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Corrigendum to: The TianQin project: current progress on science and technology

Progress of Theoretical and Experimental Physics 2021年第5期2021卷

作者： Mei, Jianwei Bai, Yan-Zheng Bao, Jiahui Barausse, Enrico Cai, Lin Canuto, Enrico Cao, Bin Chen, Wei-Ming Chen, Yu Ding, Yan-Wei Duan, Hui-Zong Fan, Huimin Feng, Wen-Fan Fu, Honglin Gao, Qing Gao, TianQuan Gong, Yungui Gou, Xingyu Gu, Chao-Zheng Gu, De-Feng He, Zi-Qi Hendry, Martin Hong, Wei Hu, Xin-Chun Hu, Yi-Ming Hu, Yuexin Huang, Shun-Jia Huang, Xiang-Qing Jiang, Qinghua Jiang, Yuan-Ze Jiang, Yun Jiang, Zhen Jin, Hong-Ming Korol, Valeriya Li, Hong-Yin Li, Ming Li, Pengcheng Li, Rongwang Li, Yuqiang Li, Zhu Li, Zhulian Li, Zhu-Xi Liang, Yu-Rong Liang, Zheng-Cheng Liao, Fang-Jie Liu, Qi Liu, Shuai Liu, Yan-Chong Liu, Li Liu, Pei-Bo Liu, Xuhui Liu, Yuan Lu, Xiong-Fei Lu, Yang Lu, Ze-Huang Luo, Yan Luo, Zhi-Cai Milyukov, Vadim Ming, Min Pi, Xiaoyu Qin, Chenggang Qu, Shao-Bo Sesana, Alberto Shao, Chenggang Shi, Changfu Su, Wei Tan, Ding-Yin Tan, Yujie Tan, Zhuangbin Tu, Liang-Cheng Wang, Bin Wang, Cheng-Rui Wang, Fengbin Wang, Guan-Fang Wang, Haitian Wang, Jian Wang, Lijiao Wang, Panpan Wang, Xudong Wang, Yan Wang, Yi-Fan Wei, Ran Wu, Shu-Chao Xiao, Chun-Yu Xu, Xiao-Shi Xue, Chao Yang, Fang-Chao Yang, Liang Yang, Ming-Lin Yang, Shan-Qing Ye, Bobing Yeh, Hsien-Chi Yu, Shenghua Zhai, Dongsheng Zhang, Caishi Zhang, Haitao Zhang, Jian-dong Zhang, Jie Zhang, Lihua Zhang, Xin Zhang, Xuefeng Zhou, Hao Zhou, Ming-Yue Zhou, Ze-Bing Zhu, Dong-Dong Zi, Tie-Guang Luo, Jun TianQin Research Center for Gravitational Physics & School of Physics and Astronomy Sun Yat-sen University (Zhuhai Campus) Zhuhai 519082 P.R. China Centre for Gravitational Experiments School of Physics MOE Key Laboratory of Fundamental Physical Quantities Measurement & Hubei Key Laboratory of Gravitation and Quantum Physics PGMF Huazhong University of Science and Technology Wuhan 430074 P.R. China SISSA Via Bonomea 265 34136 Trieste Italy and INFN Sezione di Trieste & IFPU – Institute for Fundamental Physics of the Universe Via Beirut 2 34014 Trieste Italy Former Faculty Politecnico di Torino Corso Duca degli Abruzzi 24 10129 Torino Italy Yunnan Observatories Chinese Academy of Sciences Kunming 650011 China School of Physical Science and Technology Southwest University Chongqing 400715 China Beijing Institute of Control Engineering Beijing 100094 P.R. China SUPA School of Physics and Astronomy University of Glasgow Glasgow G12 8QQ UK DFH Satellite Co. Ltd. Beijing 100094 P.R. China National Astronomical Observatories Chinese Academy of Sciences Beijing 100012 China School of Astronomy and Space Science University of Chinese Academy of Sciences Beijing 100049 China School of Physics and Astronomy University of Birmingham Birmingham B15 2TT United Kingdom Center for High Energy Physics & Department of Physics and State Key Laboratory of Nuclear Physics and Technology Peking University No. 5 Yiheyuan Rd Beijing 100871 P.R. China Lomonosov Moscow State University Sternberg Astronomical Institute Moscow 119992 Russia Dipartimento di Fisica “G. Occhialini” Universitá degli Studi Milano Bicocca Piazza della Scienza 3 I-20126 Milano Italy School of Aeronautics and Astronautics Shanghai Jiao Tong University Shanghai 200240 China Purple Mountain Observatory Chinese Academy of Sciences Nanjing 210023 & School of Astronomy and Space Science University of Science and Technology of China Hefei Anhui 230026 P.R. China Max-Planck-Institut für Gravitationsphysik (Al

