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IEEE International Conference on Bioinformatics and Bioengineering

作者： Vasileios G. Kanas Evangelia I. Zacharaki Evangelia Pippa Vasiliki Tsirka Michael Koutroumanidis Vasileios Megalooikonomou Multidimensional Data Analysis and Knowledge Management Laboratory Dept. of Computer Engineering and Informatics University of Patras Dept. of Clinical Neurophysiology and Epilepsies Guy's & St. Thomas' and Evelina Hospital for Children

ISBN: (纸本)9781467379847

Misdiagnosis of epilepsy, even by experienced clinicians, can cause exposure of patients to medical procedures and treatments with potential complications. Moreover, diagnostic delays (for 7 to 10 years on average) impose economic burden at individual and population levels. In this paper, a seizure classification framework of epileptic and non-epileptic events from multi-channel EEG data is proposed. In contrast to relevant studies found in the literature, in this study, the non-epileptic class consists of two types of paroxysmal episodes of loss of consciousness, namely the psychogenic non-epileptic seizure (PNES) and the vasovagal syncope (VVS). EEG signals are represented in the spectral-spatial-temporal domain. A tensor-based approach is employed to extract signature features to feed the classification models. TUCKER decomposition is applied to learn the essence of original, high-dimensional domain of feature space and extract a multilinear discriminative subspace. The classification models were evaluated on EEG epochs from 11 subjects in an inter-subject cross-validation setting and achieved an accuracy of 96%.

关键词： Electroencephalography Feature extraction Tensile stress Epilepsy Brain models Training

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Direct evidence for shock-powered optical emission in a nova

arXiv

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arXiv 2020年

作者： Aydi, Elias Sokolovsky, Kirill V. Chomiuk, Laura Steinberg, Elad Li, Kwan Lok Vurm, Indrek Metzger, Brian D. Strader, Jay Mukai, Koji Pejcha, Ondřej Shen, Ken J. Wade, Gregg A. Kuschnig, Rainer Moffat, Anthony F.J. Pablo, Herbert Pigulski, Andrzej Popowicz, Adam Weiss, Werner Zwintz, Konstanze Izzo, Luca Pollard, Karen R. Handler, Gerald Ryder, Stuart D. Filipović, Miroslav D. Alsaberi, Rami Z.E. Manojlović, Perica de Oliveira, Raimundo Lopes Walter, Frederick M. Vallely, Patrick J. Buckley, David A.H. Brown, Michael J.I. Harvey, Eamonn J. Kawash, Adam Kniazev, Alexei Kochanek, Christopher S. Linford, Justin Mikolajewska, Joanna Molaro, Paolo Orio, Marina Page, Kim L. Shappee, Benjamin J. Sokoloski, Jennifer L. Center for Data Intensive and Time Domain Astronomy Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Sternberg Astronomical Institute Moscow State University Universitetskii pr. 13 Moscow119992 Russia Astro Space Center of Lebedev Physical Institute Profsoyuznaya St. 84/32 Moscow117997 Russia Columbia Astrophysics Laboratory Department of Physics Columbia University New YorkNY10027 United States Racah Institute of Physics Hebrew University Jerusalem91904 Israel Department of Physics UNIST Ulsan44919 Korea Republic of Institute of Astronomy National Tsing Hua University Hsinchu30013 Taiwan Tartu Observatory University of Tartu Tõravere Tartumaa61602 Estonia CRESST X-ray Astrophysics Laboratory NASA GSFC GreenbeltMD20771 United States Department of Physics University of Maryland Baltimore County 1000 Hilltop Circle BaltimoreMD21250 United States Institute of Theoretical Physics Faculty of Mathematics and Physics Charles University Prague Czech Republic Department of Astronomy Theoretical Astrophysics Center University of California BerkeleyCA94720 United States Department of Physics and Space Science Royal Military College of Canada PO Box 17000 Station Forces KingstonONK7K 7B4 Canada Institute of Communication Networks and Satellite Communications Graz University of Technology Infeldgasse 12 Graz8010 Austria Dépt. de Physique Univ. De Montréal C.P. 6128 Succ. Centre-Ville Centre de Recherche en Astrophysique du Quéebec MontréealQCH3C 3J7 Canada AAVSO 49 Bay State Rd CambridgeMA02138 United States Instytut Astronomiczny Uniwersytet Wroclawski Kopernika 11 Wroclaw51-622 Poland Silesian University of Technology Institute of Electronics Akademicka 16 Gliwice44-100 Poland Institute for Astrophysics University of Vienna Tuerkenschanzstrasse 17 ViennaA-1180 Austria Universität Innsbruck Institut für Astro- und Teilchenphysik Technikerstrasse 25 InnsbruckA-6020 Austria D

