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Multi-label Classification with High-rank and High-order Label Correlations

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arXiv 2022年

作者： Si, Chongjie Jia, Yuheng Wang, Ran Zhang, Min-Ling Feng, Yanghe Qu, Chongxiao The Chien-Shiung Wu College Southeast University Nanjing210096 China The MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China The School of Computer Science and Engineering Southeast University Nanjing210096 China Ministry of Education China School of Computing & Information Sciences Caritas Institute of Higher Education Hong Kong The Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China The School of Mathematical Science Shenzhen University Shenzhen518060 China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China The College of Systems Engineering National University of Defense Technology China The 52nd Research Institute of China Electronics Technology Group China

Exploiting label correlations is important to multi-label classification. Previous methods capture the high-order label correlations mainly by transforming the label matrix to a latent label space with low-rank matrix factorization. However, the label matrix is generally a full-rank or approximate full-rank matrix, making the low-rank factorization inappropriate. Besides, in the latent space, the label correlations will become implicit. To this end, we propose a simple yet effective method to depict the high-order label correlations explicitly, and at the same time maintain the high-rank of the label matrix. Moreover, we estimate the label correlations and infer model parameters simultaneously via the local geometric structure of the input to achieve mutual enhancement. Comparative studies over twelve benchmark data sets validate the effectiveness of the proposed algorithm in multi-label classification. The exploited high-order label correlations are consistent with common sense empirically. Our code is publicly available at https://***/Chongjie-Si/HOMI. Copyright © 2022, The Authors. All rights reserved.

关键词： Matrix algebra

Investigate the relationship between learners’ social characteristics and academic achievements

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Journal of Physics: Conference Series 2018年第1期1113卷 012021-012021页

作者： Liu, Zhi Zhu, Su Liu, Sannyuya Sun, Jianwen Kang, Lingyun National Engineering Research Center for E-Learning Central China Normal University Wuhan P. R. China National Engineering Laboratory for Technology of Big Data Applications in Education Central China Normal University Wuhan P. R. China National Engineering Research Center for E-Learning Central China Normal University Wuhan P. R. China

The social interaction behaviors of group learning have obtained a lot of attention from researches, and social network analysis (SNA) plays a critical role in exploring collective interactive patterns. In this paper, we investigated the experimental data from the discussion forum of “Discourse Analysis” course in a university learning platform. By calculating learners social characteristics, we explored the relationship between social characteristics and academic achievements, as well as the differences between various achieving learners. The results show that there exist strong positive correlations between most social characteristics and academic achievements, and the majority of high-achieving learners are located at the core position of the network. In addition, the high-achieving learners exhibited more active participations in discussions and received more posts compared with middle- and low-achieving learners.

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FAIR for AI: An interdisciplinary and international community building perspective

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arXiv 2022年

作者： Huerta, E.A. Blaiszik, Ben Brinson, L. Catherine Bouchard, Kristofer E. Diaz, Daniel Doglioni, Caterina Duarte, Javier M. Emani, Murali Foster, Ian Fox, Geoffrey Harris, Philip Heinrich, Lukas Jha, Shantenu Katz, Daniel S. Kindratenko, Volodymyr Kirkpatrick, Christine R. Lassila-Perini, Kati Madduri, Ravi K. Neubauer, Mark S. Psomopoulos, Fotis E. Roy, Avik Rübel, Oliver Zhao, Zhizhen Zhu, Ruike Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science University of Chicago ChicagoIL60637 United States Globus University of Chicago ChicagoIL60637 United States Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Scientific Data Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Biological Systems & Engineering Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Helen Wills Neuroscience Institute University of California Berkeley BerkeleyCA94720 United States Department of Physics University of California La Jolla San DiegoCA92093 United States Lund University Department of Physics Box 118 Lund221 00 Sweden School of Physics & Astronomy The University of Manchester ManchesterM13 9PL United Kingdom Leadership Computing Facility Argonne National Laboratory LemontIL60439 United States Biocomplexity Institute Department of Computer Science University of Virginia CharlottesvilleVA22904 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Technical University Munich Arcisstraße 21 München80333 Germany Computational Science Initiative Brookhaven National Laboratory UptonNY11973 United States Electrical and Computer Engineering Rutgers The State University of New Jersey PiscatawayNJ08854 United States National Center for Supercomputing Applications University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical & Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States School of Information Sciences University of Illinois at Urbana-Champaign UrbanaIL61801 United States San Diego Supercomputer Center University of California La Jolla San DiegoCA

