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Multi-view hybrid embedding: A divide-and-conquer approach

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arXiv 2018年

作者： Xu, Jiamiao Yu, Shujian You, Xinge Leng, Mengjun Jing, Xiao-Yuan Chen, C. L. Philip School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Department of Computer Science University of Houston HoustonTX77204 United States State Key Laboratory of Software Engineering School of Computer Wuhan University China Department of Computer and Information Science Faculty of Science and Technology University of Macau Macau99999 China Dalian Maritime University Dalian116026 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

We present a novel cross-view classification algorithm where the gallery and probe data come from different views. A popular approach to tackle this problem is the multiview subspace learning (MvSL) that aims to learn a latent subspace shared by multi-view data. Despite promising results obtained on some applications, the performance of existing methods deteriorates dramatically when the multi-view data is sampled from nonlinear manifolds or suffers from heavy outliers. To circumvent this drawback, motivated by the Divideand-Conquer strategy, we propose Multi-view Hybrid Embedding (MvHE), a unique method of dividing the problem of cross-view classification into three subproblems and building one model for each subproblem. Specifically, the first model is designed to remove view discrepancy, whereas the second and third models attempt to discover the intrinsic nonlinear structure and to increase discriminability in intra-view and interview samples respectively. The kernel extension is conducted to further boost the representation power of MvHE. Extensive experiments are conducted on four benchmark datasets. Our methods demonstrate overwhelming advantages against the stateof-the-art MvSL based cross-view classification approaches in terms of classification accuracy and robustness. Copyright © 2018, The Authors. All rights reserved.

关键词： Embeddings

Self-Awareness for Autonomous systems

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PROCEEDINGS OF THE IEEE 2020年第7期108卷 971-975页

作者： Dutt, Nikil Regazzoni, Carlo S. Rinner, Bernhard Yao, Xin Nikil Dutt (Fellow IEEE) received the Ph.D. degree from the University of Illinois at Urbana–Champaign Champaign IL USA in 1989.""He is currently a Distinguished Professor of computer science (CS) cognitive sciences and electrical engineering and computer sciences (EECS) with the University of California at Irvine Irvine CA USA. He is a coauthor of seven books. His research interests include embedded systems electronic design automation (EDA) computer architecture distributed systems healthcare Internet of Things (IoT) and brain-inspired architectures and computing.""Dr. Dutt is a Fellow of ACM. He was a recipient of the IFIP Silver Core Award. He has received numerous best paper awards. He serves as the Steering Committee Chair of the IEEE/ACM Embedded Systems Week (ESWEEK). He is also on the steering organizing and program committees of several premier EDA and embedded system design conferences and workshops. He has served on the Editorial Boards for the IEEE Transactions on Very Large Scale Integration (VLSI) Systems and the ACM Transactions on Embedded Computing Systems and also previously served as the Editor-in-Chief (EiC) for the ACM Transactions on Design Automation of Electronic Systems. He served on the Advisory Boards of the IEEE Embedded Systems Letters the ACM Special Interest Group on Embedded Systems the ACM Special Interest Group on Design Automationt and the ACM Transactions on Embedded Computing Systems. Carlo S. Regazzoni (Senior Member IEEE) received the M.S. and Ph.D. degrees in electronic and telecommunications engineering from the University of Genoa Genoa Italy in 1987 and 1992 respectively.""He is currently a Full Professor of cognitive telecommunications systems with the Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN) University of Genoa and a Co-Ordinator of the Joint Doctorate on Interactive and Cognitive Environments (JDICE) international Ph.D. course started initially as EU Erasmus Mundus Project and

Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In computer systems, one can derive such behavior from the concept of a rational agent with autonomy (“control over its own actions”), reactivity (“react to events from the environment”), proactivity (“act on its own initiative”), and sociality (“interact with other agents”) as fundamental properties \n[1]\n. Autonomous systems will undoubtedly pervade into our everyday lives, and we will find them in a variety of domains and applications including robotics, transportation, health care, communications, and entertainment to name a few. \nThe articles in this month’s special issue cover concepts and fundamentals, architectures and techniques, and applications and case studies in the exciting area of self-awareness in autonomous systems.

