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Supercomputing Frontiers and Innovations 2014年第1期1卷 44-61页

作者： Fuhrer, Oliver Osuna, Carlos Lapillonne, Xavier Gysi, Tobias Cumming, Ben Bianco, Mauro Arteaga, Andrea Schulthess, Thomas C. Federal Office of Meteorology and Climatology MeteoSwiss Switzerland Center for Climate Systems Modeling C2SM ETH Zurich Switzerland Department of Computer Science ETH Zurich Switzerland Supercomputing Systems AG Zurich Switzerland Swiss National Supercomputing Centre ETH Zurich Switzerland Institute for Theoretical Physics ETH Zurich Switzerland Computer Science and Mathematics Division Oak Ridge National Laboratory United States

We propose a software implementation strategy for complex weather and climate models that produces performance portable, architecture agnostic codes. It relies on domain and data structure specific tools that are usable within common model development frameworks - Fortran today and possibly high-level programming environments like Python in the future. We present the strategy in terms of a refactoring project of the atmospheric model COSMO, where we have rewritten the dynamical core and refactored the remaining Fortran code. The dynamical core is built on top of the domain specific "Stencil Loop Language" for stencil computations on structured grids, a generic framework for halo exchange and boundary conditions, as well as a generic communication library that handles data exchange on a distributed memory system. All these tools are implemented in C++ making extensive use of generic programming and template metaprogramming. The refactored code is shown to outperform the current production code and is performance portable to various hybrid CPU-GPU node architectures.

关键词： Electronic data interchange

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Cosmic ray intensity variations in the 24th solar cycle observed by LAAS experiments 34

Cosmic ray intensity variations in the 24th solar cycle obse...

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34th International Cosmic Ray Conference, ICRC 2015

作者： Wada, Naoki Iyonoy, Atsushi Matsumoto, Hiroki Okei, Kazuhide Tsuji, Shuhei Ohara, Soji Ochi, Nobuaki Takahashi, Nobusuke Nakatsuka, Takao Yamamoto, Isao Yamamot, Saya Graduate School of Science Okayama University of Science 1-1 Ridai-cho Kita-ku Okayama-city Okayama700-0005 Japan Dept. of Fundamental Science Faculty of Science Okayama University of Science 1-1 Ridai-cho Kita-ku Okayama-city Okayama700-0005 Japan Kawasaki Medical School 577 Matsushima Kurashiki-cityOkayama701-0192 Japan Department of Informatics Naragakuen University Ikomagun-Nara6368503 Japan Faculty of Business Administration Toyo University 5-28-20 Hakusan Bunkyo-ku Tokyo112-8606 Japan Department of Advanced Physics Faculty of Science and Technology Hirosaki University 1 Bunkyo-cho Hirosaki-shi Aomori-ken036-8560 Japan Okayama Shoka University 2-10-1Tsushima Kyomachi Kita-ku Okayama-cityOkayama700-8601 Japan Department of Information and Computer Engineering Faculty of Engineering Okayama University of Science 1-1 Ridai-cho Kita-ku Okayama-city Okayama700-0005 Japan TOSHIBA MEMORY SYSTEMS Co. Ltd. Japan

In Large Area Air Shower experiments, one of EAS arrays is deployed on the second floor of four stories building in the campus of Okayama University of science to limit the zenith angle acceptance of muon detection. This system provide EAS muon intensities from zenith solid angles. To compare these intensities with solar activities such as flares, CME and magneticstorms, LAAS data have been analyzed. Faster data acquisition with Ha/CaK solar telescope have also been carried out. In this presentation, cosmic ray variations for four years since 2011 are going to be reported as well as fast image data.

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 1

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BMC Neurosci 2017年第SUPPL 1期18卷 58-58页

