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Author Correction: A portrait of the Higgs boson by the CMS experiment ten years after the discovery

Nature 2023年第7985期623卷 E4页

作者： A. Tumasyan W. Adam J. W. Andrejkovic T. Bergauer S. Chatterjee K. Damanakis M. Dragicevic A. Escalante Del Valle P. S. Hussain M. Jeitler N. Krammer L. Lechner D. Liko I. Mikulec P. Paulitsch F. M. Pitters J. Schieck R. Schöfbeck D. Schwarz S. Templ W. Waltenberger C.-E. Wulz M. R. Darwish T. Janssen T. Kello H. Rejeb Sfar P. Van Mechelen E. S. Bols J. D’Hondt A. De Moor M. Delcourt H. El Faham S. Lowette S. Moortgat A. Morton D. Müller A. R. Sahasransu S. Tavernier W. Van Doninck D. Vannerom B. Clerbaux G. De Lentdecker L. Favart J. Jaramillo K. Lee M. Mahdavikhorrami I. Makarenko A. Malara S. Paredes L. Pétré N. Postiau E. Starling L. Thomas M. Vanden Bemden C. Vander Velde P. Vanlaer D. Dobur J. Knolle L. Lambrecht G. Mestdach M. Niedziela C. Rendón C. Roskas A. Samalan K. Skovpen M. Tytgat N. Van Den Bossche B. Vermassen L. Wezenbeek A. Benecke G. Bruno F. Bury C. Caputo P. David C. Delaere I. S. Donertas A. Giammanco K. Jaffel Sa. Jain V. Lemaitre K. Mondal J. Prisciandaro A. Taliercio T. T. Tran P. Vischia S. Wertz G. A. Alves E. Coelho C. Hensel A. Moraes P. Rebello Teles W. L. Aldá Júnior M. Alves Gallo Pereira M. Barroso Ferreira Filho H. Brandao Malbouisson W. Carvalho J. Chinellato E. M. Da Costa G. G. Da Silveira D. De Jesus Damiao V. Dos Santos Sousa S. Fonseca De Souza J. Martins C. Mora Herrera K. Mota Amarilo L. Mundim H. Nogima A. Santoro S. M. Silva Do Amaral A. Sznajder M. Thiel F. Torres Da Silva De Araujo A. Vilela Pereira C. A. Bernardes L. Calligaris E. M. Gregores P. G. Mercadante S. F. Novaes Sandra S. Padula T. R. Fernandez Perez Tomei A. Aleksandrov G. Antchev R. Hadjiiska P. Iaydjiev M. Misheva M. Rodozov M. Shopova G. Sultanov A. Dimitrov T. Ivanov L. Litov B. Pavlov P. Petkov A. Petrov E. Shumka T. Cheng T. Javaid M. Mittal L. Yuan M. Ahmad G. Bauer Z. Hu S. Lezki K. Yi G. M. Chen H. S. Chen M. Chen F. Iemmi C. H. Jiang A. Kapoor H. Liao Z.-A. Liu V. Milosevic F. Monti R. Sharma J. Tao J. Thomas-Wilsker J. Wang H. Zhang J. Zhao A. Agapitos Y. An Y. Ban C. Chen A. Levin C. Li Q. Li X Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Vienna Austria TU Wien Vienna Austria Universiteit Antwerpen Antwerpen Belgium Institute of Basic and Applied Sciences Faculty of Engineering Arab Academy for Science Technology and Maritime Transport Alexandria Egypt Université Libre de Bruxelles Bruxelles Belgium Vrije Universiteit Brussel Brussel Belgium Korea University Seoul Korea Ghent University Ghent Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual de Campinas Campinas Brazil Federal University of Rio Grande do Sul Porto Alegre Brazil UFMS Nova Andradina Brazil The University of the State of Amazonas Manaus Brazil Universidade Estadual Paulista Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Beihang University Beijing China University of Chinese Academy of Sciences Beijing China Department of Physics Tsinghua University Beijing China Nanjing Normal University Department of Physics Nanjing China Institute of High Energy Physics Beijing China University of Chinese Academy of Sciences. Beijing China University of Florida Gainesville Florida USA Massachusetts Institute of Technology Cambridge Massachusetts USA State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Sun Yat-Sen University Guangzhou China Northeastern University Boston Massachusetts USA Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) -Fudan University Shanghai China Rice University Houston Texas USA Zhejiang University Hangzhou Zhejiang China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering

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A heuristic scheduling algorithm based on PSO in the cloud computing environment

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International Journal of Future Generation Communication and networking 2016年第1期9卷 349-362页

