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IEEE International Conference on Robotics and Automation

作者： Yawen Huang Fan Zhu Ling Shao Alejandro F. Frangi the Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB) Department of Electronic and Electrical Engineering The University of Sheffield Sheffield United Kingdom the Multimedia and Visual Computing Lab Department of Electrical and Computer Engineering New York University Abu Dhabi Abu Dhabi the Computer Vision and Artificial Intelligence Group Department of Computer Science and Digital Technologies Northumbria University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9781467380270

This paper addresses the object recognition problem using multiple-domain inputs. We present a novel approach that utilizes labeled RGB-D data in the training stage, where depth features are extracted for enhancing the discriminative capability of the original learning system that only relies on RGB images. The highly dissimilar source and target domain data are mapped into a unified feature space through transfer at both feature and classifier levels. In order to alleviate cross-domain discrepancy, we employ a state-of-the-art domain-adaptive dictionary learning algorithm that updates image representations in both domains and the classifier parameters simultaneously. The proposed method is trained on a RGB-D Object dataset and evaluated on the Caltech-256 dataset. Experimental results suggest that our approach can lead to significant performance gain over the state-of-the-art methods.

关键词： RGB-D enhancing discriminative capability

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Learning with privileged information using Bayesian networks

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Frontiers of computer Science 2015年第2期9卷 185-199页

作者： Shangfei WANG Menghua HE Yachen ZHU Shan HE Yue LIU Qiang JI School of Computer Science and Technology University of Science and Technology of China Hefei 230027 China Key Lab of Computing and Communicating Software of Anhui Province Hefei 230027 China School of Mathematical Sciences University of Science and Technology of China Hefei 230027 China Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY 12180-3590 USA

For many supervised learning applications, addi- tional information, besides the labels, is often available dur- ing training, but not available during testing. Such additional information, referred to the privileged information, can be exploited during training to construct a better classifier. In this paper, we propose a Bayesian network （BN） approach for learning with privileged information. We propose to in- corporate the privileged information through a three-node BN. We further mathematically evaluate different topologies of the three-node BN and identify those structures, through which the privileged information can benefit the classifica- tion. Experimental results on handwritten digit recognition, spontaneous versus posed expression recognition, and gender recognition demonstrate the effectiveness of our approach.

关键词： Bayesian network privileged information clas-sification maximum likelihood estimation

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Making advanced scientific algorithms and big scientific data management more accessible 14

Making advanced scientific algorithms and big scientific dat...

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Computational Imaging XIV 2016

作者： Venkatakrishnan, S.V. Mohan, K. Aditya Beattie, Keith Correa, Joaquin Dart, Eli Deslippe, Jack R. Hexemer, Alexander Krishnan, Harinarayan MacDowell, Alastair A. Marchesini, Stefano Patton, Simon J. Perciano, Talita Sethian, James A. Stromsness, Rune Tierney, Brian L. Tull, Craig E. Ushizima, Daniela Parkinson, Dilworth Y. Center for Advanced Mathematics for Energy Research Applications Lawrence Berkeley National Lab BerkeleyCA94720 United States Advanced Light Source Lawrence Berkeley National Lab BerkeleyCA94720 United States School of Electrical and Computer Engineering Purdue University PurdueIN47907 United States Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States National Energy Research Scientific Computing Center BerkeleyCA94720 United States Energy Sciences Network BerkeleyCA94720 United States Department of Mathematics UC Berkeley BerkeleyCA94720 United States

Synchrotrons such as the Advanced Light Source (ALS) at Lawrence Berkeley National laboratory are user facilities - they are sources of extremely bright X-ray beams, and scientists come from all over the world to perform experiments that require these beams. As the complexity of experiments has increased, and the size and rates of data sets has exploded, managing, analyzing and presenting the data collected at synchrotrons has been an increasing challenge. The ALS has partnered with high performance computing, fast networking, and applied mathematics groups to create a"super-facility", giving users simultaneous access to the experimental, computational, and algorithmic resources to overcome this challenge. This combination forms an efficient closed loop, where data despite its high rate and volume is transferred and processed, in many cases immediately and automatically, on appropriate compute resources, and results are extracted, visualized, and presented to users or to the experimental control system, both to provide immediate insight and to guide decisions about subsequent experiments during beam-time. In this paper, We will present work done on advanced tomographic reconstruction algorithms to support users of the 3D micron-scale imaging instrument (Beamline 8.3.2, hard X-ray micro-tomography). © 2016 Society for Imaging Science and Technology.

关键词： Tomography

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Segmentation as postprocessing for hyperspectral image classification

Segmentation as postprocessing for hyperspectral image class...

