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Journal of Software 2013年第12期8卷 3048-3054页

作者： Li, Wei Zhou, Changle Department of Computer Science and Technology Xiamen University of Technology Xiamen 361024 China Cognitive Science Department Xiamen University Xiamen 361005 China Fujian Key Laboratory of the Brain-like Intelligent Systems Xiamen University Xiamen 361005 China

We present an approach to creating Chinese calligraphy with particular style from learning author's written works automatically. Our first contribution is a method to represent Chinese character topology via fuzzy relative position. Our second contribution is an algorithm to take topological features as features fed into the evaluation model, which is a decision tree algorithm. Our third contribution is an algorithm of heuristic search in the optimization glyph phase. To improve the converge speed, we introduce hypothesis testing and the decay function of transformation amplitude. The experiments demonstrate our algorithm can obtain the similar style Chinese calligraphy with training samples. © 2013 Academy Publisher.

关键词： Decision trees

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Converting continuous-space language models into N-gram language models for statistical machine translation

Converting continuous-space language models into N-gram lang...

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2013 Conference on Empirical Methods in Natural Language Processing, EMNLP 2013

作者： Wang, Rui Utiyama, Masao Goto, Isao Sumita, Eiichro Zhao, Hai Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong Unviersity Shanghai200240 China Multilingual Translation Laboratory MASTAR Project National Institute of Information and Communications Technology 3-5 Hikaridai Keihanna Science City Kyoto619-0289 Japan MOE-Microsoft Key Lab. for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong Unviersity Shanghai200240 China

ISBN: (纸本)9781937284978

Neural network language models, or continuous-space language models (CSLMs), have been shown to improve the performance of statistical machine translation (SMT) when they are used for reranking n-best translations. However, CSLMs have not been used in the first pass decoding of SMT, because using CSLMs in decoding takes a lot of time. In contrast, we propose a method for converting CSLMs into back-off n-gram language models (BNLMs) so that we can use converted CSLMs in decoding. We show that they outperform the original BNLMs and are comparable with the traditional use of CSLMs in reranking. © 2013 Association for Computational Linguistics.

关键词： Decoding

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XXIV World Allergy Congress 2015 Seoul, Korea. 14-17 October 2015 Abstracts

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WORLD ALLERGY ORGANIZATION JOURNAL 2016年第1期9卷 1-206页

作者： [Anonymous] Korea University College of Medicine Guro Hospital Seoul South Korea College of Medicine Korea University Seoul South Korea Korea University Guro Hospital Seoul South Korea Seoul National University Boramae Hospital Seoul South Korea Division of Clinical Immunology Faculty of Medicine University of Debrecen Debrecen Hungary Meiji Pharmaceutical University Tokyo Japan Suri Medical Foundation Hospital Uttar Pradesh State India Shahid Beheshti University Tehran Iran Airlangga University Teaching Hospital Surabaya Indonesia Tokyo Metropolitan Police Hospital Tokyo Japan Shinichiro KogaSamsung Changwon Hospital Seoul South Korea Sapporo Tokushukai Hospital Sapporo Japan Department of Dermatology Yonsei University College of Medicine Seoul South Korea Brain Korea 21 PLUS Project for Medical Science Yonsei University College of Medicine Seoul South Korea Sureyyapasa Chest Diseases and Chest Surgery Training Hospital Istanbul Turkey Kangnam Sacred Heart Hospital Seoul South Korea Sakai City Medical Center Sakai Japan Osaka University Suita Japan Yonsei University College of Medicine Seoul South Korea Osaka-Minami Medical Center Kawachinagano Japan Inha University College of Medicine Incheon South Korea Inha University Hospital Incheon South Korea Center for Rhinology and Allergology Wiesbaden Wiesbaden Germany Leti Pharma Gmbh Witten Germany Postgraduate Institute of Medical Education and Research Chandigarh India West Bengal Technical University Kolkata India Institute of Microbiology Academy of Sciences of the Czech Republic Prague Czech Republic Seoul National University Hospital Seoul South Korea Ent and Allergy Centre(INDIA) Panchkula India University of Santo Tomas Hospital Manila Philippines University of Santo Tomas Faculty of Medicine Manila Philippines Kyung Hee University Hospital at Gang-Dong Seoul South Korea Sangji University Wonju South Korea Division of Allergy and Immunology Department of Internal Medicine Yonsei University Colle