作者： Xue, Shengjun Shi, Wenling Xu, Xiaolong School of Computer and Software Nanjing University of Information Science and Technology Nanjing China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China

In the face of a large number of task requests submitted by users, the data center in the cloud need not only to finish these massive tasks but also to satisfy the user's service demand. How to dividethe resources of the system reasonably and schedule these tasks efficiently isa problem that need be solved in the cloud computing. The task scheduling of workflow is a kind of scheduling model for the most partresearched in cloud computing. The scheduling goal is alwaysdecided by user's QoS(Quality of Service)and the scheduling goal of existing scheduling algorithm is alwayssingle. Based on the price model a scheduling algorithm is proposed which can realize the multiple targets in this article, and the scheduling algorithm is Service Cost Optimizationbased on Particle Swarm Optimization(PSO-SC).PSO-SC algorithm can adapt to dynamic cloud environment, and it not only shortens the completion time of tasks but also minimizesuser's cost of task when schedule tasks. In this paper, experiment and analyze the task scheduling of workflow under the cloud computing environment, and the experimental results show that the algorithm proposed in this paper has very good scheduling performance and can reachthe goal of task scheduling of tasks. © 2016 SERSC.

关键词： Scheduling algorithms

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A modeling methodology to facilitate safety-oriented architecture design of industrial avionics software

A modeling methodology to facilitate safety-oriented archite...

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作者： Wu, Ji Yue, Tao Ali, Shaukat Zhang, Huihui State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China Certus Software Verification and Validation Center Simula Research Laboratory Lysaker Norway

Summary Ensuring that avionics software meets safety requirements at each development stage is very important to warrant the safe operation of an avionics system. Many safety requirements are imposed by various standards and industrial regulations that must be met by avionics software. One of such standards is DO-178B/C, which provides guidelines (e.g., development process and objectives to satisfy in development activities) for meeting safety requirements. This paper presents a modeling methodology including a UML profile for specifying safety requirements on a component-based architecture model and a set of design guidelines on avionics software. These safety requirements were identified from both standards (mainly DO-178B/C) and current engineering practices in the domain of avionics systems. The methodology automatically enforces these safety requirements. We have applied the methodology on an industrial autopilot system, and several previously uncaught faults were revealed. Copyright © 2014 John Wiley & Sons, Ltd.

关键词： Accident prevention

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Virtual brain surgery simulation system based on haptic interaction

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High Technology Letters 2015年第2期21卷 185-191页

作者：张小瑞 Zhu Jiandong Sun Wei Norman I.Badler Song Aiguo Niu Jianwei Liu Jia School of Computer and Software Nanjing University of Information Science & Technology Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology School of Information and Control Nanjing University of Information Science & Technology School of Engineering and Applied Science University of Pennsylvania School of Instrument Science and Engineering Southeast University State key Laboratory of Virtual Reality Technology and Systems Beihang University

To improve the accuracy and interactivity of soft tissue delormatlon simulation, a new plate spring model based on physics is proposed. The model is parameterized and thus can be adapted to simulate different organs. Different soft tissues are modeled by changing the width, number of pieces, thickness, and length of a single plate spring. In this paper, the structural design, calcula- tion of soft tissue deformation and real-time feedback operations of our system are also introduced. To evaluate the feasibility of the system and validate the model, an experimental system of haptic in- teraction, in which users can use virtual hands to pull virtual brain tissues, is built using PHANTOM OMNI devices. Experimental results show that the proposed system is stable, accurate and promising for modeling instantaneous soft tissue deformation.

关键词： haptic feedback human-computer interaction surgery simulation soft tissue deformation

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The 5th International Conference on Biomedical engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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A separability-entanglement classifier via machine learning

arXiv

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arXiv 2017年

作者： Lu, Sirui Huang, Shilin Li, Keren Li, Jun Chen, Jianxin Lu, Dawei Ji, Zhengfeng Shen, Yi Zhou, Duanlu Zeng, Bei Department of Physics Tsinghua University Beijing100084 China Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Beijing Computational Science Research Center Beijing100193 China Department of Mathematics & Statistics University of Guelph GuelphONN1G 2W1 Canada Joint Center for Quantum Information and Computer Science University of Maryland College ParkMD United States Department of Physics Southern University of Science and Technology Shenzhen518055 China Centre for Quantum Computation & Intelligent Systems School of Software Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Department of Statistics and Actuarial Science University of Waterloo WaterlooONN2L 3G1 Canada Institute of Physics Chinese Academy of Sciences Beijing100190 China

The problem of determining whether a given quantum state is entangled lies at the heart of quantum information processing, which is known to be an NP-hard problem in general. Despite the proposed many methods such as the positive partial transpose (PPT) criterion and the k-symmetric extendibility criterion to tackle this problem in practice, none of them enables a general, effective solution to the problem even for small dimensions. Explicitly, separable states form a high-dimensional convex set, which exhibits a vastly complicated structure. In this work, we build a new separability-entanglement classifier underpinned by machine learning techniques. Our method outperforms the existing methods in generic cases in terms of both speed and accuracy, opening up the avenues to explore quantum entanglement via the machine learning approach. Copyright © 2017, The Authors. All rights reserved.

