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On Mobilizing Processes of Cyber Movement Organizations

IFAC Proceedings Volumes 2014年第3期47卷 9853-9857页

作者： Tao Wang Zhong Liu Kainan Cui Jie Zhang Fei-Yue Wang C.L. Philip Chen College of Information System and Management National University of Defense Technology Changsha 410073 China The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing 100190 China The Research Center for Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha 410073 China Department of Computer and Information Science Science and Technology University of Macau Macau China

—Cyber Movement Organization (CMO) is a special kind of social movement organization on the Web. In this paper, we propose a model to simulate the mobilizing process of CMO, which consists of the individual unit, organization unit, and the mobilizing mechanisms. The mobilizing mechanisms has three sub-mechanisms: the participation mechanism, the choice mechanism, and the inviting mechanism. A dataset of more than two million “human flesh search” related microblogs is used to validate the model. Empirical results show that our model can capture the key features of the real-world mobilizing process.

关键词： Cyber Movement Organizations (CMO) Social Network Analysis Human Flesh Search (HFS) Simulation Social Movement Organizations Resource Mobilization

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Regularized Latent Least Square Regression for cross pose face recognition

Regularized Latent Least Square Regression for cross pose fa...

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23rd International Joint Conference on Artificial Intelligence, IJCAI 2013

作者： Cai, Xinyuan Wang, Chunheng Xiao, Baihua Chen, Xue Zhou, Ji State Key Laboratory of Management and Control for Complex Systems Institute of Automations Chinese Academy of Science Beijing China

ISBN: (纸本)9781577356332

Pose variation is one of the challenging factors for face recognition. In this paper, we propose a novel cross-pose face recognition method named as Regularized Latent Least Square Regression (RLLSR). The basic assumption is that the images captured under different poses of one person can be viewed as pose-specific transforms of a single ideal object. We treat the observed images as regressor, the ideal object as response, and then formulate this assumption in the least square regression framework, so as to learn the multiple pose-specific transforms. Specifically, we incorporate some prior knowledge as two regularization terms into the least square approach: 1) the smoothness regularization, as the transforms for nearby poses should not differ too much;2) the local consistency constraint, as the distribution of the latent ideal objects should preserve the geometric structure of the observed image space. We develop an alternating algorithm to simultaneously solve for the ideal objects of the training individuals and a set of pose-specific transforms. The experimental results on the Multi-PIE dataset demonstrate the effectiveness of the proposed method and superiority over the previous methods.

关键词： Face recognition

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Online model-free RLSPI algorithm for nonlinear discrete-time non-affine systems

Online model-free RLSPI algorithm for nonlinear discrete-tim...

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20th International Conference on Neural Information Processing, ICONIP 2013

作者： Zhu, Yuanheng Zhao, Dongbin State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783642420412

Policy iteration, as one kind of reinforcement learning methods is applied here to solve the optimal problem of nonlinear discrete-time non-affine system with continuous-state and continuous-action space. By applying action-value function or Q function, the implementation of policy iteration avoids the dependence on system dynamics. Online model-free recursive least-squares policy iteration (RLSPI) algorithm is proposed with continuous policy approximation. It is the first attempt to develop online LSPI algorithm for nonlinear discrete-time non-affine systems with continuous policy. A nonlinear discrete-time system is simulated to verify the efficiency of our algorithm. © Springer-Verlag 2013.

关键词： Iterative methods

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User community discovery from multi-relational networks

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DECISION SUPPORT systems 2013年第2期54卷 870-879页

作者： Zhang, Zhongfeng Li, Qiudan Zeng, Daniel Gao, Heng Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Arizona Dept Management Informat Syst Tucson AZ 85721 USA

Online social network services (SNS) have been experiencing rapid growth in recent years. SNS enable users to identify other users with common interests, exchange their opinions, and establish forums for communication, and so on. Discovering densely connected user communities from social networks has become one of the major challenges, to help understand the structural properties of SNS and improve user-oriented services such as identification of influential users and automated recommendations. Previous work on community discovery has treated user friendship networks and user-generated contents separately. We hypothesize that these two types of information can be fruitfully integrated and propose a unified framework for user community discovery in online social networks. This framework combines the author-topic (AT) model with user friendship network analysis. We empirically show that this approach is capable of discovering interesting user communities using two real-world datasets. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Community discovery Multi-relational network Author topic model Non-negative matrix factorization

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Nonlinear metric learning with deep independent subspace analysis network for face verification

Nonlinear metric learning with deep independent subspace ana...

