检索结果-内蒙古大学图书馆

Corrigendum to “Road detection algorithm for Autonomous Navigation Systems based on dark channel prior and vanishing point in complex road scenes” [Robot. Auton. Syst. 85 (2016) 1–11]

引用

Robotics and Autonomous Systems 2017年 88卷 202-202页

作者： Yong Li Weili Ding Xuguang Zhang Zhaojie Ju Laboratory of Pattern Recognition and Intelligent Systems Key Laboratory of Industrial Computer Control Engineering of Hebei Province Department of Automation Institute of Electrical Engineering Yanshan University Qinghuangdao Hebei 066004 China College of Information Science and Engineering Northeastern University Shenyang Liaoning 110004 China School of Computing University of Portsmouth PO1 3HE UK

来源：评论

学校读者我要写书评

暂无评论

26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

引用

BMC NEUROscience 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

来源：评论

学校读者我要写书评

暂无评论

Parkes Pulsar Timing Array constraints on ultralight scalar-field dark matter

引用

Physical Review D 2018年第10期98卷 102002-102002页

作者： Nataliya K. Porayko Xingjiang Zhu Yuri Levin Lam Hui George Hobbs Aleksandra Grudskaya Konstantin Postnov Matthew Bailes N. D. Ramesh Bhat William Coles Shi Dai James Dempsey Michael J. Keith Matthew Kerr Michael Kramer Paul D. Lasky Richard N. Manchester Stefan Osłowski Aditya Parthasarathy Vikram Ravi Daniel J. Reardon Pablo A. Rosado Christopher J. Russell Ryan M. Shannon Renée Spiewak Willem van Straten Lawrence Toomey Jingbo Wang Linqing Wen Xiaopeng You Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 D-53121 Bonn Germany School of Physics and Astronomy Monash University Clayton VIC 3800 Australia School of Physics University of Western Australia Crawley WA 6009 Australia OzGrav: Australian Research Council Centre of Excellence for Gravitational Wave Discovery Hawthorn VIC 3122 Australia Center for Theoretical Physics Department of Physics Columbia University New York New York 10027 USA Center for Computational Astrophysics Flatiron Institute New York New York 10010 USA CSIRO Astronomy and Space Science P.O. Box 76 Epping NSW 1710 Australia Sternberg Astronomical Institute Lomonosov Moscow State University Universitetskii pr. 13 Moscow 119234 Russia Kazan Federal University Kremlevskaya 18 Kazan 420008 Russia Centre for Astrophysics and Supercomputing Swinburne University of Technology P.O. Box 218 Hawthorn VIC 3122 Australia International Centre for Radio Astronomy Research Curtin University Bentley WA 6102 Australia Department of Electrical and Computer Engineering University of California at San Diego La Jolla California 92093 USA CSIRO Information Management and Technology PO Box 225 Dickson ACT 2602 Australia Jodrell Bank Center for Astrophysics University of Manchester Manchester M13 9PL UK Space Science Division Naval Research Laboratory Washington DC 20375 USA Cahill Center for Astronomy and Astrophysics MC 249-17 California Institute of Technology Pasadena California 91125 USA CSIRO Scientific Computing Australian Technology Park Locked Bag 9013 Alexandria NSW 1435 Australia Institute for Radio Astronomy & Space Research Auckland University of Technology Private Bag 92006 Auckland 1142 New Zealand Xinjiang Astronomical Observatory Chinese Academy of Sciences 150 Science 1-Street Urumqi Xinjiang 830011 China School of Physical Science and Technology Southwest University Chongqing 400715 China

It is widely accepted that dark matter contributes about a quarter of the critical mass-energy density in our Universe. The nature of dark matter is currently unknown, with the mass of possible constituents spanning nearly one hundred orders of magnitude. The ultralight scalar field dark matter, consisting of extremely light bosons with m∼10−22 eV and often called “fuzzy” dark matter, provides intriguing solutions to some challenges at sub-Galactic scales for the standard cold dark matter model. As shown by Khmelnitsky and Rubakov, such a scalar field in the Galaxy would produce an oscillating gravitational potential with nanohertz frequencies, resulting in periodic variations in the times of arrival of radio pulses from pulsars. The Parkes Pulsar Timing Array (PPTA) has been monitoring 20 millisecond pulsars at two- to three-week intervals for more than a decade. In addition to the detection of nanohertz gravitational waves, PPTA offers the opportunity for direct searches for fuzzy dark matter in an astrophysically feasible range of masses. We analyze the latest PPTA data set which includes timing observations for 26 pulsars made between 2004 and 2016. We perform a search in this data set for evidence of ultralight dark matter in the Galaxy using Bayesian and Frequentist methods. No statistically significant detection has been made. We, therefore, place upper limits on the local dark matter density. Our limits, improving on previous searches by a factor of 2 to 5, constrain the dark matter density of ultralight bosons with m≤10−23 eV to be below 6 GeV cm−3 with 95% confidence in the Earth neighborhood. Finally, we discuss the prospect of probing the astrophysically favored mass range m≳10−22 eV with next-generation pulsar timing facilities.