关键词： Quantum entanglement

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Sparsity-based occlusion handling method for person re-identification 21

Sparsity-based occlusion handling method for person re-ident...

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21st International Conference on MultiMedia Modeling, MMM 2015

作者： Huang, Bingyue Chen, Jun Wang, Yimin Liang, Chao Wang, Zheng Sun, Kaimin National Engineering Research Center for Multimedia Software School of Computer Wuhan University Wuhan430072 China Research Institute of Wuhan University in Shenzhen China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University China

ISBN: (纸本)9783319144412

Person re-identification has recently attracted a lot of research interests, it refers to recognizing people across non-overlapping surveillance cameras. However, person re-identification is essentially a very challenging task due to variations in illumination, viewpoints and occlusions. Existing methods address these difficulties through designing robust feature representation or learning proper distance metric. Although these methods have achieved satisfactory performance in the case of illumination and viewpoint changes, seldom of they can genuinely handle the occlusion problem that frequently happens in the real scene. This paper proposes a sparsity-based patch matching method to handle the occlusion problem in the person re-identification. Its core idea is using a sparse representation model to determine the occlusion state of each image patch, which is further utilized to adjust the weight of patch pairs in the feature matching process. Extensive comparative experiments conducted on two widely used datasets have shown the effectiveness of the proposed method. © Springer International Publishing Switzerland 2015.

关键词： Cameras

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Coupled-view based ranking optimization for person re-identification 21

Coupled-view based ranking optimization for person re-identi...

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21st International Conference on MultiMedia Modeling, MMM 2015

作者： Ye, Mang Chen, Jun Leng, Qingming Liang, Chao Wang, Zheng Sun, Kaimin National Engineering Research Center for Multimedia Software School of Computer Wuhan University Wuhan430072 China Research Institute of Wuhan University in Shenzhen China School of Information Science and Technology Jiujiang University China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University China

ISBN: (纸本)9783319144443

Person re-identification aims to match different persons observed in non-overlapping camera views. researchers have proposed many person descriptors based on global or local descriptions, while both of them have achieved satisfying matching results, however, their ranking lists usually vary a lot for the same query person. These motivate us to investigate an approach to aggregate them to optimize the original matching results. In this paper, we proposed a coupled-view based ranking optimization method through cross KNN rank aggregation and graphbased re-ranking to revise the original ranking lists. Its core assumption is that the images of the same person should share the similar visual appearance in both global and local views. Extensive experiments on two datasets show the superiority of our proposed method with an average improvement of 20-30% over the state-of-the-art methods at CMC@1. © Springer International Publishing Switzerland 2015.

关键词： computers

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Person re-identification using data-driven metric adaptation 21

Person re-identification using data-driven metric adaptation

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21st International Conference on MultiMedia Modeling, MMM 2015

作者： Wang, Zheng Hu, Ruimin Liang, Chao Jiang, Junjun Sun, Kaimin Leng, Qingming Huang, Bingyue National Engineering Research Center for Multimedia Software School of Computer Wuhan University Wuhan China Research Institute of Wuhan University in Shenzhen China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China School of Information Science & Technology Jiujiang University Jiujiang China

ISBN: (纸本)9783319144412

Person re-identification, aiming to identify images of the same person from various cameras configured in difference places, has attracted plenty of attention in the multimedia community. In person re-identification procedure, choosing a proper distance metric is a crucial aspect [2]. Traditional methods always utilize a uniform learned metric, which ignored specific constraints given by this re-identification task that the learned metric is highly prone to over-fitting [21], and each person holding their unique characteristic brings inconsistency. Therefore, it is obviously inappropriate to merely employ a uniform metric. In this paper, we propose a data-driven metric adaptation method to improve the uniform metric. The key novelty of the approach is that we re-exploits the training data with cross-view consistency to adaptively adjust the metric. Experiments conducted on two standard data sets have validated the effectiveness of the proposed method with a significant improvement over baseline methods. © Springer International Publishing Switzerland 2015.

关键词： computers

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