关键词： Special issues and sections Autonomous systems Self-aware Robot sensing systems Computational modeling

Glucose Screening Measurements and Noninvasive Glucose Monitor Methods

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Procedia computer science 2018年 139卷 613-621页

作者： Hui Zheng Jing He Peng Li Mengjiao Guo Hui Jin Jie Shen Zhijun Xie Chihung Chi School of Software and Electrical Engineering Swinburne University of Technology Melbourne Australia School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China North China Institute of Aerospace Engineering Hebei China College of Information Engineering Nanjing University of Finance and Economics China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Jiangsu Province Nanjing China Endocrinology Department Zhongda Hospital Southeast University Nanjing Jiangsu China Endocrinology Department The Third Affiliated Hospital of Southern Medical University Guangzhou Guangdong China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo Zhejiang Province China Commonwealth Scientific and Industrial Research Organisation (CSIRO) Tasmania Australia

Diabetes alone does not affect the health, but its complications always bring danger to people’s health. To avoid the development of diabetes and posterior to discover diabetes, checking blood glucose level is an effective way. The development of glucose screening measurements is introduced. Non-invasive glucose monitor methods, as accurate, safe, convenient and comfortable methods, are also illustrated and compared in this paper. Also, an Intelligent dynamic health inspection is designed with a proposed non-invasion glucose monitor measurement.

关键词： diabetes blood glucose screening noninvasive methods

Random asynchronous iterations in distributed coordination algorithms

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arXiv 2018年

作者： Chen, Yao Xia, Weiguo Cao, Ming Lü, Jinhu Department of Computer Science Southwestern University of Finance and Economics Chengdu611130 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China Faculty of Science and Engineering ENTEG University of Groningen Groningen9747 AG Netherlands School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

Distributed coordination algorithms (DCA) carry out information processing processes among a group of networked agents without centralized information fusion. Though it is well known that DCA characterized by an SIA (stochastic, indecomposable, aperiodic) matrix generate consensus asymptotically via synchronous iterations, the dynamics of DCA with asynchronous iterations have not been studied extensively, especially when viewed as stochastic processes. This paper aims to show that for any given irreducible stochastic matrix, even non-SIA, the corresponding DCA lead to consensus successfully via random asynchronous iterations under a wide range of conditions on the transition probability. Particularly, the transition probability is neither required to be independent and identically distributed, nor characterized by a Markov chain. Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

Robust visual tracking using multi-frame multi-feature joint modeling

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arXiv 2018年

作者： Zhang, Peng Yu, Shujian Xu, Jiamiao You, Xinge Jiang, Xiubao Jing, Xiao-Yuan Tao, Dacheng School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan Hubei China Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States State Key Laboratory of Software Engineering School of Computer Wuhan University Wuhan430072 China School of Automation Nanjing University of Posts and Telecommunications Nanjing210023 China UBTech Sydney Artificial Intelligence Institute and the School of Information Technologies Faculty of Engineering and Information Technologies at the University of Sydney DarlingtonNSW2008 Australia

It remains a huge challenge to design effective and efficient trackers under complex scenarios, including occlusions, illumination changes and pose variations. To cope with this problem, a promising solution is to integrate the temporal consistency across consecutive frames and multiple feature cues in a unified model. Motivated by this idea, we propose a novel correlation filter-based tracker in this work, in which the temporal relatedness is reconciled under a multi-Task learning framework and the multiple feature cues are modeled using a multi-view learning approach. We demonstrate the resulting regression model can be efficiently learned by exploiting the structure of blockwise diagonal matrix. A fast blockwise diagonal matrix inversion algorithm is developed thereafter for efficient online tracking. Meanwhile, we incorporate an adaptive scale estimation mechanism to strengthen the stability of scale variation tracking. We implement our tracker using two types of features and test it on two benchmark datasets. Experimental results demonstrate the superiority of our proposed approach when compared with other state-of-The-Art trackers. MATLAB code is available from our project homepage http://***/ project/***. Copyright © 2018, The Authors. All rights reserved.

关键词： MATLAB

Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning

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arXiv 2019年

作者： Ho, Chi-Sing Jean, Neal Hogan, Catherine A. Blackmon, Lena Jeffrey, Stefanie S. Holodniy, Mark Banaei, Niaz Saleh, Amr A.E. Ermon, Stefano Dionne, Jennifer Dept. of Applied Physics Stanford University StanfordCA United States Dept. of Materials Science and Engineering Stanford University StanfordCA United States Dept. of Computer Science Stanford University StanfordCA United States Dept. of Electrical Engineering Stanford University StanfordCA United States Dept. of Pathology Stanford University School of Medicine StanfordCA United States Clinical Microbiology Laboratory Stanford Health Care StanfordCA United States Dept. of Surgery Stanford University School of Medicine StanfordCA United States Dept. of Medicine Stanford University School of Medicine StanfordCA United States VA Palo Alto Health Care System Palo AltoCA United States Division of Infectious Diseases and Geographic Medicine Dept. of Medicine Stanford University School of Medicine StanfordCA United States Dept. of Engineering Mathematics and Physics Faculty of Engineering Cairo University Giza Egypt

