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Author Correction: The challenge of mapping the human connectome based on diffusion tractography

Nature communications 2019年第1期10卷 5059页

作者： Klaus H Maier-Hein Peter F Neher Jean-Christophe Houde Marc-Alexandre Côté Eleftherios Garyfallidis Jidan Zhong Maxime Chamberland Fang-Cheng Yeh Ying-Chia Lin Qing Ji Wilburn E Reddick John O Glass David Qixiang Chen Yuanjing Feng Chengfeng Gao Ye Wu Jieyan Ma Renjie He Qiang Li Carl-Fredrik Westin Samuel Deslauriers-Gauthier J Omar Ocegueda González Michael Paquette Samuel St-Jean Gabriel Girard François Rheault Jasmeen Sidhu Chantal M W Tax Fenghua Guo Hamed Y Mesri Szabolcs Dávid Martijn Froeling Anneriet M Heemskerk Alexander Leemans Arnaud Boré Basile Pinsard Christophe Bedetti Matthieu Desrosiers Simona Brambati Julien Doyon Alessia Sarica Roberta Vasta Antonio Cerasa Aldo Quattrone Jason Yeatman Ali R Khan Wes Hodges Simon Alexander David Romascano Muhamed Barakovic Anna Auría Oscar Esteban Alia Lemkaddem Jean-Philippe Thiran H Ertan Cetingul Benjamin L Odry Boris Mailhe Mariappan S Nadar Fabrizio Pizzagalli Gautam Prasad Julio E Villalon-Reina Justin Galvis Paul M Thompson Francisco De Santiago Requejo Pedro Luque Laguna Luis Miguel Lacerda Rachel Barrett Flavio Dell'Acqua Marco Catani Laurent Petit Emmanuel Caruyer Alessandro Daducci Tim B Dyrby Tim Holland-Letz Claus C Hilgetag Bram Stieltjes Maxime Descoteaux Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg 69120 Germany. k.maier-hein@dkfz.de. Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg 69120 Germany. Sherbrooke Connectivity Imaging Lab (SCIL) Université de Sherbrooke Sherbrooke QC J1K 0A5 QC Canada. Department of Intelligent Systems Engineering School of Informatics and Computing Indiana University Bloomington IN 47408 USA. Krembil Research Institute University Health Network Toronto Canada M5G 2C4. Department of Neurological Surgery University of Pittsburgh School of Medicine Pittsburgh PA 15213 USA. IMT-Institute for Advanced Studies Lucca 55100 Italy. Department of Diagnostic Imaging St. Jude Children's Research Hospital Memphis TN 38105 USA. University of Toronto Institute of Medical Science Toronto Canada M5S 1A8. Institute of Information Processing and Automation Zhejiang University of Technology Hangzhou 310023 Zhejiang China. United Imaging Healthcare Co Shanghai 201807 China. Shanghai Advanced Research Institute Shanghai 201210 China. Laboratory of Mathematics in Imaging Harvard Medical School Boston MA 02215 USA. Center for Research in Mathematics Guanajuato 36023 Mexico. PROVIDI Lab Image Sciences Institute University Medical Center Utrecht Utrecht 3508 The Netherlands. Cardiff University Brain Research Imaging Centre School of Psychology Cardiff University Maindy Road Cardiff CF24 4HQ UK. Department of Radiology University Medical Center Utrecht Utrecht 3508 The Netherlands. Centre de recherche institut universitaire de geriatrie de Montreal (CRIUGM) Université de Montréal Montreal QC Canada H3W 1W5. Sorbonne Universités UPMC Univ Paris 06 CNRS INSERM Laboratoire d'Imagerie Biomédicale (LIB) 75013 Paris France. Center for Advanced Research in Sleep Medicine Hôpital du Sacré-Coeur de Montréal Montreal Canada H4J 1C5. Neuroimaging Unit Institute of Bioimaging and Molecular Physiology (IBFM) Nat

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Non-intrusive sleep pattern recognition with ubiquitous sensing in elderly assistive environment

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Frontiers of computer science 2015年第6期9卷 966-979页

作者： Shu WU Bessam ABDULRAZAK Daqing ZHANG Xiaojuan MA Xingshe ZHOU School of Computer Science Northwestern Polytechnic University Xi'an 710072 China Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100090 China Department of Computer University of Sherbrooke Sherbrooke JIK2R1 Canada Handicom Lab Institut Telecom SudParis Evry 91011 France Huawei Noah's Ark Lab Hong Kong China

The quality of sleep may be a reflection of an el- derly individual＇s health state, and sleep pattern is an im- portant measurement. Recognition of sleep pattern by itself is a challenge issue, especially for elderly-care community, due to both privacy concerns and technical limitations. We propose a novel multi-parametric sensing system called sleep pattern recognition system （SPRS）. This system, equipped with a combination of various non-invasive sensors, can mon- itor an elderly user＇s sleep behavior. It accumulates the de- tecting data from a pressure sensor matrix and ultra wide band （UWB） tags. Based on these two types of complemen- tary sensing data, SPRS can assess the user＇s sleep pattern automatically via machine learning algorithms. Compared to existing systems, SPRS operates without disrupting the users＇ sleep. It can be used in normal households with minimal deployment. Results of tests in our real assistive apartment at the Smart Elder-care Lab are also presented in this paper.

