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IEEE International Conference on Multimedia and Expo

作者： Xiameng Qing Jianbing Shen Xuelong Li Yunde Jia Beijing Key Lab of Intelligent Information Technology School of Computer Science Beijing Institute of Technology Center for OPTical IMagery Analysis and Learning (OPTIMAL) State Key Laboratory of Transient Optics and Photonics Xi'an Institute of Optics and Precision Mechanics Chinese Academy of Sciences

ISBN: (纸本)9781479947607

This paper presents a new method to compute the dense correspondences between two images by using the sparse feature-based patches in an energy optimization framework. Many transformation and deformation cues such as color, scale and rotation should be considered when we finding dense correspondences between images. However, most existing methods only consider part of these transformations, which will introduce the uncorrect correspondence results. In terms of the property of the sparse feature and the principle that nearest sub-scenes and neighbors are much more similar, we design a new energy optimization to guide the dense matching process. Both transformation and deformation are considered in our energy optimization framework since we design the feature-based patches. Thus, our algorithm can match the complicated scenes and objects robustly. At last, a local refinement technique is proposed to solve the perturbation of the matched patches. Experimental results demonstrate that our method outperforms the state-of-the-art algorithms.

关键词： Dense Correspondence Optimization Patch Match

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LEARNING TO DETECT STEREO SALIENCY

LEARNING TO DETECT STEREO SALIENCY

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IEEE International Conference on Multimedia and Expo

作者： Fang Guo Jianbing Shen Xuelong Li Beijing Key Lab of Intelligent Information Technology School of Computer Science Beijing Institute of Technology Center for OPTical IMagery Analysis and Learning (OPTIMAL) State Key Laboratory of Transient Optics and Photonics Xi'an Institute of Optics and Precision Mechanics Chinese Academy of Sciences

ISBN: (纸本)9781479947607

This paper develops a novel learning-based method for detecting stereo saliency in stereopair images. The disparity maps computed from stereopair images provide an additional depth cue for stereo saliency detection. To the best of our knowledge, our approach is the first one to simultaneously detect the stereo saliency of both left and right images using support vector machine (SVM). In our work, the disparity maps are used in two aspects. One is to improve the performance of saliency detection for monocular image. The other one is to maintain the consistency between the stereo matching and saliency maps. In order to meet the above requirements, we propose a new combinational saliency feature to train the stereo images with the labeled saliency ground truth, using support vector machine as the classifier. In the test stage, our approach generates the stereo saliency results according to the trained SVM model. Furthermore, a stereopair saliency dataset containing 400 pairs of images is created to perform the challenging experiments. The experimental results have demonstrated that our method achieves better performance than the state-of-the-art algorithms of single-image saliency detection.

关键词： Stereo saliency detection Stereopair images Support vector machine Feature detection

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How Do Open Source Communities Document Software Architecture: An Exploratory Survey

How Do Open Source Communities Document Software Architectur...

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IEEE International Conference on Engineering of Complex computer Systems

作者： Wei Ding Peng Liang Antony Tang Hans Van Vliet Mojtaba Shahin Key Laboratory of Earthquake Geodesy China Earthquake Administration China State Key Lab of Software Engineering Wuhan University China Department of Computer Science VU University Amsterdam The Netherlands Faculty of Science Engineering and Technology Swinburne University of Technology Australia VU Univ. Amsterdam Amsterdam Netherlands Islamic Azad University Iran

Software architecture (SA) documentation provides a blueprint of a software-intensive system for the communication between stakeholders about the high-level design of the system. In open source software (OSS) development, a lack of SA documentation may hinder the use and further development of OSS, but how much "architecture" documentation is enough and appropriate is largely dependent on the contextual factors of development. In order to understand the state of the practice of SA documentation in OSS projects, we conducted a documentation-based survey to explore how SA is documented in OSS projects. Out of 2,000 OSS projects from four major OSS sources, we found that 108 projects have some SA documentation, which shows that the SA documentation is scarce in OSS development. We analyzed these 108 projects to understand what SA information has been documented and how they have been described. We have found that frequently-documented architectural information is model, system, and mission, natural language is the most frequently-used architectural language for specifying architectural information in OSS SA documents. The results also show that the likelihood that an OSS project will document SA is increased when more developers are involved in the project, and industry and research OSS projects are more likely to create SA documents than freelance projects.