Raman optical spectroscopy promises label-free bacterial detection, identification, and antibiotic susceptibility testing in a single step. However, achieving clinically relevant speeds and accuracies remains challenging due to weak Raman signal from bacterial cells and numerous bacterial species and phenotypes. Here we generate an extensive dataset of bacterial Raman spectra and apply deep learning approaches to accurately identify 30 common bacterial pathogens. Even on low signal-to-noise spectra, we achieve average isolate-level accuracies exceeding 82% and antibiotic treatment identification accuracies of 97.0±0.3%. We also show that this approach distinguishes between methicillin-resistant and -susceptible isolates of Staphylococcus aureus (MRSA and MSSA) with 89±0.1% accuracy. We validate our results on clinical isolates from 50 patients. Using just 10 bacterial spectra from each patient isolate, we achieve treatment identification accuracies of 99.7%. Our approach has potential for culture-free pathogen identification and antibiotic susceptibility testing, and could be readily extended for diagnostics on blood, urine, and sputum. Copyright © 2019, The Authors. All rights reserved.

关键词： Raman spectroscopy

High-depth African genomes inform human migration and health (vol 586, pg 741, 2020)

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NATURE 2021年第7856期592卷 E26-E26页

作者： Choudhury, Ananyo Aron, Shaun Botigue, Laura R. Sengupta, Dhriti Botha, Gerrit Bensellak, Taoufik Wells, Gordon Kumuthini, Judit Shriner, Daniel Fakim, Yasmina J. Ghoorah, Anisah W. Dareng, Eileen Odia, Trust Falola, Oluwadamilare Adebiyi, Ezekiel Hazelhurst, Scott Mazandu, Gaston Nyangiri, Oscar A. Mbiyavanga, Mamana Benkahla, Alia Kassim, Samar K. Mulder, Nicola Adebamowo, Sally N. Chimusa, Emile R. Muzny, Donna Metcalf, Ginger Gibbs, Richard A. Matovu, Enock Bucheton, Bruno Hertz-Fowler, Christiane Koffi, Mathurin Macleod, Annette Mumba-Ngoyi, Dieudonne Noyes, Harry Nyangiri, Oscar A. Simo, Gustave Simuunza, Martin Rotimi, Charles Ramsay, Michele Choudhury, Ananyo Choudhury, Ananyo Aron, Shaun Botigue, Laura Sengupta, Dhriti Botha, Gerrit Bensellak, Taoufik Wells, Gordon Kumuthini, Judit Shriner, Daniel Fakim, Yasmina J. Ghoorah, Anisah W. Dareng, Eileen Odia, Trust Falola, Oluwadamilare Adebiyi, Ezekiel Hazelhurst, Scott Mazandu, Gaston Nyangiri, Oscar A. Mbiyavanga, Mamana Benkahla, Alia Kassim, Samar K. Mulder, Nicola Adebamowo, Sally N. Chimusa, Emile R. Rotimi, Charles Ramsay, Michele Adeyemo, Adebowale A. Lombard, Zane Hanchard, Neil A. Adebamowo, Clement Agongo, Godfred Boua, Romuald P. Oduro, Abraham Sorgho, Hermann Landoure, Guida Cisse, Lassana Diarra, Salimata Samassekou, Oumar Anabwani, Gabriel Matshaba, Mogomotsi Joloba, Moses Kekitiinwa, Adeodata Mardon, Graeme Mpoloka, Sununguko W. Kyobe, Samuel Mlotshwa, Busisiwe Mwesigwa, Savannah Retshabile, Gaone Williams, Lesedi Wonkam, Ambroise Moussa, Ahmed Adu, Dwomoa Ojo, Akinlolu Burke, David Salako, Babatunde O. Matovu, Enock Bucheton, Bruno Hertz-Fowler, Christiane Koffi, Mathurin Macleod, Annette Mumba-Ngoyi, Dieudonne Noyes, Harry Nyangiri, Oscar A. Simo, Gustave Simuunza, Martin Awadalla, Philip Bruat, Vanessa Gbeha, Elias Adeyemo, Adebowale A. Lombard, Zane Hanchard, Neil A. Sydney Brenner Institute for Molecular Bioscience Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa School of Electrical and Information Engineering University of the Witwatersrand Johannesburg South Africa Division of Human Genetics National Health Laboratory Service and School of Pathology Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa Navrongo Health Research Centre Navrongo Ghana Clinical Research Unit of Nanoro Institut de Recherche en Sciences de la Sante Bobo-Dioulasso Burkina Faso Center for Research in Agricultural Genomics (CRAG) Plant and Animal Genomics Program CSIC-IRTA-UAB-UB Barcelona Spain Computational Biology Division and H3ABioNet Department of Integrative Biomedical Sciences IDM University of Cape Town Cape Town South Africa System and Data Engineering Team Abdelmalek Essaadi University ENSA Tangier Morocco Centre for Proteomic and Genomic Research (CPGR) Cape Town South Africa South African National Bioinformatics Institute University of the Western Cape Bellville South Africa Africa Health Research Institute Durban South Africa Center for Research on Genomics and Global Health National Human Genome Research Institute National Institutes of Health Bethesda MD USA Department of Agriculture and Food Science Faculty of Agriculture University of Mauritius Reduit Mauritius Department of Digital Technologies Faculty of Information Communication & Digital Technologies University of Mauritius Reduit Mauritius Department of Public Health and Primary Care University of Cambridge Cambridge UK Institute of Human Virology Nigeria Abuja Nigeria Department of Epidemiology and Public Health University of Maryland School of Medicine University of Maryland Baltimore Baltimore MD USA University of Maryland Greenebaum Comprehensive Cancer Center University of Maryland School of Medicine University of Maryland Baltimore Baltimore MD USA Institute of Human Virology and Greeneba

