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International Conference on Semantics, Knowledge and Grid (SKG)

作者： Jiao Tian Mengyun Cao Jin Liu Xiaoping Sun Zhuge Hai University of Chinese Academy of Sciences Beijing China Knowledge Grid Group CAS Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Wuhan University CAS Key Lab. of Intell. Inf. Process. Institute of Computing Technology Chinese Academy of Sciences Beijing CN

Sentence ranking is one of the most important research issues in text analysis. It can be used in extracted textual summarization and information retrieval. Graph-based methods are a common way of ranking and extracting sentences. In graph based methods, sentences are nodes of graph and edges are built based on the sentence similarities or on sentence co-occurrence relationship. PageRank style algorithms can be applied to get sentence ranks. In this paper, we focus on how to rank sentences in a single scientific paper. A scientific literature has more structural information than general texts and this structural information has not been fully explored yet in graph based ranking models. We investigated several different methods that used structural information such as paragraph and section information to construct a heterogeneous graph for ranking sentences. We conducted experiments on these methods to compare the results on sentence ranking. It shows that structural information can help identify more representative sentences.

关键词： Semantics Algorithm design and analysis Web pages Data mining Iterative methods Current measurement

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A parallel scheme for three-dimensional reconstruction in large-field electron tomography

A parallel scheme for three-dimensional reconstruction in la...

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10th International Symposium on Bioinformatics research and Applications, ISBRA 2014

作者： Zhang, Jingrong Wan, Xiaohua Zhang, Fa Ren, Fei Wang, Xuan Liu, Zhiyong Key Lab. of Intelligent Information Processing and Advanced Computing Research Lab Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Yanshan University Yanshan China

ISBN: (纸本)9783319081700

Large-field high-resolution electron tomography enables visualizing detailed mechanisms under global structure. As field enlarges, the processing time increases and the distortions in reconstruction become more critical. Adopting a nonlinear projection model instead of a linear one can compensate for curvilinear trajectories, nonlinear electron optics and sample warping. But the processing time for the reconstruction with nonlinear projection model is rather considerable. In this work, we propose a new parallel strategy for block iterative reconstruction algorithms. We also adopt a page-based data transfer in this strategy so as to dramatically reduce the processing time for data transfer and communication. We have tested this parallel strategy and it can yield speedups of approximate 40 times according to our experimental results. © 2014 Springer International Publishing Switzerland.

关键词： Iterative methods

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Communication Tool for the Hard of Hearings: A Large Vocabulary Sign Language Recognition System

Communication Tool for the Hard of Hearings: A Large Vocabul...

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International Conference on Affective computing and intelligent Interaction

作者： Xiujuan Chai Hanjie Wang Fang Yin Xilin Chen Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS

ISBN: (纸本)9781479999545

Deaf person has a large social community around the world. The smooth communication is very difficult for these hard of hearings. Automatic Sign Language Recognition (SLR) can build the bridge between the deaf and the hearings and turn the seamless interaction into reality. This paper presents a visualized communication tool for the hard of hearings, i.e. a large vocabulary sign language recognition system based on the RGB-D data input. A novel Grassmann Covariance Matrix (GCM) representation is used to encode a long-term dynamics of a sign sequence and the discriminative kernel SVM is adopted for the sign classification. For continuous sign language recognition, a probability inference method is used to determine the spotting from the labels of sequential frames. Some basic evaluation and comparison of our recognition algorithms are conducted in our collected datasets. This demo will show the recognition of both isolated sign words and the continuous sign language sentences.

关键词： Sign language recognition Sentence spotting Grassmann covariance matrix Kernel SVM RGB-D data

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An improved correlation method based on rotation invariant feature for automatic particle selection

An improved correlation method based on rotation invariant f...

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10th International Symposium on Bioinformatics research and Applications, ISBRA 2014

作者： Chen, Yu Ren, Fei Wan, Xiaohua Wang, Xuan Zhang, Fa Key Lab. of Intelligent Information Processing and Advanced Computing Research Lab Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing Beijing China Yanshan University Yanshan China

ISBN: (纸本)9783319081700

Particle selection from cryo-electron microscopy (cryo-EM) images is very important for high-resolution reconstruction of macromolecular structure. However, the accuracy of existing selection methods are normally restricted to noise and low contrast of cryo-EM images. In this paper, we presented an improved correlation method based on rotation invariant features for automatic, fast particle selection. We first selected a preliminary particle set applying rotation invariant features, then filtered the preliminary particle set using correlation to reduce the interference of high noise background and improve the precision of correlation method. We used Divide and Conquer technique and cascade strategy to improve the recognition ability of features and reduce processing time. Experimental results on the benchmark of cryo-EM images show that our method can improve the accuracy of particle selection significantly. © 2014 Springer International Publishing Switzerland.

关键词： Correlation methods

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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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Semantic retrieval of geospatial datasets based on a semantic repository 6th

Semantic retrieval of geospatial datasets based on a semanti...

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6th International Workshop on information Fusion and Geographical information Systems, IF and GIS 2013

作者： Vizcarra, Julio Torres, Miguel Quintero, Rolando Intelligent Processing of Geospatial Information Lab-Centre for Computing Research National Polytechnic Institute Mexico City Mexico

ISBN: (纸本)9783642375323

In this chapter, we propose a methodology to semantically search geospatial datasets by means of their metadata. It consists of structuring a semantic repository in order to provide the inclusion mechanisms of distributed data to be retrieved, as well as the extraction of those geographic objects with respect to their conceptual similarity. The approach proposes a conceptual measure called the Conceptual Distance algorithm to determine the conceptual similarity among concepts. The features of geographic objects are conceptualized in an ontology on the domains: thematic, spatial, and temporal. The ontology is populated by using Federal Geographic Data Committee specification with instances of geographic objects distributed in several locations on a network. In the retrieval process according to a query, the objects are presented as a ranking list, which provides other results that are semantically close by means of radius of search and it avoids empty results. The approach has been implemented in a web system called SemGsearch. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Ontology

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Multipath convolutional-recursive neural networks for object recognition 8

Multipath convolutional-recursive neural networks for object...

