检索结果-内蒙古大学图书馆

2016 IEEE International Conference on systems, Man, and Cybernetics, SMC 2016

作者： Nguyen, Linh Anh Nguyen, Thi Hong Khanh Nguyen, Ngoc-Thanh Ha, Quang-Thuy Division of Knowledge and System Engineering for ICT Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam Institute of Informatics University of Warsaw Banacha 2 Warsaw02-097 Poland Electric Power University 235 Hoang Quoc Viet Tu Liem Hanoi Viet Nam Department of Information Systems Faculty of Computer Science and Management Wroclaw University of Technology Poland Faculty of Information Technology VNU University of Engineering and Technology 144 Xuan Thuy Hanoi Viet Nam

ISBN: (纸本)9781509018970

We introduce comparisons with respect to information between interpretations in paraconsistent description logics and use them to define bisimilarity for such logics. As bisimilarity is a natural notion for characterizing indiscernibility in modal and description logics, it is useful for concept learning in description logics also when inconsistencies occur. We give preservation results and the Hennessy-Milner property for comparisons with respect to information in paraconsistent description logics. As consequences, we also obtain invariance results and the Hennessy-Milner property for bisimilarity in paraconsistent description logics. © 2016 IEEE.

关键词： data description

来源：评论

学校读者我要写书评

暂无评论

SHREC'16 Track: Partial Shape Queries for 3D object retrieval 9

SHREC'16 Track: Partial Shape Queries for 3D object retrieva...

引用

9th Eurographics Workshop on 3D Object Retrieval, 3DOR 2016

作者： Pratikakis, I. Savelonas, M.A. Arnaoutoglou, F. Ioannakis, G. Koutsoudis, A. Theoharis, T. Tran, M.-T. Nguyen, V.-T. Pham, V.-K. Nguyen, H.-D. Le, H.-A. Tran, B.-H. To, Q.H. Truong, M.-B. Phan, T.V. Nguyen, M.-D. Than, T.-A. Mac, K.-N.C. Do, M.N. Duong, A.-D. Furuya, T. Ohbuchi, R. Aono, M. Tashiro, S. Pickup, D. Sun, X. Rosin, P.L. Martin, R.R. Democritus University of Thrace Department of Electrical and Computer Engineering Greece ATHENA Research Center Institute for Language and Speech Processing Greece Norwegian University of Science and Technology Department of Computer and Information Science Norway University of Science VNU-HCM Viet Nam University of Science University of Information Technology VNU-HCM Viet Nam University of Science John von Neumann Institute VNU-HCM Viet Nam University of Illinois Urbana-Champaign United States University of Information Technology VNU-HCM Viet Nam Graduate School of Medicine and Engineering University of Yamanashi Japan Knowledge Data Engineering Lab. Toyohashi University of Technology Japan School of Computer Science and Informatics Cardiff University United Kingdom

ISBN: (纸本)9783038680048

Despite numerous recent efforts, 3D object retrieval based on partial shape queries remains a challenging problem, far from being solved. The problem can be defined as: given a partial view of a shape as query, retrieve all partially similar 3D models from a repository. The objective of this track is to evaluate the performance of partial 3D object retrieval methods, for partial shape queries of various scan qualities and degrees of partiality. The retrieval problem is often found in cultural heritage applications, for which partial scans of objects query a dataset of geometrically distinct classes. © 2016 The Eurographics Association.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Computational analysis of the structural properties of economic and financial networks

arXiv

引用

arXiv 2017年

作者： Emmert-Streib, Frank Musa, Aliyu Baltakys, Kestutis Kanniainen, Juho Tripathi, Shailesh Yli-Harja, Olli Jodlbauer, Herbert Dehmer, Matthias Predictive Medicine and Data Analytics Lab Department of Signal Processing Tampere University of Technology Finland Institute of Biosciences and Medical Technology Tampere Finland Industrial and Information Management Tampere University of Technology Tampere Finland Institute for Systems Biology Seattle United States Institute for Intelligent Production Faculty for Management University of Applied Sciences Upper Austria Steyr Campus Austria Department for Biomedical Computer Science and Mechatronics UMIT - The Health and Lifesciences University Hall in Tyrol Austria College of Computer and Control Engineering Nankai University Tianjin300350 China