A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been reinterpreted or extended to include the software, tools, algorithms, and workflows that produce data. FAIR principles are now being adapted in the context of AI models and datasets. Here, we present the perspectives, vision, and experiences of researchers from different countries, disciplines, and backgrounds who are leading the definition and adoption of FAIR principles in their communities of practice, and discuss outcomes that may result from pursuing and incentivizing FAIR AI research. The material for this report builds on the FAIR for AI Workshop held at Argonne national laboratory on June 7, 2022. Copyright © 2022, The Authors. All rights reserved.

关键词： Information management

Spatial mapping of protein composition and tissue organization: A primer for multiplexed antibody-based imaging

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arXiv 2021年

作者： Hickey, John W. Neumann, Elizabeth K. Radtke, Andrea J. Camarillo, Jeannie M. Beuschel, Rebecca T. Albanese, Alexandre McDonough, Elizabeth Hatler, Julia Wiblin, Anne E. Fisher, Jeremy Croteau, Josh Small, Eliza C. Sood, Anup Caprioli, Richard M. Angelo, R. Michael Nolan, Garry P. Chung, Kwanghun Hewitt, Stephen M. Germain, Ronald N. Spraggins, Jeffrey M. Lundberg, Emma Snyder, Michael P. Kelleher, Neil L. Saka, Sinem K. Department of Pathology Stanford University School of Medicine StanfordCA94305 United States Department of Biochemistry Vanderbilt University NashvilleTN37232 United States Mass Spectrometry Research Center Vanderbilt University NashvilleTN37232 United States Lymphocyte Biology Section Center for Advanced Tissue Imaging Laboratory of Immune System Biology NIAID NIH BethesdaMD20892 United States Department of Chemistry Molecular Biosciences The National Resource for Translational and Developmental Proteomics Northwestern University EvanstonIL60208 United States Institute for Medical Engineering and Science MIT CambridgeMA United States Picower Institute for Learning and Memory MIT CambridgeMA United States GE Research NiskayunaNY12309 United States Antibody Development Department Bio-techne MinneapolisMN55413 United States Department of Research and Development Abcam PLC Cambridge Biomedical Campus Discovery Drive CambridgeCB2 0AX United Kingdom Department of Research and Development Cell Signaling Technology Inc. DanversMA01923 United States Department of Applications Science BioLegend San DiegoCA92121 United States Thermo Fisher Scientific RockfordIL United States Department of Chemistry Vanderbilt University NashvilleTN37232 United States Department of Chemical Engineering MIT CambridgeMA United States Department of Brain and Cognitive Sciences MIT CambridgeMA United States Seoul Korea Republic of Yonsei-IBS Institute Yonsei University Seoul Korea Republic of Laboratory of Pathology Center for Cancer Research National Cancer Institute National Institutes of Health BethesdaMD20892 United States Department of Cell and Developmental Biology Vanderbilt University School of Medicine NashvilleTN37240 United States Science for Life Laboratory School of Engineering Sciences in Chemistry Biotechnology and Health KTH-Royal Institute of Technology Stockholm Sweden Department of Genetics Stanford University School o

Tissues and organs are composed of distinct cell types that must operate in concert to perform physiological functions. Efforts to create high-dimensional biomarker catalogs of these cells are largely based on transcriptomic single-cell approaches that lack the spatial context required to understand critical cellular communication and correlated structural organization. To probe in situ biology with sufficient coverage depth, several multiplexed protein imaging methods have recently been developed. Though these antibody-based technologies differ in strategy and mode of immunolabeling and detection tags, they commonly utilize antibodies directed against protein biomarkers to provide detailed spatial and functional maps of complex tissues. As these promising antibody-based multiplexing approaches become more widely adopted, new frameworks and considerations are critical for training future users, generating molecular tools, validating antibody panels, and harmonizing datasets. In this perspective, we provide essential resources, key considerations for obtaining robust and reproducible imaging data, and specialized knowledge from domain experts and technology developers. Copyright © 2021, The Authors. All rights reserved.

