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Oceans MTS/IEEE Washington (Conference)

作者： Runlong Miao Shuo Pang Science and Technology on Underwater Vehicle Laboratory Harbin Engineering University Department of Electrical Computer Software and Systems Engineering Embry-Riddle Aeronautical University

ISBN: (纸本)9781467393744

Remotely Operated Vehicles (ROVs) are expensive and complicated systems for ocean exploration. ROVs are playing more and more important roles in the exploration of the ocean and fishery industry. If we could build a low-cost ROV so that people could afford it, then more and more people will have the opportunities to explore the ocean. In 2014, the science and Technology on Underwater Vehicle laboratory at Harbin engineering University started a low-cost ROV project. The goal of this project is to develop a small inspection-class ROV that carries a video camera for underwater inspection. The ROV's total weight in the air is less than 10kg. The depth rating of the ROV is 150m and the maximum speed is 4 knots. The total cost is controlled under $4000. This paper presents the details of this ROV design and test results.

关键词： ROVs Underwater thrusters Pressure housing

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25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROscience 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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Tomography is Necessary for Universal Entanglement Detection with Single-Copy Observables

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Physical Review Letters 2016年第23期116卷 230501-230501页

作者： Dawei Lu Tao Xin Nengkun Yu Zhengfeng Ji Jianxin Chen Guilu Long Jonathan Baugh Xinhua Peng Bei Zeng Raymond Laflamme Institute for Quantum Computing University of Waterloo Waterloo N2L 3G1 Ontario Canada State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics Tsinghua University Beijing 100084 China Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney NSW 2007 Australia Department of Mathematics and Statistics University of Guelph Guelph Ontario N1G 2W1 Canada State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing 100190 China Joint Center for Quantum Information and Computer Science University of Maryland College Park Maryland 20742 USA Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics University of Science and Technology of China Hefei Anhui 230036 China Canadian Institute for Advanced Research Toronto Ontario M5G 1Z8 Canada Perimeter Institute for Theoretical Physics Waterloo Ontario N2L 2Y5 Canada

Entanglement, one of the central mysteries of quantum mechanics, plays an essential role in numerous tasks of quantum information science. A natural question of both theoretical and experimental importance is whether universal entanglement detection can be accomplished without full state tomography. In this Letter, we prove a no-go theorem that rules out this possibility for nonadaptive schemes that employ single-copy measurements only. We also examine a previously implemented experiment [H. Park et al., Phys. Rev. Lett. 105, 230404 (2010)], which claimed to detect entanglement of two-qubit states via adaptive single-copy measurements without full state tomography. In contrast, our simulation and experiment both support the opposite conclusion that the protocol, indeed, leads to full state tomography, which supplements our no-go theorem. These results reveal a fundamental limit of single-copy measurements in entanglement detection and provide a general framework of the detection of other interesting properties of quantum states, such as the positivity of partial transpose and the k-symmetric extendibility.

关键词： Entanglement detection Entanglement measures Quantum tomography Nuclear magnetic resonance

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Virtual Machine Image Content Aware I/O Optimization for Mobile Virtualization

Virtual Machine Image Content Aware I/O Optimization for Mob...

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IEEE International Symposium on Cyberspace Safety and Security

作者： Renhai Chen Yi Wang Jingtong Hu Duo Liu Zili Shao Yong Guan Embedded Systems and CPS Laboratory Department of Computing The Hong Kong Polytechnic University College of Computer Science and Software Engineering Shenzhen University School of Electrical and Computer Engineering Oklahoma State University College of Computer Science Chongqing University College of Computer and Information Management Capital Normal University

ISBN: (纸本)9781479989386

Mobile virtualization introduces extra layers in software stacks, which leads to performance degradation. Especially, each I/O operation has to pass through several software layers to reach the NAND-flash-based storage systems. This paper targets at optimizing I/O for mobile virtualization, since I/O becomes one of major performance bottlenecks that seriously affects the performance of mobile devices. Among all the I/O operations, a large percentage is updating metadata. Frequent updating metadata not only degrades overall I/O performance but also severely reduces flash memory lifetime. In this paper, we propose a novel I/O optimization technique to identify the metadata of a guest file system which is stored in a VM (Virtual Machine) image file and frequently updated. Then, these metadata are stored in a small additional NVM (Non-Volatile Memory) which is faster and more endurable to greatly improve flash memory's performance and lifetime. To the best of our knowledge, this is the first work to identify the file system metadata from regular data in a guest OS VM image file under mobile virtualization. The proposed scheme is evaluated on a real hardware embedded platform. The experimental results show that the proposed techniques can improve write performance to 45.21% in mobile devices with virtualization.

关键词： IO Virtualization Metadata Mobile devices virtual machines Cache Memory System degradation of performance Mobile Conference documents

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Regression based on neural incremental attribute learning with correlation-based feature ordering

Regression based on neural incremental attribute learning wi...

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IEEE Conference on Cybernetics and Intelligent systems

作者： Ting Wang Xiaoyan Zhu Sheng-Uei Guan Ka Lok Man T. O. Ting State Key Laboratory of Intelligent Technology and Systems Tsinghua University Beijing Wuxi China Department of {Computer Science and Software Engineering Electrical and Electronic Engineering} Xi'an Jiaotong-Liverpool University Suzhou China

Incremental Attribute Learning (IAL) gradually trains features in one or more size, which can be used to solve regression problems. Previous studies showed that feature ordering is crucial to IAL, and features should be sorted by some criteria. This study proposed two new feature ordering methods based on feature's group correlation and individual correlation for different situations. Experimental results show that grouped correlation-based feature ordering approach can exhibit better performance than others based on IAL neural networks in regression. Moreover, the performance of this approach is more stable than individual correlation-based approaches and some other approaches.

