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arXiv 2019年

作者： Ichibha, Tom Benali, Anouar Hongo, Kenta Maezono, Ryo School of Information Science JAIST 1-1 Asahidai NomiIshikawa923-1292 Japan Computational Science Division Argonne National Laboratory 9700 Cass Avenue LemontIL60439 United States Research Center for Advanced Computing Infrastructure JAIST 1-1 Asahidai NomiIshikawa923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science 1-2-1 Sengen Tsukuba305-0047 Japan PRESTO Japan Science and Technology Agency 4-1-8 Honcho Kawaguchi-shi Saitama322-0012 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama351-0198 Japan

We revisited ab initio evaluations of the energy barriers along the possible diffusion paths of the defects in rutile TiO2. By using a method carefully considering the cancellation of the self-interaction, Ti interstitials hopping along c-axis are identified as the major diffusion directing to [001] surface. The conclusion is contradicting to any of previous theoretical works, and the discrepancy is explained by the overestimation of the radius of defects due to the poor cancellations in the previous works. The updated prediction here can explain the superior photocatalysis activity in [001] surface to [110]. Copyright © 2019, The Authors. All rights reserved.

关键词： Titanium dioxide

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Reports of the AAAI 2014 conference workshops

Reports of the AAAI 2014 conference workshops

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作者： Albrecht, Stefano V. Barreto, André M. S. Braziunas, Darius Buckeridge, David L. Cuayáhuitl, Heriberto Dethlefs, Nina Endres, Markus Farahmand, Amir-Massoud Fox, Mark Frommberger, Lutz Ganzfried, Sam Guillet, Sébastien Gil, Yolanda Hunter, Lawrence E. Jhala, Arnav Kersting, Kristian Konidaris, George Lecue, Freddy McIlraith, Sheila Natarajan, Sriraam Noorian, Zeinab Poole, David Ronfard, Rémi Saffiotti, Alessandro Shaban-Nejad, Arash Srivastava, Biplav Tesauro, Gerald Uceda-Sosa, Rosario Van Den Broeck, Guy Van Otterlo, Martijn Wallace, Byron C. Weng, Paul Wiens, Jenna Zhang, Jie School of Informatics University of Edinburgh United Kingdom Department of Applied and Computational Mathematics Brazilian National Laboratory Brazil Big Data Group Kobo Inc. Brazil Department of Epidemiology and Biostatistics McGill University Canada School of Mathematical and Computer Sciences Heriot-Watt University in Edinburgh United Kingdom University of Augsburg Germany Carnegie Mellon University United States University of Toronto Canada Cognitive Systems Group University of Bremen Germany LIARA UQAC Chicoutimi Canada Information Sciences Institute University of Southern California Computer Science Department United States Center for Computational Pharmacology University of Colorado School of Medicine United States Department of Computational Media University of California Santa Cruz United States Computer Science Department Technical University of Dortmund Germany MIT Computer Science and Artificial Intelligence Laboratory CambridgeMA United States IBM Research - Smarter Cities Technology Centre Dubline Ireland School of Informatics and Computing Indiana University United States Department of Computer Science University of Saskatchewan Canada University of British Columbia Canada INRIA University of Grenoble France Orebro University Sweden McGill Clinical and Health Informatics Group McGill University Canada IBM Research United States Cognitive Computing Department IBM Research Yorktown HeightsNY United States Computer Science Department Katholieke Universiteit Leuven Belgium Cognitive Artificial Intelligence Department Radboud University Nijmegen Netherlands University of Texas Austin United States Pierre and Marie Curie University Paris France University of Michigan United States School of Computer Engineering Nanyang Technological University Singapore