作者： Sue Denham Panayiota Poirazi Erik De Schutter Karl Friston Ho Ka Chan Thomas Nowotny Dongqi Han Sungho Hong Sophie Rosay Tanja Wernle Alessandro Treves Sarah Goethals Romain Brette Tomas Van Pottelbergh Rodolphe Sepulchre Alex D. Bird Hermann Cuntz Pedro J. Gonçalves Jan-Matthis Lueckmann Giacomo Bassetto Marcel Nonnenmacher Jakob H. Macke Audrey J. Sederberg Jason N. MacLean Stephanie E. Palmer Ulisse Ferrari Christophe Gardella Olivier Marre Thierry Mora Emina Ibrahimovic Martin Müller Jean-Pascal Pfister Tushar Chauhan Timothée Masquelier Alexandre Montlibert Benoit R. Cottereau Moritz Helias Jannis Schuecker David Dahmen Sven Goedeke Alexandre Hyafil Ainhoa Hermoso-Mendizabal Pavel E. Rueda-Orozco Santiago Jaramillo David Robbe Jaime de la Rocha Marie Rooy Fani Koukouli David DiGregorio Uwe Maskos Boris Gutkin Andrey Yu Verisokin Darya V. Verveyko Dmitry E. Postnov Willy Wong Omid Talakoub Robert Chen Milos Popovic Dmitriy Lisitsyn Eric Drebitz Iris Grothe Sunita Mandon Andreas Kreiter Udo Ernst Peter A. Robinson Xuelong Zhao Kevin M. Aquino John D. Griffiths Grishma Mehta-Pandejee Natasha Gabay James MacLaurin Somwrita Sarkar Tim Kunze Jens Haueisen Thomas R. Knösche Subutai Ahmad Yuwei Cui Marcus Lewis Jeff Hawkins Simona Olmi Spase Petkoski Fabrice Bartolomei Maxime Guye Viktor Jirsa Hazem Toutounji Daniel Durstewitz Matteo Cantarelli Adrian Quintana Boris Marin Matt Earnshaw Padraig Gleeson Robert Court Robert McDougal R. Angus Silver Salvador Dura-Bernal Stephen Larson William W. Lytton Giovanni Idili Lorenzo Posani Simona Cocco Karel Ježek Rémi Monasson School of Psychology Faculty of Health & Human Sciences Plymouth University Plymouth Devon PL4 8AA UK Institute of Molecular Biology and Biotechnology (IMBB) Foundation for Research and Technology-Hellas (FORTH) Heraklion Crete Greece Okinawa Institute of Science and Technology Graduate University Okinawa Japan Wellcome Trust Centre for Neuroimaging Institute of Neurology UCL London WC1N 3BG UK School of Engineering and Informatics University of Sussex Brighton BN1 9QJ UK Graduate School Okinawa Institute of Science and Technology Okinawa 904-0495 Japan Computational Neuroscience Unit Okinawa Institute of Science and Technology Okinawa 904-0495 Japan Cognitive Neuroscience Area Scuola Internazionale Superiore di Studi Avanzati Trieste 34136 Italy Kavli Institute for Systems Neuroscience NTNU Trondheim 7030 Norway Institut de la Vision Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Paris 75012 France Department of Engineering University of Cambridge Cambridge Cambridgeshire CB2 1PZ UK Ernst Strüngmann Institute for Neuroscience Max Planck Society Frankfurt Germany Frankfurt Institute for Advanced Studies Frankfurt Germany Neural Systems Analysis Research Center Caesar An Associate of the Max Planck Society Bonn Germany Max Planck Institute for Biological Cybernetics Tübingen Germany Bernstein Center for Computational Neuroscience Tübingen Germany Present Address: Biomedical Engineering Department Georgia Institute of Technology Atlanta GA 30306 USA Department of Neurobiology University of Chicago Chicago IL 60637 USA Department of Organismal Biology and Anatomy University of Chicago Chicago IL 60637 USA Committee on Computational Neuroscience University of Chicago Chicago IL 60637 USA Institut de la Vision Sorbonne Universités INSERM Paris France Laboratoire de Physique Statistique Ecole Normale Supérieure Paris France Institute of Molecular Life Sciences University of Zürich 8057 Zürich Switzerland Institute of Neuroinformatics Univer

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Streaming model transformations by complex event processing

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8767卷 68-83页

作者： Dávid, István Ráth, István Varró, Dániel Budapest University of Technology and Economics Department of Measurement and Information Systems Magyar tudósok krt. 2 Budapest1117 Hungary DIRO Université de Montréal Canada MSDL Dept. of Computer Science McGill University Montréal Canada

Streaming model transformations represent a novel class of transformations dealing with models whose elements are continuously produced or modified by a background process [1]. Executing streaming transformations requires efficient techniques to recognize the activated transformation rules on a potentially infinite input stream. Detecting a series of events triggered by compound structural changes is especially challenging for a high volume of rapid modifications, a characteristic of an emerging class of applications built on runtime models. In this paper, we propose a novel approach for streaming model transformations by combining incremental model query techniques with complex event processing (CEP) and reactive (event-driven) transformations. The event stream is automatically populated from elementary model changes by the incremental query engine, and the CEP engine is used to identify complex event combinations, which are used to trigger the execution of transformation rules. We demonstrate our approach in the context of automated gesture recognition over live models populated by Kinect sensor data. © Springer International Publishing Switzerland 2014.

