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Annual International Conference of the IEEE engineering in Medicine and Biology Society

作者： Guo-Rong Wu Gopikhrishna Deshpande Steven Laureys Daniele Marinazzo the Department of Data Analysis University of Ghent B9000 Ghent Belgium and the Key Laboratory of Cognition and Personality Southwest University Chongqing China the Department of Electrical and Computer Engineering Auburn University Auburn AL 36849 USA the Coma Science Group Cyclotron Research Centre GIGA - Research Center and Department of Neurology University of Liège Liège the Department of Data Analysis University of Ghent B9000 Ghent Belgium

ISBN: (纸本)9781424492695

In this paper we present a procedure to retrieve the hemodynamic response function (HRF) from resting state functional magnetic resonance imaging (fMRI) data. The fundamentals of the procedures are further validated by considering simultaneous electroencephalographic (EEG) recordings. The typical HRF shape at rest for a group of healthy subject is presented. Then we present the modifications to the shape of the HRF at rest following two physiological modulations: eyes open versus eyes closed and propofol-induced modulations of consciousness.

关键词： HRF EEG Hypothalamic Releasing Hormones Response function Electroencephalography fMRI rest resting state

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Methods for Evaluating Medical Tests and Biomarkers

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Diagnostic and Prognostic Research 2017年第1期1卷 1-34页

作者： Gowri Gopalakrishna Miranda Langendam Patrick Bossuyt Mariska Leeflang Rob Scholten Anna Noel-Storr James Thomas Iain Marshall Byron Wallace Penny Whiting Clare Davenport Gowri GopalaKrishna Isabel de Salis Sue Mallett Robert Wolff Marie Westwood Jos Kleinen Richard Riley Gary Collins Hans Reitsma Karel Moons Antonia Zapf Katharina Kramer Annika Hoyer Oliver Kuss J. Ensor R. D. Riley J. J. Deeks E. C. Martin Gerta Rücker Martin Schumacher Susanne Steinhauser Joie Ensor Kym Snell Brian Willis Jon Deeks Thomas Debray Lavinia Ferrante di Ruffano Sian Taylor-Phillips Chris Hyde Stuart A. Taylor Gauraang Batnagar Lavinia Ferrante Di Ruffano Farah Seedat Aileen Clarke Sarah Byron Frances Nixon Rebecca Albrow Thomas Walker Carla Deakin Zhivko Zhelev Harriet Hunt Yaling Yang Lucy Abel James Buchanan Thomas Fanshawe Bethany Shinkins Laure Wynants Sabine Van Huffel Jan Verbakel Dirk Timmerman Ben Van Calster Aeliko Zwinderman Jason Oke Jack O’Sullivan Rafael Perera Brian Nicholson Hannah L. Bromley Tracy E. Roberts Adele Francis Denniis Petrie G. Bruce Mann Kinga Malottki Holly Smith Lucinda Billingham Alice Sitch Oke Gerke Mie Holm-Vilstrup Eivind Antonsen Segtnan Ulrich Halekoh Poul Flemming Høilund-Carlsen Bernard G. Francq Jac Dinnes Julie Parkes Walter Gregory Jenny Hewison Peter Selby Doug Altman William Rosenberg Julien Asselineau Paul Perez Aïssatou Paye Emilie Bessede Cécile Proust-Lima Christiana Naaktgeboren Joris de Groot Johannes Reitsma Anne Rutjes Emmanuel Ogundimu Jonathan Cook Yannick Le Manach Yvonne Vergouwe Romin Pajouheshnia Rolf Groenwold Karen Moons Linda Peelen Bavo De Cock Daan Nieboer Ewout W. Steyerberg Micael J. Pencina Jennifer Cooper Nick Parsons Chris Stinton Steve Smith Andy Dickens Rachel Jordan Alexandra Enocson David Fitzmaurice Peymane Adab Charles Boachie Gaj Vidmar Karoline Freeman Martin Connock Rachel Court Carl Moons Jessica Harris Zoe Plummer Barnaby Reeves Chris Rogers Andrew Mumford Kurtis Lee Veerle Verheyden Gianni D. Angelini Gavin J. Murphy Jeremy Huddy Melody Ni George Hanna Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands Department of Clinical Epidemiology and Biostatistics Academic Medical Center University of Amsterdam Amsterdam Netherlands Cochrane Netherlands Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht Netherlands Cochrane Dementia and Cognitive Improvement Group University of Oxford Oxford UK EPPI-Centre Department of Social Science University College London London UK Division of Health and Social Care Research King’s College London London UK College of Computer and Information Science Northeastern University Boston USA University Hospitals Bristol NHS Foundation Trust School of Social and Community Medicine Bristol UK Institute of Applied Health Research University of Birmingham Birmingham UK School of Social and Community Medicine University of Bristol Bristol UK Kleijnen Systematic Reviews Ltd York UK Research Institute for Primary Care and Health Sciences Keele University Keele UK Centre for Statistics in Medicine Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences University of Oxford Oxford UK Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht The Netherlands Cochrane Netherlands University Medical Center Utrecht Utrecht The Netherlands Julius Center for Health Sciences and Primary Care University Medical Center Utrecht 3584 CG Utrecht Netherlands Department of Medical Statistics University Medical Center Göttingen Göttingen Germany Institute for Biometry and Epidemiology German Diabetes Center Leibniz Institute for Diabetes Research at Heinrich Heine University Düsseldorf Germany Manchester Pharmacy School University of Manchester Manchester UK Institute for Medical Biometry and Statistics Faculty of Medicine and Medical Center – University of Freiburg Stefan-Meier-Str. 26 79104 Freiburg Germany