In recent years, methods from network science are gaining rapidly interest in economics and finance. A reason for this is that in a globalized world the interconnectedness among economic and financial entities are crucial to understand and networks provide a natural framework for representing and studying such systems. In this paper, we are surveying the use of networks and network-based methods for studying economy related questions. We start with a brief overview of graph theory and basic definitions. Then we discuss descriptive network measures and network complexity measures for quantifying structural properties of economic networks. Finally, we discuss different network and tree structures as relevant for applications. Copyright © 2017, The Authors. All rights reserved.

关键词： Finance

来源：评论

学校读者我要写书评

暂无评论

Deep learning for multi-messenger astrophysics a gateway for discovery in the big data era

arXiv

引用

arXiv 2019年

作者： Allen, Gabrielle Andreoni, Igor Bachelet, Etienne Bruce Berriman, G. Bianco, Federica B. Biswas, Rahul Kind, Matias Carrasco Chard, Kyle Cho, Minsik Cowperthwaite, Philip S. Etienne, Zachariah B. George, Daniel Gibbs, Tom Graham, Matthew Gropp, William Gupta, Anushri Haas, Roland Huerta, E.A. Jennings, Elise Katz, Daniel S. Khan, Asad Kindratenko, Volodymyr Kramer, William T.C. Liu, Xin Mahabal, Ashish McHenry, Kenton Miller, J.M. Neubauer, M.S. Oberlin, Steve Olivas, Alexander R. Rosofsky, Shawn Ruiz, Milton Saxton, Aaron Schutz, Bernard Schwing, Alex Seidel, Ed Shapiro, Stuart L. Shen, Hongyu Shen, Yue Sipocz, Brigitta M. Sun, Lunan Towns, John Tsokaros, Antonios Wei, Wei Wells, Jack Williams, Timothy J. Xiong, Jinjun Zhao, Zhizhen NCSA University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Astronomy University of Illinois at Urbana-Champaign UrbanaIL61801 United States California Institute of Technology 1200 E California Blvd PasadenaCA91125 United States Las Cumbres Observatory 6740 Cortona Drive Suite 102 GoletaCA93117 United States Caltech/IPAC-NExScI 1200 E California Blvd PasadenaCA91125 United States Department of Physics and Astronomy University of Delaware NewarkDE19716 United States Joseph R. Biden Jr . School of Public Policy and Administration University of Delaware NewarkDE19716 United States Data Science Institute University of Delaware NewarkDE19716 United States Center for Urban Science and Progress New York University 370 Jay St BrooklynNY11201 United States The Oskar Klein Centre for Cosmoparticle Physics Stockholm University AlbaNova StockholmSE-106 91 Sweden University of Chicago ChicagoIL60605 United States IBM Systems AustinTX78717 United States Observatories of the Carnegie Institution for Science 813 Santa Barbara St. PasadenaCA91101 United States Department of Mathematics West Virginia University MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States NVIDIA 2788 San Tomas Expressway Santa ClaraCA95050 United States Center for Data Driven Discovery Caltech PasadenaCA91125 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States Argonne National Laboratory Leadership Computing Facility LemontIL60439 United States Department of Electrical & Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States School of Information Sciences University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 Unit

This report provides an overview of recent work that harnesses the Big data Revolution and Large Scale Computing to address grand computational challenges in Multi-Messenger Astrophysics, with a particular emphasis on real-time discovery campaigns. Acknowledging the transdisciplinary nature of Multi-Messenger Astrophysics, this document has been prepared by members of the physics, astronomy, computer science, data science, software and cyberinfrastructure communities who attended the NSF-, DOE- and NVIDIA-funded "Deep Learning for Multi-Messenger Astrophysics: Real-time Discovery at Scale" workshop, hosted at the National Center for Supercomputing Applications, October 17-19, 2018. Highlights of this report include unanimous agreement that it is critical to accelerate the development and deployment of novel, signal-processing algorithms that use the synergy between artificial intelligence (AI) and high performance computing to maximize the potential for scientific discovery with Multi-Messenger Astrophysics. We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics;(ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data;(iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups;(iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big data Era;(v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics. Copyright © 2019, The Authors. All rights reserved.