关键词： Antibodies

Sketch-based Medical Image Retrieval

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arXiv 2023年

作者： Kobayashi, Kazuma Gu, Lin Hataya, Ryuichiro Mizuno, Takaaki Miyake, Mototaka Watanabe, Hirokazu Takahashi, Masamichi Takamizawa, Yasuyuki Yoshida, Yukihiro Nakamura, Satoshi Kouno, Nobuji Bolatkan, Amina Kurose, Yusuke Harada, Tatsuya Hamamoto, Ryuji Division of Medical AI Research and Development National Cancer Center Research Institute 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Cancer Translational Research Team RIKEN Center for Advanced Intelligence Project 1-4-1 Nihonbashi Chuo-ku Tokyo103-0027 Japan Machine Intelligence for Medical Engineering Team RIKEN Center for Advanced Intelligence Project 1-4-1 Nihonbashi Chuo-ku Tokyo103-0027 Japan Research Center for Advanced Science and Technology The University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo153-8904 Japan Medical Data Deep Learning Team Advanced Data Science Project RIKEN Information R&D and Strategy Headquarters 1-4-1 Nihonbashi Chuo-ku Tokyo103-0027 Japan Department of Experimental Therapeutics National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Department of Diagnostic Radiology National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Department of Neurosurgery and Neuro-Oncology National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Department of Colorectal Surgery National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Department of Thoracic Surgery National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Radiation Safety and Quality Assurance Division National Cancer Center Hospital 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Division of Research and Development for Boron Neutron Capture Therapy National Cancer Center Exploratory Oncology Research & Clinical Trial Center 5-1-1 Tsukiji Chuo-ku Tokyo104-0045 Japan Medical Physics Laboratory Division of Health Science Graduate School of Medicine Osaka University Yamadaoka 1-7 Osaka Suita-shi565-0871 Japan Department of Surgery Kyoto University Graduate School of Medicine 54 Shogoin Kawahara-cho Sakyo-ku Kyoto606-8507 Japan

The amount of medical images stored in hospitals is increasing faster than ever;however, utilizing the accumulated medical images has been limited. This is because existing content-based medical image retrieval (CBMIR) systems usually require example images to construct query vectors;nevertheless, example images cannot always be prepared. Besides, there can be images with rare characteristics that make it difficult to find similar example images, which we call isolated samples. Here, we introduce a novel sketch-based medical image retrieval (SBMIR) system that enables users to find images of interest without example images. The key idea lies in feature decomposition of medical images, whereby the entire feature of a medical image can be decomposed into and reconstructed from normal and abnormal features. By extending this idea, our SBMIR system provides an easy-to-use two-step graphical user interface: users first select a template image to specify a normal feature and then draw a semantic sketch of the disease on the template image to represent an abnormal feature. Subsequently, it integrates the two kinds of input to construct a query vector and retrieves reference images with the closest reference vectors. Using two datasets, ten healthcare professionals with various clinical backgrounds participated in the user test for evaluation. As a result, our SBMIR system enabled users to overcome previous challenges, including image retrieval based on fine-grained image characteristics, image retrieval without example images, and image retrieval for isolated samples. Our SBMIR system achieves flexible medical image retrieval on demand, thereby expanding the utility of medical image databases. Copyright © 2023, The Authors. All rights reserved.