A1 Pirfenidone inhibits TGF-b1-induced extracellular matrix production in nasal polyp-derived fibroblasts Jae-Min Shin, Heung-Man Lee, Il-Ho Park A2 The efficacy of a 2-week course of oral steroid in the treatment of chronic spontaneous urticaria refractory to antihistamines Hyun-Sun Yoon, Gyeong Yul Park A3 The altered distribution of follicular t helper cells may predict a more pronounced clinical course of primary sjögren’s syndrome Margit Zeher A4 Betamethasone suppresses Th2 cell development induced by langerhans cell like dendritic cells Katsuhiko Matsui, Saki Tamai, Reiko Ikeda A5 An evaluation of variousallergens in cases of allergic bronchial asthma at lucknow and neighbouring districts by intradermal skintest Drsushil Suri, Dranu Suri A6 Evaluation ferqency of ADHD in childhood asthma Marzieh Heidarzadeh Arani A7 Steven johnson syndrome caused by typhoid fever in a child Azwin Lubis, Anang Endaryanto A8 Chronic Bronchitis with Radio Contrast Media Hypersensitivity: A Case with Hypothesized GINA Step 1 Asthma Shinichiro Koga A9 The association between asthma and depression in Korean adult : An analysis of the fifth korea national health and nutrition examination survey (2010-2012) Lee Ju Suk A10 Management of allergic disease exacerbations in pregnancy Yasunobu Tsuzuki A11 Subcutaneous immunotherapy mouse model for atopic dermatitis Seo Hyeong Kim, Jung U Shin, Ji Yeon Noh, Shan Jin, Shan Jin, Hemin Lee, Jungsoo Lee, Chang Ook Park, Kwang Hoon Lee, Kwang Hoon Lee A12 Atopic disease and/or atopy are risk factors for local anesthetic allergy in patients with history of hypersensitivity reactions to drugs? Fatma Merve Tepetam A13 Food hypersensitivity in patients with atopic dermatitis in Korea Chun Wook Park, Jee Hee Son, Soo Ick Cho, Yong Se Cho, Yun Sun Byun, Yoon Seok Yang, Bo Young Chung, Hye One Kim, Hee Jin Cho A14 Anaphylaxis caused by an ant (Brachyponera chinensis) in Japan Yoshinori Katada, Toshio Tanaka, Akihiko Nakabayashi, Koji Nishida, Kenichi A

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A highly efficient clustering algorithm for analyzing gene sequences

A highly efficient clustering algorithm for analyzing gene s...

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数学、计算机与生命科学交叉研究青年学者研讨会

作者： Dan Wei Qingshan Jiang Yanjie Wei Shengrui Wang Shenzhen Key Lab for High Performance Data Mining Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina Shenzhen Key Lab for High Performance Data MiningShenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina Department of Computer SciencesUniversity of SherbrookeSherbrookeQCCanada Cognitive Science Department & Fujian Key Laboratory of the Brain-like Intelligent Systems Xiamen UniversityXiamenChina

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Poster abstracts

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Sleep and Biological Rhythms 2016年第1期5卷 A1-A189页