Prototype of group heart rate monitoring with NODEMCU ESP8266 6

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Prototype of group heart rate monitoring with NODEMCU ESP826...

6th Mediterranean Conference on Embedded Computing, MECO 2017

作者： Škraba, Andrej Koložvari, Andrej Kofjač, Davorin Stojanović, Radovan Stanovov, Vladimir Semenkin, Eugene University of Maribor Cybernetics and Decision Support System Laboratory Faculty of Organizational Sciences Kidričeva cesta 55a Kranj Slovenia University of Montenegro Faculty of Electrical Engineering Džordža Vašingtona bb. Podgorica Montenegro Siberian State Aerospace University Computer Science and Telecommunications Institute 31 Krasnoyarsky Rabochy Ave. Krasnoyarsk Russia

ISBN: (纸本)9781509067411

Paper describes the development of prototype that enables monitoring of heart rate and inter beat interval for several subjects. The prototype was realized using ESP8266 hardware modules, WebSocket library, nodejs and JavaScript. system architecture is described where nodejs server acts as the signal processing and GUI code provider for clients. Signal processing algorithm was implemented in JavaScript. Application GUI is presented which can be used on mobile devices. Several important parts of the code are described which illustrate the communication between ESP8266 modules, server and clients. Developed prototype shows one of the possible realizations of group monitoring of biomedical data. © 2017 IEEE.

关键词： Graphical user interfaces

GRID: A Student Project to Monitor the Transient Gamma-Ray Sky in the Multi-Messenger Astronomy Era

