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Correction to "Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning"

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IEEE transactions on bio-medical engineering 2017年第1期64卷 250页

作者： Guorong Wu Minjeong Kim Qian Wang Brent C Munsell Dinggang Shen Department of Radiology and BRIC The University of North Carolina at Chapel Hill Chapel Hill NC USA Department of Brain and Cognitive Engineering Korea University Seoul South Korea Department of Radiology and BRIC The University of North Carolina at Chapel Hill Med-X Research Institute of Shanghai Jiao Tong University Shanghai China Department of Computer Science The College of Charleston Charleston SC USA

Presents corrections to the paper, "Scalable high performance image registration framework by unsupervised deep feature representations", (Wu, G. et al.), IEEE Trans. Biomed. Eng., vol. 63, no. 7, pp. 1505–... 详细信息

关键词： Machine learning Image registration Unsupervised learning Scalability

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Facial Micro-Expression Recognition Using Spatiotemporal Local Binary Pattern with Integral Projection

Facial Micro-Expression Recognition Using Spatiotemporal Loc...

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International Conference on computer Vision Workshops (ICCV Workshops)

作者： Xiaohua Huang Su-Jing Wang Guoying Zhao Matti Piteikäinen Department of Computer Science and Engineering University of Oulu Finland State Key Laboratory of Brain and Cognitive Science Chinese Academy of Science Beijing China Oulun Yliopisto Oulu FI

Recently, there are increasing interests in inferring mirco-expression from facial image sequences. For micro-expression recognition, feature extraction is an important critical issue. In this paper, we proposes a novel framework based on a new spatiotemporal facial representation to analyze micro-expressions with subtle facial movement. Firstly, an integral projection method based on difference images is utilized for obtaining horizontal and vertical projection, which can preserve the shape attributes of facial images and increase the discrimination for micro-expressions. Furthermore, we employ the local binary pattern operators to extract the appearance and motion features on horizontal and vertical projections. Intensive experiments are conducted on three available published micro-expression databases for evaluating the performance of the method. Experimental results demonstrate that the new spatiotemporal descriptor can achieve promising performance in micro-expression recognition.

关键词： Face Shape Feature extraction Face recognition Spatiotemporal phenomena Databases Histograms

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Sustainable computational science: The Rescience initiative

arXiv

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arXiv 2017年

作者： Rougier, Nicolas P. Hinsen, Konrad Alexandre, Frédéric Arildsen, Thomas Barba, Lorena Benureau, Fabien C.Y. Titus Brown, C. de Buyl, Pierre Caglayan, Ozan Davison, Andrew P. Delsuc, Marc André Detorakis, Georgios Diem, Alexandra K. Drix, Damien Enel, Pierre Girard, Benoît Guest, Olivia Hall, Matt G. Henriques, Rafael Neto Hinaut, Xavier Jaron, Kamil S. Khamassi, Mehdi Klein, Almar Manninen, Tiina Marchesi, Pietro McGlinn, Dan Metzner, Christoph Petchey, Owen L. Plesser, Hans Ekkehard Poisot, Timothée Ram, Karthik Ram, Yoav Roesch, Etienne Rossant, Cyrille Rostami, Vahid Shifman, Aaron Stimberg, Marcel Stachelek, Joseph Stollmeier, Frank Vaggi, Federico Viejo, Guillaume Vitay, Julien Vostinar, Anya Yurchak, Roman Zito, Tiziano INRIA Bordeaux Sud-Ouest Talence France Institut des Maladies Neurodégénératives Université de Bordeaux CNRS UMR 5293 Bordeaux France LaBRI Université de Bordeaux Bordeaux INP CNRS UMR 5800 Talence France Centre de Biophysique Moléculaire CNRS UPR4301 Orléans France Synchrotron SOLEIL Division Expériences Gif sur Yvette France Department of Electronic Systems Technical Faculty of IT and Design Aalborg University Denmark Department of Mechanical and Aerospace Engineering George Washington University WashingtonDC United States Department of Population Health and Reproduction University of California Davis DavisCA United States Instituut voor Theoretische Fysica KU Leuven Belgium Le Mans France Unité de Neurosciences Information et Complexité CNRS FRE 3693 Gif sur Yvette France Institut de Génétique et de Biologie Moléculaire et Cellulaire INSERM U964 CNRS UMR 7104 Université de Strasbourg Illkirch France Department of Cognitive Sciences University of California Irvine United States Computational Engineering and Design Faculty of Engineering and the Environment University of Southampton United Kingdom Department of Computer Science Humboldt-Universität zu Berlin Friedman Brain Institute Icahn School of Medicine Mount SinaiNY United States Institut des Systèmes Intelligent et de Robotique Sorbonne Universités UPMC Univ Paris 06 CNRS Paris France Experimental Psychology University College London United Kingdom UCL Great Ormond St Institute of Child Health University College London London United Kingdom MRC Cognition and Brain Sciences Unit Cambridge United Kingdom Department of Ecology and Evolution University of Lausanne Lausanne Switzerland Independent scholar Enschede Netherlands BioMediTech Institute and Faculty of Biomedical Sciences and Engineering Tampere University of Technology Tampere Finland Swammerdam Institute for Life Sciences Center for Neuroscience Faculty of Science University of Amsterdam Amsterdam Netherlands Depart