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Keeping speed and distance for aligned motion

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Physical Review E 2015年第1期91卷 012807-012807页

作者： Illés J. Farkas Jeromos Kun Yi Jin (金祎) Gaoqi He (何高奇) Mingliang Xu (徐明亮) MTA-ELTE Statistical and Biological Physics Research Group (Hungarian Academy of Sciences) Pázmány Péter sétány 1A Budapest 1117 Hungary Regional Knowledge Center ELTE Faculty of Sciences Irányi Dániel u. 4. Székesfehérvár 8000 Hungary Department of Biological Physics Eötvös University Pázmány Péter sétány 1A Budapest 1117 Hungary Department of Computer Science and Engineering East China University of Science and Technology 130 Meilong Road Shanghai 200237 China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing 100191 China School of Information Engineering Zhengzhou University 100 Science Avenue Zhengzhou 450001 China

The cohesive collective motion (flocking, swarming) of autonomous agents is ubiquitously observed and exploited in both natural and man-made settings, thus, minimal models for its description are essential. In a model with continuous space and time we find that if two particles arrive symmetrically in a plane at a large angle, then (i) radial repulsion and (ii) linear self-propelling toward a fixed preferred speed are sufficient for them to depart at a smaller angle. For this local gain of momentum explicit velocity alignment is not necessary, nor are adhesion or attraction, inelasticity or anisotropy of the particles, or nonlinear drag. With many particles obeying these microscopic rules of motion we find that their spatial confinement to a square with periodic boundaries (which is an indirect form of attraction) leads to stable macroscopic ordering. As a function of the strength of added noise we see—at finite system sizes—a critical slowing down close to the order-disorder boundary and a discontinuous transition. After varying the density of particles at constant system size and varying the size of the system with constant particle density we predict that in the infinite system size (or density) limit the hysteresis loop disappears and the transition becomes continuous. We note that animals, humans, drones, etc., tend to move asynchronously and are often more responsive to motion than positions. Thus, for them velocity-based continuous models can provide higher precision than coordinate-based models. An additional characteristic and realistic feature of the model is that convergence to the ordered state is fastest at a finite density, which is in contrast to models applying (discontinuous) explicit velocity alignments and discretized time. To summarize, we find that the investigated model can provide a minimal description of flocking.

关键词： ELECTROSTATIC flocking ANISOTROPY INTELLIGENT agents (computer software) UBIQUITOUS computing DISCRETIZATION methods computer engineering

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HiEve: A corpus for extracting event hierarchies from news stories 9

HiEve: A corpus for extracting event hierarchies from news s...

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9th International Conference on Language Resources and Evaluation, LREC 2014

作者： Glavaš, Goran Šnajder, Jan Kordjamshidi, Parisa Moens, Marie-Francine University of Zagreb Faculty of Electrical Engineering and Computing Text Analysis and Knowledge Engineering Lab. Unska 3 Zagreb10000 Croatia KU Leuven Department of Computer Science Language Intelligence and Information Retrieval Group Celestijnenlaan 200A LeuvenB-3000 Belgium

ISBN: (纸本)9782951740884

In news stories, event mentions denote real-world events of different spatial and temporal granularity. Narratives in news stories typically describe some real-world event of coarse spatial and temporal granularity along with its subevents. In this work, we present HiEve, a corpus for recognizing relations of spatiotemporal containment between events. In HiEve, the narratives are represented as hierarchies of events based on relations of spatiotemporal containment (i.e., superevent-subevent relations). We describe the process of manual annotation of HiEve. Furthermore, we build a supervised classifier for recognizing spatiotemporal containment between events to serve as a baseline for future research. Preliminary experimental results are encouraging, with classifier performance reaching 58% F1-score, only 11% less than the inter-annotator agreement.