关键词： Engines

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Agents in grid extended to clouds

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AIP Conference Proceedings 2016年第1期1773卷

作者： K. Wasielewska M. Ganzha M. Paprzycki C. Bădică M. Ivanovic I. Lirkov S. Fidanova 1Systems Research Institute Polish Academy of Sciences Warsaw Poland 2Warsaw University of Technology Warsaw Poland 3Warsaw Management Academy Warsaw Poland 4Department of Computer Science University of Craiova Craiova Romania 5Department of Mathematics and Informatics Faculty of Sciences University of Novi Sad Novi Sad Serbia 6Institute of Information and Communication Technologies Bulgarian Academy of Sciences Sofia Bulgaria

The presented work is an attempt to extend considerations from the Agents in Grid (AiG) project to the Clouds. The AiG project is aimed at the development of an agent-semantic infrastructure for efficient resource management in the grid. Decision support within the AIG system helps the user, without in-depth knowledge, to choose optimal algorithm and/or resource to solve a problem from a given domain, and later to choose the best contract defining terms of collaboration with the provider of a resource used to solve the problem. Cloud computing refers to an architecture, in which groups of remote servers are networked, to allow online access to computer services or resources. The general vision is the same as in the case of computational grids, i.e., to reduce cost of computing, as well as to increase flexibility and reliability of the infrastructure. However, there are also important differences. It is relatively easy to notice that solutions considered in the context of the AiG system can be easily extended to computational clouds that evolved from computational grids. As it was shown in the case of grids, integrating software agents, semantics and cloud computing could enable highly efficient, intelligent systems, making clouds even more flexible, autonomic and usable.

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International Society for Therapeutic Ultrasound Conference 2016

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Journal of Therapeutic Ultrasound 2017年第1期5卷 1-114页

作者： Brian Fowlkes Pejman Ghanouni Narendra Sanghvi Constantin Coussios Paul C. Lyon Michael Gray Christophoros Mannaris Marie de Saint Victor Eleanor Stride Robin Cleveland Robert Carlisle Feng Wu Mark Middleton Fergus Gleeson Jean-Franҫois Aubry Kim Butts Pauly Chrit Moonen Jacob Vortman Shirley Sharabi Dianne Daniels David Last David Guez Yoav Levy Alexander Volovick Javier Grinfeld Itay Rachmilevich Talia Amar Zion Zibly Yael Mardor Sagi Harnof Michael Plaksin Yoni Weissler Shy Shoham Eitan Kimmel Omer Naor Nairouz Farah Dong-Guk Paeng Zhiyuan Xu John Snell Anders H. Quigg Matthew Eames Changzhu Jin Ashli C. Everstine Jason P. Sheehan Beatriz S. Lopes Neal Kassell Thomas Looi Vera Khokhlova Charles Mougenot Kullervo Hynynen James Drake Michael Slayton Richard C. Amodei Keegan Compton Ashley McNelly Daniel Latt John Kearney David Melodelima Aurelien Dupre Yao Chen David Perol Jeremy Vincenot Jean-Yves Chapelon Michel Rivoire Wei Guo Guoxin Ren Guofeng Shen Michael Neidrauer Leonid Zubkov Michael S. Weingarten David J. Margolis Peter A. Lewin Nathan McDannold Jonathan Sutton Natalia Vykhodtseva Margaret Livingstone Thiele Kobus Yong-Zhi Zhang Michael Schwartz Yuexi Huang Nir Lipsman Jennifer Jain Martin Chapman Tejas Sankar Andres Lozano Robert Yeung Christakis Damianou Nikolaos Papadopoulos Omer Brokman Eyal Zadicario Ori Brenner David Castel Shih-Ying Wu Julien Grondin Wenlan Zheng Marc Heidmann Maria Eleni Karakatsani Carlos J. Sierra Sánchez Vincent Ferrera Elisa E. Konofagou Marinos Yiannakou HongSeok Cho Hwayoun Lee Mun Han Jong-Ryul Choi Taekwan Lee Sanghyun Ahn Yongmin Chang Juyoung Park Nicholas Ellens Ari Partanen Keyvan Farahani Raag Airan Alexandre Carpentier Michael Canney Alexandre Vignot Cyril Lafon Jean-yves Delattre Ahmed Idbaih Henrik Odéen Bradley Bolster Eun Kee Jeong Dennis L. Parker Pooja Gaur Xue Feng Samuel Fielden Craig Meyer Beat Werner William Grissom Michael Marx Hans Weber Valentina Taviani Brian Hargreaves Jun Tanaka Kentaro Kikuchi Ayumu Ishijima Takashi Azuma Kosuke Minamihata Satoshi Basic Radiological Sciences Division Department of Radiology University of Michigan Ann Arbor USA Stanford School of Medicine Stanford USA SonaCare Medical Indianapolis USA Institute of Biomedical Engineering University of Oxford Oxford UK HIFU Unit Churchill Hospital Oxford UK Department of Oncology University of Oxford Oxford UK Department of Radiology Churchill Hospital Oxford UK Institut Langevin Paris France Center for Imaging Sciences Imaging Division University Medical Center Utrecht The Netherlands InSighTec Haifa Israel Sheba Medical Center Ramat-Gan Israel Tel-Aviv University Tel-Aviv Israel Faculty of Biomedical Engineering & Russell Berrie Nanotechnology Institute Technion – Israel Institute of Technology Haifa Israel ELSC Center for Brain Sciences Hebrew University Jerusalem Israel Biomedical Engineering Technion- Israel Institute of Technology Haifa Israel Faculty of Life Sciences Bar Ilan University Ramat Gan Israel Focused Ultrasound Foundation Charlottesville USA Ocean System Engineering Jeju National University Jeju Republic of Korea Neurosurgery University of Virginia Charlottesville USA Biology University of Virginia Charlottesville USA Pathology University of Virginia Charlottesville USA Hospital for Sick Children Toronto Canada University of Toronto Toronto Canada University of Washington Seattle USA Philips Healthcare Markham Canada Sunnybrook Research Institute Toronto Canada Guided Therapy Systems LLC Mesa USA School of Medicine University of Arizona Tucson USA The CORE Institute Phoenix USA LabTAU - U1032 INSERM Lyon France Centre Leon Berard Lyon France Department of Oral Maxillofacial and Head Neck Oncology Shanghai 9th People’s Hospital Shanghai China The School of Biomedical Engineering Science and Health Systems Drexel University Philadelphia USA Department of Surgery College of Medicine Drexel University Philadelphia USA Biostatistics and Epidemiology University of Pennsylvania Philadelphi