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8th IFIP International Conference on intelligent information processing, IIP 2014

作者： Li, Xiangyang Jiang, Shuqiang Song, Xinhang Herranz, Luis Shi, Zhiping Capital Normal University College of Information Engineering Beijing China Key Lab of Intelligent Information Processing Institute of Computing Tech Beijing China

ISBN: (纸本)9783662449790

Extracting good representations from images is essential for many computer vision tasks. While progress in deep learning shows the importance of learning hierarchical features, it is also important to learn features through multiple paths. This paper presents Multipath Convolutional-Recursive Neural Networks(M-CRNNs), a novel scheme which aims to learn image features from multiple paths using models based on combination of convolutional and recursive neural networks (CNNs and RNNs). CNNs learn low-level features, and RNNs, whose inputs are the outputs of the CNNs, learn the efficient high-level features. The final features of an image are the combination of the features from all the paths. The result shows that the features learned from M-CRNNs are a highly discriminative image representation that increases the precision in object recognition. © IFIP International Federation for information processing 2014.

关键词： Object recognition

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Shape driven kernel adaptation in Convolutional Neural Network for robust facial trait recognition

Shape driven kernel adaptation in Convolutional Neural Netwo...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Shaoxin Li Junliang Xing Zhiheng Niu Shiguang Shan Shuicheng Yan Department of Electrical and Computer Engineering National University of Singapore Singapore Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China National Laboratory of Pattern Recognition Institute of Automation CAS Beijing China

ISBN: (纸本)9781467369657

One key challenge of facial trait recognition is the large non-rigid appearance variations due to some irrelevant real world factors, such as viewpoint and expression changes. In this paper, we explore how the shape information, i.e. facial landmark positions, can be explicitly deployed into the popular Convolutional Neural Network (CNN) architecture to disentangle such irrelevant non-rigid appearance variations. First, instead of using fixed kernels, we propose a kernel adaptation method to dynamically determine the convolutional kernels according to the spatial distribution of facial landmarks, which helps learning more robust features. Second, motivated by the intuition that different local facial regions may demand different adaptation functions, we further propose a tree-structured convolutional architecture to hierarchically fuse multiple local adaptive CNN subnetworks. Comprehensive experiments on WebFace, Morph II and MultiPIE databases well validate the effectiveness of the proposed kernel adaptation method and tree-structured convolutional architecture for facial trait recognition tasks, including identity, age and gender recognition. For all the tasks, the proposed architecture consistently achieves the state-of-the-art performances.

关键词： Kernel Shape Face Face recognition Robustness Neural networks Computer architecture

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Using pLink to analyze cross-linked peptides

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Current Protocols in Bioinformatics 2015年第1期2015卷 8.21.1-8.21.19页

作者： Fan, Sheng-Bo Meng, Jia-Ming Lu, Shan Zhang, Kun Yang, Hao Chi, Hao Sun, Rui-Xiang Dong, Meng-Qiu He, Si-Min Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of the Chinese Academy of Sciences Beijing China National Institute of Biological Sciences Beijing China

pLink is a search engine for high-throughput identification of cross-linked peptides from their tandem mass spectra, which is the data-analysis step in chemical cross-linking of proteins coupled with mass spectrometry analysis. pLink has accumulated more than 200 registered users from all over the world since its first release in 2012. After 2 years of continual development, a new version of pLink has been released, which is at least 40 times faster, more versatile, and more user-friendly. Also, the function of the new pLink has been expanded to identifying endogenous protein cross-linking sites such as disulfide bonds and SUMO (Small Ubiquitin-like MOdifier) modification sites. Integrated into the new version are two accessory tools: plabel, to annotate spectra of cross-linked peptides for visual inspection and publication, and pConfig, to assist users in setting up search parameters. Here, we provide detailed guidance on running a database search for identification of protein cross-links using the 2014 version of pLink. © 2015 by John Wiley & Sons, Inc.

关键词： Cross-linking Mass spectrometry pLink

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Orientational Spatial Part Modeling for Fine-Grained Visual Categorization

Orientational Spatial Part Modeling for Fine-Grained Visual ...

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IEEE International Conference on Mobile Services (MS)

作者： Hantao Yao Shiliang Zhang Fei Xie Yongdong Zhang Dongming Zhang Yu Su Qi Tian CAS Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology Beijing China NEC Labs America USA Department of Computer Science University of Texas at San Antonio San Antonio USA Xin Hua News Agency China

Although significant success has been achieved in fine-grained visual categorization, most of existing methods require bounding boxes or part annotations for training and test, resulting in limited usability and flexibility. To conquer these limitations, we aim to automatically detect the bounding box and parts for fine-grained object classification. The bounding boxes are acquired by a transferring strategy which infers the locations of objects from a set of annotated training images. Based on the generated bounding box, we propose a multiple-layer Orientational Spatial Part (OSP) model to generate a refined description for the object. Finally, we employ the output of deep Convolutional Neural Network (dCNN) as the feature and train a linear SVM as object classifier. Extensive experiments on public benchmark datasets manifest the impressive performance of our method, i.e., Classification accuracy achieves 63.9% on CUB-200-2011 and 75.6% on Aircraft, which are actually higher than many existing methods using manual annotations.

关键词： Feature extraction Training Visualization Testing Aircraft Histograms Computational modeling

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