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作者： Cai, Xinyuan Wang, Chunheng Xiao, Baihua Shao, Yunxue State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Face verification is the task of determining whether two given face images represent the same person or not. It is a very challenging task, as the face images, captured in the uncontrolled environments, may have large variations in illumination, expression, pose, background, etc. The crucial problem is how to compute the similarity of two face images. Metric learning has provided a viable solution to this problem. Until now, many metric learning algorithms have been proposed, but they are usually limited to learning a linear transformation. In this paper, we propose a nonlinear metric learning method, which leams an explicit mapping from the original space to an optimal subspace using deep Independent Subspace Analysis (ISA) network. Compared to the linear or kernel based metric learning methods, the proposed deep ISA network is a deep and local learning architecture, and therefore exhibits more powerful ability to learn the nature of highly variable dataset. We evaluate our method on the labeled Faces in the Wild dataset, and results show superior performance over some state-of-the-art methods.© 2013 The Institute of Electronics, Information and Communication Engineers.

关键词： Learning algorithms

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Unmanned Aerial Vehicles (UAV) Heading Optimal Tracking control Using Online Kernel-based HDP Algorithm

Unmanned Aerial Vehicles (UAV) Heading Optimal Tracking Cont...

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International Joint Conference on Neural Networks

作者： Fuxiao Tan Derong Liu Xinping Guan Bin Luo School of Computer and Information Fuyang Teachers College State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University School of Computer Science and Technology Anhui University

ISBN: (纸本)9781479914821

UAV can work in places that are dangerous, or not easy to reach for humans. However, due to active control and operating difficulties, it is still a challenge to develop fully autonomous flight in complex environments. This paper applies a novel heuristic dynamic programming for the UAV heading optimal tracking controller design, using kernel-based heuristic dynamic programming (KHDP). Kernel-based HDP is developed by integrating kernel methods and approximately linear dependence (ALD) analysis with the critic learning of HDP algorithm. Compared with conventional HDP where neural networks are widely used and their features were manually designed, the proposed algorithm can obtain better generalization capability and learning efficiency through applying the sparse kernel machine into the critic learning process of HDP algorithm. Simulation and experimental results of UAV heading optimal tracking control problems demonstrate the effectiveness of the proposed kernel-based HDP algorithm.

关键词： Kernel Algorithm design and analysis Heuristic algorithms Equations Vectors Approximation algorithms Mathematical model

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Non-Gaussian Approximation Iterative Receiver for MIMO-OFDM Vehicular Networks

Non-Gaussian Approximation Iterative Receiver for MIMO-OFDM ...

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17th International IEEE Conference on Intelligent Transportation systems & The Asia-Pacific Council on systems Engineering Conference 2014

作者： Shuangshuang Han Liuqing Yang Xiang Cheng Shichao Chen Fenghua Zhu Zhongdong Yu Haitao Xie Qingdao Academy of Intelligent Industries State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Electronics Engineering and Computer Science Peking University Guang Zhou Communication Information Construction Investment and Operation Co.Ltd.

Multiple-Input Multiple-Output- Orthogonal Frequency Division Multiplexing(MIMO-OFDM) is adopted to vehicular networks to increase the capacity,reliability and *** this paper,iterative demodulation and decoding algorithms are studied to approach the capacity of MIMOOFDM vehicular *** analysing the drawbacks of the Gaussian approximation on the interference cancellation,NonGaussian approximation is proposed to enhance the performance of interference cancellation based detectors with large *** results demonstrate that the proposed non-Gaussian algorithm can achieve a significant performance gain over existing ones with high order constellations.