关键词： Documentation computer architecture Software Unified modeling language Standards Educational institutions Web pages

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Harmonious Hashing

Harmonious Hashing

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23rd International Joint Conference on Artificial Intelligence, IJCAI 2013

作者： Xu, Bin Bu, Jiajun Lin, Yue Chen, Chun He, Xiaofei Cai, Deng Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University China State Key Lab of CAD and CG College of Computer Science Zhejiang University China

ISBN: (纸本)9781577356332

Hashing-based fast nearest neighbor search technique has attracted great attention in both research and industry areas recently. Many existing hashing approaches encode data with projection-based hash functions and represent each projected dimension by 1-bit. However, the dimensions with high variance hold large energy or information of data but treated equivalently as dimensions with low variance, which leads to a serious information loss. In this paper, we introduce a novel hashing algorithm called Harmonious Hashing which aims at learning hash functions with low information loss. Specifically, we learn a set of optimized projections to preserve the maximum cumulative energy and meet the constraint of equivalent variance on each dimension as much as possible. In this way, we could minimize the information loss after binarization. Despite the extreme simplicity, our method outperforms superiorly to many state-of-the-art hashing methods in large-scale and high-dimensional nearest neighbor search experiments.

关键词： Hash functions

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Automatic Target Segmentation based on Texture for Microscopic Images of Chinese Herbal Powders

Automatic Target Segmentation based on Texture for Microscop...

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第25届中国控制与决策会议

作者： Jun Li Yixu Song Yaoli Li Shaoqin Cai Zehong Yang State Key Laboratory of Intelligent Technology and Systems Computer Science and Technology Department Tsinghua University State Key Lab. of Natural and Biological Drugs School of Pharmaceutical Sciences Peking University

ISBN: (纸本)9781467355339

The identification of Chinese herbal powders is usually based on physical or chemical detection, but that is far from enough to identity dozens of herbal species. Microscopic images of these powders contain variety of information, and important evidence for identification. These images usually contain variety of substance, and most of them are noises, which makes the target segmentation become a difficult job. An effective automatic target segmentation algorithm based on texture is proposed in this paper. Our method consists of two steps: "Preliminary Segmentation" and "Further Segmentation". Firstly, feature vector of texture is extracted and clustered into two groups: background and foreground;secondly, taking the continuity of edge and the locality of target into consideration, energy equations are established, and Maximum flow-Minimum cut Algorithm is applied to solve them. Three groups of images are used to test our method: microscopic images of Chinese herbal powders, Brodaze Images, and natural texture images. And the experimental results show that our method achieves a better segmentation compared with Grab-Cut, and additionally user inter-action is not required in our method.

关键词： Chinese herbal powder microscopic images automatic segmentation texture feature

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A semi-informative aware approach using topic model for medical search

A semi-informative aware approach using topic model for medi...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qinmin Vivian Hu Liang He Mingyao Li Jimmy Xiangji Huang E. Mark Haacke Shanghai Key Laboratory of Multidimensional Information Processing MR Research Facility Wayne State University Detroit MI USA Department of Computer Science & Technology East China Normal University Shanghai China Information Retrieval and Knowledge Management Research Lab York University Toronto Canada

We propose a semi-informative aware approach using the topic model on query expansion problem in the biomedicine domain. the demographics and disease information is applied to semi-structure the topic model as the “known” label, compared to the traditional latent topics in topic modelling. Then, we suggest to select three terms from the top ranked documents to expand the query, based on the assumption in the pseudo relevance feedback method that the top ranked results in the first retrieval around are relevant. After that, we conduct the experiments on the TREC medical records data sets with extensive analysis and discussions. Numerically, we achieve the improvements of 7.41% on MAP, 9.29% on Bpref and 5.60% on P@10 respectively over the strong baselines.

关键词： Indexes Diseases Information retrieval Numerical models Biological system modeling Educational institutions Mathematical model

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Whom to mention: Expand the diffusion of tweets by @ recommendation on micro-blogging systems

Whom to mention: Expand the diffusion of tweets by @ recomme...

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22nd International Conference on World Wide Web, WWW 2013

作者： Wang, Beidou Wang, Can Bu, Jiajun Chen, Chun Zhang, Wei Vivian Cai, Deng He, Xiaofei Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University China Microsoft Corporation Redmond WA United States State Key Lab of CAD and CG College of Computer Science Zhejiang University China

ISBN: (纸本)9781450320351

Nowadays, micro-blogging systems like Twitter have become one of the most important ways for information sharing. In Twitter, a user posts a message (tweet) and the others can forward the message (retweet). Mention is a new feature in micro-blogging systems. By mentioning users in a tweet, they will receive notifications and their possible retweets may help to initiate large cascade diffusion of the tweet. To enhance a tweet's diffusion by finding the right persons to mention, we propose in this paper a novel recommendation scheme named as whom-to-mention. Specifically, we present an in-depth study of mention mechanism and propose a recommendation scheme to solve the essential question of whom to mention in a tweet. In this paper, whom-to-mention is formulated as a ranking problem and we try to address several new challenges which are not well studied in the traditional information retrieval tasks. By adopting features including user interest match, content-dependent user relationship and user influence, a machine learned ranking function is trained based on newly defined information diffusion based relevance. The extensive evaluation using data gathered from real users demonstrates the advantage of our proposed algorithm compared with the traditional recommendation methods. Copyright is held by the International World Wide Web Conference Committee (IW3C2).