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Integrated vehicle control of in-wheel electric vehicle

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Periodica Polytechnica Transportation Engineering 2014年第1期42卷 19-25页

作者： Mihály, András Németh, Balázs Gáspár, Péter Department of Control for Transportation and Vehicle Systems Budapest University of Technology and Economics Stoczek J. u. 2 H-1111 Budapest Hungary Institute for Computer Science and Control Hungarian Academy of Sciences Kende u. 13-17 H-1111 Budapest Hungary

The paper presents a control method for in-wheel electric vehicles, integrating several vehicle components such as steering, braking and propulsion system. The aim is to design a cruise control system with trajectory tracking ability. First a control oriented vehicle model is introduced and the trajectory and velocity tracking problem is formulated. Next the robust LPV (Linear Parameter Varying) trajectory tracking cruise control design is introduced. In the implementation of the integrated control system, the limitations of the actuators has to be considered as well as the road surface adhesion. The operation of the proposed system is demonstrated in CarSim simulation environment with maneuvering the vehicle along a racetrack with altering speed limits.

关键词： Maneuverability

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Global Nonlinear Kernel Prediction for Large Data Set With a Particle Swarm-Optimized Interval Support Vector Regression

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IEEE Transactions on Neural Networks and Learning systems 2015年第10期26卷 2521-34页

作者： Ding, Yongsheng Cheng, Lijun Pedrycz, Witold Hao, Kuangrong Engineering Research Center of Digitized Textile and Apparel Technology Ministry of Education Shanghai 201620 China College of Information Science and Technology Donghua University Shanghai 201620 China Centers for Computational Biology and Bioinformatics Department of Medical and Molecular Genetics School of Medicine Indiana University Indianapolis 46202 IN United States Department of Electrical and Computer Engineering University of Alberta Edmonton T6G 2R3 AB Canada Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah 21589 Saudi Arabia Systems Research Institute Polish Academy of Sciences Warsaw 00-656 Poland

A new global nonlinear predictor with a particle swarm-optimized interval support vector regression (PSO-ISVR) is proposed to address three issues (viz., kernel selection, model optimization, kernel method speed) encountered when applying SVR in the presence of large data sets. The novel prediction model can reduce the SVR computing overhead by dividing input space and adaptively selecting the optimized kernel functions to obtain optimal SVR parameter by PSO. To quantify the quality of the predictor, its generalization performance and execution speed are investigated based on statistical learning theory. In addition, experiments using synthetic data as well as the stock volume weighted average price are reported to demonstrate the effectiveness of the developed models. The experimental results show that the proposed PSO-ISVR predictor can improve the computational efficiency and the overall prediction accuracy compared with the results produced by the SVR and other regression methods. The proposed PSO-ISVR provides an important tool for nonlinear regression analysis of big data.

