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Bulletin of the American Meteorological Society 2024年第8期105卷 S12-S155页

作者： Dunn, R.J.H. Blannin, J. Gobron, N. Miller, J.B. Willett, K.M. Met Office Hadley Centre Exeter United Kingdom European Commission Joint Research Centre Ispra Italy NOAA Global Monitoring Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland European Centre for Medium-Range Weather Forecasts Bonn Germany Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Hampton University Hampton Virginia National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Laboratoire des Sciences du Climat et de l’Environnement (LSCE) LSCE/IPSL CEA-CNRS-UVSQ Gif-sur-Yvette France Instituto de Geografía Pontificia Universidad Católica de Valparaíso Valparaíso Chile Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia University of Reading Reading United Kingdom Institute Pierre Simon Laplace (IPSL) Guyancourt France School of Engineering Newcastle University Newcastle-upon-Tyne United Kingdom Université Paris Cité Institut de physique du globe de Paris CNRS IGN F-75005 Paris France Univ Gustave Eiffel ENSG IGN F-77455 Marne-la-Vallée France Laboratoire EDYTEM CNRS/Université Savoie Mont-Blanc Le Bourget-du-Lac France Global Modeling and Assimilation Office NASA Goddard Space Flight Center Greenbelt Maryland Universität Hamburg Hamburg Germany Climate Dynamics Lab University of St Andrews St Andrews United Kingdom Ea

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A role of asymmetry in linear response of globally coupled oscillator systems

arXiv

引用

arXiv 2018年

作者： Terada, Yu Ito, Keigo Yoneda, Ryosuke Aoyagi, Toshio Yamaguchi, Yoshiyuki Y. Department of Mathematical and Computing Science Tokyo Institute of Technology Tokyo152-8552 Japan Shimadzu Corporation Kyoto604-8511 Japan Faculty of Engineering Kyoto University Kyoto606-8501 Japan Graduate School of Informatics Kyoto University Kyoto606-8501 Japan

The linear response is studied in globally coupled oscillator systems including the Kuramoto model. We develop a linear response theory which can be applied to systems whose coupling functions are generic. Based on the theory, we examine the role of asymmetry introduced to the natural frequency distribution, the coupling function, or the coupling constants. A remarkable difference appears in coexistence of the divergence of susceptibility at the critical point and a nonzero phase gap between the order parameter and the applied external force. The coexistence is not allowed by the asymmetry in the natural frequency distribution but can be realized by the other two types of asymmetry. This theoretical prediction and the coupling-constant dependence of the susceptibility are numerically verified by performing simulations in N-body systems and in reduced systems obtained with the aid of the Ott-Antonsen ansatz. Copyright © 2018, The Authors. All rights reserved.

关键词： Natural frequencies

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Inexact Orthant-Wise Quasi-Newton Method

Inexact Orthant-Wise Quasi-Newton Method

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2018 2nd International Conference on Applied Mathematics, Modelling and Statistics Application (AMMSA 2018)

作者： Faguo Wu Wang Yao Xiao Zhang Chenxu Wang Zhiming Zheng Key Laboratory of Mathematics Informatics and Behavioral SemanticsMinistry of EducationSchool of Mathematics and Systems ScienceBeihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Big Data Management Department Information Technology Department China Minsheng Bank

The Orthant-Wise Limited-memory QuasiNewton method(OWL-QN), based on the L-BFGS method, is an effective algorithm for solving the '1-regularized sparse learning problem. In order to deal with the '1-regularization, OWL-QN restrict the point to an orthant on which the quadratic model is valid and differentiable. In this paper, we propose an Inexact Orthant-Wise Limited-memory Quasi-Newton method(IOWLQN). This method, at every iteration, compute an approximate solution satisfied the inexactness conditions to estimate the exact solution. We give brief proof to the convergence and report the numerical results.

