检索结果-内蒙古大学图书馆

Erratum to: Improving official statistics in emerging markets using machine learning and mobile phone data

EPJ data science 2017年第1期6卷 1-1页

作者： Jahani, Eaman Sundsøy, Pål Bjelland, Johannes Bengtsson, Linus Pentland, Alex ‘Sandy’ de Montjoye, Yves-Alexandre Institute for Data Systems and Society Massachusetts Institute of Technology Cambridge USA Telenor Research Oslo Norway Flowminder Stockholm Sweden Department of Public Health Sciences Karolinska Institute Stockholm Sweden Media Lab Massachusetts Institute of Technology Cambridge USA Department of Computing Imperial College London London UK Data Science Institute William Penney Laboratory Imperial College London London UK

来源：评论

学校读者我要写书评

暂无评论

Image segmentation using membrane computing: A literature survey 11th

Image segmentation using membrane computing: A literature su...

引用

11th International Conference on Bio-inspired computing – Theories and Applications, BIC-TA 2016

作者： Yahya, Rafaa I. Shamsuddin, Siti Mariyam Yahya, Salah I. Hasan, Shafatnnur Al-Salibi, Bisan Al-Khafaji, Ghada Faculty of Computing Universiti Teknologi Malaysia UTM SkudaiJohor81310 Malaysia Department of Computer College of Science University of Al-Mustansiriyah Baghdad Iraq UTM Big Data Center Ibnu Sina Institute for Scientific and Industrial Research Universiti Teknologi Malaysia UTM SkudaiJohor81310 Malaysia Department of Software Engineering Koya University Iraq School of Computer Sciences Universiti Sains Malaysia USM GelugorPenang11800 Malaysia Department of Computer Science College of Science Baghdad University Baghdad Iraq

ISBN: (纸本)9789811036101

Membrane computing, a recent branch of natural computing, has been gaining momentum attention in the last few decades due to its massive parallelism and efficient computation. Many researchers in the field of membrane computing have proposed sophisticated techniques inspired by cell biology for computer science applications, especially when they considered cell organization in tissues, organs, and most recently, from the organization of neurons. The interdisciplinary applications of membrane computing include, but not limited to computer science, biology, biomedicine, bioinformatics and several other fields such as mathematics, artificial intelligence, automation, economics, to name but a few. Their applications are pertaining to computer graphics, approximate optimization, cryptography, parallel computing and image processing. Hence, in this paper we present an up to date comprehensive literature review pertaining to the application of membrane computing in the area of digital image analysis, especially image segmentation, comprehensively and systematically. We thoroughly investigate the recent advancement in the field of image segmentation using membrane system. Furthermore, we highlight the merits and demerits of various software tools and methods. Finally, we suggest some intuitive future directions in light of the current limitations. © Springer Nature Singapore Pte Ltd. 2016.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Bright γ-ray flares observed in GRB131108A