关键词： Event hierarchies Relation extraction Spatiotemporal containment

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RIVER: Reconfigurable flow and fabric for real-time signal processing on FPGAs

RIVER: Reconfigurable flow and fabric for real-time signal p...

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作者： Brugger, Christian Hillenbrand, Dominic Balzer, Matthias Microelectronic Systems Design Research Group University of Kaiserslautern Germany Green Computing Systems Research and Development Center Department of Computer Science and Engineering Waseda University Tokyo Japan Institute for Data Processing and Electronics Karlsruhe Institute of Technology Germany

For high-performance embedded hard-real-time systems, ASICs and FPGAs hold advantages over generalpurpose processors and graphics accelerators (GPUs). However, developing signal processing architectures from scratch requires significant resources. Our design methodology is based on sets of configurable building blocks that provide storage, dataflow, computation, and control. Based on our building blocks, we generate hundreds of thousands of our dynamic streaming engine processors that we call DSEs. We store our DSEs in a repository that can be queried for (online) design space exploration. From this repository, DSEs can be downloaded and instantiated within milliseconds on FPGAs. If a loss of flexibility can be tolerated then ASIC implementations are feasible as well. In this article we focus on FPGA implementations. Our DSEs vary in cores, computational lanes, bitwidths, power consumption, and frequency. To the best of our knowledge we are the first to propose online design space exploration based on repositories of precompiled cores that are assembled of common building blocks. For demonstration purposes we map algorithms for image processing and financial mathematics to DSEs and compare the performance to existing highly optimized signal and graphics accelerators. © 2014 ACM.

关键词： Field programmable gate arrays (FPGA)

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Principal Component Analysis Anomaly Detector for Rotor Broken Bars

Principal Component Analysis Anomaly Detector for Rotor Brok...

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Annual Conference of the IEEE Industrial Electronics Society

作者： Mohammed Obaid Mustafa George Georgoulas George Nikolakopoulos Control Engineering Group Department of Computer Science Electrical and Space Engineering Lulea University of Technology Laboratory of Knowledge and Intelligent Computing (KIC) Department of Computer Engineering Technological Educational Institute of Epirus

ISBN: (纸本)9781479940318

In this article a method for the detection of broken rotor bars in asynchronous machines operating under full load is presented. Unlike most Motor Current Signature Analysis (MCSA) approaches, which operate in the frequency domain, our method operates in the time domain. The scheme is based on the use of a Principal Component Analysis (PCA) fault/anomaly detector. PCA is applied on the three stator currents to subsequently calculate the Q statistic which is employed for detecting the presence/absence of a fault. The efficiency of the proposed scheme was experimentally evaluated using different fault severity levels, ranging from 1/4 of a broken bar to three broken bars. The obtained results indicate that the method can detect the caused asymmetry with a very restricted amount of data.

关键词： Principal component analysis Rotors Bars Induction motors Detectors Stators Fault detection

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A Comparative Study of Autonomous Learning Outlier Detection Methods Applied to Fault Detection

A Comparative Study of Autonomous Learning Outlier Detection...

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IEEE International Conference on Fuzzy Systems

作者： Clauber Gomes Bezerra Bruno Sielly Jales Costa Luiz Affonso Guedes Plamen Parvanov Angelov Campus EaD Federal Institute of Rio Grande do Norte - IFRN Natal Brazil Campus Natal - Zona Norte Federal Institute of Rio Grande do Norte - IFRN Natal Brazil Department of Computer Engineering and Automation Federal University of Rio Grande do Norte - UFRN Lancaster University Data Science Group School of Computing and Communications Lancaster LA1 4WA United Kingdom Chair of Excellence Carlos III University Madrid Spain

ISBN: (纸本)9781467374293

Outlier detection is a problem that has been largely studied in the past few years due to its great applicability in real world problems (e.g. financial, social, climate, security). Fault detection in industrial processes is one of these problems. In that context, several methods have been proposed in literature to address fault detection. In this paper we propose a comparative analysis of three recently introduced outlier detection methods: RDE, RDE with Forgetting and TEDA. Such methods were applied to the data set provided in DAMADICS benchmark, a very well-known real data tool for fault detection applications. The results, however, can be extended to similar problems of the area. Therewith, in this work we compare the main features of each method as well as the results obtained with them.