关键词： orthant-based sparse optimization inexact Newton proximal gadient

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Genomic basis for RNA alterations in cancer (vol 578, pg 129, 2020)

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NATURE 2023年第7948期614卷 E37-E37页

作者： Calabrese, Claudia Davidson, Natalie R. Demircioglu, Deniz Fonseca, Nuno A. He, Yao Lehmann, Kjong-Van Liu, Fenglin Shiraishi, Yuichi Soulette, Cameron M. Urban, Lara Greger, Liliana Li, Siliang Liu, Dongbing Perry, Marc D. Xiang, Qian Zhang, Fan Zhang, Junjun Bailey, Peter Erkek, Serap Hoadley, Katherine A. Hou, Yong Huska, Matthew R. Kilpinen, Helena Korbel, Jan O. Marin, Maximillian G. Markowski, Julia Nandi, Tannistha Pan-Hammarstrom, Qiang Pedamallu, Chandra Sekhar Siebert, Reiner Stark, Stefan G. Su, Hong Tan, Patrick Waszak, Sebastian M. Yung, Christina Zhu, Shida Awadalla, Philip Creighton, Chad J. Meyerson, Matthew Ouellette, B. F. Francis Wu, Kui Yang, Huanming Brazma, Alvis Brooks, Angela N. Goke, Jonathan Ratsch, Gunnar Schwarz, Roland F. Stegle, Oliver Zhang, Zemin European Molecular Biology Laboratory European Bioinformatics Institute Hinxton UK European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany CIBIO/InBIO - Research Center in Biodiversity and Genetic Resources Universidade do Porto Vairão Portugal Berlin Institute for Medical Systems Biology Max Delbruck Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) partner site Berlin Germany German Cancer Research Center (DKFZ) Heidelberg Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) Partner site Berlin Berlin Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Division of Computational Genomics and Systems Genetics German Cancer Research Center (DKFZ) Heidelberg Germany ETH Zurich Zurich Switzerland Memorial Sloan Kettering Cancer Center New York NY USA Weill Cornell Medical College New York NY USA SIB Swiss Institute of Bioinformatics Lausanne Switzerland University Hospital Zurich Zurich Switzerland Computational Biology Center Memorial Sloan Kettering Cancer Center New York NY USA Department of Biology ETH Zurich Zürich Switzerland Department of Computer Science ETH Zurich Zurich Switzerland Korea University Seoul South Korea Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA National University of Singapore Singapore Singapore Genome Institute of Singapore Singapore Singapore Computational and Systems Biology Genome Institute of Singap

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Lithium-Ion Batteries: Operando Magnetometry Probing the Charge Storage Mechanism of CoO Lithium-Ion Batteries (Adv. Mater. 12/2021)

引用

Advanced Materials 2021年第12期33卷

作者： Hongsen Li Zhengqiang Hu Qingtao Xia Hao Zhang Zhaohui Li Huaizhi Wang Xiangkun Li Fengkai Zuo Fengling Zhang Xiaoxiong Wang Wanneng Ye Qinghao Li Yunze Long Qiang Li Shishen Yan Xiaosong Liu Xiaogang Zhang Guihua Yu Guo-Xing Miao College of Physics Center for Marine Observation and Communications Qingdao University Qingdao 266071 China School of Physics State Key Laboratory of Crystal Materials Shandong University Jinan 250100 China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology (SIMIT) Chinese Academy of Sciences Shanghai 200050 China College of Material Science and Engineering Nanjing University of Aeronautics and Astronautics Nanjing 210016 China Materials Science and Engineering Program and Department of Mechanical Engineering The University of Texas at Austin Austin TX 78712 USA Department of Electrical and Computer Engineering & Institute for Quantum Computing University of Waterloo Waterloo ON N2L 3G1 Canada

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NIMG-67. MULTI-PARAMETRIC MRI-BASED MACHINE LEARNING ANALYSIS FOR PREDICTION OF NEOPLASTIC INFILTRATION AND RECURRENCE IN PATIENTS WITH GLIOBLASTOMA: UPDATES FROM THE MULTI-INSTITUTIONAL RESPOND CONSORTIUM

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Neuro-Oncology 2022年第SUPPLEMENT_7期24卷 vii179–vii180页