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Formation and temporal evolution of social groups during coffee breaks 1

引用

5th International Workshop on Mining Ubiquitous and Social Environments, MUSE 2014

作者： Atzmueller, Martin Ernst, Andreas Krebs, Friedrich Scholz, Christoph Stumme, Gerd Knowledge and Data Engineering Group Research Center for Information System Design University of Kassel Wilhelmshöher Allee 73 Kassel34121 Germany Center for Environmental Systems Research University of Kassel Wilhelmshöher Allee 47 Kassel34117 Germany Department Energy Informatics and Information Systems Fraunhofer Institute for Wind Energy and Energy System Technology Königstor 59 Kassel34119 Germany

ISBN: (数字)9783319290096

ISBN: (纸本)9783319290089

Group formation and evolution are prominent topics in social contexts. This paper focuses on the analysis of group evolution events in networks of face-to-face proximity. We first analyze statistical properties of group evolution, e.g., individual activity and typical group sizes. After that, we define a set of specific group evolution events. These are analyzed in the context of an academic conference, where we provide different patterns according to phases of the conference. Specifically, we investigate group formation and evolution using real-world data collected at the LWA 2010 conference utilizing the Conferator system, and discuss patterns according to different phases of the conference. © Springer International Publishing Switzerland 2016.

关键词： Computers

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Developing a framework for digital objects in the Big data to Knowledge (BD2K) commons: Report from the Commons Framework Pilots workshop

Developing a framework for digital objects in the Big Data t...

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作者： Jagodnik, Kathleen M. Koplev, Simon Jenkins, Sherry L. Ohno-Machado, Lucila Paten, Benedict Schurer, Stephan C. Dumontier, Michel Verborgh, Ruben Bui, Alex Ping, Peipei McKenna, Neil J. Madduri, Ravi Pillai, Ajay Ma'ayan, Avi Department of Pharmacological Sciences BD2K-LINCS Data Coordination and Integration Center Mount Sinai Center for Bioinformatics Icahn School of Medicine at Mount Sinai One Gustave L. Levy Place Box 1215 New YorkNY10029 United States Health System Department of Biomedical Informatics University of California San Diego 9500 Gilman Dr. La Jolla CA92083 United States Health Services Research San Diego Veterans Administration Health System San DiegoCA92083 United States UC Santa Cruz Genomics Institute University of California Santa Cruz 1156 High St. Santa CruzCA95060 United States MiamiFL33136 United States Institute for Data Science Universiteit Maastricht Minderbroedersberg 4-6 LK Maastricht6211 Netherlands Ghent University – iMinds Research Foundation Flanders St. Pietersnieuwstraat 33 Gent9000 Belgium Department of Radiological Sciences UCLA School of Medicine Los AngelesCA90095 United States Department of Bioengineering UCLA Henri Samueli School of Engineering Los AngelesCA90095 United States Departments of Physiology Medicine and Bioinformatics UCLA School of Medicine Los AngelesCA90095 United States Department of Molecular and Cellular Biology Baylor College of Medicine 1 Baylor Plaza HoustonTX77030 United States Department of Mathematics and Computer Science Argonne National Laboratory 9700 S. Cass Avenue ArgonneIL60439 United States Division of Genome Sciences National Human Genome Research Institute National Institutes of Health 31 Center Drive MSC 2152 9000 Rockville Pike Bethesda MD20892 United States

