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Congress on Image and Signal Processing, CISP

作者： Shaorong Wang Yisong Chen Sheng Li Guoping Wang Hua Li HCI & Multimedia Laboratory of Department of Computer Science Peking University Beijing China National Research Center for Intelligent Computing Systems Institute of Computing Technology Chinese Academy and Sciences Beijing China

A novel Weighted Graph Partitioning Active Contours method based on weighted dissimilarity is introduced. This method is easy to be extended by defining different types of similarities. And it has been greatly acceler... 详细信息

关键词： Image segmentation Active contours Pixel Cost function Minimization Level set Mathematical model

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ERRATUM: 'FERMI DETECTION OF γ-RAY EMISSION FROM THE M2 SOFT X-RAY FLARE ON 2010 JUNE 12' (2012, ApJ, 745, 144)

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The Astrophysical Journal 2012年第2期748卷

作者： M. Ackermann M. Ajello A. Allafort W. B. Atwood L. Baldini G. Barbiellini D. Bastieri K. Bechtol R. Bellazzini P. N. Bhat R. D. Blandford E. Bonamente A. W. Borgland J. Bregeon M. S. Briggs M. Brigida P. Bruel R. Buehler J. M. Burgess S. Buson G. A. Caliandro R. A. Cameron J. M. Casandjian C. Cecchi E. Charles A. Chekhtman J. Chiang S. Ciprini R. Claus J. Cohen-Tanugi V. Connaughton J. Conrad S. Cutini B. R. Dennis F. de Palma C. D. Dermer S. W. Digel E. do Couto e Silva P. S. Drell A. Drlica-Wagner R. Dubois C. Favuzzi S. J. Fegan E. C. Ferrara P. Fortin Y. Fukazawa P. Fusco F. Gargano S. Germani N. Giglietto F. Giordano M. Giroletti T. Glanzman G. Godfrey L. Grillo J. E. Grove D. Gruber S. Guiriec D. Hadasch M. Hayashida E. Hays D. Horan G. Iafrate G. Jóhannesson A. S. Johnson W. N. Johnson T. Kamae R. M. Kippen J. Knödlseder M. Kuss J. Lande L. Latronico F. Longo F. Loparco B. Lott M. N. Lovellette P. Lubrano M. N. Mazziotta J. E. McEnery C. Meegan J. Mehault P. F. Michelson W. Mitthumsiri C. Monte M. E. Monzani A. Morselli I. V. Moskalenko S. Murgia R. Murphy M. Naumann-Godo E. Nuss T. Nymark M. Ohno T. Ohsugi A. Okumura N. Omodei E. Orlando W. S. Paciesas J. H. Panetta D. Parent M. Pesce-Rollins V. Petrosian M. Pierbattista F. Piron G. Pivato H. Poon T. A. Porter R. Preece S. Rainò R. Rando M. Razzano S. Razzaque A. Reimer O. Reimer S. Ritz C. Sbarra R. A. Schwartz C. Sgrò G. H. Share E. J. Siskind P. Spinelli H. Takahashi T. Tanaka Y. Tanaka J. B. Thayer L. Tibaldo M. Tinivella A. K. Tolbert G. Tosti E. Troja Y. Uchiyama T. L. Usher J. Vandenbroucke V. Vasileiou G. Vianello V. Vitale A. vonKienlin A. P. Waite C. Wilson-Hodge D. L. Wood K. S. Wood Z. Yang Deutsches Elektronen Synchrotron DESY D-15738 Zeuthen Germany W. W. Hansen Experimental Physics Laboratory Kavli Institute for Particle Astrophysics and Cosmology Department of Physics and SLAC National Accelerator Laboratory Stanford University Stanford CA 94305 USA Santa Cruz Institute for Particle Physics Department of Physics and Department of Astronomy and Astrophysics University of California at Santa Cruz Santa Cruz CA 95064 USA Istituto Nazionale di Fisica Nucleare Sezione di Pisa I-56127 Pisa Italy Istituto Nazionale di Fisica Nucleare Sezione di Trieste I-34127 Trieste Italy Dipartimento di Fisica Università di Trieste I-34127 Trieste Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova I-35131 Padova Italy Dipartimento di Fisica 'G. Galilei ' Università di Padova I-35131 Padova Italy Center for Space Plasma and Aeronomic Research (CSPAR) University of Alabama in Huntsville Huntsville AL 35899 USA Istituto Nazionale di Fisica Nucleare Sezione di Perugia I-06123 Perugia Italy Dipartimento di Fisica Università degli Studi di Perugia I-06123 Perugia Italy Dipartimento di Fisica 'M. Merlin ' dell'Università e del Politecnico di Bari I-70126 Bari Italy Istituto Nazionale di Fisica Nucleare Sezione di Bari I-70126 Bari Italy Laboratoire Leprince-Ringuet École polytechnique CNRS/IN2P3 Palaiseau France Institut de Ciències de l'Espai (IEEE-CSIC) Campus UAB E-08193 Barcelona Spain Laboratoire AIM CEA-IRFU/CNRS/Université Paris Diderot Service d'Astrophysique CEA Saclay 91191 Gif sur Yvette France Artep Inc. 2922 Excelsior Springs Court Ellicott City MD 21042 USA Resident at Naval Research Laboratory Washington DC 20375 USA. ASI Science Data Center I-00044 Frascati (Roma) Italy Laboratoire Univers et Particules de Montpellier Université Montpellier 2 CNRS/IN2P3 Montpellier France Department of Physics Stockholm University AlbaNova SE-106 91 Stockholm Sweden The Oskar Klein Centre for Cosmoparticle Physics AlbaNova SE-106 91 Stockhol