作者： J. Ferrie M. Shipley F. Cappuccio E. Brunner M. Miller M. Kumari M. Marmot A. Coenen J. L. Castillo F. Araya G. Bustamante L. Montecino C. Torres S. Oporto J. Gronli E. Fiske R. Murison B. Bjorvatn E. Sorensen R. Ursin C. M. Portas S. Rajaraman A. Gribok N. Wesensten T. Balkin J. Reifman N. Dursunoglu S. Ozkurt S. Baser O. Delen S. Sarikaya P. Sadler P. Mitchell D. Françon M. Decobert B. Herve A. Richard G. Griebel P. Avenet B. Scatton G. L. Fur D. Eckert A. Jordan A. Wellman S. Smith A. Malhotra D. White D. Bruck I. Thomas A. Kritikos W. Oertel K. Stiasny-Kolster D. Garcia-Borreguero W. Poewe B. Hoegl R. Kohnen E. Schollmayer J. Keffel C. Trenkwalder A. Valle S. Roizenblatt F. Fregni P. Boggio S. Tufik K. Ward L. Robertson L. Palmer P. Eastwood D. Hillman J. Lee S. Mukherjee V. de Padova G. Barbato G. Ficca I. Zilli P. Salzarulo M. Veldi T. Hion V. Vasar M. Kull L. Nowak J. Davis Y. Latzer O. Tzischinsky S. Crowley M. Carskadon M. Anca-Herschkovitsch D. Frey J. Ortega C. Wiseman C. Farley K. Wright A. Campbell A. Neill K. Spiegel R. Leproult E. Tasali N. Scherberg E. van Cauter A. T. Noradina N. A. Karim I. Norlinah A. A. Raymond R. Sahathevan B. Hamidon E. Werth R. Poryazova R. Khatami C. Bassetti R. G. Beran L. Ainley G. Holand J. Duncan H. Kinney B. Davis B. Hood S. Frey C. Schmidt M. Hofstetter P. Peigneux C. Cajochen W.-P. Hu J.-D. Li C. Zhang L. Boehmer J. Siegel Q.-Y. Zhou Y. Sagawa H. Kondo T. Takemura H. Kanayama Y. Kaneko M. Sato T. Kanbayashi Y. Hishikawa T. Shimizu A. Viola L. James L. Schlangen D.-J. Dijk R. Andretic Y.-C. Kim K.-A. Han F. Jones R. Greenspan L. Sanford L. Yang X. Tang K. Dieter E. Uta H. Sven M. Richard N. Oyane S. Pallesen F. Holsten Y. Inoue M. Fujita N. Emura K. Kuroda N. Uchimura A. Johnston J. Astbury G. Kennedy K. Hoedlmoser M. Schabus T. Pecherstorfer S. Moser G. Gruber P. Anderer W. Klimesch N. Naidoo M. Ferber A. Pack D. Neu O. Mairesse G. Hoffmann A. Dris L. Lambrecht P. Linkowski P. Verbanck O. Le Bon N. Matsuura M. Yamao N. Adachi R. Aritomi Y. Komada H. Tanaka S. Shirak University College London London UK University College London London USA Warwick Medical School London UK Warwick Medical School Coventry USA Radboud University Nijmegen Nijmegen Netherlands Department of Neurology University of Chile Santiago Chile Neurology Service Hospital del Salvador Santiago Chile Family Health Center Juan Pablo II Santiago Chile Department of Biomedicine Section on Physiology Univeristy of Bergen Bergen Norway Department of Public Health and Primary Health Care Univeristy of Bergen Bergen Norway Department of Public Health and Primary Health Care University of Bergen Bergen Norway Norwegian Competence Center for Sleep Disorders Haukeland University Hospital Bergen Norway Department of Child and Adolescent Psychiatry Haukeland University Hospital Univeristy of Bergen Bergen Norway Department of Biomedicine University of Bergen Bergen Norway US Army Medical Research and Materiel Command Fort Detrick Frederick USA Walter Reed Army Institute of Research Silver Spring USA Department of Behavioral Biology Walter Reed Army Institute of Research Silver Spring USA Faculty Department of Pulmonology Pamukkale University Medical Denizli Turkey University of Ballarat Ballarat Australia Sanofi-Aventis Recherche et Developpement Paris France Brigham and Women’s Hospital Harvard Medical School Boston USA The University of Queensland Adelaide Australia School of Psychology University of Queensland Brisbane Australia The University of Queensland Brisbane Australia Victoria University Melbourne Australia Department of Psychology Victoria University Melbourne Australia School of Psychology Victoria University Melbourne Australia Philips-Universität Marburg Germany Philips-Universitaet-Marburg Marburg Germany Instituto de Investigaciones del Sueno Madrid Spain Universitätsklinik für Neurologie Innsbruck Austria Universitaetsklinik fuer Neurologie Innsbruck Austria IMEREM GmbH Nuernberg Germany Schwarz Pharma AG Monheim Germany

