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arXiv 2021年

作者： Ajello, M. Baldini, L. Bastieri, D. Bellazzini, R. Berretta, A. Bissaldi, E. Blandford, R.D. Bonino, R. Bruel, P. Buson, S. Cameron, R.A. Caputo, R. Cavazzuti, E. Cheung, C.C. Chiaro, G. Costantin, D. Cutini, S. D’Ammando, F. de Palma, F. Desiante, R. Di Lalla, N. Di Venere, L. Fana Dirirsa, F. Fegan, S.J. Fukazawa, Y. Funk, S. Fusco, P. Gargano, F. Gasparrini, D. Giordano, F. Giroletti, M. Green, D. Guiriec, S. Hays, E. Hewitt, J.W. Horan, D. Jóhannesson, G. Kovacevic, M. Kuss, M. Larsson, S. Latronico, L. Li, J. Longo, F. Lovellette, M.N. Lubrano, P. Maldera, S. Manfreda, A. Martí-Devesa, G. Mazziotta, M.N. Mereu, I. Michelson, P.F. Mizuno, T. Monzani, M.E. Morselli, A. Moskalenko, I.V. Negro, M. Omodei, N. Orienti, M. Orlando, E. Paneque, D. Pei, Z. Persic, M. Pesce-Rollins, Melissa Petrosian, V. Piron, F. Porter, T.A. Principe, G. Racusin, J.L. Rainò, S. Rando, R. Rani, B. Razzano, M. Razzaque, S. Reimer, A. Reimer, O. Serini, D. Sgrò, C. Siskind, E.J. Spandre, G. Spinelli, P. Tak, D. Troja, E. Valverde, J. Wood, K. Zaharijas, G. Department of Physics and Astronomy Clemson University Kinard Lab of Physics ClemsonSC29634-0978 United States Università di Pisa Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova PadovaI-35131 Italy Dipartimento di Fisica e Astronomia "G. Galilei" Università di Padova PadovaI-35131 Italy Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy Dipartimento di Fisica Università degli Studi di Perugia PerugiaI-06123 Italy Dipartimento di Fisica"M. Merlin" dell’Università e del Politecnico di Bari via Amendola 173 BariI-70126 Italy Istituto Nazionale di Fisica Nucleare Sezione di Bari BariI-70126 Italy W. W. Hansen Experimental Physics Laboratory Kavli Institute for Particle Astrophysics and Cosmology Department of Physics SLAC National Accelerator Laboratory Stanford University StanfordCA94305 United States Istituto Nazionale di Fisica Nucleare Sezione di Torino TorinoI-10125 Italy Dipartimento di Fisica Università degli Studi di Torino TorinoI-10125 Italy Laboratoire Leprince-Ringuet École polytechnique CNRS/IN2P3 PalaiseauF-91128 France NASA Goddard Space Flight Center GreenbeltMD20771 United States Italian Space Agency Via del Politecnico snc Roma00133 Italy Space Science Division Naval Research Laboratory WashingtonDC20375-5352 United States INAF-Istituto di Astrofisica Spaziale e Fisica Cosmica Milano via E. Bassini 15 MilanoI-20133 Italy University of Padua Department of Statistical Science Via 8 Febbraio 2 Padova35122 Italy Istituto Nazionale di Fisica Nucleare Sezione di Perugia PerugiaI-06123 Italy INAF Istituto di Radioastronomia BolognaI-40129 Italy Department of Physics University of Johannesburg PO Box 524 Auckland Park2006 South Africa Department of Physical Sciences Hiroshima University Higashi-HiroshimaHiroshima739-8526 Japan Friedrich-Alexander Universität Erlangen-Nürnberg Erlangen Centre for Astroparticle Ph