arXiv

引用

arXiv 2019年

作者： Ajello, M. Arimoto, M. Asano, K. Axelsson, M. Baldini, L. Barbiellini, G. Bastieri, D. Bellazzini, R. Berretta, A. Bissaldi, E. Blandford, R.D. Bonino, R. Bottacini, E. Bregeon, J. Bruel, P. Buehler, R. Buson, S. Cameron, R.A. Caputo, R. Caraveo, P.A. Cavazzuti, E. Chen, S. Chiaro, G. Ciprini, S. Costantin, D. Cutini, S. D'Ammando, F. de la Torre Luque, P. de Palma, F. Di Lalla, N. Di Venere, L. Fana Dirirsa, F. Fegan, S.J. Franckowiak, A. Fukazawa, Y. Fusco, P. Gargano, F. Gasparrini, D. Giglietto, N. Giordano, F. Giroletti, M. Green, D. Grenier, I.A. Grondin, M.-H. Guiriec, S. Hays, E. Horan, D. Jóhannesson, G. Kocevski, D. Kovac'evic, M. Kuss, M. Larsson, S. Latronico, L. Li, J. Liodakis, I. Longo, F. Loparco, F. Lovellette, M.N. Lubrano, P. Maldera, S. Manfreda, A. Martí-Devesa, G. Mazziotta, M.N. McEnery, J.E. Mereu, I. Michelson, P.F. Mizuno, T. Monzani, M.E. Moretti, E. Morselli, A. Moskalenko, I.V. Negro, M. Nuss, E. Ohno, M. Omodei, N. Orienti, M. Orlando, E. Palatiello, M. Paliya, V.S. Paneque, D. Pei, Z. Persic, M. Pesce-Rollins, M. Petrosian, V. Piron, F. Poon, H. Porter, T.A. Principe, G. Racusin, J.L. Rainò, S. Rando, R. Rani, B. Razzano, M. Reimer, A. Reimer, O. Serini, D. Sgrò, C. Siskind, E.J. Spandre, G. Spinelli, P. Tajima, H. Takagi, K. Tak, D. Torres, D.F. Valverde, J. Wood, K. Yamazaki, R. Yassine, M. Zhu, S. Lucas Uhm, Z. Zhang, Bing Department of Physics and Astronomy Clemson University Kinard Lab of Physics ClemsonSC29634-0978 United States Faculty of Mathematics and Physics Institute of Science and Engineering Kanazawa University Kakuma Ishikawa Kanazawa920-1192 Japan Institute for Cosmic-Ray Research University of Tokyo 5-1-5 Kashiwanoha Chiba Kashiwa277-8582 Japan Department of Physics Stockholm University AlbaNova StockholmSE-106 91 Sweden Department of Physics KTH Royal Institute of Technology AlbaNova StockholmSE-106 91 Sweden Università di Pisa Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy Istituto Nazionale di Fisica Nucleare Sezione di Trieste TriesteI-34127 Italy Dipartimento di Fisica Università di Trieste TriesteI-34127 Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova PadovaI-35131 Italy Dipartimento di Fisica e Astronomia "G. Galilei" Università di Padova PadovaI-35131 Italy Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy Dipartimento di Fisica Università degli Studi di Perugia PerugiaI-06123 Italy Dipartimento di Fisica "M. Merlin" dell'Università e del Politecnico di Bari BariI-70126 Italy Istituto Nazionale di Fisica Nucleare Sezione di Bari BariI-70126 Italy W. W. Hansen Experimental Physics Laboratory Kavli Institute for Particle Astrophysics and Cosmology Department of Physics SLAC National Accelerator Laboratory Stanford University StanfordCA94305 United States Istituto Nazionale di Fisica Nucleare Sezione di Torino TorinoI-10125 Italy Dipartimento di Fisica Università degli Studi di Torino TorinoI-10125 Italy Department of Physics and Astronomy University of Padova Vicolo Osservatorio 3 PadovaI-35122 Italy Laboratoire Univers et Particules de Montpellier Université Montpellier CNRS/IN2P3 MontpellierF-34095 France Laboratoire Leprince-Ringuet École polytechnique CNRS/IN2P3 PalaiseauF-91128 France Deutsches Elektronen Synchrotron DESY ZeuthenD-15738 Germany Insti

GRB 131108A is a bright long Gamma-Ray Burst (GRB) detected by the Large Area Telescope and the Gamma-ray Burst Monitor on board the Fermi Gamma-ray Space Telescope. Dedicated temporal and spectral analyses reveal three γ-ray flares dominating above 100 MeV, which are not directly related to the prompt emission in the GBM band (10 keV-10 MeV). The high-energy light curve of GRB 131108A (100 MeV - 10 GeV) shows an unusual evolution: a steep decay, followed by three flares with an underlying emission, and then a long-lasting decay phase. The detailed analysis of the γ-ray flares finds that the three flares are 6-20 times brighter than the underlying emission and are similar to each other. The fluence of each flare, (1.6 ∼ 2.0) × 10−6 erg cm−2, is comparable to that of emission during the steep decay phase, 1.7 × 10−6 erg cm−2. The total fluence from three γ-ray flares is 5.3 × 10−6 erg cm−2. The three γ-ray flares show properties similar to the usual X-ray flares that are sharp flux increases, occurring in ∼ 50% of afterglows, in some cases well after the prompt emission. Also, the temporal and spectral indices during the early steep decay phase and the decaying phase of each flare show the consistency with a relation of the curvature effect (Equation presented), which is the first observational evidence of the high-latitude emission in the GeV energy band. Copyright © 2019, The Authors. All rights reserved.

关键词： Gamma rays

来源：评论

学校读者我要写书评

暂无评论

Unsupervised classification of data streams based on typicality and eccentricity data analytics

Unsupervised classification of data streams based on typical...