The AAAI-14 Workshop program was held Sunday and Monday, July 27-28, 2014, at the Québec City Convention Centre in Québec, Canada. The AAAI-14 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Artificial Intelligence and Robotics;Artificial Intelligence Applied to Assistive Technologies and Smart Environments;Cognitive computing for Augmented Human Intelligence;Computer Poker and Imperfect Information;Discovery Informatics;Incentives and Trust in Electronic Communities;Intelligent Cinematography and Editing;Machine Learning for Interactive systems: Bridging the Gap Between Perception, Action, and Communication;Modern Artificial Intelligence for Health Analytics;Multiagent Interaction Without Prior Coordination;Multidisciplinary Workshop on Advances in Preference Handling;Semantic Cities - Beyond Open data to Models, Standards, and Reasoning;Sequential Decision Making with big data;Statistical Relational AI;and the World Wide Web and Public Health Intelligence. This article presents short summaries of those events. © 2015, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Decision making

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Linked data Crowdsourcing Quality Assessment based on Domain Professionalism

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Journal of Physics: Conference Series 2019年第5期1187卷

作者： Lu Yang Li Huang Zhenzhen Liu School of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430065 China Key Laboratory of Intelligent Information Processing and Real-time Industrial System in Hubei Province Wuhan 430065 China Institute of Big Data Science and Engineering Wuhan University of Science and Technology Wuhan 430065 China Key Laboratory of Rich-media Knowledge Organization and Service of Digital Publishing Content National Press and Publication Administration Beijing 100038 China

With the rapid development of Internet technology, crowdsourcing, as a flexible, effective and low-cost problem-solving method, has begun to receive more and more attention. The use of crowdsourcing to evaluate the quality of linked data has also become a research hotspot. This paper proposes the concept of Domain Specialization Test (DST), which uses domain professional testing tasks DSTs to evaluate the professionalism of workers, and combines the idea of Mini-batch Gradient Descent (MBGD) to improve the EM algorithm, and the MBEM algorithm is proposed to achieve efficient and accurate evaluation of task results. The experimental results show that the proposed method can screen out the appropriate workers for the linked data crowdsourcing task and improve the accuracy and iteration efficiency of the results.

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Emergency Event Matching using Hierarchical Blocking Method

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Journal of Physics: Conference Series 2019年第5期1187卷

作者： Chang Wen Yu Liu College of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430065 China Key Laboratory of Intelligent Information Processing and Real-time Industrial System in Hubei Province Wuhan 430065 China Institute of Big Data Science and Engineering Wuhan University of Science and Technology Wuhan 430065 China Key Laboratory of Rich-media Knowledge Organization and Service of Digital Publishing Content National Press and Publication Administration Beijing 100038 China

With the extensive application of the knowledge base (KB), how to complete it is a hot topic on Semantic Web. However, many problems go with the big data, and the event matching is one of these problems, which is finding out the entities referring to the same things in the real world and also the key point in the extending process. To enrich the emergency knowledge base (E-SKB) we constructed before, we need to filter out the news from several web pages and find the same news to avoid data redundancy. In this paper, we proposed a hierarchy blocking method to reduce the times of comparisons and narrow down the scope by extracting the news properties as the blocking keys. The method transforms the event matching problem into a clustering problem. Experimental results show that the proposed method is superior to the existing text clustering algorithm with high precision and less comparison times.

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The effects of different travel modes and travel destinations on COVID-19 transmission in global cities

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Science Bulletin 2022年第6期67卷 588-592,M0003页

