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IEEE Conference on Computer Vision and Pattern Recognition

作者： Zhiwu Huang Ruiping Wang Shiguang Shan Xilin Chen Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology

ISBN: (纸本)9781479951192

In this paper, we focus on the problem of point-to-set classification, where single points are matched against sets of correlated points. Since the points commonly lie in Euclidean space while the sets are typically modeled as elements on Riemannian manifold, they can be treated as Euclidean points and Riemannian points respectively. To learn a metric between the heterogeneous points, we propose a novel Euclidean-to-Riemannian metric learning framework. Specifically, by exploiting typical Riemannian metrics, the Riemannian manifold is first embedded into a high dimensional Hilbert space to reduce the gaps between the heterogeneous spaces and meanwhile respect the Riemannian geometry of the manifold. The final distance metric is then learned by pursuing multiple transformations from the Hilbert space and the original Euclidean space (or its corresponding Hilbert space) to a common Euclidean sub-space, where classical Euclidean distances of transformed heterogeneous points can be measured. Extensive experiments clearly demonstrate the superiority of our proposed approach over the state-of-the-art methods.

关键词： Manifolds Hilbert space Kernel Extraterrestrial measurements Geometry

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Stacked Progressive Auto-Encoders (SPAE) for Face Recognition Across Poses

Stacked Progressive Auto-Encoders (SPAE) for Face Recognitio...

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IEEE Conference on Computer Vision and Pattern Recognition

作者： Meina Kan Shiguang Shan Hong Chang Xilin Chen Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology

ISBN: (纸本)9781479951192

Identifying subjects with variations caused by poses is one of the most challenging tasks in face recognition, since the difference in appearances caused by poses may be even larger than the difference due to identity. Inspired by the observation that pose variations change non-linearly but smoothly, we propose to learn pose-robust features by modeling the complex non-linear transform from the non-frontal face images to frontal ones through a deep network in a progressive way, termed as stacked progressive auto-encoders (SPAE). Specifically, each shallow progressive auto-encoder of the stacked network is designed to map the face images at large poses to a virtual view at smaller ones, and meanwhile keep those images already at smaller poses unchanged. Then, stacking multiple these shallow auto-encoders can convert non-frontal face images to frontal ones progressively, which means the pose variations are narrowed down to zero step by step. As a result, the outputs of the topmost hidden layers of the stacked network contain very small pose variations, which can be used as the pose-robust features for face recognition. An additional attractiveness of the proposed method is that no pose estimation is needed for the test images. The proposed method is evaluated on two datasets with pose variations, i.e., MultiPIE and FERET datasets, and the experimental results demonstrate the superiority of our method to the existing works, especially to those 2D ones.

关键词： Face Three-dimensional displays Transforms Face recognition Solid modeling Decoding Training

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Continuous Travel Time Prediction for Transit Signal Priority Based on a Deep Network

Continuous Travel Time Prediction for Transit Signal Priorit...

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International Conference on intelligent Transportation

作者： Xiong Gang Wenwen Kang Feiyue Wang Fenghua Zhu Yisheng Lv Xisong Dong Jukka Riekki Susanna Pirttikangas State Key Lab. of Manage. & Control for Complex Syst. Institute of Automation Chinese Academy of Sciences Beijing Beijing CN The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China Cloud Computing Center Chinese Academy of Sciences Dongguan CHINA Faculty of Information Technology and Electrical Engineering University of Oulu Finland

It has been recognized by many researchers that accurate bus travel time prediction is critical for successful deployment of traffic signal priority (TSP) systems. Although there exist a lot of studies on travel time prediction for Advanced Traveler information Systems (ATIS), this problem for TSP purpose is a little different and the amount of literature is limited. This paper proposes a deep learning based approach for continuous travel time prediction problem. Parameters of the deep network are fine-tuned following a layer-by-layer pre-training procedure on a dataset generated by traffic simulations. Variables that may affect continuous travel time are selected carefully. Experiments are conducted to validate the performance of the proposed model. The results indicate that the proposed model produces prediction with mean absolute error less than 4 seconds, which is accurate enough for TSP operations. This paper also reveals that, except for obvious factors like speed, travel distance and traffic density, the signal time when the prediction is made is also an important factor affecting travel time.

关键词： Vehicles Predictive models Object oriented modeling Training Artificial neural networks Data models

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Semi-paired probabilistic canonical correlation analysis 8

Semi-paired probabilistic canonical correlation analysis

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8th IFIP International Conference on intelligent information processing, IIP 2014

作者： Zhang, Bo Hao, Jie Ma, Gang Yue, Jinpeng Shi, Zhongzhi Chinese Academy of Sciences Institute of Computing Technology The Key Laboratory of Intelligent Information Processing Beijing China University of Chinese Academy of Sciences Beijing China Xuzhou Medical College School of Medicine Information Xuzhou China China University of Mining and Technology School of Computer Science and Technology Xuzhou China

ISBN: (纸本)9783662449790

CCA is a powerful tool for analyzing paired multi-view data. However, when facing semi-paired multi-view data which widely exist in real-world problems, CCA usually performs poorly due to its requirement of data pairing between different views in nature. To cope with this problem, we propose a semi-paired variant of CCA named SemiPCCA based on the probabilistic model for CCA. Experiments with artificially generated samples demonstrate the effectiveness of the proposed method. © IFIP International Federation for information processing 2014.