The volume and diversity of data in biomedical research have been rapidly increasing in recent years. While such data hold significant promise for accelerating discovery, their use entails many challenges including: the need for adequate computational infrastructure, secure processes for data sharing and access, tools that allow researchers to find and integrate diverse datasets, and standardized methods of analysis. These are just some elements of a complex ecosystem that needs to be built to support the rapid accumulation of these data. The NIH Big data to Knowledge (BD2K) initiative aims to facilitate digitally enabled biomedical research. Within the BD2K framework, the Commons initiative is intended to establish a virtual environment that will facilitate the use, interoperability, and discoverability of shared digital objects used for research. The BD2K Commons Framework Pilots Working Group (CFPWG) was established to clarify goals and work on pilot projects that address existing gaps toward realizing the vision of the BD2K Commons. This report reviews highlights from a two-day meeting involving the BD2K CFPWG to provide insights on trends and considerations in advancing Big data science for biomedical research in the United States. © 2017 Elsevier Inc.

关键词： Reusability

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Two‐Dimensional Perovskite Oxynitride K2LaTa2O6N with an H+/K+Exchangeability in Aqueous Solution Forming a Stable Photocatalyst for Visible‐Light H2Evolution

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Angewandte Chemie 2020年第24期132卷

作者： Takayoshi Oshima Tom Ichibha Kenji Oqmhula Keisuke Hibino Hiroto Mogi Shunsuke Yamashita Kotaro Fujii Yugo Miseki Kenta Hongo Daling Lu Ryo Maezono Kazuhiro Sayama Masatomo Yashima Koji Kimoto Hideki Kato Masato Kakihana Hiroshi Kageyama Kazuhiko Maeda Department of Chemistry School of Science Tokyo Institute of Technology 2-12-1-NE-2 Ookayama Meguro-ku Tokyo 152-8550 Japan Japan Society for the Promotion of Science Kojimachi Business Center Building 5-3-1 Kojimachi Chiyoda-ku Tokyo 102-0083 Japan School of Information Science JAIST Asahidai 1-1 Nomi Ishikawa 923-1292 Japan Research Center for Advanced Measurement and Characterization National Institute for Materials Science 1-1 Namiki Tsukuba Ibaraki 305-0044 Japan Research Center for Photovoltaics (RCPV) and Global Zero Emission Research Center (GZR) National Institute of Advanced Industrial Science and Technology (AIST) Central 5 1-1-1 Higashi Tsukuba Ibaraki 305-8565 Japan Research Center for Advanced Computing Infrastructure JAIST Asahidai 1-1 Nomi Ishikawa 923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba 305-0047 Japan PRESTO Japan Science and Technology Agency 4-1-8 Honcho Kawaguchi-shi Saitama 322-0012 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama 351-0198 Japan Suzukakedai Materials Analysis Division Technical Department Tokyo Institute of Technology 4259 Nagatsuta-cho Midori-ku Yokohama 226-8503 Japan Institute of Multidisciplinary Research for Advanced Materials Tohoku University 2-1-1 Katahira Aoba-ku Sendai 980-8577 Japan Graduate School of Engineering Kyoto University Nishikyo-ku Kyoto 615-8510 Japan

Undoped layered oxynitrides have not been considered as promising H 2 ‐evolution photocatalysts because of the low chemical stability of oxynitrides in aqueous solution. Here, we demonstrate the synthesis of a new layered perovskite oxynitride, K 2 LaTa 2 O 6 N, as an exceptional example of a water‐tolerant photocatalyst for H 2 evolution under visible light. The material underwent in‐situ H + /K + exchange in aqueous solution while keeping its visible‐light‐absorption capability. Protonated K 2 LaTa 2 O 6 N, modified with an Ir cocatalyst, exhibited excellent catalytic activity toward H 2 evolution in the presence of I − as an electron donor and under visible light; the activity was six times higher than Pt/ZrO 2 /TaON, one of the best‐performing oxynitride photocatalysts for H 2 evolution. Overall water splitting was also achieved using the Ir‐loaded, protonated K 2 LaTa 2 O 6 N in combination with Cs‐modified Pt/WO 3 as an O 2 evolution photocatalyst in the presence of an I 3 − /I − shuttle redox couple.