作者： Rui Zhu Luc Anselin Michael Batty Mei-Po Kwan Min Chen Wei Luo Tao Cheng Che Kang Lim Paolo Santi Cheng Cheng Qiushi Gu Man Sing Wong Kai Zhang Guonian Lü Carlo Ratti Department of Land Surveying and Geo-Informatics the Hong Kong Polytechnic UniversityHong KongChina Key Laboratory of Virtual Geographic Environment(Ministry of Education) Nanjing Normal UniversityNanjing 210023China Senseable City Laboratory Future Urban Mobility IRGSingapore-MIT Alliance for Research and TechnologySingapore 138602Singapore Center for Spatial Data Science Division of the Social SciencesUniversity of ChicagoChicago IL 60637USA School of Geography Science Nanjing Normal UniversityNanjing 210023China Centre for Advanced Spatial Analysis Faculty of the Built EnvironmentUniversity College LondonLondon WC1E 6BTUK Department of Geography and Resource Management the Chinese University of Hong KongHong KongChina Institute of Space and Earth Information Science the Chinese University of Hong KongHong KongChina Department of Human Geography and Spatial Planning Utrecht UniversityUtrecht 3584 CSNetherlands State Key Laboratory Cultivation Base of Geographical Environment Evolution Nanjing 210023China Department of Geography National University of SingaporeSingapore 117570Singapore SpaceTimeLab Department of CivilEnvironmental and Geomatic EngineeringUniversity College LondonLondon WC1E 6BTUK Department of Clinical Translational Research Singapore General HospitalSingapore 169856Singapore Division of Clinical Immunology and Transfusion Medicine Department of Laboratory MedicineKarolinska InstitutetStockholm 17177Sweden Senseable City Laboratory Department of Urban Studies and PlanningMassachusetts Institute of TechnologyCambridgeMA 02139USA Istituto di Informatica e Telematica del CNR Pisa 56124Italy Department of System Science Institute of High Performance Computing Agency for ScienceTechnology and ResearchSingapore 138632Singapore Department of Tourism Studies School of HumanitiesSoutheast UniversityNanjing 210096China

The international community has made significant efforts to flatten the COVID-19 curve,including predicting transmission[1,2],executing unprecedented global lockdowns and social distancing[3,4],promoting the wearing of facemasks and social distancing measures[5],and isolating confirmed cases and contacts[6].Because of the adverse consequences of these lockdown measures[7],many cities have reopened so they can rebuild their ***,as mobility has gradually returned towards normal,imported cases from unknown sources have disrupted the recovery situation,and cities are continually at high risk of new waves of infection[8,9]since airborne transmission is the dominant transmission route[10].

关键词：多变量时间序列疫情传播出行方式谷歌公司交通出行病毒传播出行行为大城市

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Scientific data Lake for High Luminosity LHC project and other data-intensive particle and astro-particle physics experiments

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Journal of Physics: Conference Series 2020年第1期1690卷

作者： A Alekseev A Kiryanov A Klimentov T Korchuganova V Mitsyn D Oleynik A Smirnov S Smirnov A Zarochentsev Compiler Technology Department Ivannikov Institute for System Programming of the RAS Moscow 109004 Russia Cloud technologies and Big Data analytics laboratory Plekhanov Russian University of Economics Moscow 117997 Russia ATLAS group Universidad Andres Bello Santiago 7550196 Chile Department of computing resources and technologies NRC 'Kurchatov Institute' - PNPI 188300 Gatchina Russia Physics Department Brookhaven National Laboratory Upton NY 11973 USA Laboratory of Information Technologies Joint Institute for Nuclear Research Dubna 141980 Russia Institute of Nuclear Physics and Engineering National Research Nuclear University MEPhI Moscow 115409 Russia Laboratory of Ultra-High Energy Physics Saint Petersburg State University Saint Petersburg 198504 Russia

The next phase of LHC Operations – High Luminosity LHC (HL-LHC), which is aimed at ten-fold increase in the luminosity of proton-proton collisions at the energy of 14 TeV, is expected to start operation in 2027-2028 and will deliver an unprecedented scientific data volume of multi-exabyte scale. This amount of data has to be stored and the corresponding storage system should ensure fast and reliable data delivery for processing by scientific groups distributed all over the world. The present LHC computing and data processing model will not be able to provide the required infrastructure growth even taking into account the expected hardware technology evolution. To address this challenge the new state-of-the-art computing infrastructure technologies are now being developed and are presented here. The possibilities of application of the HL-LHC distributed data handling technique for other particle and astro-particle physics experiments dealing with large-scale data volumes like DUNE, LSST, Belle-II, JUNO, SKAO etc. are also discussed.