Classical novae are thermonuclear explosions that occur on the surfaces of white dwarf stars in interacting binary systems1. It has long been thought that the luminosity of classical novae is powered by continued nuclear burning on the surface of the white dwarf after the initial runaway2. However, recent observations of GeV γrays from classical novae have hinted that shocks internal to the nova ejecta may dominate the nova emission. Shocks have also been suggested to power the luminosity of events as diverse as stellar mergers3, supernovae4, and tidal disruption events5, but observational confirmation has been lacking. Here we report simultaneous space-based optical and γ-ray observations of the 2018 nova V906 Carinae (ASASSN-18fv), revealing a remarkable series of distinct correlated flares in both bands. The optical and γ-ray flares occur simultaneously, implying a common origin in shocks. During the flares, the nova luminosity doubles, implying that the bulk of the luminosity is shock-powered. Furthermore, we detect concurrent but weak X-ray emission from deeply embedded shocks, confirming that the shock power does not appear in the X-ray band and supporting its emergence at longer wavelengths. Our data, spanning the spectrum from radio to γ-ray, provide direct evidence that shocks can power substantial luminosity in classical novae and other optical transients. Copyright © 2020, The Authors. All rights reserved.

关键词： Luminance

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PennyLane: Automatic differentiation of hybrid quantum-classical computations

