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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROscience 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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The Null Field and the Interior Field Methods For Mixed Types of Boundary Conditions of Laplace's Equations in Elliptic Domains with Elliptic Holes

The Null Field and the Interior Field Methods For Mixed Type...

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Joint International Conference on Trefftz Method Ⅶ and Method of Fundamental Solutions Ⅲ(Trefftz/MFS 2015)

作者： Liping Zhang Ming-Gong Lee Zi-Cai Li College of Science Zhejiang University of Technology Department of Leisure and Recreation Management Ph.D.Program in Engineering ScienceChung Hua University Department of Applied Mathematics Department of Computer Science and EngineeringNational Sun Yat-sen University

The particular solutions(PS) and fundamental solutions(FS) in polar coordinates can be found in many textbooks, but with much less coverage in elliptic coordinates [1]. Since the elliptic domains with elliptic holes may be found in some engineering problems, the PS and the FS expansions in elliptic coordinates are essential for numerical computations. For Dirichlet problems of Laplace's equation in elliptic domains, the null field method(NFM), the interior field method(IFM) and the collocation Trefftz method(CTM) are reported in [2]. There seems to exist very few reports for mixed problems, where the Dirichlet and Neumann conditions are assigned on the exterior and the interior boundaries, simultaneously. This paper is devoted to such mixed problems by the NFM and the IFM, and the explicit algebraic equations are derived for elliptic domains. Besides, other effective particular solutions(PS) are sought, and the collocation Trefftz method(CTM) [3] is employed. The CTM may be used for Robin problems in elliptic domains. The effective algorithms for the mixed problems of Laplace's equation on elliptic domains are the main goal of this paper.

关键词： Null field method Interior field method collocation Trefftz method circular domains elliptic domains Elliptic Holes fundamental solutions Particular Solutions Dirichlet condition Neumann condition Robin Problems

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Intrinsic Raman signatures of pristine hybrid perovskite CH3NH3PbI3 and its multiple stages of structure transformation

Intrinsic Raman signatures of pristine hybrid perovskite CH3...

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2016 IEEE 43rd Photovoltaic Specialists Conference (PVSC)

作者： Qiong Chen Henan Liu Hui-Seon Kim Yucheng Liu Mengjin Yang Naili Yue Gang Ren Kai Zhu Shengzhong Frank Liu Nam-Gyu Park Yong Zhang Department of Electrical and Computer Engineering The University of North Carolina at Charlotte Charlotte NC USA Optical Science and Engineering graduate program The University of North Carolina at Charlotte Charlotte NC USA Department of Energy Science Sungkyunkwan University Suwon South Korea National Ministry of Education Shaanxi Normal University Xi'an China Chemical and Materials Science Center National Renewable Energy Laboratory CO USA Molecular Foundry Lawrence Berkeley National Laboratory Berkeley CA USA Key Laboratory of Applied Surface and Colloid Chemistry National Ministry of Education Shaanxi Normal University Xi'an Shaanxi CN School of Chemical Engineering Sungkyunkwan University Suwon South Korea University of North Carolina at Charlotte Charlotte NC US

By performing spatially resolved Raman and photoluminescence spectroscopy with different illumination conditions, we have achieved a unified understanding towards the spectroscopy signatures of the organic-inorganic hybrid perovskite, transforming from the pristine state (CH 3 NH 3 PbI 3 or MAPbI 3 ) to fully degraded state (i.e., PbI 2 ), for samples with varying crystalline domain size from mesoscopic scale to macroscopic size, synthesized by three different techniques.

关键词： Measurement by laser beam Density measurement Chemistry Lighting Optical films Degradation Spectroscopy

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Publisher Correction: Alpha-synuclein facilitates to form short unconventional microtubules that have a unique function in the axonal transport

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Scientific reports 2018年第1期8卷 8019页

作者： Shiori Toba Mingyue Jin Masami Yamada Kanako Kumamoto Sakiko Matsumoto Takuo Yasunaga Yuko Fukunaga Atsuo Miyazawa Sakiko Fujita Kyoko Itoh Shinji Fushiki Hiroaki Kojima Hideki Wanibuchi Yoshiyuki Arai Takeharu Nagai Shinji Hirotsune Department of Genetic Disease Research Osaka City University Graduate School of Medicine Asahi-machi 1-4-3 Abeno Osaka 545-8585 Japan. Department of Bioscience and Bioinformatics Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Kawazu 680-4 Iizuka Fukuoka 820-850 Japan. JST-SENTAN 4-1-8 Honcho Kawaguchi Saitama 332-0012 Japan. JST-CREST 4-1-8 Honcho Kawaguchi Saitama 332-0012 Japan. Graduate School of Life Science University of Hyogo 3-2-1 Kouto Kamigori-cho Ako-gun Hyogo 678-1297 Japan. RSC-University of Hyogo Leading Program Center RIKEN SPring-8 Center 1-1-1 Kouto Sayo-cho Sayo-gun Hyogo 679-5148 Japan. Graduate School of Materials Science Nara Institute of Science and Technology 8916-5 Takayama Ikoma Nara 630-0101 Japan. Department of Pathology and Applied Neurobiology Kyoto Prefectural University of Medicine Graduate School of Medical Sciences Kajii-cho Kawaramachi-Hirokoji Kamigyo-ku Kyoto 602-8566 Japan. Advanced ICT Research Institute National Institute of Information and Communications Technology 588-2 Iwaoka Nishi-ku Kobe 651-2492 Japan. Department of Pathology Osaka City University Graduate School of Medicine Asahi-machi 1-4-3 Abeno Osaka 545-8586 Japan. Department of Biomolecular Science and Engineering Institute of Scientific and Industrial Research Osaka University Mihoga-oka 8-1 Osaka 567-0047 Japan. Department of Genetic Disease Research Osaka City University Graduate School of Medicine Asahi-machi 1-4-3 Abeno Osaka 545-8585 Japan. shinjih@med.osaka-cu.ac.jp.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