Due to an error at the publisher, the times given for the major tick marks in the X-axis in Figure 1 of the published article are incorrect. The correctly labeled times should be '00:52:00,' '00:54:00,'..., and '01:04:00.' The correct version of Figure 1 and its caption is shown below. IOP Publishing sincerely regrets this error.

关键词： acceleration of particles Sun: flares Sun: particle emission Sun: X-rays, gamma rays

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Heterogeneous artificial agents for triage nurse assistance

Heterogeneous artificial agents for triage nurse assistance

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IEEE-RAS International Conference on Humanoid Robots

作者： D.M. Wilkes Stan Franklin Erdem Erdemir Stephen Gordon Steve Strain Karen Miller Kazuhiko Kawamura Center of Intelligent Systems and Electrical and Computer Engineering Vanderbilt University USA Cognitive Computing Research Group Institute of Intelligent Systems University of Memphis Memphis TN USA Department of Electrical Engineering and Computer Science Vanderbilt University USA Electrical Engineering Vanderbilt University USA Cognitive Computing Research Group Institute of Intelligent Systems University of Memphis USA Clinical Research Operations for Emergency Medicine Vanderbilt University USA Center of Intelligent Systems and Electrical and Computer Engineering Vanderbilt University Nashville TN USA

A dream of humanoid robot researchers is to develop a complete “human-like” (whatever that means) artificial agent both in terms of body and brain. We now have seen an increasing number of humanoid robots (such as Honda's ASIMO, Aldebaran's Nao and many others). These, however, display only a limited number of cognitive skills in terms of perception, learning and decision-making. On the other hand, brain research has begun to produce computational models such as LIDA. In this paper, we propose an intermediate approach for body-brain integration in a form of a scenario-based distributed system. Busy hospital Emergency Departments (ED) are concerned with shortening the waiting times of patients, with relieving overburdened triage team physicians, nurses and medics, and with reducing the number of mistakes. Here we propose a system of cognitive robots and a supervisor, dubbed the TriageBot System that would gather both logistical and medical information, as well as take diagnostic measurements, from an incoming patient for later use by the triage team. TriageBot would also give tentative, possible diagnoses to the triage nurse, along with recommendations for non-physician care Some of the robots in the TriageBot System would be humanoid in form, but it is not necessary that all of them take this form. Advances in humanoid robotic design, in sensor technology, and in cognitive control architectures make such a system feasible, at least in principle.

关键词： Robot sensing systems Robot kinematics Computer architecture Humanoid robots Pain

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Knowledge extraction from multiple criteria linear programming classification approach

Knowledge extraction from multiple criteria linear programmi...