arXiv

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arXiv 2018年

作者： Bergholm, Ville Izaac, Josh Schuld, Maria Gogolin, Christian Ahmed, Shahnawaz Ajith, Vishnu Alam, M. Sohaib Alonso-Linaje, Guillermo AkashNarayanan, B. Asadi, Ali Arrazola, Juan Miguel Azad, Utkarsh Banning, Sam Blank, Carsten Bromley, Thomas R. Cordier, Benjamin A. Ceroni, Jack Delgado, Alain Matteo, Olivia Di Dusko, Amintor Garg, Tanya Guala, Diego Hayes, Anthony Hill, Ryan Ijaz, Aroosa Isacsson, Theodor Ittah, David Jahangiri, Soran Jain, Prateek Jiang, Edward Khandelwal, Ankit Kottmann, Korbinian Lang, Robert A. Lee, Christina Loke, Thomas Lowe, Angus McKiernan, Keri Meyer, Johannes Jakob Montañez-Barrera, J.A. Moyard, Romain Niu, Zeyue O’Riordan, Lee James Oud, Steven Panigrahi, Ashish Park, Chae-Yeun Polatajko, Daniel Quesada, Nicolás Roberts, Chase Sá, Nahum Schoch, Isidor Shi, Borun Shu, Shuli Sim, Sukin Singh, Arshpreet Strandberg, Ingrid Soni, Jay Száva, Antal Thabet, Slimane Vargas-Hernández, Rodrigo A. Vincent, Trevor Vitucci, Nicola Weber, Maurice Wierichs, David Wiersema, Roeland Willmann, Moritz Wong, Vincent Zhang, Shaoming Killoran, Nathan Xanadu 777 Bay Street Toronto Canada Wallenberg Centre for Quantum Technology Department of Microtechnology and Nanoscience Chalmers University of Technology Gothenburg412 96 Sweden Indian Institute of Information Technology Kottayam India NASA Ames Research Center Moffett FieldCA94035 United States Mountain ViewCA94043 United States data cybernetics Martin-Kolmsperger-Str 26 Landsberg86899 Germany Department of Medical Informatics and Clinical Epidemiology Oregon Health and Science University PortlandOR97202 United States Dept. of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada Department of Physics Indian Institute of Technology Roorkee Uttarakhand Roorkee India qBraid 5235 South Harper Court ChicagoIL60615 United States Factal Analytics Level 2 Chimes Building Plot 61 Sector - 44 Haryana Gurgaon122003 India Centre for High Energy Physics Indian Institute of Science Karnataka Bengaluru560012 India ICFO - Institut de Ciencies Fotoniques The Barcelona Institute of Science and Technology Av. Carl Friedrich Gauss 3 Castelldefels Barcelona08860 Spain Chemical Physics Theory Group Department of Chemistry University of Toronto Canada DUG Technology 76 Kings Park Rd West PerthWA6005 Australia Rigetti Computing 2919 Seventh Street BerkeleyCA94710 United States Dahlem Center for Complex Quantum Systems Freie Universität Berlin Berlin14195 Germany Institute for Advanced Simulation Jülich Supercomputing Centre Forschungszentrum Jülich Jülich52425 Germany University of Amsterdam Netherlands School of Physical Sciences National Institute of Science Education and Research HBNI Odisha Jatni752 050 India Cervest United Kingdom Centro Brasileiro de Pesquisas Físicas Rua Dr. Xavier Sigaud 150 Rio de Janeiro22290-180 Brazil Zurich8092 Switzerland Neo4j UK Ltd United Kingdom Zapata Computing Inc. United States ITC Infotech Bangalore India Department of Microtechnology and Nanoscienc

PennyLane is a Python 3 software framework for differentiable programming of quantum computers. The library provides a unified architecture for near-term quantum computing devices, supporting both qubit and continuous-variable paradigms. PennyLane’s core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation. PennyLane thus extends the automatic differentiation algorithms common in optimization and machine learning to include quantum and hybrid computations. A plugin system makes the framework compatible with any gate-based quantum simulator or hardware. We provide plugins for hardware providers including the Xanadu Cloud, Amazon Braket, and IBM Quantum, allowing PennyLane optimizations to be run on publicly accessible quantum devices. On the classical front, PennyLane interfaces with accelerated machine learning libraries such as TensorFlow, PyTorch, JAX, and Autograd. PennyLane can be used for the optimization of variational quantum eigensolvers, quantum approximate optimization, quantum machine learning models, and many other applications. Copyright © 2018, The Authors. All rights reserved.

关键词： Machine learning

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FML-based Intelligent Adaptive Assessment Platform for Learning Materials Recommendation

FML-based Intelligent Adaptive Assessment Platform for Learn...

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IEEE International Conference on Fuzzy Systems

作者： Chang-Shing Lee Mei-Hui Wang Jian-Lin Yu Koun-Hong Lin Ting-Tzu Lin Sheng-Chi Yang Sheng-Lun Cho Dept. of Computer Science and Information Engineering National University of Tainan Center for Research of Knowledge Application & Web Service National University of Tainan

ISBN: (纸本)9781467374293

There are many students learning their academic studies via on-line education platform with many learning materials;however, how to select learning materials that exactly fit to their competence is not easy for them. This paper proposes an intelligent adaptive assessment platform (IAAP) to allow students to do adaptive testing to assess their learning ability. Additionally, this paper also proposes an FML-based fuzzy inference mechanism to infer the rank of the recommended learning materials based on the constructed fuzzy ontology, including knowledge base and rule base. The learning-material recommendation mechanism then outputs the recommended learning materials to the students based on the constructed ontologies and the inferred rank of the recommended learning materials. Finally, after learning, students provide a feedback to the IAAP and starts next learning iteration to achieve the goal of students' learning progress. Experimental results show that the developed IAAP is able to correctly estimate students' ability and the proposed approach is feasible for learning materials' recommendation and self-learning.