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Author Correction: The influence of natural variability on extreme monsoons in Pakistan

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npj Climate and Atmospheric Science 2023年第1期6卷 1页

作者： Moetasim Ashfaq Matthew F. Horan Salil Mahajan Katherine J. Evans Nathaniel Johnson Fred Kucharski Muhammad Adnan Abid Noah S. Diffenbaugh Deepti Singh Subimal Ghosh Auroop R. Ganguly Shafiqul Islam Computational Sciences and Engineering Division (CSED) Oak Ridge National Laboratory Oak Ridge TN USA Bredesen Center University of Tennessee Knoxville TN USA National Oceanic and Atmospheric Administration/Geophysical Fluid Dynamics Laboratory Princeton NJ USA Earth System Physics Abdus Salam International Centre for Theoretical Physics Trieste Italy Doerr School of Sustainability Stanford University Stanford CA USA School of the Environment Washington State University Vancouver WA USA Department of Civil Engineering Indian Institute of Technology Bombay Mumbai India Sustainability and Data Sciences Laboratory Civil and Environmental Engineering Northeastern University Boston MA USA Advanced Computing Mathematics and Data Division Pacific Northwest National Laboratory Richland WA USA Indian Institute of Technology Gandhinagar Gandhinagar GJ India Department of Civil and Environmental Engineering The Fletcher School of Law and Diplomacy Tufts University Medford MA USA

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Mapping essential urban land use categories in China(EULUC-China):preliminary results for 2018

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Science Bulletin 2020年第3期65卷 182-187页

作者： Peng Gong Bin Chen Xuecao Li Han Liu Jie Wang Yuqi Bai Jingming Chen Xi Chen Lei Fang Shuailong Feng Yongjiu Feng Yali Gong Hao Gu Huabing Huang Xiaochun Huang Hongzan Jiao Yingdong Kang Guangbin Lei Ainong Li Xiaoting Li Xun Li Yuechen Li Zhilin Li Zhongde Li Chong Liu Chunxia Liu Maochou Liu Shuguang Liu Wanliu Mao Changhong Miao Hao Ni Qisheng Pan Shuhua Qi Zhehao Ren Zhuoran Shan Shaoqing Shen Minjun Shi Yimeng Song Mo Su Hoi Ping Suen Bo Sun Fangdi Sun Jian Sun Lin Sun Wenyao Sun Tian Tian Xiaohua Tong Yihsing Tseng Ying Tu Hong Wang Lan Wang Xi Wan Zongming Wang Tinghai Wu Yaowen Xie Jian Yang Jun Yang Man Yuan Wenze Yue Hongda Zeng Kuo Zhang Neng Zhang Tao Zhang Yu Zhang Feng Zhao Yichen Zheng Qiming Zhou Nicholas Clinton Zhiliang Zhu Bing Xu Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System ScienceTsinghua UniversityBeijing 100084China Tsinghua Urban Institute Tsinghua UniversityBeijing 100084China Center for Healthy Cities Institute for China Sustainable UrbanizationTsinghua UniversityBeijing 100084China Department of Land Air and Water ResourcesUniversity of CaliforniaDavisCA 95616-8627USA Department of Geological and Atmospheric Sciences Iowa State UniversityAmesIA 50011USA State Key Laboratory of Remote Sensing Science Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijing 100101China AI for Earth Laboratory Cross-Strait InstituteTsinghua UniversityBeijing 100084China International Institute for Earth System Sciences Nanjing UniversityNanjing 210093China Research Center for Ecology and Environment of Central Asia Chinese Academy of SciencesUrumchi 830011China Key Laboratory of Forest Ecology and Management Institute of Applied EcologyChinese Academy of SciencesShenyang 110016China National Engineering Laboratory for Applied Technology in Forestry&Ecology in South China College of Life Science and TechnologyCentral South University of Forestry and TechnologyChangsha 410004China College of Surveying&Geo-Informatics Tongji UniversityShanghai 200092China Department of Urban Planning College of Architecture and Urban PlanningTongji UniversityShanghai 200092China Beijing Institute of Urban Planning Beijing 100045China Department of Urban Planning School of Urban DesignWuhan UniversityWuhan 430072China College of Earth Sciences Jilin UniversityChangchun 130061China Research Center for Digital Mountain and Remote Sensing Application Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610041China Institute of Remote Sensing and Geographic Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Department of Urban and Regional Planning School of Geography and PlanningSun Yat-sen Univ