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

Exploring outliers in crowdsourced ranking for QoE

arXiv

引用

arXiv 2017年

作者： Xu, Qianqian Yan, Ming Huang, Chendi Xiong, Jiechao Huang, Qingming Yao, Yuan Institute of Information Engineering Cas Beijing100093 Department of Computational Mathematics Michigan State University East LansingMI48824 United States BICMR-LMAM-LMEQF-LMP School of Mathematical Sciences Peking University Beijing100871 Tencent Ai Lab Shenzhen518057 University of Chinese Academy of Sciences Beijing100049 China Key Lab of Intell. Info. Process. Inst. of Comput. Tech. Cas Beijing100190 Key Lab of Big Data Mining and Knowledge Management Cas Beijing100190 Department of Mathematics Hong Kong University of Science and Technology 100871 Hong Kong

Outlier detection is a crucial part of robust evaluation for crowd-sourceable assessment of Quality of Experience (QoE) and has attracted much attention in recent years. In this paper, we propose some simple and fast algorithms for outlier detection and robust QoE evaluation based on the nonconvex optimization principle. Several iterative procedures are designed with or without knowing the number of outliers in samples. Theoretical analysis is given to show that such procedures can reach statistically good estimates under mild conditions. Finally, experimental results with simulated and real-world crowdsourcing datasets show that the proposed algorithms could produce similar performance to Huber-LASSO approach in robust ranking, yet with nearly 8 or 90 times speed-up, without or with a prior knowledge on the sparsity size of outliers, respectively. Therefore the proposed methodology provides us a set of helpful tools for robust QoE evaluation with crowdsourcing data. Copyright © 2017, The Authors. All rights reserved.

关键词： Crowdsourcing

来源：评论

学校读者我要写书评

暂无评论

The linked data mining challenge 2015 4

The linked data mining challenge 2015

引用

4th Workshop on knowledge Discovery and data Mining Meets Linked Open data, Know@LOD 2015, co-located with 12th Extended Semantic Web Conference, ESWC 2015

作者： Ristoski, Petar Paulheim, Heiko Svátek, Vojtěch Zeman, Václav University of Mannheim Research Group Data and Web Science Germany University of Economics Department of Information and Knowledge Engineering Czech Republic

The 2015 edition of the Linked data Mining Challenge, conducted in conjunction with Know@LOD 2015, has been the third edition of this challenge. This year's dataset collected movie ratings, where the task was to classify well and badly rated movies. The solutions submitted reached an accuracy of almost 95%, which is a clear advancement over the baseline of 60%. However, there is still headroom for improvement, as the majority vote of the three best systems reaches an even higher accuracy. Copyright © 2015 for the individual papers by the papers' authors.

关键词： Linked data

来源：评论

学校读者我要写书评

暂无评论

Improving the Visualization of WordNet Large Lexical database through Semantic Tag Clouds

Improving the Visualization of WordNet Large Lexical Databas...

引用

IEEE International Conference on Services Economics (SE)

作者： Enrico Giacinto Caldarola Antonio M. Rinaldi National Research Council Institute of Industrial Technologies and Automation Bari Italy Department of Electrical Engineering and Information Technologies University of Naples Federico II Napoli Italy IKNOS-LAB Intelligent and Knowledge Systems LUPT

In the Big data era, the visualization of large data sets is becoming an increasingly relevant task due to the great impact that data have from a human perspective. Since the visualization is the closer phase to the users within the data life cycles phases, there is no doubt that an effective, efficient and impressive representation of the analyzed data may result as important as the analytic process itself. Starting from previous experiences in importing, querying and visualizing WordNet database within Neo4J and Cytoscape, this work aims at improving the WordNet Graph visualization by exploiting the features and concepts behind tag clouds. The objective of this study is twofold: firstly, we argue that the proposed visualization strategy is able to put order in the messy and dense structure of nodes and edges of large knowledge bases as WordNet, showing as much as possible information from this knowledge source and in a clearer way; secondly, we think that the tag cloud approach applied to the synonyms rings reinforces the human cognition in recognizing the different usages of words in natural languages like English. In this regard, we also propose a formal strategy in order to evaluate the information perception in the use of our methodology by means of a questionnaire asked to a group of users. Finally, we compare these results with those resulting from the adoption of well known representations of WordNet within existing GUIs.