We present the first Fermi-Large Area Telescope (LAT) solar flare catalog covering the 24th solar cycle. This catalog contains 45 Fermi-LAT solar flares (FLSFs) with emission in the γ-ray energy band (30 MeV - 10 GeV) detected with a significance ≥5σ over the years 2010-2018. A subsample containing 37 of these flares exhibit delayed emission beyond the prompt-impulsive hard X-ray phase with 21 flares showing delayed emission lasting more than two hours. No prompt-impulsive emission is detected in four of these flares. We also present in this catalog the observations of GeV emission from 3 flares originating from Active Regions located behind the limb (BTL) of the visible solar disk. We report the light curves, spectra, best proton index and localization (when possible) for all the FLSFs. The γ-ray spectra is consistent with the decay of pions produced by >300 MeV protons. This work contains the largest sample of high-energy γ-ray flares ever reported and provides the unique opportunity to perform population studies on the different phases of the flare and thus allowing to open a new window in solar physics. Copyright © 2021, The Authors. All rights reserved.

关键词： Germanium alloys

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EyeQual: Accurate, Explainable, Retinal Image Quality Assessment

EyeQual: Accurate, Explainable, Retinal Image Quality Assess...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Pedro Costa Aurelio Campilho Bryan Hooi Asim Smailagic Kris Kitani Shenghua Liu Christos Faloutsos Adrian Galdran Department of Electrical and Computer Engineering Carnegie Mellon University INESC TEC Porto Faculty of Engineering University of Porto Department of Statistics and Data Science Carnegie Mellon University Robotics Institute Carnegie Mellon University Chinese Academy of Sciences Institute of Computing Technology China School of Computer Science Carnegie Mellon University

Given a retinal image, can we automatically determine whether it is of high quality (suitable for medical diagnosis)? Can we also explain our decision, pinpointing the region or regions that led to our decision? Images from human retinas are vital for the diagnosis of multiple health issues, like hypertension, diabetes, and Alzheimer's; low quality images may force the patient to come back again for a second scanning, wasting time and possibly delaying treatment. However, existing retinal image quality assessment methods are either black boxes without explanations of the results or depend heavily on feature engineering or on complex and error-prone anatomical structures' segmentation. Therefore, we propose EyeQual, that solves exactly this problem. EyeQual is novel, fast for inference, accurate and explainable, pinpointing low-quality regions on the image. We evaluated EyeQual on two real datasets where it achieved 100% accuracy taking just 36 milliseconds for each image.

关键词： Retina Image quality Diabetes Standards Retinopathy Machine learning Graphical models

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Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits (vol 50, pg 1412, 2018)

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NATURE GENETICS 2018年第12期50卷 1755-1755页