Land use reflects human activities on *** land use is the highest level human alteration on Earth,and it is rapidly changing due to population increase and *** areas have widespread effects on local hydrology,climate,biodiversity,and food production[1,2].However,maps,that contain knowledge on the distribution,pattern and composition of various land use types in urban areas,are limited to city *** mapping standard on data sources,methods,land use classification schemes varies from city to city,due to differences in financial input and skills of mapping *** address various national and global environmental challenges caused by urbanization,it is important to have urban land uses at the national and global scales that are derived from the same or consistent data sources with the same or compatible classification systems and mapping *** is because,only with urban land use maps produced with similar criteria,consistent environmental policies can be made,and action efforts can be compared and assessed for large scale environmental ***,despite of the fact that a number of urban-extent maps exist at global scales[3,4],more detailed urban land use maps do not exist at the same *** at big country or regional levels such as for the United States,China and European Union,consistent land use mapping efforts are rare[5,6](e.g.,https://***/open_land_use/).

关键词： EULUC-China 土地利用类型

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Learning from learning machines: A new generation of AI technology to meet the needs of science

arXiv

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arXiv 2021年

作者： Pion-Tonachini, Luca Bouchard, Kristofer Martin, Hector Garcia Peisert, Sean Holtz, W. Bradley Aswani, Anil Dwivedi, Dipankar Wainwright, Haruko Pilania, Ghanshyam Nachman, Benjamin Marrone, Babetta L. Falco, Nicola Prabhat Arnold, Daniel Wolf-Yadlin, Alejandro Powers, Sarah Climer, Sharlee Jackson, Quinn Carlson, Ty Sohn, Michael Zwart, Petrus Kumar, Neeraj Justice, Amy Tomlin, Claire Jacobson, Daniel Micklem, Gos Gkoutos, Georgios V. Bickel, Peter J. Cazier, Jean-Baptiste Müller, Juliane Webb-Robertson, Bobbie-Jo Stevens, Rick Anderson, Mark Kreutz-Delgado, Ken Mahoney, Michael W. Brown, James B. Pattern Computer Inc. Friday HarborWA98250 United States Biosciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Helen Wils Neuroscience Institute Redwood Center for Theoretical Neuroscience Uc Berkeley BerkeleyCA94720 United States Doe Agile BioFoundry Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Joint BioEnergy Institute Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Bcam Basque Center for Applied Mathematics Bilbao48009 Spain Computer Science University of California Davis DavisCA95616 United States Cenic La MiradaCA90638 United States Health Informatics University of California Davis School of Medicine SacramentoCA95817 United States Berkeley Institute for Data Science University of California Berkeley BerkeleyCA94720 United States Industrial Engineering and Operations Research University of California Berkeley BerkeleyCA94720 United States Environmental & Earth Sciences Area Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Nuclear Engineering University of California Berkeley BerkeleyCA94720 United States Materials Science and Technology Division Los Alamos National Laboratory Los AlamosNM87545 United States Physics Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Bioscience Division Los Alamos National Laboratory Los AlamosNM87545 United States Earth and Environmental Sciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Energy Technologies Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computing and Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Industrial & Systems Engineering The University of Tennessee KnoxvilleTN37996 United States Department of Computer Science University of Missouri-Saint Louis St. LouisMO63121 United

We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data. If we address the fundamental challenges associated with "bridging the gap" between domain-driven scientific models and data-driven AI learning machines, then we expect that these AI models can transform hypothesis generation, scientific discovery, and the scientific process itself. © 2021, CC BY.