关键词： Correlation methods

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Sign language recognition by 3D trajectory matching in frequency domain

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Ruan Jian Xue Bao/Journal of Software 2014年 25卷 36-43页

作者： Lin, Yu-Shun Chai, Xiu-Juan Xu, Zhi-Hao Yin, Fang Chen, Xi-Lin Key Laboratory of Intelligent Information Processing Institute of Computing Technology The Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

For hearing-impaired people, sign language is a common communication means just like spoken language to ordinary people. Because most of ordinary people cannot understand sign language, it's difficult for hearing-impaired community to participate in social activities. This paper proposes an effective method for sign language recognition with simple 3D trajectory information to break down the barriers between hearing-impaired and normal persons. First of all, the 3D trajectory captured from Kinect is preprocessed, and both the trajectories from the probe and galleries is normalized by the size of the signer. Then the trajectories are resampled evenly by a fast, easy, and usable interpolation algorithm. Before matching of two curves, the trajectory of the probe is aligned to the gallery trajectory and both aligned trajectories are transferred into frequency domain by DFT, and vectors linking the real part, imaginary part and amplitude are obtained. Finally, Euclidean distance between two trajectories in frequency domain is calculated to evaluate the dis-similarity of two trajectory vectors according to the minimized distance. The experimental results on a data set of 239 sign words show that the presented approach is effective to recognizing isolated words of Chinese sign language. ©2014 ISCAS.

关键词： Trajectories

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A hybrid approach of PTSVM and ELM inspired by samples' geometric distribution structure

A hybrid approach of PTSVM and ELM inspired by samples' geom...

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IEEE International Conference on Granular computing (GRC)

作者： Hong Hu Panyi Ouyang Liang Pang Zhongzhi Shi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

In this paper, we try to hybrid projection twin support vector machine (PTSVM) and Extreme Learning Machine(ELM). The experiments shows that ELM generally out performs SVM/LS-SVM in various kinds of cases. PTELM tries to use ELM to overcome the shortness of PTSVM, which lacks of flexibility to change nonlinear kernel mapping for complex samples distribution regions. In order to overcome the shortness of ELMs, we try to maintain the geometric structure of the samples distribution in the initial setting of ELM parameters by starting with an auto-encoder ELM, a sufficient condition for such kind auto-encoder ELM to keep the equivalence of topological homology before and after nonlinear mapping is proved by us. For an auto-encoder, in order to do feature abstraction and find the exact manifold in which samples are located, the number of inner layers nodes should be small enough. For this purpose, we prove that the whole samples set can be at least replaced by a small subset which located on the boundary of distribution region. For more, the weights are modified by a ELM approach to make samples of a class move toward this classs hyperplane found by PTSVM. The experimental results on several UCI benchmark data sets show the feasibility and effectiveness of the proposed method.

关键词： Support vector machines Kernel Training Transforms Shape Vectors Manifolds

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A Gentzen system for the description logic

A Gentzen system for the description logic

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International Conference on Computer Science & Education (ICCSE)

作者： Yu Sun Yuefei Sui Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing CN

The Gentzen systems for a sequent Γ⇒Δ have been proposed in the propositional logic, the predicate calculus and other logics. In this paper, based on the Gentzen system for the predicate calculus, we propose the Gentzen system for a sequent Γ⇒Δ in the description logic, where Γ and Δ are two sets of assertions in ALC: Assertions with universal qualification and inclusion assertions are decomposed as those of universal quantification formulas in the Gentzen system for the predicate calculus. It is proved that the Gentzen system for description logic is sound and complete; but it is not decidable.

关键词： Computers Calculus

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Generalized Unsupervised Manifold Alignment 14

Generalized Unsupervised Manifold Alignment

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Proceedings of the 28th International Conference on Neural information processing Systems - Volume 2

作者： Zhen Cui Hong Chang Shiguang Shan Xilin Chen Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China and School of Computer Science and Technology Huaqiao University Xiamen China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China

In this paper, we propose a Generalized Unsupervised Manifold Alignment (GU-MA) method to build the connections between different but correlated datasets without any known correspondences. Based on the assumption that datasets of the same theme usually have similar manifold structures, GUMA is formulated into an explicit integer optimization problem considering the structure matching and preserving criteria, as well as the feature comparability of the corresponding points in the mutual embedding space. The main benefits of this model include: (1) simultaneous discovery and alignment of manifold structures; (2) fully unsuper-vised matching without any pre-specified correspondences; (3) efficient iterative alignment without computations in all permutation cases. Experimental results on dataset matching and real-world applications demonstrate the effectiveness and the practicability of our manifold alignment method.

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Modeling term translation for document-informed machine translation

Modeling term translation for document-informed machine tran...

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2014 Conference on Empirical Methods in Natural Language processing, EMNLP 2014

作者： Meng, Fandong Xiong, Deyi Jiang, Wenbin Liu, Qun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China School of Computer Science and Technology Soochow University China Centre for Next Generation Localisation Dublin City University Ireland

ISBN: (纸本)9781937284961

Term translation is of great importance for statistical machine translation (SMT), especially document-informed SMT. In this paper, we investigate three issues of term translation in the context of documentinformed SMT and propose three corresponding models: (a) a term translation disambiguation model which selects desirable translations for terms in the source language with domain information, (b) a term translation consistency model that encourages consistent translations for terms with a high strength of translation consistency throughout a document, and (c) a term bracketing model that rewards translation hypotheses where bracketable source terms are translated as a whole unit. We integrate the three models into hierarchical phrase-based SMT and evaluate their effectiveness on NIST Chinese- English translation tasks with large-scale training data. Experiment results show that all three models can achieve significant improvements over the baseline. Additionally, we can obtain a further improvement when combining the three models. © 2014 Association for Computational Linguistics.

关键词： Machine translation

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25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROSCIENCE 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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