作者： Akbari, Hamed Mohan, Suyash Garcia, Jose Kazerooni, Anahita Fathi Sako, Chiharu Bakas, Spyridon Bilello, Michel Bagley, Stephen Baid, Ujjwal Brem, Steven Lustig, Robert Nasrallah, MacLean O'Rourke, Donald Barnholtz-Sloan, Jill Badve, Chaitra Sloan, Andrew Jain, Rajan Lee, Matthew Chakravarti, Arnab Palmer, Joshua Taylor, William Cepeda, Santiago Dicker, Adam Flanders, Adam Shi, Wenyin Shukla, Gaurav Calabrese, Evan Rudie, Jeffrey Villanueva-Meyer, Javier LaMontagne, Pamela Marcus, Daniel Balana, Carmen Capellades, Jaume Puig, Josep Ak, Murat Colen, Rivka Ahn, Sung Soo Chang, Jong Hee Choi, Yoon Seong Lee, Seung-Koo Griffith, Brent Poisson, Laila Rogers, Lisa Booth, Thomas Mahajan, Abhishek Wiestler, Benedikt Davatzikos, Christos University of Pennsylvania Philadelphia PA USA Center for Biomedical Image Computing and Analytics Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA University of Pennsylvania Philadelphia USA Center for Biomedical Image Computing and Analytics and Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia USA Center for Biomedical Image Computing and Analytics Department of Radiology and Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Hospital of the University of Pennsylvania Philadelphia PA USA Hospital of the University of Pennsylvania Philadelphia USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Center for Biomedical Informatics and Information Technology and Division of Cancer Epidemiology and Genetics National Cancer Institute Bethesda MD USA Department of Radiology Case Western Reserve University and University Hospitals Cleveland Medical Center Cleveland USA Department of Pathology and Department of Neurosurgery Case Western Reserve University and University Hospitals Cleveland Medical Center Seidman Cancer Center and Case Comprehensive Cancer Center Cleveland USA Department of Radiology NYU Grossman School of Medicine New York NY USA Department of Radiation Oncology Ohio State University Wexner Medical Center Columbus OH USA The Department of Radiation Oncology The James Cancer Hospital Ohio State University Wexner Medical Center Columbus OH USA The Ohio State University Columbus USA University Hospital Río Hortega Valladolid Spain Department of Radiation Oncology Sidney Kimmel Cancer Center Thomas Jefferson University Philadelphia PA USA Department of Radiology Sidney Kimmel Cancer Center Thomas Jefferson University Philadelphia PA USA Department of Radiation Oncology Thomas Jefferson University Hospita

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PERFORMANCE OF MANET WITH IPSEC UNDER JELLY FISH ATTACK

PERFORMANCE OF MANET WITH IPSEC UNDER JELLY FISH ATTACK

引用

2017 2nd Joint International Mechanical,Electronic and Information technology Conference(JIMET 2017)

作者： Fatin Hamadah Rahman Thien-Wan Au Wida Susanty Suhaili Yan Liu School of Computing and Informatics Universiti Teknologi Brunei Jalan Tungku Link Harbin University of Science and Technology

MANET is a self-organizing network that uses multi-hop routing for data communication. Without any centralization, network monitoring is problematic thus making it vulnerable to attacks such as Jellyfish attack. Hence IPSec protocol can be used as one of its security means. The aims of this paper is to study the performance of MANET using OLSR and TORA routing protocols alongside with the application of IPSec when the MANET is under Jellyfish attack, to see which routing protocol is least affected by the attack and to see which routing protocol benefitted the most with the application of IPSec using Riverbed Modeler Academic Edition. Based on the simulations, it shows that OLSR performs better under Jellyfish attack, and TORA benefits the most with the use of IPSec.

关键词： MANET Jellyfish attack IPSec OLSR TORA

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NIMG-33. PROGNOSTIC STRATIFICATION OF DE NOVO GLIOBLASTOMA PATIENTS ACROSS 22 GEOGRAPHICALLY DISTINCT INSTITUTIONS: UPDATES FROM THE RESPOND CONSORTIUM

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Neuro-Oncology 2022年第SUPPLEMENT_7期24卷 vii170–vii170页