computer science offers a large set of tools for prototyping, writing, running, testing, validating, sharing and reproducing results, however computational science lags behind. In the best case, authors may provide their source code as a compressed archive and they may feel confident their research is reproducible. But this is not exactly true. Jonathan Buckheit and David Donoho proposed more than two decades ago that an article about computational results is advertising, not scholarship. The actual scholarship is the full software environment, code, and data that produced the result. This implies new workflows, in particular in peer-reviews. Existing journals have been slow to adapt: source codes are rarely requested, hardly ever actually executed to check that they produce the results advertised in the article. Rescience is a peer-reviewed journal that targets computational research and encourages the explicit replication of already published research, promoting new and open-source implementations in order to ensure that the original research can be replicated from its description. To achieve this goal, the whole publishing chain is radically different from other traditional scientific journals. Rescience resides on GitHub where each new implementation of a computational study is made available together with comments, explanations, and software tests. Copyright © 2017, The Authors. All rights reserved.

关键词： Open source software

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English to Chinese translation: How Chinese character matters? 29

English to Chinese translation: How Chinese character matter...

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29th Pacific Asia Conference on Language, Information and Computation, PACLIC 2015

作者： Wang, Rui Zhao, Hai Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai200240 China

Word segmentation is helpful in Chinese natural language processing in many aspects. However it is showed that different word segmentation strategies do not affect the performance of Statistical Machine Translation (SMT) from English to Chinese significantly. In addition, it will cause some confusions in the evaluation of English to Chinese SMT. So we make an empirical attempt to translation English to Chinese in the character level, in both the alignment model and language model. A series of empirical comparison experiments have been conducted to show how different factors affect the performance of character-level English to Chinese SMT. We also apply the recent popular continuous s- pace language model into English to Chinese SMT. The best performance is obtained with the BLEU score 41.56, which improve base- line system (40.31) by around 1.2 BLEU s- core.

关键词： computer aided language translation

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A machine learning method to distinguish machine translation from human translation 29

A machine learning method to distinguish machine translation...

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29th Pacific Asia Conference on Language, Information and Computation, PACLIC 2015

作者： Li, Yitong Wang, Rui Zhai, Hai Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai200240 China

This paper introduces a machine learning ap-proach to distinguish machine translation texts from human texts in the sentence level au-Tomatically. In stead of traditional methods, we extract some linguistic features only from the target language side to train the predic-Tion model and these features are independent of the source language. Our prediction mod-el presents an indicator to measure how much a sentence generated by a machine translation system looks like a real human translation. Furthermore, the indicator can directly and ef-fectively enhance statistical machine transla-Tion systems, which can be proved as BLEU score improvements.

关键词： Machine translation

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Neural network language model for Chinese pinyin input method engine 29

Neural network language model for Chinese pinyin input metho...