关键词： Computational efficiency

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The three dimensional visualization growth of bone tissue in microstructure of surface analysis using drishti open-source software

Advances in Intelligent Systems and Computing

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Advances in Intelligent systems and Computing 2014年 283卷 91-102页

作者： Stolarz, Mateusz Ficek, Krzysztof Binkowski, Marcin Wójcicka, Anna Wróbel, Zygmunt X-ray Microtomography Laboratory Department of Biomedical Computer Systems University of Silesia Institute of Computer Science Bedzinska 39 Sosnowiec41-200 Poland Department of Orthopedics and Traumatology City Hospital in Zabrze Zamkowa 4 Zabrze41-803 Poland Galen-Orthopaedics Jerzego 6 Bierun43-150 Poland Department of Technology and Engineering of Material Pedagogical University Institute of Technology Podchorążych 2 Cracow30-084 Poland

Nowadays, computed tomography and three dimensional visualization provide anatomic images structures with an impressive richness of anatomical details. They are ubiquitous used in various fields of medical knowledge. In addition, X-ray microtomography (XMT) next to standard quantitative computed tomography (QCT) provide data with much higher spatial resolution. Use them for three dimensional visualization of the surface of animal tissue for macroscopic and microscopic analysis of the structure of tissue is a tool of immense possibilities that successfully is widely use in structural studies of hard tissues. The research article presents the disadvantages and advantages of the creation and use of three dimensional visualization of images using Drishti opensource software on the example of growth of sheep bone tissue. © Springer International Publishing Switzerland 2014.

关键词： Open source software

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11th German Conference on Chemoinformatics (GCC 2015) Abstracts

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JOURNAL OF CHEMINFORMATICS 2016年第SUPPL 1期8卷 1-27页

作者： [Anonymous] GDCh-CIC Division Associated Board Member Beilstein-Institut zur Förderung der Chemischen Wissenschaften Trakehner Str. 7-9 60487 Frankfurt Germany. ufechner@beilstein-institut.de. Division Medicinal Chemistry Amsterdam Institute for Molecules Medicines and Systems (AIMMS) VU University Amsterdam The Netherlands. Centre for Bioinformatics Uni Hamburg Bundesstr. 43 20146 Hamburg Germany. Sanofi-Aventis Deutschland GmbH 65926 Frankfurt am Main Germany. stefan.guessregen@***. Sanofi-Aventis Deutschland GmbH 65926 Frankfurt am Main Germany. GlaxoSmithKline Stevenage SG1 2NY UK. nicola.j.richmond@***. Discngine Paris 75011 France. Organisch-Chemisches Institut Westfälische Wilhelms-Universität Münster Germany. marwin.segler@wwu.de. Organisch-Chemisches Institut Westfälische Wilhelms-Universität Münster Germany. Bundeskriminalamt Wiesbaden Central Analytics II 65173 Wiesbaden Germany. Department of Chemistry and Biochemistry University of California Santa Barbara CA 93111 USA. shea@chem.ucsb.edu. Department of Chemistry and Biochemistry University of California Santa Barbara CA 93111 USA. Chemistry Department Ludwig-Maximilians-Universität München Butenandtstr. 7 81377 Munich Germany. Inorganic Chemistry and Center for Nanointegration University of Duisburg-Essen Essen Germany. CAM-D Technologies Essen Germany. Institute for Bioinformatics and Chemoinformatics Westphalian University of Applied Sciences Recklinghausen Germany. Institute for Bioinformatics and Chemoinformatics Westphalian University of Applied Sciences Recklinghausen Germany. achim.zielesny@w-hs.de. Leiden University Leiden Netherlands. j.fraaije@chem.leidenuniv.nl. Culgi BV Berlin Germany. Physikalische Chemie III TU Dortmund 44227 Dortmund Germany. stefan.kast@tu-dortmund.de. Centre for Molecular Informatics Department of Chemistry University of Cambridge Lensfield Road Cambridge CB2 1EW United Kingdom. kcb27@cam.ac.uk. Centre for Molecular Informatics Department of Chemistry

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