引用

2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016

作者： Costa, Bruno Sielly Jales Bezerra, Clauber Gomes Guedes, Luiz Affonso Angelov, Plamen Parvanov Federal Institute of Rio Grande do Norte - IFRN Campus Natal - Zona Norte Natal Brazil Federal Institute of Rio Grande do Norte - IFRN Campus EaD Natal Brazil Department of Computer Engineering and Automation Federal University of Rio Grande do Norte - UFRN Brazil Data Science Group School of Computing and Communications Lancaster University LancasterLA14WA United Kingdom Department of Excellence Carlos III University Madrid Spain

ISBN: (纸本)9781509006250

In this paper, we propose a novel approach to unsupervised and online data classification. The algorithm is based on the statistical analysis of selected features and development of a self-evolving fuzzy-rule-basis. It starts learning from an empty rule basis and, instead of offline training, it learns "on-The-fly". It is free of parameters and, thus, fuzzy rules, number, size or radius of the classes do not need to be pre-defined. It is very suitable for the classification of online data streams with realtime constraints. The past data do not need to be stored in memory, since that the algorithm is recursive, which makes it memory and computational power efficient. It is able to handle concept-drift and concept-evolution due to its evolving nature, which means that, not only rules/classes can be updated, but new classes can be created as new concepts emerge from the data. It can perform fuzzy classification/soft-labeling, which is preferred over traditional crisp classification in many areas of application. The algorithm was validated with an industrial pilot plant, where online calculated period and amplitude of control signal were used as input to a fault diagnosis application. The approach, however, is generic and can be applied to different problems and with much higher dimensional inputs. The results obtained from the real data are very significant. © 2016 IEEE.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Ebolavirus database: Gene and protein information resource for ebolaviruses

引用

Advances in Bioinformatics 2016年第1期2016卷 1673284页

作者： Swetha, Rayapadi G. Ramaiah, Sudha Anbarasu, Anand Sekar, Kanagaraj Medical and Biological Computing Laboratory School of Biosciences and Technology VIT University Vellore 632 014 India Laboratory for Structural Biology and Bio-Computing Department of Computational and Data Sciences Indian Institute of Science Bangalore 560 012 India

Ebola Virus Disease (EVD) is a life-threatening haemorrhagic fever in humans. Even though there are many reports on EVD, the protein precursor functions and virulent factors of ebolaviruses remain poorly understood. Comparative analyses of Ebolavirus genomes will help in the identification of these important features. This prompted us to develop the Ebolavirus database (EDB) and we have provided links to various tools that will aid researchers to locate important regions in both the genomes and proteomes of Ebolavirus. The genomic analyses of ebolaviruses will provide important clues for locating the essential and core functional genes. The aim of EDB is to act as an integrated resource for ebolaviruses and we strongly believe that the database will be a useful tool for clinicians, microbiologists, health care workers, and bioscience researchers. © 2016 Rayapadi G. Swetha et al.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with care

引用

Nature communications 2019年第1期10卷 588页

作者： Lena Maier-Hein Matthias Eisenmann Annika Reinke Sinan Onogur Marko Stankovic Patrick Scholz Tal Arbel Hrvoje Bogunovic Andrew P Bradley Aaron Carass Carolin Feldmann Alejandro F Frangi Peter M Full Bram van Ginneken Allan Hanbury Katrin Honauer Michal Kozubek Bennett A Landman Keno März Oskar Maier Klaus Maier-Hein Bjoern H Menze Henning Müller Peter F Neher Wiro Niessen Nasir Rajpoot Gregory C Sharp Korsuk Sirinukunwattana Stefanie Speidel Christian Stock Danail Stoyanov Abdel Aziz Taha Fons van der Sommen Ching-Wei Wang Marc-André Weber Guoyan Zheng Pierre Jannin Annette Kopp-Schneider Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. l.maier-hein@dkfz.de. Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Centre for Intelligent Machines McGill University Montreal QC H3A0G4 Canada. Christian Doppler Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University Vienna 1090 Vienna Austria. Science and Engineering Faculty Queensland University of Technology Brisbane QLD 4001 Australia. Department of Electrical and Computer Engineering Department of Computer Science Johns Hopkins University Baltimore MD 21218 USA. CISTIB - Center for Computational Imaging & Simulation Technologies in Biomedicine The University of Leeds Leeds Yorkshire LS2 9JT UK. Department of Radiology and Nuclear Medicine Medical Image Analysis Radboud University Center 6525 GA Nijmegen The Netherlands. Institute of Information Systems Engineering TU Wien 1040 Vienna Austria. Complexity Science Hub Vienna 1080 Vienna Austria. Heidelberg Collaboratory for Image Processing (HCI Heidelberg University 69120 Heidelberg Germany. Centre for Biomedical Image Analysis Masaryk University 60200 Brno Czech Republic. Electrical Engineering Vanderbilt University Nashville TN 37235-1679 USA. Institute of Medical Informatics Universität zu Lübeck 23562 Lübeck Germany. Division of Medical Image Computing (MIC) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Institute for Advanced Studies Department of Informatics Technical University of Munich 80333 Munich Germany. Information System Institute HES-SO Sierre 3960 Switzerland. Departments of Radiology Nuclear Medicine and Medical Informatics Erasmus MC 3015 GD Rotterdam The Netherlands. Department of Computer Science University of Warwick Coventry CV4 7AL UK. Department of Radiation Oncology Massachusetts General Hospital Boston MA