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29th Pacific Asia Conference on Language, Information and Computation, PACLIC 2015

作者： Chen, Shen-Yuan Wang, Rui Zhao, Hai Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai200240 China

Neural network language models (NNLMs) have been shown to outperform traditional n-gram language model. However, too high computational cost of NNLMs becomes the main obstacle of directly integrating it into pinyin IME that normally requires a real-Time response. In this paper, an efficient solution is proposed by converting NNLMs into back-off n-gram language models, and we integrate the converted NNLM into pinyin IME. Our exper-imental results show that the proposed method gives better decoding predictive performance for pinyin IME with satisfied efficiency.

关键词： Computational linguistics

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High-order graph-based neural dependency parsing 29

High-order graph-based neural dependency parsing

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29th Pacific Asia Conference on Language, Information and Computation, PACLIC 2015

作者： Zhang, Zhisong Zhao, Hai Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai200240 China

In this work, we present a novel way of using neural network for graph-based dependency parsing, which fits the neural network into a simple probabilistic model and can be furthermore generalized to high-order parsing. Instead of the sparse features used in traditional methods, we utilize distributed dense feature representations for neural network, which give better feature representations. The proposed parsers are evaluated on English and Chinese Penn Treebanks. Compared to existing work, our parsers give competitive performance with much more efficient inference.

关键词： Graphic methods

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Erratum to: Towards the abstract system theory of system science for cognitive and intelligent systems

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Complex & Intelligent Systems 2016年第1期1卷 23-23页

作者： Yingxu Wang Laboratory for Computational Intelligence and Software Science International Institute of Cognitive Informatics and Cognitive Computing (ICIC) Department of Electrical and Computer Engineering Schulich School of Engineering and Hotchkiss Brain Institute University of Calgary Calgary Canada

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Scalable clustering based on enhanced-SMART for large-scale FMRI datasets 40

Scalable clustering based on enhanced-SMART for large-scale ...

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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Liu, Chao Fa, Rui Abu-Jamous, Basel Brattico, Elvira Nandi, Asoke Department of Electronic and Computer Engineering Brunel University United Kingdom Cognitive Brain Research Unit Institute of Behavioural Science University of Helsinki Finland Helsinki Collegium of Advanced Studies University of Helsinki Finland Department of Mathematical Information Technology University of Jyvaskyla Finland

ISBN: (纸本)9781467369978

In this paper, we propose a scalable clustering paradigm to address the problems of excessive computational load and limited clustering performance in large-scale data. The proposed method employs the enhanced splitting merging awareness tactics (E-SMART) algorithm. The large-scale dataset is divided into many sub-datasets sampled randomly from original data. These sub-datasets are clustered using E-SMART with the number of clusters K detected automatically and the resulting partitions are combined and re-clustered. We evaluate our method using synthetic fMRI datasets with different noise levels and one real fMRI dataset. Results show that the accuracy and execution time outperforms the traditional clustering algorithms in large-scale datasets. © 2015 IEEE.

关键词： E-SMART large-scale data sampling scalable clustering

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Detection of event related optical signal (EROS) in episodic memory retrieval task using ICA-based Kalman filter

Detection of event related optical signal (EROS) in episodic...

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International Conference on Control, Automation and Systems ( ICCAS)

作者： Sunghee Dong Jaeyoung Shin Jichae Jeong Department of Brain and Cognitive Engineering Korea University Seoul Korea Department of Computer Science Berlin Institute of Technology Berlin Germany

We investigate the noise reduction performance for event related optical signal (EROS) measured from 5-channel near-infrared spectroscopy to reduce noise by using grand average and independent component analysis (ICA)-based Kalman filter. Episodic memory retrieval task was performed and expected to show brain activity around 500 ms from right prefrontal cortex. We verified that positive deviation in response to old word occurred around 500 ms using grand average for three subjects. After applying ICA-based Kalman filter on data from each subject, the clear positive deviation was shown graphically. Also, the ratio of positively deviated epoch in response to old word increased by 14 % after ICA-based Kalman filtering.

关键词： Spectroscopy Area measurement Integrated optics

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