关键词： artificial photosynthesis layered materials mixed anion compound solar fuels water splitting

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Developing Evaluation Module for Battery Lifetime

Developing Evaluation Module for Battery Lifetime

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2016 International Conference on Artificial Intelligence: Techniques and Applications(AITA 2016)

作者： Jing ZHANG Kenji TANAKA Shen-ming GU Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhejiang Ocean University Department of System Innovation Graduate School of EngineeringThe University of Tokyo

Lithium-ion batteries(Li Bs) have seen increasing use in automobiles and buildings over the past decade. Usable lifetimes of several years are sufficient for Li Bs used in electronic devices,but infrastructure applications require lifetimes measured in decades. Users of Li Bs are faced with uncertainty about Li B lifetimes,which hinders the spread of Li Bs. Predicting Li B performance degradation and evaluating useful lifetimes by examining present performance is therefore important for planning. Our research group has developed an evaluation model for Li Bs that comprises an empirically obtained degradation speed database and a method for recognizing Li B usage patterns.

关键词： Battery performance Evaluation Model Simulation

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Balanced truncation for linear switched systems

arXiv

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arXiv 2017年

作者： Gosea, I.V. Petreczky, M. Antoulas, A.C. Fiter, C. Data-Driven System Reduction and Identification Group Max Planck Institute for Dynamics of Complex Technical Systems Sandtorstrasse 1 Magdeburg39106 Germany UMR CNRS 9189 CNRS Ecole Centrale de Lille France Department of Electrical and Computer Engineering Rice University 6100 Main St MS-366 HoustonTX77005 United States CNRS CRIStAL UMR 9189 Université de Lille 1 Sciences et Technologies Villeneuve d'Ascq59651 France

We propose a model order reduction approach for balanced truncation of linear switched systems. Such systems switch among a finite number of linear subsystems or modes. We compute pairs of controllability and observability Gramians corresponding to each active discrete mode by solving systems of coupled Lyapunov equations. Depending on the type, each such Gramian corresponds to the energy associated to all possible switching scenarios that start or, respectively end, in a particular operational mode. In order to guarantee that hard to control and hard to observe states are simultaneously eliminated, we construct a transformed system, whose Gramians are equal and diagonal. Then, by truncation, directly construct reduced order models. One can show that these models preserve some properties of the original model, such as stability and that it is possible to obtain error bounds relating the observed output, the control input and the entries of the diagonal *** Codes 93A15, 93A30, 93B11, 93C05, 93C10 Copyright © 2017, The Authors. All rights reserved.

关键词： Lyapunov functions

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Publisher Correction: Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes

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Nature genetics 2019年第7期51卷 1192-1193页