关键词： Dark matter Neutron stars & pulsars Electromagnetic radiation astronomy Gravitational wave detection

来源：评论

学校读者我要写书评

暂无评论

Changes in the EEG amplitude as a biomarker for early detection of Alzheimer's disease

Changes in the EEG amplitude as a biomarker for early detect...

引用

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Ali H. Al-nuaimi Emmanuel Jammeh Lingfen Sun Emmanuel Ifeachor Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing P. R. China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University Shanghai China

The rapid increase in the number of older people with Alzheimer's disease (AD) and other forms of dementia represents one of the major challenges to the health and social care systems. Early detection of AD makes it possible for patients to access appropriate services and to benefit from new treatments and therapies, as and when they become available. The onset of AD starts many years before the clinical symptoms become clear. A biomarker that can measure the brain changes in this period would be useful for early diagnosis of AD. Potentially, the electroencephalogram (EEG) can play a valuable role in early detection of AD. Damage in the brain due to AD leads to changes in the information processing activity of the brain and the EEG which can be quantified as a biomarker. The objective of the study reported in this paper is to develop robust EEG-based biomarkers for detecting AD in its early stages. We present a new approach to quantify the slowing of the EEG, one of the most consistent features at different stages of dementia, based on changes in the EEG amplitudes (ΔEEG A ). The new approach has sensitivity and specificity values of 100% and 88.88%, respectively, and outperformed the Lempel-Ziv Complexity (LZC) approach in discriminating between AD and normal subjects.

关键词： Electroencephalography Dementia Complexity theory information processing Electrodes

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Byzantine fault tolerance for collaborative editing with commutative operations

Byzantine fault tolerance for collaborative editing with com...

引用

IEEE International Conference on Electro-information Technology

作者： Wenbing Zhao Mamdouh Babi William Yang Xiong Luo Yueqin Zhu Jack Yang Chaomin Luo Mary Yang Department of Electrical Engineering and Computer Science Cleveland State University Cleveland OH Texas Advanced Computing Center University of Texas at Austin Austin Texas School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Development Research Center of China Geological Survey Key Laboratory of Geological Information Technology Beijing China Division of Biostatistics & Biomathematics Massachusetts General Hospital & Harvard Medical School Boston MA Department of Electrical and Computer Engineering University of Detroit Mercy Detroit Michigan Department of Information Science Little Rock and University of Arkansas for Medical Sciences Little Rock Arkansas

ISBN: (纸本)9781467399869

In this paper, we present a study on how to achieve Byzantine fault tolerance for collaborative editing systems with commutative operations. Recent research suggests that Conflict-free Replicated Data Types (CRDTs) can be used to construct collaborative editing systems where concurrent update operations are commutative. This new approach is shown to avoid the complex issue of conflict resolution for concurrent updates to a shared document. The shared document is often modeled as a linear text buffer where each basic element is assigned a globally unique and totally ordered identifier. The linear text buffer constructed this way would constitute as a CRDT, which would make concurrent update operations issued by different users commutative. State convergence at all users can be achieved automatically as long as the users could receive the same set of operations irrespective of their relative ordering. However, it is not straightforward to guarantee state convergence in the presence of malicious users and external adversaries. In this paper, we carefully analyze the threats towards this type of systems, and propose a lightweight solution to achieve Byzantine fault tolerance with low runtime overhead. We define a set of correctness properties for such systems and prove that the proposed Byzantine fault tolerance mechanisms guarantee these properties.

关键词： Collaboration Fault tolerant systems Fault tolerance Convergence Algorithm design and analysis Mechanical factors

来源：评论

学校读者我要写书评

暂无评论

Denoising Auto-Encoders toward Robust Unsupervised Feature Representation

Denoising Auto-Encoders toward Robust Unsupervised Feature R...