作者： Akbari, Hamed Bakas, Spyridon Sako, Chiharu Kazerooni, Anahita Fathi Villanueva-Meyer, Javier Garcia, Jose Bagley, Stephen Baid, Ujjwal Bilello, Michel Brem, Steven Lustig, Robert Mohan, Suyash Nasrallah, MacLean O'Rourke, Donald Calabrese, Evan Rudie, Jeffrey LaMontagne, Pamela Marcus, Daniel Balana, Carmen Capellades, Jaume Puig, Josep Barnholtz-Sloan, Jill Badve, Chaitra Sloan, Andrew Ak, Murat Colen, Rivka Ahn, Sung Soo Chang, Jong Hee Choi, Yoon Seong Lee, Seung-Koo Dicker, Adam Flanders, Adam Shi, Wenyin Shukla, Gaurav Griffith, Brent Poisson, Laila Rogers, Lisa Booth, Thomas Jain, Rajan Lee, Matthew Mahajan, Abhishek Chakravarti, Arnab Palmer, Joshua Taylor, William Cepeda, Santiago Wiestler, Benedikt Davatzikos, Christos University of Pennsylvania Philadelphia PA USA Center for Biomedical Image Computing and Analytics Department of Radiology and Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Center for Biomedical Image Computing and Analytics and Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia USA University of Pennsylvania Philadelphia USA University of California San Francisco San Francisco CA USA Hospital of the University of Pennsylvania Philadelphia PA USA Hospital of the University of Pennsylvania Philadelphia USA Center for Biomedical Image Computing and Analytics Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA University of California San Francisco San Francisco USA Washington University School of Medicine St. Louis USA Department of Radiology Washington University School of Medicine St. Louis MO USA Medical Oncology Department Catalan Institute of Oncology Barcelona Spain Department of Medical Imaging Consorci MAR Parc de Salut Barcelona Spain Department of Radiology (IDI) and Girona Biomedical Research Institute (IDIBGI) Hospital Universitari de Girona Dr Josep Trueta Girona Spain Center for Biomedical Informatics and Information Technology and Division of Cancer Epidemiology and Genetics National Cancer Institute Bethesda MD USA Department of Radiology Case Western Reserve University and University Hospitals Cleveland Medical Center Cleveland USA Department of Pathology and Department of Neurosurgery Case Western Reserve University and University Hospitals Cleveland Medical Center Seidman Cancer Center and Case Comprehensive Cancer Center Cleveland USA University of Pittsburgh Pittsburgh USA Department of Radiology University of Pittsburgh Pittsburgh PA USA Yons

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Characterizing the analogy between hyperbolic embedding and community structure of complex networks

arXiv

引用

arXiv 2018年

作者： Faqeeh, Ali Osat, Saeed Radicchi, Filippo MACSI Department of Mathematics and Statistics University of Limerick Limerick Ireland Center for Complex Networks and Systems Research School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Quantum Complexity Science Initiative Skolkovo Institute of Science and Technology Skoltech Building 3 Moscow143026 Russia

We show that the community structure of a network can be used as a coarse version of its embedding in a hidden space with hyperbolic geometry. The finding emerges from a systematic analysis of several real-world and synthetic networks. We take advantage of the analogy for reinterpreting results originally obtained through network hyperbolic embedding in terms of community structure only. First, we show that the robustness of a multiplex network can be controlled by tuning the correlation between the community structures across different layers. Second, we deploy an efficient greedy protocol for network navigability that makes use of routing tables based on community structure. Copyright © 2018, The Authors. All rights reserved.

关键词： Complex networks

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Scientific mobility indicators in practice: International mobility profiles at the country level

arXiv

引用

arXiv 2018年

作者： Robinson-Garcia, Nicolas Sugimoto, Cassidy R. Murray, Dakota Yegros-Yegros, Alfredo Larivière, Vincent Costas, Rodrigo School of Public Policy Georgia Institute of Technology United States School of Informatics and Computing Indiana University Bloomington United States Leiden University Netherlands École de bibliothéconomie et des sciences de l’information Université de Montréal Canada Leiden University Netherlands DST-NRF Centre of Excellence in Scientometrics and Science Technology and Innovation Policy Stellenbosch University South Africa

This paper presents and describes the methodological opportunities offered by bibliometric data to produce indicators of scientific mobility. Large bibliographic datasets of disambiguated authors and their affiliations allow for the possibility of tracking the affiliation changes of scientists. Using the Web of Science as data source, we analyze the distribution of types of mobile scientists for a selection of countries. We explore the possibility of creating profiles of international mobility at the country level, and discuss potential interpretations and caveats. Five countries—Canada, The Netherlands, South Africa, Spain, and the United States—are used as examples. These profiles enable us to characterize these countries in terms of their strongest links with other countries. This type of analysis reveals circulation among and between countries with strong policy implications. Copyright © 2018, The Authors. All rights reserved.

关键词： Large dataset

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