作者： Rainer Malik Ganesh Chauhan Matthew Traylor Muralidharan Sargurupremraj Yukinori Okada Aniket Mishra Loes Rutten-Jacobs Anne-Katrin Giese Sander W van der Laan Solveig Gretarsdottir Christopher D Anderson Michael Chong Hieab H H Adams Tetsuro Ago Peter Almgren Philippe Amouyel Hakan Ay Traci M Bartz Oscar R Benavente Steve Bevan Giorgio B Boncoraglio Robert D Brown Adam S Butterworth Caty Carrera Cara L Carty Daniel I Chasman Wei-Min Chen John W Cole Adolfo Correa Ioana Cotlarciuc Carlos Cruchaga John Danesh Paul I W de Bakker Anita L DeStefano Marcel den Hoed Qing Duan Stefan T Engelter Guido J Falcone Rebecca F Gottesman Raji P Grewal Vilmundur Gudnason Stefan Gustafsson Jeffrey Haessler Tamara B Harris Ahamad Hassan Aki S Havulinna Susan R Heckbert Elizabeth G Holliday George Howard Fang-Chi Hsu Hyacinth I Hyacinth M Arfan Ikram Erik Ingelsson Marguerite R Irvin Xueqiu Jian Jordi Jiménez-Conde Julie A Johnson J Wouter Jukema Masahiro Kanai Keith L Keene Brett M Kissela Dawn O Kleindorfer Charles Kooperberg Michiaki Kubo Leslie A Lange Carl D Langefeld Claudia Langenberg Lenore J Launer Jin-Moo Lee Robin Lemmens Didier Leys Cathryn M Lewis Wei-Yu Lin Arne G Lindgren Erik Lorentzen Patrik K Magnusson Jane Maguire Ani Manichaikul Patrick F McArdle James F Meschia Braxton D Mitchell Thomas H Mosley Michael A Nalls Toshiharu Ninomiya Martin J O'Donnell Bruce M Psaty Sara L Pulit Kristiina Rannikmäe Alexander P Reiner Kathryn M Rexrode Kenneth Rice Stephen S Rich Paul M Ridker Natalia S Rost Peter M Rothwell Jerome I Rotter Tatjana Rundek Ralph L Sacco Saori Sakaue Michele M Sale Veikko Salomaa Bishwa R Sapkota Reinhold Schmidt Carsten O Schmidt Ulf Schminke Pankaj Sharma Agnieszka Slowik Cathie L M Sudlow Christian Tanislav Turgut Tatlisumak Kent D Taylor Vincent N S Thijs Gudmar Thorleifsson Unnur Thorsteinsdottir Steffen Tiedt Stella Trompet Christophe Tzourio Cornelia M van Duijn Matthew Walters Nicholas J Wareham Sylvia Wassertheil-Smoller James G Wilson Kerri L Wiggins Qiong Yang Salim Yusuf Joshua C Bis Tomi Institute for Stroke and Dementia Research (ISD) University Hospital LMU Munich Munich Germany. Centre for Brain Research Indian Institute of Science Bangalore India. INSERM U1219 Bordeaux Population Health Research Center University of Bordeaux Bordeaux France. Stroke Research Group Division of Clinical Neurosciences University of Cambridge Cambridge UK. Department of Neurology Institute for Neurodegenerative Disease Bordeaux University Hospital Bordeaux France. Laboratory for Statistical Analysis RIKEN Center for Integrative Medical Sciences Yokohama Japan. Department of Statistical Genetics Osaka University Graduate School of Medicine Osaka Japan. Laboratory of Statistical Immunology Immunology Frontier Research Center (WPI-IFReC) Osaka University Suita Japan. Department of Neurology Massachusetts General Hospital (MGH) Harvard Medical School Boston MA USA. Laboratory of Experimental Cardiology Department of Cardiology Division of Heart and Lungs University Medical Center Utrecht University of Utrecht Utrecht the Netherlands. deCODE genetics/AMGEN Inc. Reykjavik Iceland. Center for Genomic Medicine MGH Boston MA USA. J. Philip Kistler Stroke Research Center Department of Neurology MGH Boston MA USA. Program in Medical and Population Genetics Broad Institute Cambridge MA USA. Population Health Research Institute McMaster University Hamilton Ontario Canada. Department of Epidemiology Erasmus University Medical Center Rotterdam the Netherlands. Department of Radiology and Nuclear Medicine Erasmus University Medical Center Rotterdam the Netherlands. Department of Medicine and Clinical Science Graduate School of Medical Sciences Kyushu University Fukuoka Japan. Department of Clinical Sciences Lund University Malmö Sweden. INSERM Institut Pasteur de Lille LabEx DISTALZ-UMR1167 Risk Factors and Molecular Determinants of Aging-Related Diseases Université Lille Lille France. Centre Hospitalier Université Lille Epidemiology and Public Health D

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Biogeography-based optimization for cluster analysis

Biogeography-based optimization for cluster analysis

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International Conference on Computer, Communication and Computational Sciences, ICCCCS 2016

作者： Wu, Xueyan Wang, Hainan Chen, Zhimin Lu, Zhihai Phillips, Preetha Wang, Shuihua Zhang, Yudong School of Computer Science and Technology Nanjing Normal University NanjingJiangsu210023 China Key Laboratory of Statistical Information Technology & Data Mining State Statistics Bureau ChengduSichuan610225 China School of Computer and Information Engineering Henan Normal University XinxiangHenan453000 China School of Electronic Information Shanghai Dianji University Shanghai200240 China Key Laboratory of Symbolic Computation & Knowledge Engineering of Ministry of Education Jilin University ChangchunJilin130012 China Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology GuilinGuangxi541004 China School of Natural Sciences and Mathematics Shepherd University ShepherdstownWV25443 United States West Virginia School of Osteopathic Medicine 400 N Lee St LewisburgWV24901 United States Department of Electrical Engineering The City College of New York CUNY New YorkNY10031 United States State Key Lab of CAD & CG Zhejiang University HangzhouZhejiang310027 China Manchester Metropolitan University ManchesterM156BH United Kingdom