关键词： Learning systems

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Redefining pandemic preparedness:Multidisciplinary insights from the CERP modelling workshop in infectious diseases,workshop report

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Infectious Disease Modelling 2024年第2期9卷 501-518页

作者： Marta C.Nunes Edward Thommes Holger Fröhlich Antoine Flahault Julien Arino Marc Baguelin Matthew biggerstaff Gaston Bizel-Bizellot Rebecca Borchering Giacomo Cacciapaglia Simon Cauchemez Alex Barbier-Chebbah Carsten Claussen Christine Choirat Monica Cojocaru Catherine Commaille-Chapus Chitin Hon Jude Kong Nicolas Lambert Katharina B.Lauer Thorsten Lehr Cédric Mahe Vincent Marechal Adel Mebarki Seyed Moghadas Rene Niehus Lulla Opatowski Francesco Parino Gery Pruvost Andreas Schuppert Rodolphe Thiébaut Andrea Thomas-Bachli Cecile Viboud Jianhong Wu Pascal Crépey Laurent Coudeville Center of Excellence in Respiratory Pathogens(CERP) Hospices Civils de Lyon(HCL)and Centre International de Recherche en Infectiologie(CIRI)Équipe SantéPubliqueÉpidémiologie etÉcologieÉvolutive des Maladies Infectieuses(PHE3ID)Inserm U1111CNRS UMR5308ENS de LyonUniversitéClaude Bernard Lyon 1LyonFrance South African Medical Research Council Vaccines&Infectious Diseases Analytics Research UnitFaculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa New Products and Innovation(NPI) Sanofi Vaccines(Global)TorontoOntarioCanada Department of Mathematics and Statistics University of GuelphGuelphOntarioCanada Fraunhofer Institute for Algorithms and Scientific Computing(SCAI) Department of BioinformaticsSchloss BirlinghovenSankt AugustinGermany University of Bonn Bonn-Aachen International Center for IT(b-it)BonnGermany Institute of Global Health Faculty of MedicineUniversity of GenevaGenevaSwitzerland and Swiss School of Public HealthZürichSwitzerland Department of Mathematics University of ManitobaWinnipegManitobaCanada MRC Centre for Global Infectious Disease Analysis School of Public HealthImperial College LondonLondonUK Centre for Mathematical Modelling of Infectious Diseases Department of Infectious Disease EpidemiologyLondon School of Hygiene&Tropical MedicineLondonUK National Center for Immunization and Respiratory Diseases(NCIRD) US Centers for Disease Control and Prevention(CDC)AtlantaGAUSA Departement of Computational Biology Departement of Global HealthInstitut PasteurParisFrance Institut de Physique des Deux Infinis de Lyon(IP2I) UMR5822IN2P3/CNRSUniversitéClaude Bernard Lyon 1VilleurbanneFrance Mathematical Modelling of Infectious Diseases Unit Institut PasteurUniversitéParis CitéUMR2000 CNRSParisFrance Decision and Bayesian Computation Institut PasteurUniversitéParis CitéCNRS UMR 3571France Fraunhofer-Institute for Translational Medicine and Pharmacology HamburgGermany Institute of Global Health Faculty of MedicineUniversity of

In July 2023,the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 *** report summarizes the rich discussions that occurred during the *** workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data,social media,and wastewater *** advancements were noted in the development of predictive models,with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease *** role of open collaboration between various stakeholders in modelling was stressed,advocating for the continuation of such partnerships beyond the pandemic.A major gap identified was the absence of a common international framework for data sharing,which is crucial for global pandemic ***,the workshop underscored the need for robust,adaptable modelling frameworks and the integration of different data sources and collaboration across sectors,as key elements in enhancing future pandemic response and preparedness.

关键词： Modelling Covid-19 Infectious diseases Pandemic preparedness Workshop

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