关键词： Correlation Correlation coefficient Neural networks Regression analysis Training Error analysis Pattern recognition

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software architecture documentation for developers: A survey

Software architecture documentation for developers: A survey

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software engineering 2014

作者： Rost, Dominik Naab, Matthias Lima, Crescendo Von Flach Chavez, Christina Fraunhofer Institute for Experimental Software Engineering Kaiserslautern Germany Fraunhofer Project Center on Software and Systems Engineering Software Engineering Laboratory Department of Computer Science Bahia Brazil

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A method of recommendation the most used XML tags

A method of recommendation the most used XML tags

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5th KKU International engineering Conference 2014, KKU-IENC 2014

作者： Vacharaskunee, Sutheetutt Intakosum, Sarun Software Systems Engineering Laboratory Department of Mathematics and Computer Science King Mongkut's Institute of Technology Ladkrabang Bangkok 10250 Thailand

ISBN: (纸本)9783038350903

Processing of a large data set which is known for today as big data processing is still a problem that has not yet a well-defined solution. The data can be both structured and unstructured. For the structured part, eXtensible Markup Language (XML) is a major tool that freely allows document owners to describe and organize their data using their markup tags. One major problem, however, behind this freedom lies in the big data retrieving process. The same or similar information that are described using the different tags or different structures may not be retrieved if the query statements contains different keywords to the one used in the markup tags. The best way to solve this problem is to specify a standard set of the markup tags for each problem domain. The creation of such a standard set if done manually requires a lot of hard work and is a time consuming process. In addition, it may be hard to define terms that are acceptable by all people. This research proposes a model for a new technique, "XML Tag Recommendation" (XTR) that aims to solve this problem. This technique applies the idea of Case Base Reasoning (CBR) by collecting the most used tags in each domain as a case. These tags come from the collection of related words in WordNet. The WordCount that is the web site to find the frequency of words is applied to choose the most used one. The input (problem) to the XTR system is an XML document contains the tags specified by the document owners. The solution is a set of the recommended tags, which is the most used tags, for the problem domain of the document. Document owners have a freedom to change or not change the tags in their documents and can provide feedback to the XTR system. © (2014) Trans Tech Publications, Switzerland.

关键词： XML

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Large-scale supervised multimodal hashing with semantic correlation maximization 28

Large-scale supervised multimodal hashing with semantic corr...

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28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014

作者： Zhang, Dongqing Li, Wu-Jun Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University China National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China

ISBN: (纸本)9781577356790

Due to its low storage cost and fast query speed, hashing has been widely adopted for similarity search in multimedia data. In particular, more and more attentions have been payed to multimodal hashing for search in multimedia data with multiple modalities, such as images with tags. Typically, supervised information of semantic labels is also available for the data points in many real applications. Hence, many supervised multimodal hashing (SMH) methods have been proposed to utilize such semantic labels to further improve the search accuracy. However, the training time complexity of most existing SMH methods is too high, which makes them unscalable to large-scale datasets. In this paper, a novel SMH method, called semantic correlation maximization (SCM), is proposed to seamlessly integrate semantic labels into the hashing learning procedure for large-scale data modeling. Experimental results on two real-world datasets show that SCM can significantly outperform the state-of-the-art SMH methods, in terms of both accuracy and scalability. Copyright © 2014, Association for the Advancement of Artificial Intelligence.

关键词： Semantics

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Supervised hashing with latent factor models 14

Supervised hashing with latent factor models

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37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2014

作者： Zhang, Peichao Zhang, Wei Li, Wu-Jun Guo, Minyi Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University China National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China

ISBN: (纸本)9781450322591

Due to its low storage cost and fast query speed, hashing has been widely adopted for approximate nearest neighbor search in large-scale datasets. Traditional hashing methods try to learn the hash codes in an unsupervised way where the metric (Euclidean) structure of the training data is preserved. Very recently, supervised hashing methods, which try to preserve the semantic structure constructed from the semantic labels of the training points, have exhibited higher accuracy than unsupervised methods. In this paper, we propose a novel supervised hashing method, called latent factor hashing (LFH), to learn similarity-preserving binary codes based on latent factor models. An algorithm with convergence guarantee is proposed to learn the parameters of LFH. Furthermore, a linear-time variant with stochastic learning optimization is proposed for training LFH on large-scale datasets. Experimental results on two large datasets with semantic labels show that LFH can achieve superior accuracy than state-of-the-art methods with comparable training time. Copyright 2014 ACM.

关键词： Big data

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Distributed stochastic ADMM for matrix factorization 14

Distributed stochastic ADMM for matrix factorization

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23rd ACM International Conference on Information and Knowledge Management, CIKM 2014

作者： Yu, Zhi-Qin Shi, Xing-Jian Yan, Ling Li, Wu-Jun Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China

ISBN: (纸本)9781450325981

Matrix factorization (MF) has become the most popular technique for recommender systems due to its promising performance. Recently, distributed (parallel) MF models have received much attention from researchers of big data community. In this paper, we propose a novel model, called distributed stochastic alternating direction methods of multipliers (DS-ADMM), for large-scale MF problems. DS-ADMM is a distributed stochastic variant of ADMM. In particular, we first devise a new data split strategy to make the distributed MF problem fit for the ADMM framework. Then, a stochastic ADMM scheme is designed for learning. Finally, we implement DS-ADMM based on message passing interface (MPI), which can run on clusters with multiple machines (nodes). Experiments on several data sets from recommendation applications show that our DS-ADMM model can outperform other state-of-the-art distributed MF models in terms of both efficiency and accuracy. Copyright 2014 ACM.

关键词： Recommender systems

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