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作者： Zhang, Yuejin Zhang, Peng Zhang, Lingling Shi, Yong Research Center on Fictitious Economy and Data Sciences Chinese Academy of Sciences 100190 Beijing China School of Management Graduate University of Chinese Academy of Sciences 100190 Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska Ant Omaha NE68182 Omaha United States

As an effective model for classification, Multiple Criteria Linear Programming (MCLP) has been widely used in business intelligence. However, a possible limitation of MCLP is that it generates unexplainable black-box models which can only tell us results without reasons. To overcome this shortage, in this paper, we present a knowledge mining strategy which mines explainable decision rules from black-box MCLP models. Firstly, we use the rough set theory to distinguish the definable set where samples are perfectly classified, from the rough set where misclassified samples may exist. Then, to get explainable knowledge, we present a clustering-based decision rule extraction approach to extract knowledge from the definable set, and a rough set-based rule extraction approach to the rough set. Finally, empirical studies on real world VIP Email data sets demonstrate that our method can effectively extract explicit rules from MCLP model with only a little lost in performance.

关键词： Linear programming

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A value-oriented model for managing service supply chains

A value-oriented model for managing service supply chains

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IEEE International Conference on Industrial Engineering and Engineering Management

作者： T. He W. Ho X.F. Xu Research Center of Intelligent Computing for Enterprises and Services School of Computer Science and Technology Harbin Institute of Technology Harbin China Operations & Information Management Group Aston Business School Aston University Birmingham UK

Service supply chain (SSC) has attracted more and more attention from academia and industry. Although there exists extensive product-based supply chain management models and methods, they are not applicable to the SSC as the differences between service and product. Besides, the existing supply chain management models and methods possess some common deficiencies. Because of the above reasons, this paper develops a novel value-oriented model for the management of SSC using the modeling methods of E3-value and Use Case Maps (UCMs). This model can not only resolve the problems of applicability and effectiveness of the existing supply chain management models and methods, but also answer the questions of `why the management model is this?' and `how to quantify the potential profitability of the supply chains?'. Meanwhile, the service business processes of SSC system can be established using its logic procedure. In addition, the model can also determine the value and benefits distribution of the entire service value chain and optimize the operations management performance of the service supply.

关键词： Biological system modeling Supply chains Supply chain management Analytical models Industries Adaptation model

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On Dynamic Weighting of data in Clustering with K-Alpha Means

On Dynamic Weighting of Data in Clustering with K-Alpha Mean...

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International Conference on Pattern Recognition

作者： Si-Bao Chen Hai-Xian Wang Bin Luo Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei Anhui China Key Laboratory of Child Development and Learning Science of Ministry of Education Research Center of Learning Science South-East University Nanjing Jiangsu China

ISBN: (纸本)9781424475421

Although many methods of refining initialization have appeared, the sensitivity of K-Means to initial centers is still an obstacle in applications. In this paper, we investigate a new class of clustering algorithm, K-Alpha Means (KAM), which is insensitive to the initial centers. With K-Harmonic Means as a special case, KAM dynamically weights data points during iteratively updating centers, which deemphasizes data points that are close to centers while emphasizes data points that are not close to any centers. Through replacing minimum operator in K-Means by alpha-mean operator, KAM significantly improves the clustering performances.

关键词： Clustering algorithms Heuristic algorithms Indexes Partitioning algorithms Signal processing algorithms Euclidean distance Iris

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Constraining Dark Matter Models from a Combined Analysis of Milky Way Satellites with the Fermi Large Area Telescope

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Physical Review Letters 2011年第24期107卷 241302-241302页