关键词： Search engines

Correction to: Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning

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BMC medical informatics and decision making 2019年第1期19卷 153页

作者： Muhammad Naseer Bajwa Muhammad Imran Malik Shoaib Ahmed Siddiqui Andreas Dengel Faisal Shafait Wolfgang Neumeier Sheraz Ahmed Fachbereich Informatik Technische Universität Kaiserslautern 67663 Kaiserslautern Germany. bajwa@dfki.uni-kl.de. Deutsche Forschungszentrum für KünstlicheIntelligenz GmbH (DFKI) 67663 Kaiserslautern Germany. bajwa@dfki.uni-kl.de. Deep Learning Laboratory National Center of Artificial Intelligence Islamabad 46000 Pakistan. School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology H-12 Islamabad 46000 Pakistan. Fachbereich Informatik Technische Universität Kaiserslautern 67663 Kaiserslautern Germany. Deutsche Forschungszentrum für KünstlicheIntelligenz GmbH (DFKI) 67663 Kaiserslautern Germany. Ophthalmology Clinic Rittersberg 9 67657 Kaiserslautern Germany.

Following publication of the original article.

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Outcomes from a Workshop on a national Center for Quantum Education

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arXiv 2024年

作者： Barnes, Edwin Bennett, Michael B. Boltasseva, Alexandra Borish, Victoria Brown, Bennett Carr, Lincoln D. Ceballos, Russell R. Dukes, Faith Easton, Emily W. Economou, Sophia E. Edwards, E.E. Finkelstein, Noah D. Fracchiolla, C. Franklin, Diana Freericks, J.K. Goss, Valerie Hannum, Mark Holincheck, Nancy Kelly, Angela M. Lanes, Olivia Lewandowski, H.J. Matsler, Karen Jo Mercurio, Emily Montaño, Inès Murdock, Maajida Peltz, Kiera Perron, Justin K. Richardson, Christopher J.K. Rosenberg, Jessica L. Ross, Richard S. Ryu, Minjung Samuel, Raymond E. Schrode, Nicole Schwamberger, Susan Searles, Thomas A. Singh, Chandralekha Tingle, Alexandra Zwickl, Benjamin M. Department of Physics Virginia Tech Center for Quantum Information Science and Engineering Virginia Tech 850 West Campus Drive BlacksburgVA24061 United States Q-SEnSE NSF QLCI University of Colorado 440 UCB BoulderCO80301 United States Elmore Family School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue Quantum Science and Engineering Institute Purdue University 610 Purdue Mall West LafayetteIN47907 United States Quantum Science Center A National Quantum Information Science Research Center of The U.S. Department of Energy Oak Ridge National Laboratory 1 Bethel Valley Road Oak RidgeTN37830 United States JILA National Institute of Standards and Technology the University of Colorado BoulderCO80309 United States Department of Physics University of Colorado 390 UCB BoulderCO80309 United States QuSTEAM 510 Ronalds St. Iowa CityIA52245 United States Quantum Engineering Program Department of Physics Colorado School of Mines 1523 Illinois St. GoldenCO80401 United States QuSTEAM Non-Profit Wilbur Wright College City Colleges of Chicago 4300 N Narragansett Ave ChicagoIL60634 United States Government and Community Relations Office - K-12 Programs Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA United States Chicago Quantum Exchange 5235 S. Harper Court ChicagoIL60615 United States Department of Physics Virginia Tech 850 West Campus Drive BlacksburgVA24060 United States Department of Electrical and Computer Engineering Duke Quantum Center Duke University Duke University 130 Hudson Hall DurhamNC27708 United States American Physical Society 555 12th St. NW WashingtonDC20004 United States Computer Science University of Chicago 5730 S. Ellis Ave. ChicagoIL60637 United States Department of Physics Georgetown University 552 Reiss Science Building WashingtonDC20057 United States Chemistry Physics and Engineering Studies Chicago State University 9501 S. King Drive WSC 309 ChicagoIL606