ISBN: (纸本)9789811037696

With the aim of resolving the issue of cluster analysis more precisely and validly, a new approach was proposed based on biogeography-based optimization (abbreviated as BBO) algorithm. (Method) First, we reformulated the problem with an optimization model based on the variance ratio criterion (VARAC). Then, BBO was presented to search the optimal solution of the VARAC. There are 400 data of four groups in the experimental dataset, which have the degrees of overlapping of three distinct scales. The first one is nonoverlapping, the second one is partial overlapping, and the last is severely overlapping. BBO algorithm was compared with three different state-of-the-art approaches. We ran every algorithm 20 times. In this experiment, our results demonstrate the maximum VARAC values that can be found by BBO. The conclusion is that BBO is predominant which is extremely quick for the issue of clustering analysis. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Cluster analysis

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Temporal and spatial evolutions of a large sunspot group and great auroral storms around the carrington event in 1859

arXiv

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arXiv 2019年

作者： Hisashi, Hayakawa Ebihara, Yusuke Willis, David M. Toriumi, Shin Iju, Tomoya Hattori, Kentaro Wild, Matthew N. Oliveira, Denny M. Ermolli, Ilaria Ribeiro, José R. Correia, Ana P. Ribeiro, Ana I. Knipp, Delores J. Graduate School of Letters Osaka University JSPS Research Toyonaka5600043 Japan UK Solar System Data Centre Space Physics and Operations Division RAL Space Science and Technology Facilities Council Rutherford Appleton Laboratory Harwell Oxford Didcot OxfordshireOX11 0QX United Kingdom Research Institute for Sustainable Humanosphere Kyoto University Uji6100011 Japan Unit of Synergetic Studies for Space Kyoto University Kyoto6068306 Japan Centre for Fusion Space and Astrophysics Department of Physics University of Warwick CoventryCV4 7AL United Kingdom 3-1-1 Yoshinodai Chuo-ku Sagamihara Kanagawa252-5210 Japan National Astronomical Observatory of Japan Mitaka1818588 Japan Graduate School of Science Kyoto University Kyoto Kitashirakawa Oiwake-cho Sakyo-ku Kyoto6068502 Japan Goddard Planetary Heliophysics Institute University of Maryland Baltimore County 1000 Hilltop Circle BaltimoreMD21250 United States NASA Goddard Space Flight Center 8800 Greenbelt Road GreenbeltMD21021 United States INAF Osservatorio Astronomico di Roma Via Frascati 33 Monte Porzio Catone00078 Italy Escola Secundária Henrique Medina Esposende Av. Dr. Henrique Barros Lima Esposende4740-203 Portugal EPIUnit - Instituto de Saúde Pública Universidade do Porto Rua das Taipas Porto135 4050-600 Portugal Departamento de Ciências da Saúde Pública e Forenses e Educação Médica Faculdade de Medicina Universidade do Porto Porto4200-319 Portugal High Altitude Observatory National Center for Atmospheric Research BoulderCOCO 80307 United States Smead Aerospace Engineering Sciences Department University of Colorado Boulder BoulderCOCO 80309 United States

The Carrington event is considered to be one of the most extreme space weather events in observational history within a series of magnetic storms caused by extreme interplanetary coronal mass ejections (ICMEs) from a large and complex active region (AR) emerged on the solar disk. In this article, we study the temporal and spatial evolutions of the source sunspot active region and visual aurorae, and compare this storm with other extreme space weather events on the basis of their spatial evolution. Sunspot drawings by Schwabe, Secchi, and Carrington describe the position and morphology of the source AR at that time. Visual auroral reports from the Russian Empire, Iberia, Ireland, Oceania, and Japan fill the spatial gap of auroral visibility and revise the time series of auroral visibility in mid to low magnetic latitudes (MLATs). The reconstructed time series is compared with magnetic measurements and shows the correspondence between low to mid latitude aurorae and the phase of magnetic storms. The spatial evolution of the auroral oval is compared with those of other extreme space weather events in 1872, 1909, 1921, and 1989 as well as their storm intensity, and contextualizes the Carrington event, as one of the most extreme space weather events, but likely not unique. Copyright © 2019, The Authors. All rights reserved.

关键词： Visibility

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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