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arXiv 2019年

作者： Wen, Jiaxing Long, Xiangyun Zheng, Xutao An, Yu Cai, Zhengyang Cang, Jirong Che, Yuepeng Chen, Changyu Chen, Liangjun Chen, Qianjun Chen, Ziyun Cheng, Yingjie Deng, Litao Deng, Wei Ding, Wenqing Du, Hangci Duan, Lian Gan, Quan Gao, Tai Gao, Zhiying Han, Wenbin Han, Yiying He, Xinbo He, Xinhao Hou, Long Hu, Fan Hu, Junling Huang, Bo Huang, Dongyang Huang, Xuefeng Jia, Shihai Jiang, Yuchen Jin, Yifei Li, Ke Li, Siyao Li, Yurong Liang, Jianwei Liang, Yuanyuan Lin, Wei Liu, Chang Liu, Gang Liu, Mengyuan Liu, Rui Liu, Tianyu Liu, Wanqiang Lu, Di'an Lu, Peiyibin Lu, Zhiyong Luo, Xiyu Ma, Sizheng Ma, Yuanhang Mao, Xiaoqing Mo, Yanshan Nie, Qiyuan Qu, Shuiyin Shan, Xiaolong Shi, Gengyuan Song, Weiming Sun, Zhigang Tan, Xuelin Tang, Songsong Tao, Mingrui Wang, Boqin Wang, Yue Wang, Zhiang Wu, Qiaoya Wu, Xuanyi Xia, Yuehan Xiao, Hengyuan Xie, Wenjin Xu, Dacheng Xu, Rui Xu, Weili Yan, Longbiao Yan, Shengyu Yang, Dongxin Yang, Hang Yang, Haoguang Yang, Yi-Si Yang, Yifan Yao, Lei Yu, Huan Yu, Yangyi Zhang, Aiqiang Zhang, Bingtao Zhang, Lixuan Zhang, Maoxing Zhang, Shen Zhang, Tianliang Zhang, Yuchong Zhao, Qianru Zhao, Ruining Zheng, Shiyu Zhou, Xiaolong Zhu, Runyu Zou, Yu An, Peng Cai, Yifu Chen, Hongbing Dai, Zigao Fan, Yizhong Feng, Changqing Feng, Hua Gao, He Huang, Liang Kang, Mingming Li, Lixin Li, Zhuo Liang, Enwei Lin, Lin Lin, Qianqian Liu, Congzhan Liu, Hongbang Liu, Xuewen Liu, Yinong Lu, Xiang Mao, Shude Shen, Rongfeng Shu, Jing Su, Meng Sun, Hui Tam, Pak-Hin Tang, Chi-Pui Tian, Yang Wang, Fayin Wang, Jianjun Wang, Wei Wang, Zhonghai Wu, Jianfeng Wu, Xuefeng Xiong, Shaolin Xu, Can Yu, Jiandong Yu, Wenfei Yu, Yunwei Zeng, Ming Zeng, Zhi Zhang, Bin-Bin Zhang, Bing Zhao, Zongqing Zhou, Rong Zhu, Zonghong Department of Engineering Physics Center for Astrophysics Tsinghua University Beijing100084 China Science and Technology on Plasma Physics Laboratory Laser Fusion Research Center Chinese Academy of Engineering Physics Mianyang621900 China Department of Physics Center for Astrophysics Tsinghua University Beijing100084 China Guangxi Key Laboratory for Relativistic Astrophysics Department of Physics Guangxi University NanningGuangxi530004 China School of Electronic and Information Engineering Ningbo University of Technology NingboZhejiang315211 China Department of Instruments Science & Engineering Shanghai Jiaotong University Shanghai200240 · China School of Materials Science & Chemical Engineering Ningbo University Ningbo315211 China School of Astronomy and Space Science Nanjing University NanjingJiangsu210093 China School of Aerospace Engineering Tsinghua University Beijing100084 China College of Physical Science and Technology Sichuan University Chengdu610065 China School of Physics and Astronomy Sun Yat-Sen University Zhuhai519082 China Department of Astronomy Xiamen University XiamenFujian361005 China School of Mathematics and Physics University of Science and Technology Beijing Beijing100083 China Department of Foreign Languages and Literatures Tsinghua University Beijing100084 China Department of Electrical Engineering Tsinghua University Beijing100084 China School of Software Tsinghua University Beijing100084 China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China Department of Modern Physics School of Physical Sciences University of Science and Technology of China HefeiAnhui230026 China Department of Astronomy Beijing Normal University Beijing100875 China Department of Astronomy School of Physical Sciences University of Science and Technology of China HefeiAnhui230026 China Purple Mountain Observatory Chinese Academy of Sciences Nanjing210008 China Department of Astrono

The Gamma-Ray Integrated Detectors (GRID) is a space mission concept dedicated to monitoring the transient gamma-ray sky in the energy range from 10 keV to 2 MeV using scintillation detectors onboard CubeSats in low Earth orbits. The primary targets of GRID are the gamma-ray bursts (GRBs) in the local universe. The scientific goal of GRID is, in synergy with ground-based gravitational wave (GW) detectors such as LIGO and VIRGO, to accumulate a sample of GRBs associated with the merger of two compact stars and study jets and related physics of those objects. It also involves observing and studying other gamma-ray transients such as long GRBs, soft gamma-ray repeaters, terrestrial gamma-ray flashes, and solar flares. With multiple CubeSats in various orbits, GRID is unaffected by the Earth occultation and serves as a full-time and all-sky monitor. Assuming a horizon of 200 Mpc for ground-based GW detectors, we expect to see a few associated GW-GRB events per year. With about 10 CubeSats in operation, GRID is capable of localizing a faint GRB like 170817A with a 90% error radius of about 10 degrees, through triangulation and flux modulation. GRID is proposed and developed by students, with considerable contribution from undergraduate students, and will remain operated as a student project in the future. The current GRID collaboration involves more than 20 institutes and keeps growing. On August 29th, the first GRID detector onboard a CubeSat was launched into a Sun-synchronous orbit and is currently under test. Copyright © 2019, The Authors. All rights reserved.

关键词： Gravity waves