作者： Evangelou, Evangelos Warren, Helen R. Mosen-Ansorena, David Mifsud, Borbala Pazoki, Raha Gao, He Ntritsos, Georgios Dimou, Niki Cabrera, Claudia P. Karaman, Ibrahim Fu Liang Ng Evangelou, Marina Witkowska, Katarzyna Tzanis, Evan Hellwege, Jacklyn N. Giri, Ayush Edwards, Digna R. Velez Sun, Yan V. Cho, Kelly Gaziano, J. Michael Wilson, Peter W. F. Tsao, Philip S. Kovesdy, Csaba P. Esko, Tonu Magi, Reedik Milani, Lili Almgren, Peter Boutin, Thibaud Debette, Stephanie Ding, Jun Giulianini, Franco Holliday, Elizabeth G. Jackson, Anne U. Li-Gao, Ruifang Lin, Wei-Yu Luan, Jian'an Mangino, Massimo Oldmeadow, Christopher Prins, Bram Peter Qian, Yong Sargurupremraj, Muralidharan Shah, Nabi Surendran, Praveen Theriault, Sebastien Verweij, Niek Willems, Sara M. Zhao, Jing-Hua Amouyel, Philippe Connell, John de Mutsert, Renee Doney, Alex S. F. Farrall, Martin Menni, Cristina Morris, Andrew D. Noordam, Raymond Pare, Guillaume Poulter, Neil R. Shields, Denis C. Stanton, Alice Thom, Simon Abecasis, Goncalo Amin, Najaf Arking, Dan E. Ayers, Kristin L. Barbieri, Caterina M. Batini, Chiara Bis, Joshua C. Blake, Tineka Bochud, Murielle Boehnke, Michael Boerwinkle, Eric Boomsma, Dorret I. Bottinger, Erwin P. Braund, Peter S. Brumat, Marco Campbell, Archie Campbell, Harry Chakravarti, Aravinda Chambers, John C. Chauhan, Ganesh Ciullo, Marina Cocca, Massimiliano Collins, Francis Cordell, Heather J. Davies, Gail de Borst, Martin H. de Geus, Eco J. Deary, Ian J. Deelen, Joris Del Greco, Fabiola M. Demirkale, Cumhur Yusuf Dorr, Marcus Ehret, Georg B. Elosua, Roberto Enroth, Stefan Erzurumluoglu, A. Mesut Ferreira, Teresa Franberg, Mattias Franco, Oscar H. Gandin, Ilaria Gasparini, Paolo Giedraitis, Vilmantas Gieger, Christian Girotto, Giorgia Goel, Anuj Gow, Alan J. Gudnason, Vilmundur Guo, Xiuqing Gyllensten, Ulf Hamsten, Anders Harris, Tamara B. Harris, Sarah E. Hartman, Catharina A. Havulinna, Aki S. Hicks, Andrew A. Hofer, Edith Hofman, Albert Hottenga, Jouke-Jan Huffman, Jennifer E. Hwang, Shih-Jen Ingelsson, Erik James, Alan Jansen Department of Epidemiology and Biostatistics Imperial College London London UK Department of Hygiene and Epidemiology University of Ioannina Medical School Ioannina Greece William Harvey Research Institute Barts and The London School of Medicine and Dentistry Queen Mary University of London London UK National Institute for Health Research Barts Cardiovascular Biomedical Research Center Queen Mary University of London London UK MRC-PHE Centre for Environment and Health Imperial College London London UK Department of Mathematics Imperial College London London UK Division of Epidemiology Department of Medicine Institute for Medicine and Public Health Vanderbilt Genetics Institute Vanderbilt University Medical Center Tennessee Valley Healthcare System (626)/Vanderbilt University Nashville TN USA Vanderbilt Genetics Institute Vanderbilt Epidemiology Center Department of Obstetrics and Gynecology Vanderbilt University Medical Center Tennessee Valley Health Systems VA Nashville TN USA Department of Epidemiology Emory University Rollins School of Public Health Atlanta GA USA Department of Biomedical Informatics Emory University School of Medicine Atlanta GA USA Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC) VA Boston Healthcare System Boston MA USA Division of Aging Department of Medicine Brigham and Women’s Hospital Department of Medicine Harvard Medical School Boston MA USA Atlanta VAMC and Emory Clinical Cardiovascular Research Institute Atlanta GA USA VA Palo Alto Health Care System Division of Cardiovascular Medicine Stanford University School of Medicine Stanford CA USA Nephrology Section Memphis VA Medical Center and University of Tennessee Health Science Center Memphis TN USA Estonian Genome Center University of Tartu Tartu Estonia Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA Department Clinical Sciences Malmö Lund University Malmö Sweden MRC Human Genetics Un

High blood pressure is a highly heritable and modifiable risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits (systolic, diastolic and pulse pressure) to date in over 1 million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also highlight shared genetic architecture between blood pressure and lifestyle exposures. Our findings identify new biological pathways for blood pressure regulation with potential for improved cardiovascular disease prevention in the future.

关键词： Cardiovascular diseases Genome informatics Genome-wide association studies

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An interview based study of pioneering experiences in teaching and learning complex systems in higher education

arXiv

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arXiv 2018年

作者： Lizier, Joseph T. Harré, Michael S. Mitchell, Melanie DeDeo, Simon Finn, Conor Lindgren, Kristian Lizier, Amanda L. Sayama, Hiroki Centre for Complex Systems Complex Systems Research Group Faculty of Engineering and IT University of Sydney NSW Australia Department of Computer Science Portland State University PortlandOR United States Santa Fe Institute Santa FeNM United States School of Informatics and Computing Indiana University BloomingtonIN United States Department of Social and Decision Sciences Carnegie Mellon University PittsburghPA United States CSIRO Data 61 MarsfieldNSW Australia Department of Space Earth and Environment Chalmers University of Technology Göteborg Sweden School of Education University of Technology Sydney NSW Australia Center for Collective Dynamics of Complex Systems Department of Systems Science and Industrial Engineering Binghamton University State University of New York NY United States