作者： M. Ackermann M. Ajello A. Albert W. B. Atwood L. Baldini J. Ballet G. Barbiellini D. Bastieri K. Bechtol R. Bellazzini B. Berenji R. D. Blandford E. D. Bloom E. Bonamente A. W. Borgland J. Bregeon M. Brigida P. Bruel R. Buehler T. H. Burnett S. Buson G. A. Caliandro R. A. Cameron B. Cañadas P. A. Caraveo J. M. Casandjian C. Cecchi E. Charles A. Chekhtman J. Chiang S. Ciprini R. Claus J. Cohen-Tanugi J. Conrad S. Cutini A. de Angelis F. de Palma C. D. Dermer S. W. Digel E. do Couto e Silva P. S. Drell A. Drlica-Wagner L. Falletti C. Favuzzi S. J. Fegan E. C. Ferrara Y. Fukazawa S. Funk P. Fusco F. Gargano D. Gasparrini N. Gehrels S. Germani N. Giglietto F. Giordano M. Giroletti T. Glanzman G. Godfrey I. A. Grenier S. Guiriec M. Gustafsson D. Hadasch M. Hayashida E. Hays R. E. Hughes T. E. Jeltema G. Jóhannesson R. P. Johnson A. S. Johnson T. Kamae H. Katagiri J. Kataoka J. Knödlseder M. Kuss J. Lande L. Latronico A. M. Lionetto M. Llena Garde F. Longo F. Loparco B. Lott M. N. Lovellette P. Lubrano G. M. Madejski M. N. Mazziotta J. E. McEnery J. Mehault P. F. Michelson W. Mitthumsiri T. Mizuno C. Monte M. E. Monzani A. Morselli I. V. Moskalenko S. Murgia M. Naumann-Godo J. P. Norris E. Nuss T. Ohsugi A. Okumura N. Omodei E. Orlando J. F. Ormes M. Ozaki D. Paneque D. Parent M. Pesce-Rollins M. Pierbattista F. Piron G. Pivato T. A. Porter S. Profumo S. Rainò M. Razzano A. Reimer O. Reimer S. Ritz M. Roth H. F.-W. Sadrozinski C. Sbarra J. D. Scargle T. L. Schalk C. Sgrò E. J. Siskind G. Spandre P. Spinelli L. Strigari D. J. Suson H. Tajima H. Takahashi T. Tanaka J. G. Thayer J. B. Thayer D. J. Thompson L. Tibaldo M. Tinivella D. F. Torres E. Troja Y. Uchiyama J. Vandenbroucke V. Vasileiou G. Vianello V. Vitale A. P. Waite P. Wang B. L. Winer K. S. Wood M. Wood Z. Yang S. Zimmer M. Kaplinghat G. D. Martinez W. W. Hansen Experimental Physics Laboratory Kavli Institute for Particle Astrophysics and Cosmology Department of Physics and SLAC National Accelerator Laboratory Stanford University Stanford California 94305 USA Department of Physics Center for Cosmology and Astro-Particle Physics The Ohio State University Columbus Ohio 43210 USA Santa Cruz Institute for Particle Physics Department of Physics and Department of Astronomy and Astrophysics University of California at Santa Cruz Santa Cruz California 95064 USA Istituto Nazionale di Fisica Nucleare Sezione di Pisa I-56127 Pisa Italy Laboratoire AIM CEA-IRFU/CNRS/Université Paris Diderot Service d’Astrophysique CEA Saclay 91191 Gif sur Yvette France Istituto Nazionale di Fisica Nucleare Sezione di Trieste I-34127 Trieste Italy Dipartimento di Fisica Università di Trieste I-34127 Trieste Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova I-35131 Padova Italy Dipartimento di Fisica “G. Galilei ” Università di Padova I-35131 Padova Italy Istituto Nazionale di Fisica Nucleare Sezione di Perugia I-06123 Perugia Italy Dipartimento di Fisica Università degli Studi di Perugia I-06123 Perugia Italy Dipartimento di Fisica “M. Merlin” dell’Università e del Politecnico di Bari I-70126 Bari Italy Istituto Nazionale di Fisica Nucleare Sezione di Bari 70126 Bari Italy Laboratoire Leprince-Ringuet École polytechnique CNRS/IN2P3 Palaiseau France Department of Physics University of Washington Seattle Washington 98195-1560 USA Institut de Ciències de l’Espai (IEEE-CSIC) Campus UAB 08193 Barcelona Spain Istituto Nazionale di Fisica Nucleare Sezione di Roma “Tor Vergata” I-00133 Roma Italy Dipartimento di Fisica Università di Roma “Tor Vergata” I-00133 Roma Italy INAF-Istituto di Astrofisica Spaziale e Fisica Cosmica I-20133 Milano Italy Artep Inc. 2922 Excelsior Springs Court Ellicott City Maryland 21042 USA ASI Science Data Center I-00044 Frascati (Roma) Italy Laboratoire Univers et Particules de Montpell