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A naïve hypergraph model of brain networks

A naïve hypergraph model of brain networks

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2012 International Conference on brain Informatics, BI 2012

作者： Wang, Zhijiang Liu, Jiming Zhong, Ning Qin, Yulin Zhou, Haiyan Yang, Jian Li, Kuncheng International WIC Institute Beijing University of Technology China Beijing Key Laboratory of MRI and Brain Informatics China Dept. of Computer Science Hong Kong Baptist University Hong Kong Dept. of Life Science and Informatics Maebashi Institute of Technology Japan Dept. of Psychology Carnegie Mellon University United States Dept. of Radiology Xuanwu Hospital Capital Medical University China

ISBN: (纸本)9783642351389

This paper extended the concept of motif by maximum cliques defined as "hyperedges" in brain networks, as novel and flexible characteristic network building blocks. Based on the definition of hyperedge, a naïve brain hypergraph model was constructed from a graph model of large-scale brain functional networks during rest. Nine intrinsic hub hyperedges of functional connectivity were identified, which could be considered as the most important intrinsic information processing blocks (or units), and they also covered many components of the core brain intrinsic networks. Furthermore, these overlapped hub hyperedges were assembled into a compound structure as a core subsystem of the intrinsic brain organization. © 2012 Springer-Verlag.

关键词： computers

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Associative information processing in parahippocampal place area (PPA): An fMRI study

Associative information processing in parahippocampal place ...

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2012 International Conference on brain Informatics, BI 2012

作者： Li, Mi Han, Dongning Lu, Shengfu Liu, Zheng Zhong, Ning School of Computer and Communication Engineering Liaoning ShiHua University Liaoning 113001 China International WIC Institute Beijing University of Technology Beijing 100024 China College of Optical and Electronical Information Changchun University of Science and Technology Jilin 130022 China Dept. of Biomedical Engineering Tianjing University Tianjing 300072 China Dept. of Life Science and Informatics Maebashi Institute of Technology Maebashi-City 371-0816 Japan Beijing Key Laboratory of MRI and Brain Informatics China

ISBN: (纸本)9783642351389

The function of the parahippocampal place area (PPA) is a matter of debate. We investigated this issue in fMRI experiment by 36 normal subjects using two types of tasks: figure and text. Furthermore, the figure tasks contain two kinds of contextual associations: one is among items;the other is among items and their locations which refers to the spatial contextual associations. In contrast, the text tasks contain only item contextual associations - they do not have any spatial information. The results showed that the right PPA located at anterior lingual gyrus and left anterior parahippocampal gyrus nearby amygdala were significantly activated during both figure and text tasks, which indicate that the PPA is more involved in associative information processing. © 2012 Springer-Verlag.

关键词： Associative processing

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{\cal U}Boost: Boosting with the Universum

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2012年第4期34卷 825-832页

作者： Chunhua Shen Peng Wang Fumin Shen Hanzi Wang Australian Center for Visual Technologies and School of Computer Science University of Adelaide North Terrace SA Australia School of Automation Science and Electrical Engineering Beihang University Beijing China School of Computer Science and Technology Nanjing University of Science and Technology of PLA Nanjing China Cognitive Science Department School of Information Science and Technology and Fujian Key Laboratory of the Brain-like Intelligent Systems Xiamen University Xiamen Fujian China School of Computer Science University of Adelaide SA Australia

It has been shown that the Universum data, which do not belong to either class of the classification problem of interest, may contain useful prior domain knowledge for training a classifier [1], [2]. In this work, we design a novel boosting algorithm that takes advantage of the available Universum data, hence the name UBoost. UBoost is a boosting implementation of Vapnik's alternative capacity concept to the large margin approach. In addition to the standard regularization term, UBoost also controls the learned model's capacity by maximizing the number of observed contradictions. Our experiments demonstrate that UBoost can deliver improved classification accuracy over standard boosting algorithms that use labeled data alone.

