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arXiv 2018年

作者： Terayama, Kei Tamura, Ryo Nose, Yoshitaro Hiramatsu, Hidenori Hosono, Hideo Okuno, Yasushi Tsuda, Koji RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Medical Sciences Innovation Hub Program RIKEN Cluster for Science Technology and Innovation Hub Kanagawa230-0045 Japan Graduate school of Medicine Kyoto University Kyoto606-8507 Japan National Institute for Materials Science Ibaraki305-0044 Japan Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Ibaraki305-0047 Japan Graduate school of Frontier Sciences University of Tokyo Chiba277-8568 Japan Department of Materials Science and Engineering Kyoto University Kyoto606-8501 Japan Laboratory for Materials and Structures Institute of Innovative Research Tokyo Institute of Technology Yokohama226-8503 Japan Materials Research Center for Element Strategy Tokyo Institute of Technology Yokohama226-8503 Japan

We develop a method to efficiently construct phase diagrams using machine learning. Uncertainty sampling (US) in active learning is utilized to intensively sample around phase boundaries. Here, we demonstrate constructions of three known experimental phase diagrams by the US approach. Compared with random sampling, the US approach decreases the number of sampling points to about 20%. In particular, the reduction rate is pronounced in more complicated phase diagrams. Furthermore, we show that using the US approach, undetected new phase can be rapidly found, and smaller number of initial sampling points are sufficient. Thus, we conclude that the US approach is useful to construct complicated phase diagrams from scratch and will be an essential tool in materials science. Copyright © 2018, The Authors. All rights reserved.

关键词： Graphic methods

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Neural network potential for Al-Mg-Si alloys

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Physical Review Materials 2017年第5期1卷 053604-053604页

作者： Ryo Kobayashi Daniele Giofré Till Junge Michele Ceriotti William A. Curtin Department of Scientific and Engineering Simulation Nagoya Institute of Technology Gokiso-cho Showa-ku Nagoya 466-8555 Japan Center for Materials research by Information Integration (CMI2) Research and Services Division of Materials Data and Integrated System (MaDIS) National Institute for Materials Science (NIMS) 1-2-1 Sengen Tsukuba Ibaraki 305-0047 Japan Institute of Materials Science École Polytechnique Fédérale de Lausanne CH-1015 Vaud Switzerland Institute of Mechanical Engineering École Polytechnique Fédérale de Lausanne CH-1015 Vaud Switzerland

The 6000 series Al alloys, which include a few percent of Mg and Si, are important in automotive and aviation industries because of their low weight, as compared to steels, and the fact their strength can be greatly improved through engineered precipitation. To enable atomistic-level simulations of both the processing and performance of this important alloy system, a neural network (NN) potential for the ternary Al-Mg-Si has been created. Training of the NN uses an extensive database of properties computed using first-principles density functional theory, including complex precipitate phases in this alloy. The NN potential accurately reproduces most of the pure Al properties relevant to the mechanical behavior as well as heat of solution, solute-solute, and solute-vacancy interaction energies, and formation energies of small solute clusters and precipitates that are required for modeling the early stage of precipitation and mechanical strengthening. This success not only enables future detailed studies of Al-Mg-Si but also highlights the ability of NN methods to generate useful potentials in complex alloy systems.

关键词： Defects Plasticity Alloys Face-centered cubic Solid solutions Machine learning Molecular dynamics

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Bias Reduction of Abilities for Adaptive Online IRT Testing systems

Bias Reduction of Abilities for Adaptive Online IRT Testing ...

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IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Takenori Sakumura Hideo Hirose Department of Industrial and System Engineering Chuo University Tokyo Japan Data Science Research Center Hiroshima Institute of Technology Hiroshima Japan

ISBN: (纸本)9781467389860

To evaluate the ability of examinees, the item response theory (IRT) gives us useful information. The IRT evaluates the examinee's ability and provides the item characteristics. Recently, we have configured the adaptive online testing using the IRT as one of the CBT (Computer Based Testing) methods, and we have been able to obtain the examinee's ability rating in short testing time. However, when the number of items is too small, we observed the bias of estimates for the ability parameter. In this study, we have performed simulation studies under various conditions, and have known such biases. To circumvent the biases due to the Bayes procedure, we have proposes a simple method to reduce the biases.

关键词： Testing Maximum likelihood estimation Adaptation models Standards Bayes methods Mathematical model

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

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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

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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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