Due to the interdisciplinary nature of complex systems as a field, students studying complex systems at University level have diverse disciplinary backgrounds. This brings challenges (e.g. wide range of computer programming skills) but also opportunities (e.g. facilitating interdisciplinary interactions and projects) for the classroom. However, there is little published regarding how these challenges and opportunities are handled in teaching and learning Complex Systems as an explicit subject in higher education, and how this differs in comparison to other subject areas. We seek to explore these particular challenges and opportunities via an interview-based study of pioneering teachers and learners (conducted amongst the authors) regarding their experiences. We compare and contrast those experiences, and analyse them with respect to the educational literature. Our discussions explored: approaches to curriculum design, how theories/models/frameworks of teaching and learning informed decisions and experience, how diversity in student backgrounds was addressed, and assessment task design. We found a striking level of commonality in the issues expressed as well as the strategies to handle them, for example a significant focus on problem-based learning, and the use of major student-led creative projects for both achieving and assessing learning outcomes. Copyright © 2018, The Authors. All rights reserved.

关键词： Students

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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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Event-driven emotion cause extraction with corpus construction

Event-driven emotion cause extraction with corpus constructi...

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2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016

作者： Gui, Lin Wu, Dongyin Xu, Ruifeng Lu, Qin Zhou, Yu School of Computer Science and Technology Harbin Institute of Technology Shenzhen Graduate School Shenzhen China Guangdong Provincial Engineering Technology Research Center for Data Science China Department of Computing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9781945626258

In this paper, we present our work in emotion cause extraction. Since there is no open dataset available, the lack of annotated resources has limited the research in this area. Thus, we first present a dataset we built using SINA city news. The annotation is based on the scheme of the W3C Emotion Markup Language. Second, we propose a 7-tuple definition to describe emotion cause events. Based on this general definition, we propose a new event-driven emotion cause extraction method using multi-kernel SVMs where a syntactical tree based approach is used to represent events in text. A convolution kernel based multi-kernel SVM are used to extract emotion causes. Because traditional convolution kernels do not use lexical information at the terminal nodes of syntactic trees, we modify the kernel function with a synonym based improvement. Even with very limited training data, we can still extract sufficient features for the task. Evaluations show that our approach achieves 11.6% higher F-measure compared to referenced methods. The contributions of our work include resource construction, concept definition and algorithm development. © 2016 Association for Computational Linguistics

关键词： Extraction

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Power structure in Chilean news media

arXiv

引用

arXiv 2017年

作者： Bahamonde, Jorge Bollen, Johan Elejalde, Erick Ferres, Leo Poblete, Barbara Department of Computer Science University of Chile Santiago Chile School of Informatics and Computing Indiana University BloomingtonIN United States Computer Science Department Faculty of Engineering Universidad de Concepción Concepción Chile Institute of Data Science Faculty of Engineering Universidad del Desarrollo & Telefónica R&D Santiago Chile

Even democracies endowed with the most active free press struggle to maintain diversity of news coverage. Consolidation and market forces may cause only a few dominant players to control the news cycle. Editorial policies may be biased by corporate ownership relations, narrowing news coverage and focus. To an increasing degree this problem also applies to social media news distribution, since it is subject to the same socio-economic drivers. To study the effects of consolidation and ownership on news diversity, we model the diversity of Chilean coverage on the basis of ownership records and social media data. We create similarity networks of news outlets on the basis of their ownership and the topics they cover. We then examine the relationships between the topology of ownership networks and content similarity to characterize how ownership affects news coverage. A network analysis reveals that Chilean media is highly concentrated both in terms of ownership as well as in terms of topics covered. Our method can be used to determine which groups of outlets and ownership exert the greatest influence on news coverage. Copyright © 2017, The Authors. All rights reserved.

关键词：

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Dictionary pair classifier driven convolutional neural networks for object detection

Dictionary pair classifier driven convolutional neural netwo...