关键词： Boosting Training Educational institutions Optimization Algorithm design and analysis Histograms Accuracy

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A multi-features fusion support vector machine method (MF-SVM) for classification of mangrove remote sensing image

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Journal of Computational Information Systems 2012年第1期8卷 323-334页

作者： Luo, Yanmin Liao, Minghong Yan, Jing Zhang, Caiyun Cognitive Science Department Fujian Key Laboratory of the Brain-like Intelligent Systems Xiamen University Xiamen 361005 China College of Computer Science and Technology Huaqiao University Xiamen 361005 China Software School of Xiamen University Xiamen 361005 China College of Oceanography and Environmental Science Xiamen University Xiamen 361005 China

Remote sensing technology has been the major monitoring and analysing technologies of the mangrove ecosystem. Because of the similar spectral features between mangroves and other types of land cover such as agricultural land and forests, serious confusion in mangrove classification using conventional methods can develop. As one of the popular and advanced statistical learning algorithms, Support Vector Machine (SVM) has been the important study area of pattern recognition and machine learning in recent years. SVM is the useful technique for data classification which based on structural risk minimization (SRM) principle. SVM has such advantages as suitableness to high dimensional data, requirement of few samples and robustness to uncertainty, so it can be used to remote sensing image classification effectively. In order to improve the accuracy of mangrove remote sensing images classification, this paper discusses the SVM models including kernel functions and multi-class methods, and proposes a Multi-features fusion support vector machine classification method (MF-SVM) for classification of mangrove remote sensing image using multi-temporal Landsat TM data, ancillary GIS data and image information. The SVM with the RBF kernel function and one against one multiclass method is used in the MF-SVM. Experiments are performed on the remote sensing image of Zhangjiangkou Mangrove Forestry National Nature Reserve which is located in Yunxiao, Fujian, China. The result shows that the proposed method can improve the extraction accuracy of mangroves. © 2012 Binary Information Press January 2012.

关键词： Classification (of information)

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A secure and high-efficient dynamic key management scheme for group communication using optimized GDH

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ICIC Express Letters 2012年第7期6卷 1815-1820页

作者： Wang, Zhiwen Li, Shaozi Department of Computer and Engineering Guangxi University of Technology No. 268 Donghuan Road Liuzhou 545006 China Cognitive Science Department Xiamen University No. 422 Siming South Road Siming District Xiamen 361005 China Key Laboratory of the Brain-like Intelligent Systems Xiamen University No. 422 Siming South Road Siming District Xiamen 361005 China

Currently, relatively popular and representative group key agreement management schemes are Group Diffie-Hellman (GDH), Centralized key Distribution (CKD), Tree Group Diffie-Hellman (TGDH), Steer(STR) and Burmester-Desmedt (BD). There exist some defects in these schemes;for example, the node or the server may become the bottleneck of system and computational complexity and cost of communications and storage complexity are much higher than some centralized group key management scheme. A secure and high-efficient dynamic key management scheme for group communications using optimized GDH is proposed and implemented and its security is proven in this article. The volume of calculating and traffic of optimized GDH consensus agreement has great advantage compared with GDH. At the same time, the optimized GDH consensus agreement can quickly produce or update group key when there are members who want to add to or leave the group, and it owns highly practicability. © 2012 ICIC International.

关键词： Dynamic subgroup Forward and backward security Gdh Group communication Group key management agreement Optimized gdh

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