In response to numerous programs seeking to advance quantum education and workforce development in the United States, experts from academia, industry, government, and professional societies convened for a national Science Foundation-sponsored workshop in February 2024 to explore the benefits and challenges of establishing a national center for quantum education. Broadly, such a center would foster collaboration and build the infrastructure required to develop a diverse and quantum-ready workforce. The workshop discussions centered around how a center could uniquely address gaps in public, K-12, and undergraduate quantum information science and engineering (QISE) education. Specifically, the community identified activities that, through a center, could lead to an increase in student awareness of quantum careers, boost the number of educators trained in quantum-related subjects, strengthen pathways into quantum careers, enhance the understanding of the U.S. quantum workforce, and elevate public engagement with QISE. Core proposed activities for the center include professional development for educators, coordinated curriculum development and curation, expanded access to educational laboratory equipment, robust evaluation and assessment practices, network building, and enhanced public engagement with quantum science. © 2024, CC BY.

关键词： Students

Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021

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The Lancet 2023年第10397期402卷 203-234页

作者： Ong, Kanyin Liane Stafford, Lauryn K. McLaughlin, Susan A. Boyko, Edward J. Vollset, Stein Emil Smith, Amanda E. Dalton, Bronte E. Duprey, Joe Cruz, Jessica A. Hagins, Hailey Lindstedt, Paulina A. Aali, Amirali Abate, Yohannes Habtegiorgis Abate, Melsew Dagne Abbasian, Mohammadreza Abbasi-Kangevari, Zeinab Abbasi-Kangevari, Mohsen ElHafeez, Samar Abd Abd-Rabu, Rami Abdulah, Deldar Morad Abdullah, Abu Yousuf Md Abedi, Vida Abidi, Hassan Aboagye, Richard Gyan Abolhassani, Hassan Abu-Gharbieh, Eman Abu-Zaid, Ahmed Adane, Tigist Demssew Adane, Denberu Eshetie Addo, Isaac Yeboah Adegboye, Oyelola A. Adekanmbi, Victor Adepoju, Abiola Victor Adnani, Qorinah Estiningtyas Sakilah Afolabi, Rotimi Felix Agarwal, Gina Aghdam, Zahra Babaei Agudelo-Botero, Marcela Arriagada, Constanza Elizabeth Aguilera Agyemang-Duah, Williams Ahinkorah, Bright Opoku Ahmad, Danish Ahmad, Rizwan Ahmad, Sajjad Ahmad, Aqeel Ahmadi, Ali Ahmadi, Keivan Ahmed, Ali Ahmed, Luai A. Ahmed, Syed Anees Ajami, Marjan Akinyemi, Rufus Olusola Al Hamad, Hanadi Al Hasan, Syed Mahfuz AL-Ahdal, Tareq Mohammed Ali Alalwan, Tariq A. Al-Aly, Ziyad AlBataineh, Mohammad T. Alcalde-Rabanal, Jacqueline Elizabeth Alemi, Sharifullah Ali, Hassam Alinia, Tahereh Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Amare, Firehiwot Ameyaw, Edward Kwabena Amiri, Sohrab Amusa, Ganiyu Adeniyi Andrei, Catalina Liliana Anjana, Ranjit Mohan Ansar, Adnan Ansari, Golnoosh Ansari-Moghaddam, Alireza Anyasodor, Anayochukwu Edward Arabloo, Jalal Aravkin, Aleksandr Y. Areda, Demelash Arifin, Hidayat Arkew, Mesay Armocida, Benedetta Arnlov, Johan Artamonov, Anton A. Arulappan, Judie Aruleba, Raphael Taiwo Arumugam, Ashokan Aryan, Zahra Asemu, Mulu Tiruneh Asghari-Jafarabadi, Mohammad Askari, Elaheh Asmelash, Daniel Astell-Burt, Thomas Athar, Mohammad Athari, Seyyed Shamsadin Atout, Maha Moh'd Wahbi Avila-Burgos, Leticia Awaisu, Ahmed Azadnajafabad, Sina Darshan, B.B. Babamohamadi, Hassan Badar, Muhammad Badawi, Alaa Badiye, Ashish D. Baghcheghi, Nayereh Baghe Institute for Health Metrics and Evaluation School of Medicine General Medicine Service Department of Veterans Affairs Seattle WA United States Department of Health Metrics Sciences School of Medicine Faculty of Medicine France Department of Clinical Governance and Quality Improvement Aleta Wondo Hospital Aleta Wondo Ethiopia Department of Nursing Woldia University Woldia Ethiopia Department of Nursing Department of Orthopedic Surgery Orthopaedic Surgery Social Determinants of Health Research Center Non-communicable Diseases Research Center Epidemiology Department Evidence-Based Practice Center Community and Maternity Nursing Unit University of Duhok Duhok Iraq School of Planning Faculty of Environment Department of Public Health Sciences Penn State College of Medicine Pennsylvania State University Hershey PA United States Biocomplexity Institute Virginia Tech Blacksburg VA United States College of Medicine Department of Public Health Laboratory Technology Sciences Department Department of Family and Community Health University of Health and Allied Sciences Ho Ghana Research Center for Immunodeficiencies Department of Biosciences and Nutrition Karolinska University Hospital Huddinge Sweden Clinical Sciences Department Department of Surgery Department of Surgery Department of Obstetrics & Gynecology College of Graduate Health Sciences University of Tennessee Memphis TN United States Department of Obstetrics & Gynecology Department of Clinical and Psychosocial Epidemiology Department of Anesthesia and Critical Care Centre for Social Research in Health Quality and Systems Performance Unit Cancer Institute NSW Sydney NSW Australia Department of Public Health and Tropical Medicine HIV and Infectious Diseases Department Jhpiego Abuja Nigeria Adolescent Research and Care Department Adolescent Friendly Research Initiative and Care Ado Ekiti Nigeria Department of Epidemiology Department of Epidemiology and Medical Statistics Department of Family Medicine McMaster University