关键词： data visualization Visualization Layout Semantics Tag clouds Springs Visual databases

来源：评论

学校读者我要写书评

暂无评论

Intelligent Cities: Translating architectural models into a virtual gaming environment for event simulation

Intelligent Cities: Translating architectural models into a ...

引用

2015 systems and information engineering Design Symposium, SIEDS 2015

作者： Brady, Daniel Lee, Ashley Pearce, Adam Shintaku, Nicole Guerlain, Stephanie Data Science University of Virginia United States Department of Systems and Information Engineering University of Virginia United States

ISBN: (纸本)9781479918324

The value of virtual reality simulation for emergency response training and improvement has great potential, but obstacles remain. The utility of simulators is dependent on creating a highly realistic environment. Rather than building a virtual world from scratch, using existing architectural models may be a viable option due to the large quantity of data embedded within three-dimensional architectural designs. However, it can be difficult to maintain and translate these huge and highly detailed models into an interactive, user friendly interface. The MITRE Intelligent Cities project aims to blend methods of data science with interactive 3D modeling tools, using software packages like Revit, 3DS Max, and the Unity gaming engine. The goal of the project is to provide a procedure for visualizing and interacting with the structural properties and metadata of facilities encoded in architectural designs. Using MITRE campus data as a test case, several options were explored to preserve the metadata across software platforms, and a means of easily and efficiently accomplishing this task was discovered. However, limitations occur due to the large size of the model, slowing down the gaming engine and requiring the use of a data subset. This procedure allows anybody with a standard Revit architectural model to visualize their environment in a 3D simulation. Interactivity can be added by creating scripts to highlight key features of the models. These implementations can be used by emergency personnel or facility operators to peer through walls, or to find locations of key facility systems, such as HVAC or data centers. © 2015 IEEE.

关键词： Architectural design

来源：评论

学校读者我要写书评

暂无评论

Extended Measurement of the Cosmic-Ray Electron and Positron Spectrum from 11 GeV to 4.8 TeV with the Calorimetric Electron Telescope on the International Space Station