关键词： Fuzzy Ontology Learning Materials Recommendation Adaptive Assessment Fuzzy Markup Language

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Clustering EU's Countries According to I. Th. Mazi's Systemic Geopolitical Theory Using K-Means and MPI

Clustering EU's Countries According to I. Th. Mazi's Systemi...

引用

IEEE International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises (WET ICE)

作者： Ilias K. Savvas Alekos Stogiannos Ioannis Th. Mazis Dept. of Computer Science and Engineering Software and Knowledge Engineering Lab Larissa Greece Faculty of Turkish and Modern Asian Studies National and Kapodistrian University of Athens Greece

As a geographical method of analyzing power redistribution, Systemic Geopolitical Analysis (according to Ioannis Th. Mazis theoretical basis) proposes a multi-dimensional, interdisciplinary research pattern, which embraces economic, cultural, political and defensive facts. The amount of data produced combining these attributes is extremely large and complex. One of the solutions to explore and analyze this data is clustering it and one of the most popular and useful techniques in order to group data within appropriate sets is k-means algorithm which clusters data according to its characteristics. The main disadvantage is its computational complexity which makes the technique very difficult to apply on big and dynamic data sets. In this study, a parallel version of k-means is used in order to cluster the European Union countries according to their attributes and the results obtained prove the importance of this research.

关键词： Peer-to-peer computing Cultural differences Europe Geography Economics Clustering algorithms Complexity theory

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Eye blink artifact removal in eeg using tensor decomposition

IFIP Advances in Information and Communication Technology

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IFIP Advances in Information and Communication Technology 2014年 437卷 155-164页

作者： Triantafyllopoulos, Dimitrios Megalooikonomou, Vasileios Multidimensional Data Analysis and Knowledge Management Laboratory Dept. of Computer Engineering and Informatics University of Patras Rion-Patras26500 Greece

EEG data are usually contaminated with signals related to subject's activities, the so called artifacts, which degrade the information contained in recordings. The removal of this additional information is essential to the improvement of EEG signals' interpretation. The proposed method is based on the analysis, using Tucker decomposition, of a tensor constructed using continuous wavelet transform. Our contribution is an automatic method which processes simultaneously spatial, temporal and frequency information contained in EEG recordings in order to remove eye blink related information. The proposed method is compared with a matrix based removal method and shows promising results regarding reconstruction error and retaining the texture of the artifact free signal. © IFIP International Federation for Information Processing 2014.

关键词： Tensors

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Comparative evaluation of feature extraction methods for human motion detection

IFIP Advances in Information and Communication Technology

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IFIP Advances in Information and Communication Technology 2014年 437卷 146-154页

作者： Politi, Olga Mporas, Iosif Megalooikonomou, Vasileios Multidimensional Data Analysis and Knowledge Management Laboratory Dept. of Computer Engineering and Informatics University of Patras Rion-Patras26500 Greece

In this article we conduct an evaluation of feature extraction methods for the problem of human motion detection based on 3-dimensional inertial sensor data. For the purpose of this study, different preprocessing methods are used, and statistical as well as physical features are extracted from the motion signals. At each step, state-of-the-art methods are applied, and the produced results are finally compared in order to evaluate the importance of the applied feature extraction and preprocessing combinations, for the human activity recognition task. © IFIP International Federation for Information Processing 2014.