Background: Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050. Methods: Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined;these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs;defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims;type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative r

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Special Issue on Variable Structure System Control — New Designs and applications

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Asian Journal of Control 2008年第4期5卷

作者： Prof. Jian-Xin Xu Prof. Leonid Fridman Department of Electrical and Computer Eng. National University of Singapore 4 Engineering Drive 3 Singapore 117576 Tel +65 6874-2566 Fax +65 6779-1103 Dr Jian-Xin Xu received his Bachelor degree from Zhejiang University China in 1982. He attended the University of Tokyo Japan where he received his Master's and Ph.D. degrees in 1986 and 1989 respectively. All his degrees are in Electrical Engineering. He worked for one year in the Hitachi research Laboratory Japan and for more than one year in Ohio State University U.S.A. as a Visiting Scholar. In 1991 he joined the National University of Singapore and is currently an associate professor in the Department of Electrical Engineering. His research interests lie in the fields of learning control variable structure control fuzzy logic control discontinuous signal processing and applications to motion control and process control problems. He is the associate editor of Asian Journal of Control member of TC on variable structure systems and sliding mode control of IEEE Control Systems Society and a senior member of IEEE. He has produced more than 90 peer-refereed journal papers near 160 technical papers in conference proceedings and authored/edited 4 books. Division de Estudios de Posgrado Facultad de Ingenieria National Autonomous University of Mexico DEP-FI UNAM Edificio “A” Circuito Exterior Ciudad Universitaria A. P. 70–256 C.P.04510 Mexico D.F. Mexico Tel +52 55 56223014 Fax +52 55 56161719 Dr. Leonid M. Fridman received his M.S in mathematics from Kuibyshev (Samara) State University Russia Ph.D. in Applied Mathematics from Institute of Control Science (Moscow) and Dr. of Science degrees in Control Science from Moscow State University of Mathematics and Electronics in 1976 1988 and 1998 respectively. In 1976–1999 Dr. Fridman was with the Department of Mathematics at the Samara State Architecture and Civil Engineering Academy Samara Russia. In 2000–2002 he was with the Department of Postgraduate Study and Investigations at the Chihuahu