In the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in the PDF and HTML versions of the Article.

关键词：

来源：评论

学校读者我要写书评

暂无评论

ACID encountering the CAP theorem: Two bank case studies 12

ACID encountering the CAP theorem: Two bank case studies

引用

12th Web Information System and Application Conference, WISA 2015

作者： Kong, Chao Gao, Ming Qian, Weining Zhou, Minqi Gong, Xueqing Zhang, Rong Zhou, Aoying Institute for Data Science and Engineering and Shanghai Key Lab for Trustworthy Computing East China Normal University Shanghai China

ISBN: (纸本)9781467393720

In the era of big data, we may adopt the distributed architecture for a transaction processing system due to some reasons, including distributed branches, heavy demand and operational expenditure, etc. In terms of the CAP Theorem, a transaction processing system associated with ACID properties is infeasible to work well in the distributed architecture. It is indispensable to address how to make a trade-off between availability and partition tolerance for a bank as it favors the consistency in the distributed system. In this research, we conduct two case studies to address the question using two transaction logs collected from a bank in China. We mainly analyze the table dependency and the table concurrency, and find that (1) it is arduous to partition the data in the database system associated with ACID properties, (2) in-memory architecture for updating transactions may be an alternative for building a transaction processing system. © 2015 IEEE.

关键词： Memory architecture

来源：评论

学校读者我要写书评

暂无评论

Authorship Correction: International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study

引用

Journal of medical Internet research 2021年第11期23卷 e34625页

作者： Griffin M Weber Harrison G Zhang Sehi L'Yi Clara-Lea Bonzel Chuan Hong Paul Avillach Alba Gutiérrez-Sacristán Nathan P Palmer Amelia Li Min Tan Xuan Wang William Yuan Nils Gehlenborg Anna Alloni Danilo F Amendola Antonio Bellasi Riccardo Bellazzi Michele Beraghi Mauro Bucalo Luca Chiovato Kelly Cho Arianna Dagliati Hossein Estiri Robert W Follett Noelia García Barrio David A Hanauer Darren W Henderson Yuk-Lam Ho John H Holmes Meghan R Hutch Ramakanth Kavuluru Katie Kirchoff Jeffrey G Klann Ashok K Krishnamurthy Trang T Le Molei Liu Ne Hooi Will Loh Sara Lozano-Zahonero Yuan Luo Sarah Maidlow Adeline Makoudjou Alberto Malovini Marcelo Roberto Martins Bertrand Moal Michele Morris Danielle L Mowery Shawn N Murphy Antoine Neuraz Kee Yuan Ngiam Marina P Okoshi Gilbert S Omenn Lav P Patel Miguel Pedrera Jiménez Robson A Prudente Malarkodi Jebathilagam Samayamuthu Fernando J Sanz Vidorreta Emily R Schriver Petra Schubert Pablo Serrano Balazote Byorn Wl Tan Suzana E Tanni Valentina Tibollo Shyam Visweswaran Kavishwar B Wagholikar Zongqi Xia Daniela Zöller Isaac S Kohane Tianxi Cai Andrew M South Gabriel A Brat Department of Biomedical Informatics Harvard Medical School Boston MA United States. BIOMERIS (BIOMedical Research Informatics Solutions) Pavia Italy. Clinical Research Unit Botucatu Medical School São Paulo State University Botucatu Brazil. Division of Nephrology Department of Medicine Ente Ospedaliero Cantonale Lugano Switzerland. Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy. Information Technology Department Azienda Socio-Sanitaria Territoriale di Pavia Pavia Italy. Unit of Internal Medicine and Endocrinology Istituti Clinici Scientifici Maugeri SpA SB IRCCS Pavia Italy. Massachusetts Veterans Epidemiology Research and Information Center Veterans Affairs Boston Healthcare System Boston MA United States. Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy. Department of Medicine Massachusetts General Hospital Boston MA United States. Department of Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles CA United States. Health Informatics Hospital Universitario 12 de Octubre Madrid Spain. Department of Learning Health Sciences University of Michigan Medical School Ann Arbor MI United States. Department of Biomedical Informatics University of Kentucky Lexington KY United States. Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine Philadelphia PA United States. Institute for Biomedical Informatics University of Pennsylvania Perelman School of Medicine Philadelphia PA United States. Department of Preventive Medicine Northwestern University Chicago IL United States. Institute for Biomedical Informatics University of Kentucky Lexington KY United States. Medical University of South Carolina Charleston SC United States. Department of Computer Science Renaissance Computing Institute University of North Carolina at Chapel Hill Chapel Hill NC United States. Departm