关键词： sleep pattern elder-care pressure sensor UWB tags Naive Bayes Random Forest

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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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SHREC'15 track: 3D object retrieval with multimodal views 8

SHREC'15 track: 3D object retrieval with multimodal views

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8th Eurographics Workshop on 3D Object Retrieval, 3DOR 2015

作者： Gao, Yue Liu, Anan Nie, Weizhi Su, Yuting Dai, Qionghai Chen, Fuhai Chen, Yingying Cheng, Yanhua Dong, Shuilong Duan, Xingyue Fu, Jianlong Gao, Zan Guo, Haiyun Guo, Xin Huang, Kaiqi Ji, Rongrong Jiang, Yingfeng Li, Haisheng Lu, Hanqing Song, Jianming Sun, Jing Tan, Tieniu Wang, Jinqiao Yin, Huanpu Zhang, Chaoli Zhang, Guotai Zhang, Yan Zhao, Chaoyang Zhao, Xin Zhu, Guibo School of Medicine University of North Carolina Chapel Hill United States School of Electronic Information Engineering Tianjin University China Department of Automation Tsinghua University China School of Information Science and Engineering Xiamen University China Center for Research on Intelligent Perception and Computing Institute of Automation Chinese Academy of Sciences China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China College of Computing and Digital Media DePaul University United States School of Computing Tianjin University of Technology China School of Computer Science and Information Engineering Beijing Technology and Business University China

ISBN: (纸本)9783905674781

This paper reports the results of the SHREC'15 track: 3D Object Retrieval with Multimodal Views, which goal is to evaluate the performance of retrieval algorithms when multimodal views are employed for 3D object representation. In this task, a collection of 505 objects is generated and both the color images and the depth images are provided for each object. 311 objects are selected as the queries and average retrieval performance is measured. The track attracted six participants and the submission of 26 runs, to two tasks. The evaluation results show a promising scenario about multimodal view-based 3D retrieval methods, and reveal interesting insights in dealing with multimodal data. © The Eurographics Association 2015.

关键词： Information retrieval

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QACtools: A Quality Assessment and Quality Control Tool for Next-Generation Sequencing Data

QACtools: A Quality Assessment and Quality Control Tool for ...

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2015年中国智能自动化学术会议

作者： Dandan Song Ning Li Lejian Liao Beijing Lab of Intelligent Information Technology School of Computer Science and Technology Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology

In recent years, next-generation sequencing technologies have been widely used, and are rapidly changing the landscape of genetics with unimaginable speed. However, the preliminary quality control regulations including trimming adaptor sequences, quality statistics, and depth and coverage statistics need to be applied to raw reads before further analysis. QACtools implements various utilities for processing raw reads in the FASTQ format and alignments in the BAM *** analyzes some relevant properties of next-generation sequencing data such as average read length, base quality scores, and so on. Additionally, to our knowledge,none of the other tools support depth and coverage statistic that have been provided in our tool for analysis of large DNA sequencing data. It is crucial for overcoming errors in base calling and assembly. We suppose tool is useful for the quality control of NGS data to perform further analysis. This software is an open source application freely available at https://***/projects/qactools/.

关键词： NGS data analysis Quality control FASTQ format BAM format

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An improved method for semantic similarity calculation based on stop-words 13

An improved method for semantic similarity calculation based...

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13th International Conference on Machine Learning and Cybernetics, ICMLC 2014

作者： Li, Haodi Chen, Qingcai Wang, Xiaolong Intelligent Computing Research Center Computer Science and Technology Harbin Institute of Technology Shenzhen Graduate School Shenzhen China

ISBN: (纸本)9783662456514

Text similarity calculation has become one of the key issues of many applications such as information retrieval, semantic disambiguation, automatic question answering. There are increasing needs of similarity calculations in different levels, e.g. characters, vocabularies, syntactic structures and semantic etc. Most of existing semantic similarity algorithms can be categorized into statistical based methods, rule based methods and combination of these two methods. Statistical methods use knowledge bases to incorporate more comprehensive knowledge and have the capability of reducing knowledge noise. So they are able to obtain better performance. Nevertheless, for the unbalanced distribution of different items in the knowledge base, semantic similarity calculation performance for low-frequency words is usually poor. In this work, based on the distributions of stop-words, we proposes a weights normalization method for semantic dimensions. The proposed method uses the semantic independence of stop-words to avoid semantic bias of corpus in statistical methods. It further improves the accuracy of semantic similarity computation. Experiments compared with several existing algorithms show the effectiveness of the proposed method. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Semantics

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A short texts matching method using shallow features and deep features 3

A short texts matching method using shallow features and dee...