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IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015

作者： Jimenez, L.I. Ayma, V.A. Achanccaray, P. Costa, G.A.O.P. Feitosa, R.Q. Plaza, A. Hyperspectral Computing Laboratory Department of Technology of Computers and Communications University of Extremadura Caceres Spain Computer Vision Lab Department of Electrical Engineering Catholic University of Rio de Janeiro Rio de Janeiro Brazil Department of Computer Engineering Rio de Janeiro State University Rio de Janeiro Brazil

ISBN: (纸本)9781479979295

Hyperspectral imaging is a new technique in remote sensing that collects hundreds of images at differents wavelength values for the same area of the Earth. For instance the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) sensor of NASA capable to obtain 224 spectral channels in a wavelength range between 40 and 250 nanometers. As a result each pixel of the image can be represented as a spectral signature. Image segmentation is the process of dividing a digital image into groups of pixels or objects. Hyperspectral image classification is an important and active area dedicated to identifying each pixel in the image with an exclusive material/object class. Several efforts had been done in this field using spectral and spatial information separately or simultaneously in order to improve the performance of the classification techniques. In this work we have developed a new technique that uses a segmentation algorithm to post-process the classification results obtained using a widely used classifier such as the support vector machine (SVM). Experimental results with a real hyperspectral data set collected over the city of Pavia, Italy, are provided. © 2015 IEEE.

关键词： classification hyperspectral imaging Segmentation

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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TKLBLIIR: Detecting Twitter Paraphrases with TweetingJay 9

TKLBLIIR: Detecting Twitter Paraphrases with TweetingJay

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9th International Workshop on Semantic Evaluation, SemEval 2015

作者： Karan, Mladen Glavaš, Goran Šnajder, Jan Bašić, Bojana Dalbelo Vulić, Ivan Moens, Marie-Francine University of Zagreb Faculty of Electrical Engineering and Computing Text Analysis and Knowledge Engineering Lab Unska 3 Zagreb10000 Croatia KU Leuven Department of Computer Science Language Intelligence Information Retrieval Group Celestijnenlaan 200A Leuven Belgium

ISBN: (纸本)9781941643402

When tweeting on a topic, Twitter users often post messages that convey the same or similar meaning. We describe TweetingJay, a system for detecting paraphrases and semantic similarity of tweets, with which we participated in Task 1 of SemEval 2015. TweetingJay uses a supervised model that combines semantic overlap and word alignment features, previously shown to be effective for detecting semantic textual similarity. TweetingJay reaches 65.9% F1-score and ranked fourth among the 18 participating systems. We additionally provide an analysis of the dataset and point to some peculiarities of the evaluation setup. © 2015 Association for Computational Linguistics

关键词： Semantics

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An algorithm to optimize ECoG stimulus current patterns while constraining local current density across the entire brain

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Brain Stimulation 2017年第4期10卷 e38-e38页

作者： Seyhmus Guler Moritz Dannhauer Biel Roig-Solvas Alexis Gkogkidis Rob MacLeod Tonio Ball Jeffrey G. Ojemann Dana H. Brooks Computational Radiology Lab Boston Children’s Hospital and Harvard Medical School Boston MA USA Center for Integrative Biomedical Computing (CIBC) University of Utah Salt Lake City UT USA Scientific Computing Institute (SCI) University of Utah Salt Lake City UT USA B-SPIRAL Group Department of Electrical and Computer Engineering Northeastern University Boston MA USA Intracranial EEG and Brain Imaging Lab Epilepsy Center University Hospital Freiburg Freiburg Germany BrainLinks-BrainTools Cluster of Excellence and Bernstein Center Freiburg University of Freiburg Freiburg Germany Department of Neurological Surgery and the Center for Sensorimotor Neural Engineering University of Washington Seattle WA USA

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Modeling and optimizing cortical electric stimulation

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Brain Stimulation 2017年第4期10卷 e29-e29页

作者： Kimia Shayestehfard Moritz Dannhauer Seyhmus Guler Alexis Gkogkidis David Caldwell Jeneva Cronin Tonio Ball Jeffrey G. Ojemann Rob MacLeod Dana H. Brooks B-SPIRAL Group Department of Electrical and Computer Engineering Northeastern University Boston MA USA Center for Integrative Biomedical Computing (CIBC) University of Utah Salt Lake City UT USA Scientific Computing Institute (SCI) University of Utah Salt Lake City UT USA Computational Radiology Lab Boston Children’s Hospital and Harvard Medical School Boston MA USA Intracranial EEG and Brain Imaging Lab Epilepsy Center University Hospital Freiburg Freiburg Germany BrainLinks-BrainTools Cluster of Excellence and Bernstein Center Freiburg University of Freiburg Freiburg Germany Department of Neurological Surgery and the Center for Sensorimotor Neural Engineering University of Washington Seattle WA USA

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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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