引用

International Joint Conference on Neural Networks

作者： Wei Xiong Bo Du Lefei Zhang Liangpei Zhang Dacheng Tao State Key Laboratory of Software Engineering School of Computer Science Wuhan University China Department of Computing The Hong Kong Polytechnic University Kowloon Hong Kong State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University China Centre for Quantum Computation & Intelligent Systems University of Technology Sydney NSW 2007 Australia

ISBN: (纸本)9781509006212

Deep networks like the convolutional neural network and its variants usually learn hierarchical features from labeled images, which is very expensive to obtain. How can we find an unsupervised way to effectively extract deep and abstract features from images without annotations? Even from large qualities of images with noise? In this paper, we propose a robust deep neural network, named as stacked convolutional denoising auto-encoders (SCDAE), which can map raw images to hierarchical representations in an unsupervised manner. Our network is elaborately designed to fit for the visual recognition tasks. It is established by stacking the denoising auto-encoders. Unlike the prior works, in the training phase, the auto-encoders are trained patch-wisely so that the latent features can be applied to powerful regularizers for better representation;in the inference phase, the denoising auto-encoders are stacked convolutionally, hence the generated feature maps in the higher layers can preserve the coherent structures within the features in the lower layers. To achieve better performance, we apply whitening to each layer to sphere the input features. Our network is evaluated on the challenging image datasets MNIST, CIFAR-10 and STL-10 and demonstrates superior performance to the state-of-the-art unsupervised networks.

关键词： Noise reduction Convolutional codes Training Feature extraction Kernel Optimized production technology Robustness

来源：评论

学校读者我要写书评

暂无评论

Comment on 'Inherent security of phase coding quantum key distribution systems against detector blinding attacks' (2018 Laser Phys. Lett. 15 095203)

引用

Laser Physics Letters 2018年第1期16卷

作者： Aleksey Fedorov Ilja Gerhardt Anqi Huang Jonathan Jogenfors Yury Kurochkin Antía Lamas-Linares Jan-Åke Larsson Gerd Leuchs Lars Lydersen Vadim Makarov Johannes Skaar Russian Quantum Center Skolkovo Moscow 143025 Russia QRate Skolkovo Moscow 143025 Russia QApp Skolkovo Moscow 143025 Russia Institute of Physics University of Stuttgart and Institute for Quantum Science and Technology Pfaffenwaldring 57 D-70569 Stuttgart Germany Max Planck Institute for Solid State Research Heisenbergstraße 1 D-70569 Stuttgart Germany Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha 410073 People's Republic of China Department of Electrical Engineering Linköping University SE-58183 Linköping Sweden Texas Advanced Computing Center The University of Texas at Austin Austin Texas United States of America Max Planck Institute for the Science of Light and University of Erlangen-Nürnberg D-91058 Erlangen Germany Kringsjåvegen 3E NO-7032 Trondheim Norway National University of Science and Technology MISIS Moscow 119049 Russia Department of Technology Systems University of Oslo Box 70 NO-2027 Kjeller Norway

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

A New Logistic Map Based Chaotic Biogeography-based Optimization Approach for Cluster Analysis

A New Logistic Map Based Chaotic Biogeography-based Optimiza...

引用

2016 IEEE International Conference on Progress in Informatics and computing

作者： Shui-Hua Wang Hui-Min Lu Yu-Jie Li Yang Wang Zhi-Min Chen Yu-Min Wei Wen-Juan Jia Fang-Yuan Liu Yu-Dong Zhang School of Computer Science and Technology Nanjing Normal University Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Policy Academy Department of Electrical Engineering The city college of New YorkCUNY Department of Mechanical and Control Engineering Kyushu Institute of Technology School of Information Engineering Yangzhou University School of Physics and Electronic Engineering Taizhou University School of Electronic Information Shanghai Dianji University School of Energy and Mechanical Engineering Nanjing Normal University Key Laboratory of Intelligent Computing and Information Processing in Fujian Provincial University Quanzhou Normal University

Cluster analysis is important in scientific and industrial fields. In this study, we proposed a novel chaotic biogeography-based optimization(CBBO) method, and applied it in centroid-based clustering methods. The results over three types of simulation data showed that this proposed CBBO method gave better performance than chaotic particle swarm optimization, genetic algorithm, firefly algorithm, and quantum-behaved particle swarm optimization. In all, our CBBO method is effective in centroid-based clustering.

关键词： chaotic biogeography-based optimization cluster analysis centroid-based clustering simulation particle swarm optimization

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：