关键词： Motion estimation

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159 Dermoscopy versus videocapillaroscopy in the assessment of nailfold capillaroscopy images in patients with systemic sclerosis and healthy controls

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Rheumatology 2018年第SUPPL_3期57卷

作者： Dinsdale, Graham Peytrignet, Sebastien Moore, Tonia Berks, Michael Roberts, Christopher Manning, Joanne Allen, John Anderson, Marina Cutolo, Maurizio Hesselstrand, Roger Howell, Kevin Pizzorni, Carmen Smith, Vanessa Sulli, Alberto Wildt, Marie Taylor, Christopher Murray, Andrea Herrick, Ariane Centre for Musculoskeletal Research University of Manchester Salford Royal Hospital NHS Foundation Trust MAHSC Manchester UNITED KINGDOM Centre for Imaging Sciences Division of Informatics Imaging & Data Sciences University of Manchester Manchester UNITED KINGDOM Centre for Biostatistics Division of Population Health Health Services Research & Primary Care University of Manchester Manchester UNITED KINGDOM Microvascular Diagnostics Northern Medical Physics and Clinical Engineering Freeman Hospital Newcastle-upon-Tyne UNITED KINGDOM Institute of Ageing and Chronic Disease University of Liverpool Liverpool UNITED KINGDOM Research Laboratory and Academic Division of Clinical Rheumatology Department of Internal Medicine University of Genova Genova ITALY Department of Clinical Sciences Section of Rheumatology Lund University Lund SWEDEN Institute of Immunity and Transplantation University College London Royal Free Campus London UNITED KINGDOM Research Laboratory and Academic Division of Clinical Rheumatology Dept. Internal Medicine University of Genova Genova ITALY Department of Rheumatology Ghent University Hospital Faculty of Internal Medicine Ghent University Ghent BELGIUM Research Laboratory and Academic Division of Clinical Rheumatology Dept. Internal Medicine University of Genova Manchester ITALY NIHR Manchester Musculoskeletal Biomedical Research Centre Central Manchester NHS Foundation Trust Manchester Academic Health Science Centre Manchester UNITED KINGDOM

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Geological evidence for Geode AI

引用

Journal of Mines, Metals and Fuels 2015年第8期63卷 237-238页

作者： Singh, Ankuj Mahor, Ajay Singh, Hargyan Singh, N.P. Saraswat, Pradeep Singh, Bharat Swarup, R. Dept. of Computer Science Vivekananda College of Technology and Management Aligarh India Accurate Institute of Tech. Prince Institute of Innovative Technology Knowledge Park Greater Noida India IEC College of Engineering and Technology India Aligarh India

Al is regarded as self-decision making computational systems. The prevailing status of AI is mainly restricted up to intelligent software programming as well as AI generation from clouding process. On AI equipped hardware front discovery of super intelligent matters is still in the preliminary phase. An attempt is being made to yield AI on geo-matters, high temperature superconductors (HTS) an other anatomic and unantomic matters under magneto radio frequency stimulations. The magnetic field H between 0-20,000 gauss, radio frequency F 0-7 MHz had been used in this experimentation.

关键词： High temperature superconductors

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Empirical evaluation of software development effort estimation based on upper phase development activity

Computer Software

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Computer Software 2014年第2期31卷 129-143页

作者： Tsunoda, Masateru Toda, Koji Fushida, Kyohei Kamei, Yasutaka Ubayashi, Naoyasu Dept. of Informatics Kinki University Japan Dept. of Information and Systems Engineering Fukuoka Institute of Technology Japan Research and Development Headquarters NTT DATA Corporation Japan Graduate School Faculty of Information Science and Electrical Engineering Kyushu University Japan

We focus on effort estimation based on the effort for early phase activities, and built effort estimation models using early phase effort as an explanatory variable, and compared the estimation accuracies of these models to the effort estimation models based on software size. In addition, we built estimation models using both early phase effort and software size. In our experiment, we used ISBSG dataset, which was collected from software development companies, and regarded planning phase effort and requirement analysis effort as early phase effort. The result of the experiment showed that when both software size and sum of planning and requirement analysis phase effort were used as explanatory variables, the estimation accuracy was most improved (Average Balanced Relative Error was improved to 75.4% from 148.4%).

关键词： Software design

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