关键词： Social networking (online)

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Parallel Bayesian Network Structure Learning for Genome-Scale Gene Networks

Parallel Bayesian Network Structure Learning for Genome-Scal...

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Supercomputing Conference

作者： Sanchit Misra Md. Vasimuddin Kiran Pamnany Sriram P. Chockalingam Yong Dong Min Xie Maneesha R. Aluru Srinivas Aluru Parallel Computing Lab Intel Corporation Bangalore India Dept. of Computer Science and Engineering Indian Institute of Technology Bombay Mumbai India State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China School of Biology Georgia Institute of Technology Atlanta USA School of Computational Science and Engineering Georgia Institute of Technology Atlanta USA

Learning Bayesian networks is NP-hard. Even with recent progress in heuristic and parallel algorithms, modeling capabilities still fall short of the scale of the problems encountered. In this paper, we present a massively parallel method for Bayesian network structure learning, and demonstrate its capability by constructing genome-scale gene networks of the model plant Arabidopsis thaliana from over 168.5 million gene expression values. We report strong scaling efficiency of 75% and demonstrate scaling to 1.57 million cores of the Tianhe-2 supercomputer. Our results constitute three and five orders of magnitude increase over previously published results in the scale of data analyzed and computations performed, respectively. We achieve this through algorithmic innovations, using efficient techniques to distribute work across all compute nodes, all available processors and coprocessors on each node, all available threads on each processor and coprocessor, and vectorization techniques to maximize single thread performance.

关键词： Bayes methods Hypercubes Instruction sets Genomics Bioinformatics Coprocessors Vectors

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Induced Magnetization in La0.7Sr0.3MnO3/BiFeO3 Superlattices

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Physical Review Letters 2014年第4期113卷 047204-047204页

作者： Surendra Singh J. T. Haraldsen J. Xiong E. M. Choi P. Lu D. Yi X.-D. Wen J. Liu H. Wang Z. Bi P. Yu M. R. Fitzsimmons J. L. MacManus-Driscoll R. Ramesh A. V. Balatsky Jian-Xin Zhu Q. X. Jia Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Solid State Physics Division Bhabha Atomic Research Center Mumbai 400085 India Department of Physics and Astronomy James Madison University Harrisonburg Virginia 22807 USA State Key Lab of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu 610051 China Department of Materials Science and Metallurgy University of Cambridge Cambridge CB2 3QZ United Kingdom Sandia National Laboratory Albuquerque New Mexico 87185 USA Department of Materials Science and Engineering University of California at Berkeley Berkeley California 94720 USA Department of Electrical and Computer Engineering Texas A&M University College Station Texas 77843 USA

Using polarized neutron reflectometry, we observe an induced magnetization of 75±25 kA/m at 10 K in a La0.7Sr0.3MnO3 (LSMO)/BiFeO3 superlattice extending from the interface through several atomic layers of the BiFeO3 (BFO). The induced magnetization in BFO is explained by density functional theory, where the size of band gap of BFO plays an important role. Considering a classical exchange field between the LSMO and BFO layers, we further show that magnetization is expected to extend throughout the BFO, which provides a theoretical explanation for the results of the neutron scattering experiment.

关键词： Iron compounds

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Tumor gene expressive data classification based on locally linear representation Fisher criterion 1

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9th International Conference on Intelligent Computing, ICIC 2013

作者： Li, Bo Tian, Bei-Bei Liu, Jin School of Computer Science and Technology Wuhan University of Science and Technology 430081 Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China State Key Lab. of Software Engineering Wuhan China

ISBN: (数字)9783642394829

ISBN: (纸本)9783642394812

In this paper, a discriminant manifold learning method based on Locally Linear Embedding (LLE), which is named Locally Linear Representation Fisher Criterion (LLRFC), is proposed for the classification of tumor gene expressive data. In the proposed LLRFC, an inter-class graph and intra-class graph is constructed based on the class information of tumor gene expressive data, where the weights between nodes in both graph are optimized using locally linear representation trick. Moreover, a Fisher criterion is modeled to maximize the inter-class scatter and minimize the intra-class scatter simultaneously. Experiments on some benchmark tumor gene expressive data validate its efficiency. © 2013 Springer-Verlag.

关键词： Genes

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