Satellite galaxies of the Milky Way are among the most promising targets for dark matter searches in gamma rays. We present a search for dark matter consisting of weakly interacting massive particles, applying a joint likelihood analysis to 10 satellite galaxies with 24 months of data of the Fermi Large Area Telescope. No dark matter signal is detected. Including the uncertainty in the dark matter distribution, robust upper limits are placed on dark matter annihilation cross sections. The 95% confidence level upper limits range from about 10−26 cm3 s−1 at 5 GeV to about 5×10−23 cm3 s−1 at 1 TeV, depending on the dark matter annihilation final state. For the first time, using gamma rays, we are able to rule out models with the most generic cross section (∼3×10−26 cm3 s−1 for a purely s-wave cross section), without assuming additional boost factors.

关键词： MILKY Way ARTIFICIAL satellites DARK matter (Astronomy) GAMMA rays TELESCOPES

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Subspace selection using semi-supervised harmonic mean of Kullback-Leibler divergences

Subspace selection using semi-supervised harmonic mean of Ku...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Si-Bao Chen Hai-Xian Wang Bin Luo Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei Anhui China Key Lab of Child Development and Learning Science of Ministry of Education Research Center of Learning Sciences South-East University Nanjing Jiangsu China

In many areas of pattern recognition and machine learning, subspace selection is an essential step. Fisher's linear discriminant analysis (LDA) is one of the most well-known linear subspace selection methods. However, LDA suffers from the class separation problem. The projection to a subspace tends to merge close class pairs. A recent result, named maximizing the geometric mean of Kullback-Leibler (KL) divergences of class pairs (MGMD), can significantly reduce the class separation problem. Furthermore, maximizing the harmonic mean of Kullback-Leibler (KL) divergences of class pairs (MHMD) emphasizes smaller divergences more than MGMD, and deals with the class separation problem more effectively. However, in many applications, labeled data are very limited while unlabeled data can be easily obtained. The estimation of divergences of class pairs is unstable using inadequate labeled data. To take advantage of unlabeled data for subspace selection, semi-supervised MHMD (SSMHMD) is proposed using graph Laplacian as normalization. Quasi-Newton method is adopted to solve the optimization problem. Experiments on synthetic data and real image data show the validity of SSMHMD.

关键词： Harmonic analysis Training Laplace equations Manifolds Covariance matrix Machine learning Symmetric matrices

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Adaptive generic learning for face recognition from a single sample per person

Adaptive generic learning for face recognition from a single...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yu Su Shiguang Shan Xilin Chen Wen Gao School of Computer Science and Technology Harbin Institute of Technology China Digital Media Research Center Institute of Computing Technology CAS China Key Laboratory of Intelligent Information Processing Chinese Academy and Sciences China Institute for Digital Media Peking University China

Real-world face recognition systems often have to face the single sample per person (SSPP) problem, that is, only a single training sample for each person is enrolled in the database. In this case, many of the popular face recognition methods fail to work well due to the inability to learn the discriminatory information specific to the persons to be identified. To address this problem, in this paper, we propose an Adaptive Generic Learning (AGL) method, which adapts a generic discriminant model to better distinguish the persons with single face sample. As a specific implementation of the AGL, a Coupled Linear Representation (CLR) algorithm is proposed to infer, based on the generic training set, the within-class scatter matrix and the class mean of each person given its single enrolled sample. Thus, the traditional Fisher's Linear Discriminant (FLD) can be applied to SSPP task. Experiments on the FERET and a challenging passport face database show that the proposed method can achieve better results compared with other common solutions to the SSPP problem.

关键词： Face recognition Principal component analysis Linear discriminant analysis Computer science Content addressable storage Laboratories Deductive databases Information processing Scattering Image databases

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Knowledge extraction from multiple criteria linear programming classification approach

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Procedia Computer science 2010年第1期1卷 2441-2448页

作者： Yuejin Zhang Peng Zhang Lingling Zhang Yong Shi Research Center on Fictitious Economy and Data Sciences Chinese Academy of Sciences 100190 Beijing China School of Management Graduate University of Chinese Academy of Sciences 100190 Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska ant Omaha NE68182 Omaha USA

关键词： MCLP Rule extraction Clustering Rough set

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