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3rd CCF Conference on Natural Language Processing and Chinese computing, NLPCC 2014

作者： Kang, Longbiao Hu, Baotian Wu, Xiangping Chen, Qingcai He, Yan Intelligent Computing Research Center School of Computer Science and Technology Harbin Institute of Technology Shenzhen Graduate School Shenzhen China Zunyi Medical and Pharmaceutical College Zunyi China

ISBN: (纸本)9783662459232

Semantic matching is widely used in many natural language processing tasks. In this paper, we focus on the semantic matching between short texts and design a model to generate deep features, which describe the semantic relevance between short "text object". Furthermore, we design a method to combine shallow features of short texts (i.e., LSI, VSM and some other handcraft features) with deep features of short texts (i.e., word embedding matching of short text). Finally, a ranking model (i.e., RankSVM) is used to make the final judgment. In order to evaluate our method, we implement our method on the task of matching posts and responses. Results of experiments show that our method achieves the state-of-the-art performance by using shallow features and deep features. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Semantics

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Integrated management for network virtualization infrastructure

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Journal of the National Institute of Information and Communications Technology 2015年第2期62卷 33-43页

作者： Yamada, Kazuhisa Nakao, Akihiro Kanada, Yasusi Saida, Yoshinori Amemiya, Koichiro Future Networking Research Group Media Innovation Laboratory NTT Network Innovation Laboratories Network Japan Interfaculty Initiative in Information Studies Graduate School of Interdisciplinary Information Studies University of Tokyo Network Computer Science Japan Network Research Department Center for Technology Innovation - Information and Telecommunications Network Computer Vision Japan System Integration and Services Technologies Management Division Technological Strategy Planning Department NEC Network Japan Software Laboratory Fujitsu Labratories Ltd. Cloud Computing Network Japan

This paper introduces research and development results of extended functions on our network virtualization infrastructure which achieve advanced network virtualization. Our results achieved to meet five requirements which are resource abstraction, resource isolation, resource elasticity, deep programmability and authentication. In addition, we proposed evolvable architecture and extended network virtualization for edge network and terminals. We also deployed our system on JGN-X testbed.

关键词： Virtual reality

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Discriminative Representative Selection via Structure Sparsity

Discriminative Representative Selection via Structure Sparsi...

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International Conference on Pattern Recognition

作者： Baoxing Wang Qiyue Yin Shu Wu Liang Wang Guiquan Liu School of Software and Engineering University of Science and Technology of China NLPR CASIA Center for Research on Intelligent Perception and Computing (CRIPAC) School of Computer Science and Technology University of Science and Technology of China

This paper focuses on the problem of finding a few representatives for a given dataset, which have both representation and discrimination ability. To solve this problem, we propose a novel algorithm, called Structure Sparsity based Discriminative Representative Selection (SSDRS), to find a representative subset of data points. The selected representative subset keeps the representation ability based on sparse representation models assuming that each data point can be expressed as a linear combination of those representatives. Meanwhile, we employ the Fisher discrimination criterion to make the coefficient matrix possess small within-class scatter but big between-class scatter, which leads to the discriminant ability of representatives. Since such a selected subset is representative and discriminative, it can be used to properly describe the entire dataset and achieve a good classification performance simultaneously. Experimental results in terms of video summarization and image classification indicate that our proposed algorithm outperforms the state-of-the-art methods.

关键词： Databases Linear programming Sparse matrices Training Support vector machines Mathematical model Equations

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A multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis in the frequency domain

Advances in Intelligent Systems and Computing

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Advances in intelligent Systems and computing 2014年 215卷 603-616页

作者： Chen, Guang Song, Dandan Liao, Lejian Beijing Eng. Research Center of High Volume Language Inf. Processing and Cloud Computing App. Beijing Lab of Intelligent Information Technology School of Computer Science Beijing Institute of Technology Beijing China

Brain-computer interface (BCI) is a new way for man-machine interaction with wide applications, in which steady-state visual evoked potentials (SSVEP) is a promising option. However, many characteristics of SSVEP show great user variation. So parameter optimization and channel selection for each subject were applied to improve the performance of BCI. These optimizations limit the practical applicability of the SSVEP-based BCI. The use of a canonical correlation analysis (CCA) method for multi-channel SSVEP in the time domain detection showed highly increased detection accuracy, but it is sensitive to the noise when the stimulate frequency is low. In this paper, a method of CCA in the frequency domain is presented for classifying multi-channel SSVEPs. First overlapping average is conducted on the original training signals. Then fast Fourier transform (FFT) is used to transform the signals from time domain to frequency domain to produce the reference data. Finally, according to the correlation coefficients of the new data and the references in the frequency domain, the SSVEP is classified. The experimental results show the enhanced accuracy of our method when applied to low stimulate frequencies. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Brain computer interface

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