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2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016

作者： Wang, Keze Lin, Liang Zuo, Wangmeng Gu, Shuhang Zhang, Lei School of Data and Computer Science Sun Yat-sen University Guangzhou China School of Computer Science and Technology Harbin Institute of Technology China Department of Computing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9781467388511

Feature representation and object category classification are two key components of most object detection methods. While significant improvements have been achieved for deep feature representation learning, traditional SVM/softmax classifiers remain the dominant methods for the final object category classification. However, SVM/softmax classifiers lack the capacity of explicitly exploiting the complex structure of deep features, as they are purely discriminative methods. The recently proposed discriminative dictionary pair learning (DPL) model involves a fidelity term to minimize the reconstruction loss and a discrimination term to enhance the discriminative capability of the learned dictionary pair, and thus is appropriate for balancing the representation and discrimination to boost object detection performance. In this paper, we propose a novel object detection system by unifying DPL with the convolutional feature learning. Specifically, we incorporate DPL as a Dictionary Pair Classifier Layer (DPCL) into the deep architecture, and develop an end-to-end learning algorithm for optimizing the dictionary pairs and the neural networks simultaneously. Moreover, we design a multi-task loss for guiding our model to accomplish the three correlated tasks: objectness estimation, categoryness computation, and bounding box regression. From the extensive experiments on PASCAL VOC 2007/2012 benchmarks, our approach demonstrates the effectiveness to substantially improve the performances over the popular existing object detection frameworks (e.g., R-CNN [13] and FRCN [12]), and achieves new state-of-the-arts.

关键词： Object detection

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The JOREK non-linear extended MHD code and applications to large-scale instabilities and their control in magnetically confined fusion plasmas

arXiv

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arXiv 2020年

作者： Hoelzl, Matthias Huijsmans, G.T.A. Pamela, S.J.P. Bécoulet, M. Nardon, E. Artola, F.J. Nkonga, B. Atanasiu, C.V. Bandaru, V. Bhole, A. Bonfiglio, D. Cathey, A. Czarny, O. Dvornova, A. Fehér, T. Fil, A. Franck, E. Futatani, S. Gruca, M. Guillard, H. Haverkort, J.W. Holod, I. Hu, D. Kim, S.K. Korving, S.Q. Kos, L. Krebs, I. Kripner, L. Latu, G. Liu, F. Merkel, P. Meshcheriakov, D. Mitterauer, V. Mochalskyy, S. Morales, J.A. Nies, R. Nikulsin, N. Orain, F. Pratt, J. Ramasamy, R. Ramet, P. Reux, C. Särkimäki, K. Schwarz, N. Singh Verma, P. Smith, S.F. Sommariva, C. Strumberger, E. van Vugt, D.C. Verbeek, M. Westerhof, E. Wieschollek, F. Zielinski, J. Max Planck Institute for Plasma Physics Boltzmannstr. 2 Garching b. M.85748 Germany CEA IRFM Saint-Paul-Lez-Durance13108 France Eindhoven University of Technology P.O. Box 513 Eindhoven5600 MB Netherlands CCFE Culham Science Centre OX14 3DB United Kingdom ITER Organization St. Paul Lez Durance Cedex13067 France Université Côte d’Azur & Inria Sophia-Antipolis Méditerranée CNRS LJAD Nice France National Institute for Laser Plasma and Radiation Physics Atomistilor 409 P.O. BoxMG-36 Magurele-Bucharest077125 Romania Consorzio RFX-CNR ENEA INFN Università di Padova Acciaierie Venete SpA. PadovaI-35127 Italy Department of Physics Technical University Munich James-Franck-Str. 1 Garching b. M.85748 Germany FRAMATOME 10 Rue Juliette Récamier Lyon Cedex69456 France Inria Nancy Grand Est and IRMA Strasbourg France Universitat Politècnica de Catalunya Barcelona Spain Institute of Plasma Physics and Laser Microfusion P.O. Box 49 Warsaw 49PL-00-908 Poland Inria Sophia-Antipolis Méditerranée & Université Côte d’Azur Inria CNRS LJAD Nice France Delft University of Technology Delft Netherlands Max Planck Computing and Data Facility Boltzmannstr. 2 Garching b. M.85748 Germany School of Physics Beihang University Beijing100191 China Department of Nuclear Engineering Seoul National University Gwanak Seoul08826 Korea Republic of LECAD laboratory Mech.Eng University of Ljubljana Aškerčeva 6 LjubljanaSI-1000 Slovenia DIFFER - Dutch Institute for Fundamental Energy Research De Zaale 20 Eindhoven5612 AJ Netherlands Institute of Plasma Physics Prague Czech Republic Department of Astrophysical Sciences Princeton University PrincetonNJ08543 United States Princeton Plasma Physics Laboratory PrincetonNJ08540 United States Georgia State University Department of Physics and Astronomy AtlantaGA30303 United States Max-Planck/Princeton Research Center for Plasma Physics LaBRI INRIA Bordeaux Sud-Ouest Université de Bordeaux 351 cours de la Libérati