[This corrects the article DOI: 10.2196/31400.].

关键词：

来源：评论

学校读者我要写书评

暂无评论

Chinese wall security policies information flows in business cloud 3

Chinese wall security policies information flows in business...

引用

3rd IEEE International Conference on Big data, IEEE Big data 2015

作者： Lin, Tsau-Young T. Y. Institute of Data Science and Computing San Jose State University and GrC Society San JoseCA95192 United States

ISBN: (纸本)9781479999255

A Business Cloud is defined to be a collection of company datasets that are stored on the «Cloud». For simplicity, we have assumed: Each company only has one dataset. There are information flows among these datasets. Within such an environment Chinese Wall Security Policy (CWSP) is revisited. Based on the «physical» view of Brewer and Nash, the Chinese Wall policy that regulates «Conflict of Interest» binary Relation, denoted by CIR among company datasets, is investigated. CIR has found to be anti-reflexive, symmetric and anti-transitive, in other words, the compliment of CIR is an equivalence relation. The main theorem states that, from the owner (a company) view, a dataset under CWSP will neve flow into enemies hands (companies in conflicts). More generally, if CIR is anti-reflexive and symmetric, then there are Aggressive Chinese Wall that surrounds the company datasets of those companies who are in conflict, and a Conservative Chinese Wall that that surrounds the company datasets whose owner are friends;note that the set friends is a subset of «not in conflict». The main theorem states that no information of those company datasets that are stored within the Aggressive Chinese Wall may never penetrate the Wall, similar conclusion for Conservative Chinese Wall. However those company datasets that are outside these two walls may penetrate the two walls. © 2015 IEEE.

关键词： Set theory

来源：评论

学校读者我要写书评

暂无评论

A new evolving clustering algorithm for online data streams 10

A new evolving clustering algorithm for online data streams

引用

10th IEEE Conference on Evolving and Adaptive Intelligent Systems, EAIS 2016

作者： Bezerra, Clauber Gomes Costa, Bruno Sielly Jales Guedes, Luiz Affonso Angelov, Plamen Parvanov Campus EaD Federal Institute of Rio Grande do Norte - IFRN Natal Brazil Campus Natal - Zona Norte Federal Institute of Rio Grande do Norte - IFRN Natal Brazil Department of Computer Engineering and Automation Federal University of Rio Grande do Norte - UFRN Brazil Lancaster University Data Science Group School of Computing and Communications LancasterLA1 4WA United Kingdom Department of Excellence Carlos III University Madrid Spain

ISBN: (纸本)9781509025831

In this paper, we propose a new approach to fuzzy data clustering. We present a new algorithm, called TEDA-Cloud, based on the recently introduced TEDA approach to outlier detection. TEDA-Cloud is a statistical method based on the concepts of typicality and eccentricity able to group similar data observations. Instead of the traditional concept of clusters, the data is grouped in the form of granular unities called data clouds, which are structures with no pre-defined shape or set boundaries. TEDA-Cloud is a fully autonomous and self-evolving algorithm that can be used for data clustering of online data streams and applications that require real-time response. Since it is fully autonomous, TEDA-Cloud is able to "start from scratch" (from an empty knowledge basis), create, update and merge data clouds, in a fully autonomous manner, without requiring any user-defined parameters (e.g. number of clusters, size, radius) or previous training. Moreover, TEDA-Cloud, unlike most of the traditional statistical approaches, does not rely on a specific data distribution or on the assumption of independence of data samples. The results, obtained from multiple data sets that are very well known in literature, are very encouraging. ©2016 IEEE.

关键词： Clustering algorithms

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：