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Absolute change pivot rule for the simplex algorithm

Absolute change pivot rule for the simplex algorithm

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International MultiConference of Engineers and computer Scientists, IMECS 2014

作者： Chankong, Kittiphong Intiyot, Boonyarit Sinapiromsaran, Krung Applied Mathematics and Computational Science Program Department of Mathematics and Computer Science Faculty of Science Chulalongkorn University Bangkok Thailand

ISBN: (纸本)9789881925336

The simplex algorithm is a widely used method for solving a linear programming problem (LP) which is first presented by George B. Dantzig. One of the important steps of the simplex algorithm is applying an appropriate pivot rule, the rule to select the entering variable. An effective pivot rule can lead to the optimal solution of LP with the small number of iterations. In a minimization problem, Dantzig's pivot rule selects an entering variable corresponding to the most negative reduced cost. The concept is to have the maximum improvement in the objective value per unit step of the entering variable. However, in some problems, Dantzig's rule may visit a large number of extreme points before reaching the optimal solution. In this paper, we propose a pivot rule that could reduce the number of such iterations over the Dantzig's pivot rule. The idea is to have the maximum improvement in the objective value function by trying to block a leaving variable that makes a little change in the objective function value as much as possible. Then we test and compare the efficacy of this rule with Dantzig' original rule.

关键词： Linear programming

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROscience 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Optimal resource allocation for survivable virtual infrastructures

Optimal resource allocation for survivable virtual infrastru...

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10th International Conference on Design of Reliable Communication Networks, DRCN 2014

作者： De S. Cavalcanti, Gustavo A. Obelheiro, Rafael R. Koslovski, Guilherme Graduate Program in Applied Computing Department of Computer Science Santa Catarina State University Brazil

ISBN: (纸本)9781479940097

Virtual infrastructures (VIs) consolidated the dynamic provisioning of computing and communication resources. A VI is a set of virtual machines interconnected by virtual links and switches/routers. Infrastructure providers (InPs) manage the physical substrates in which virtual resources requested by VIs (such as CPU, disk, memory, bandwidth) are reserved and allocated. Resource allocation is a complex problem that needs to satisfy different goals: users expect to run their applications on survivable VIs, while InPs aim to maximize profits, minimize costs and reduce substrate fragmentation. However, there is a dichotomy between minimizing substrate fragmentation, by co-locating VIs, and maximizing VI survivability, by sparsely allocating resources in order to decrease the impact of substrate failures. In this context, this paper discusses VI survivability and its impact on substrate fragmentation. We propose a mixed integer linear programming model to allocate resources considering the joint coordination of fragmentation and survivability. Experimental results suggest that it is possible to enhance VI survivability without significantly impacting substrate fragmentation. © 2014 IEEE.

关键词： Substrates

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Customer-oriented diagnosis of memory provisioning for IaaS clouds

Customer-oriented diagnosis of memory provisioning for IaaS ...

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Brazilian Symposium on computer System Engineering, SBESC 2013

作者： Pfitscher, Ricardo J. Pillon, Mauricio A. Obelheiro, Rafael R. Santa Catarina State University Graduate Program in Applied Computing Department of Computer Science Brazil

Infrastructure-as-a-service clouds enable customers to use computing resources in a flexible manner to satisfy their needs, and pay only for the allocated resources. One challenge for IaaS customers is the correct provisioning of their resources. Many users end up underprovisioning, hurting application performance, or overprovisioning, paying for resources that are not really necessary. Memory is an essential resource for any computing system, and is frequently a performance-limiting factor in cloud environments. In this work, we propose a model that enables cloud customers to determine whether the memory allocated to their virtual machines is correctly provisioned, underprovisioned, or overprovisioned. The model uses two metrics collected inside a VM, resident and committed memory, and defines thresholds for these metrics that characterize each provisioning level. Experimental results with Linux guests on Xen, running four benchmarks with different workloads and varying memory capacity, show that the model was able to accurately diagnose memory provisioning in 98% of the scenarios evaluated. Copyright 2013 by the Authors.

关键词： Customer satisfaction

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Microwave Sensing of Sand Production from Petroleum Wells

Microwave Sensing of Sand Production from Petroleum Wells

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IEEE International Instrumentation and Measurement Technology Conference

作者： Jaswanth N. Vutukury Kristen M. Donnell Steven Hilgedick Applied Microwave Nondestructive Testing Laboratory (amntl) Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO 65409 Petroleum Engineering Program Department of Geological Sciences and Engineering Missouri University of Science and Technology Rolla MO 65409

ISBN: (纸本)9781479961153

Sand production in petroleum wells is a major concern for the petroleum industry. Sand production results in erosion of pipelines and damage to equipment which may lead to production shutdown and large economic losses. Detection of produced sand is important for optimizing flow rates and measuring the success of sand mitigation techniques. Recently, microwave nondestructive testing was considered as a potential new sensing solution for the detection of sand production. This paper presents preliminary simulations and measurements for such a sensing approach using open-ended waveguides. Different production scenarios are considered for a stratified flow regime. The results indicate that this sensing method can detect the presence of sand. Preliminary measurements are also presented for varying amounts of sand that further support the potential of this technique.

关键词： microwave nondestructive testing sandproduction petroleum oil and gas microwave sensing open-ended waveguide material characterization

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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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