JOREK is a massively parallel fully implicit non-linear extended MHD code for realistic tokamak X-point plasmas. It has become a widely used versatile simulation code for studying large-scale plasma instabilities and their control and is continuously developed in an international community with strong involvements in the European fusion research program and ITER organization. This article gives a comprehensive overview of the physics models implemented, numerical methods applied for solving the equations and physics studies performed with the code. A dedicated section highlights some of the verification work done for the code. A hierarchy of different physics models is available including a free boundary and resistive wall extension and hybrid kinetic-fluid models. The code allows for flux-surface aligned iso-parametric finite element grids in single and double X-point plasmas which can be extended to the true physical walls and uses a robust fully implicit time stepping. Particular focus is laid on plasma edge and scrape-off layer (SOL) physics as well as disruption related phenomena. Among the key results obtained with JOREK regarding plasma edge and SOL, are deep insights into the dynamics of edge localized modes (ELMs), ELM cycles, and ELM control by resonant magnetic perturbations, pellet injection, as well as by vertical magnetic kicks. Also ELM free regimes, detachment physics, the generation and transport of impurities during an ELM, and electrostatic turbulence in the pedestal region are investigated. Regarding disruptions, the focus is on the dynamics of the thermal quench and current quench triggered by massive gas injection (MGI) and shattered pellet injection (SPI), runaway electron (RE) dynamics as well as the RE interaction with MHD modes, and vertical displacement events (VDEs). Also the seeding and suppression of tearing modes (TMs), the dynamics of naturally occurring thermal quenches triggered by locked modes, and radiative collapses are being stu

关键词： Ablation

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Network slicing for service-oriented networks under resource constraints

arXiv

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arXiv 2017年

作者： Zhang, Nan Liu, Ya-Feng Farmanbar, Hamid Chang, Tsung-Hui Hong, Mingyi Luo, Zhi-Quan School of Mathematical Sciences Peking University State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Huawei Canada Research Center Ottawa Canada Department of Electrical and Computer Engineering University of Minnesota United States Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen China

To support multiple on-demand services over fixed communication networks, network operators must allow flexible customization and fast provision of their network resources. One effective approach to this end is network virtualization, whereby each service is mapped to a virtual subnetwork providing dedicated on-demand support to network users. In practice, each service consists of a prespecified sequence of functions, called a service function chain (SFC), while each service function in a SFC can only be provided by some given network nodes. Thus, to support a given service, we must select network function nodes according to the SFC and determine the routing strategy through the function nodes in a specified order. A crucial network slicing problem that needs to be addressed is how to optimally localize the service functions in a physical network as specified by the SFCs, subject to link and node capacity constraints. In this paper, we formulate the network slicing problem as a mixed binary linear program and establish its strong NP-hardness. Furthermore, we propose efficient penalty successive upper bound minimization (PSUM) and PSUM-R(ounding) algorithms, and two heuristic algorithms to solve the problem. Simulation results are shown to demonstrate the effectiveness of the proposed algorithms. Copyright © 2017, The Authors. All rights reserved.

关键词： Network function virtualization

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