关键词： Comparative Study Autonomous Learning Outlier Detection Methods Applied Fault Detection

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Reports on the 2013 AAAI fall symposium series

Reports on the 2013 AAAI fall symposium series

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作者： Burns, Gully A.P.C. Gil, Yolanda Villanueva-Rosales, Natalia Liu, Yan Risi, Sebastian Lehman, Joel Clune, Jeff Lebiere, Christian Rosenbloom, Paul S. Van Harmelen, Frank Hendler, James A. Hitzler, Pascal Janowicz, Krzysztof Swarup, Samarth Biomedical Knowledge Engineering Group Information Sciences Institute University of Southern California United States Interactive Knowledge Capture Research Group Information Sciences Institute United States Department of Computer Science University of Southern California United States Department of Computer Science and Cyber-ShARE Center of Excellence University of Texas El Paso United States Center for Computer Games Research University of Copenhagen Denmark Department of Computer Science University of Texas Austin United States University of Wyoming United States Department of Psychology Carnegie Mellon University United States Institute for Creative Technologies United States Vrije Universiteit Amsterdam Netherlands Rensselaer Polytechnic Institute United States Wright State University United States University of California Santa Barbara United States Virginia Bioinformatics Institute Virginia Tech. United States

The Association for the Advancement of Artificial Intelligence was pleased to present the 2013 Fall Symposium Series, held Friday through Sunday, November 15-17, at the Westin Arlington Gateway in Arlington, Virginia, near Washington, D.C., USA. The titles of the five symposia were Discovery Informatics: AI Takes a science- Centered View on Big data (FS-13-01);How Should Intelligence Be Abstracted in AI Research: MDPs, Symbolic Representations, Artificial Neural Networks, or - ? (FS-13-02);Integrated Cognition (FS-13-03);Semantics for Big data (FS- 13-04);and Social Networks and Social Contagion: Web Analytics and Computational Social science (FS-13-05). The highlights of each symposium are presented in this report. Copyright © 2014, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Big data

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Ontology engineering in the era of linked data

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Bulletin of the Association for Information science and Technology 2015年第4期41卷

作者： Oscar Corcho Associate professor María Poveda-Villalón Ph.D. student Asunción Gómez-Pérez Full professor Universidad Politécnica de Madrid (UPM) and co-founder of the spin-off Localidata. His main research interests focus on linked data ontology-based data integration ontological engineering and semantic e-Science. Ontology Engineering Group (UPM). Her research activities focus on ontological engineering knowledge representation and the Semantic Web. UPM director of the artificial intelligence department and director of the Ontology Engineering Group. Her main research interests are ontologies semantic technologies linked data and the Semantic Web.

EDITOR'S SUMMARY Ontology engineering encompasses the method, tools and techniques used to develop ontologies. Without requiring ontologies, linked data is driving a paradigm shift, bringing benefits and drawbacks to the publishing world. Ontologies may be heavyweight, supporting deep understanding of a domain, or lightweight, suited to simple classification of concepts and more adaptable for linked data. They also vary in domain specificity, usability and reusabilty. Hybrid vocabularies drawing elements from diverse sources often suffer from internally incompatible semantics. To serve linked data purposes, ontology engineering teams require a range of skills in philosophy, computer science, web development, librarianship and domain expertise.

关键词： ontologies linked data index language construction information reuse logic professional competencies

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Incremental visualization of growing music collections 14

Incremental visualization of growing music collections

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14th International Society for Music Information Retrieval Conference, ISMIR 2013

作者： Stober, Sebastian Low, Thomas Gossen, Tatiana Nürnberger, Andreas Data and Knowledge Engineering Group Faculty of Computer Science University of Magdeburg DE Germany

ISBN: (纸本)9780615900650

Map-based visualizations – sometimes also called projections – are a popular means for exploring music collections. But how useful are they if the collection is not static but grows over time? Ideally, a map that a user is already familiar with should be altered as little as possible and only as much as necessary to reflect the changes of the underlying collection. This paper demonstrates to what extent existing approaches are able to incrementally integrate new songs into existing maps and discusses their technical limitations. To this end, Growing Self-Organizing Maps, (Landmark) Multidimensional Scaling, Stochastic Neighbor Embedding, and the Neighbor Retrieval Visualizer are considered. The different algorithms are experimentally compared based on objective quality measurements as well as in a user study with an interactive user interface. In the experiments, the well-known Beatles corpus comprising the 180 songs from the twelve official albums is used – adding one album at a time to the collection. © 2013 International Society for Music Information Retrieval.

关键词： Visualization

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