引用

Physical Review Letters 2018年第26期120卷 261102-261102页

作者： O. Adriani Y. Akaike K. Asano Y. Asaoka M. G. Bagliesi E. Berti G. Bigongiari W. R. Binns S. Bonechi M. Bongi P. Brogi J. H. Buckley N. Cannady G. Castellini C. Checchia M. L. Cherry G. Collazuol V. Di Felice K. Ebisawa H. Fuke T. G. Guzik T. Hams M. Hareyama N. Hasebe K. Hibino M. Ichimura K. Ioka W. Ishizaki M. H. Israel K. Kasahara J. Kataoka R. Kataoka Y. Katayose C. Kato N. Kawanaka Y. Kawakubo K. Kohri H. S. Krawczynski J. F. Krizmanic T. Lomtadze P. Maestro P. S. Marrocchesi A. M. Messineo J. W. Mitchell S. Miyake A. A. Moiseev K. Mori M. Mori N. Mori H. M. Motz K. Munakata H. Murakami S. Nakahira J. Nishimura G. A. de Nolfo S. Okuno J. F. Ormes S. Ozawa L. Pacini F. Palma P. Papini A. V. Penacchioni B. F. Rauch S. B. Ricciarini K. Sakai T. Sakamoto M. Sasaki Y. Shimizu A. Shiomi R. Sparvoli P. Spillantini F. Stolzi J. E. Suh A. Sulaj I. Takahashi M. Takayanagi M. Takita T. Tamura N. Tateyama T. Terasawa H. Tomida S. Torii Y. Tsunesada Y. Uchihori S. Ueno E. Vannuccini J. P. Wefel K. Yamaoka S. Yanagita A. Yoshida K. Yoshida Department of Physics University of Florence Via Sansone 1—50019 Sesto Fiorentino Italy INFN Sezione di Florence Via Sansone 1—50019 Sesto Fiorentino Italy Department of Physics University of Maryland Baltimore County 1000 Hilltop Circle Baltimore Maryland 21250 USA Astroparticle Physics Laboratory NASA/GSFC Greenbelt Maryland 20771 USA Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa Chiba 277-8582 Japan Research Institute for Science and Engineering Waseda University 3-4-1 Okubo Shinjuku Tokyo 169-8555 Japan JEM Utilization Center Human Spaceflight Technology Directorate Japan Aerospace Exploration Agency 2-1-1 Sengen Tsukuba Ibaraki 305-8505 Japan Department of Physical Sciences Earth and Environment University of Siena via Roma 56 53100 Siena Italy INFN Sezione di Pisa Polo Fibonacci Largo B. Pontecorvo 3—56127 Pisa Italy Department of Physics Washington University One Brookings Drive St. Louis Missouri 63130-4899 USA Department of Physics and Astronomy Louisiana State University 202 Nicholson Hall Baton Rouge Louisiana 70803 USA Institute of Applied Physics (IFAC) National Research Council (CNR) Via Madonna del Piano 10 50019 Sesto Fiorentino Italy Department of Physics and Astronomy University of Padova Via Marzolo 8 35131 Padova Italy INFN Sezione di Padova Via Marzolo 8 35131 Padova Italy University of Rome “Tor Vergata” Via della Ricerca Scientifica 1 00133 Rome Italy INFN Sezione di Rome “Tor Vergata” Via della Ricerca Scientifica 1 00133 Rome Italy Institute of Space and Astronautical Science Japan Aerospace Exploration Agency 3-1-1 Yoshinodai Chuo Sagamihara Kanagawa 252-5210 Japan CRESST and Astroparticle Physics Laboratory NASA/GSFC Greenbelt Maryland 20771 USA St. Marianna University School of Medicine 2-16-1 Sugao Miyamae-ku Kawasaki Kanagawa 216-8511 Japan Kanagawa University 3-27-1 Rokkakubashi Kanagawa Yokohama Kanagawa 221-8686 Japan Faculty of Science and Technol

Extended results on the cosmic-ray electron + positron spectrum from 11 GeV to 4.8 TeV are presented based on observations with the Calorimetric Electron Telescope (CALET) on the International Space Station utilizing the data up to November 2017. The analysis uses the full detector acceptance at high energies, approximately doubling the statistics compared to the previous result. CALET is an all-calorimetric instrument with a total thickness of 30 X0 at normal incidence and fine imaging capability, designed to achieve large proton rejection and excellent energy resolution well into the TeV energy region. The observed energy spectrum in the region below 1 TeV shows good agreement with Alpha Magnetic Spectrometer (AMS-02) data. In the energy region below ∼300 GeV, CALET’s spectral index is found to be consistent with the AMS-02, Fermi Large Area Telescope (Fermi-LAT), and Dark Matter Particle Explorer (DAMPE), while from 300 to 600 GeV the spectrum is significantly softer than the spectra from the latter two experiments. The absolute flux of CALET is consistent with other experiments at around a few tens of GeV. However, it is lower than those of DAMPE and Fermi-LAT with the difference increasing up to several hundred GeV. The observed energy spectrum above ∼1 TeV suggests a flux suppression consistent within the errors with the results of DAMPE, while CALET does not observe any significant evidence for a narrow spectral feature in the energy region around 1.4 TeV. Our measured all-electron flux, including statistical errors and a detailed breakdown of the systematic errors, is tabulated in the Supplemental Material in order to allow more refined spectral analyses based on our data.

关键词： Cosmic ray acceleration Cosmic ray composition & spectra Cosmic ray propagation Cosmic ray sources Cosmic rays & astroparticles Particle dark matter

来源：评论

学校读者我要写书评

暂无评论

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

引用

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：