关键词： Gaussian Non-Gaussian Approximation Iterative Receiver for MIMO-OFDM Vehicular Networks

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Dissociation of glutamate and cortical thickness is restricted to regions subserving trait but not state markers in major depressive disorder

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JOURNAL OF AFFECTIVE DISORDERS 2014年 169卷 91-100页

作者： Li, Meng Metzger, Coraline D. Li, Wenjing Safron, Adam van Tol, Marie-Jose Lord, Anton Krause, Anna Linda Borchardt, Viola Dou, Weiqiang Genz, Axel Heinze, Hans-Jochen He, Huiguang Walter, Martin Otto Von Guericke Univ Dept Neurol D-39120 Magdeburg Germany Otto Von Guericke Univ Dept Psychiat & Psychotherapy D-39120 Magdeburg Germany Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Beijing Univ Technol Coll Elect & Control Engn Beijing Peoples R China Northwestern Univ Dept Psychol Evanston IL 60208 USA Univ Groningen Univ Med Ctr Groningen Neuroimaging Ctr Groningen Netherlands Leibniz Inst Neurobiol Magdeburg Germany Otto Von Guericke Univ D-39120 Magdeburg Germany Otto Von Guericke Univ Ctr Behav Brain Sci D-39120 Magdeburg Germany

Background: The anterior cingulate cortex (ACC) plays an important role in the neuropathology of major depressive disorder (MDD). So far, the effect of local cortical alteration on metabolites in multiple subdivisions of ACC has not been studied. We aimed to investigate structural and biochemical changes and their relationship in the pregenual ACC (pgACC), dorsal ACC (dACC) in MDD. Methods: We obtained magnetic resonance spectroscopy (MRS) in two investigated regions for 24 depressed patients and matched controls. In each region, cortical thickness (CTh) was calculated within a template mask based on its MRS voxel. We investigated neurotransmitter concentrations of Glx, N-acetyl aspartate (NAA), and myo-inositol (m-lns) in two investigated regions, as well as their relationships with CTh in depressed individuals and healthy controls. Results: Patients showed significantly lower cortical thickness in dACC compared to controls. Glx in dACC significantly correlated with CTh in healthy controls but not MDD patients, while NAA and CTh in dACC significantly correlated in both groups. A marginal decrease of Glx in pgACC was found in the subgroup of more severely depressive patients, compared to the mildly depressed patients. Limitations: Modest sample size and lack of episodes of depression may limit the generalizability of our findings. Conclusion: Our results indicate an abolished CTh-MRS relation in dACC associated with structural decline but not in pgACC, where acute MRS alterations prevailed. Our study provides the first evidence of a neurochemical basis explaining some of the inter-individual variability in CTh in MDD. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Glutamate Cortical thickness Major depressive disorder

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A PARALLEL FUZZY LEARNING APPROACH TO DETERMINE THE HITTING POINT FOR PING-PONG PLAYING ROBOT

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND control 2013年第10期9卷 4181-4192页

作者： Huang, Yanlong Xu, De Tan, Min Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst 95 Zhongguancun East Rd Beijing 100190 Peoples R China

This paper concentrates on determining the hitting point where the racket attached to the ping-pong playing robot will intercept the incoming ball. The nearest neighbor method is used to estimate the racket velocity at the hitting point;then the racket acceleration during the hitting process is analyzed. A parallel fuzzy learning system consisting of two fuzzy subsystems is used to compute the success rate for the racket acceleration. Both these subsystems will be updated online based on the feedback. A performance function of the acceleration and the success rate is formulated to evaluate the candidate hitting points obtained by trajectory prediction;then the optimal hitting point is chosen. In comparison with the virtual hitting plane method, the proposed method shows better performance.

关键词： Ping-pong playing robot Hitting point Nearest neighbor method Parallel fuzzy learning

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Vehicle Detection Based on the AND-OR Graph for Congested Traffic Conditions

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2013年第2期14卷 984-993页

作者： Li, Ye Li, Bo Tian, Bin Yao, Qingming Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Engn Res Ctr Intelligent Syst & Technol Beijing 100190 Peoples R China

In urban traffic video monitoring systems, traffic congestion is a common scene that causes vehicle occlusion and is a challenge for current vehicle detection methods. To solve the occlusion problem in congested traffic conditions, we have proposed an effective vehicle detection approach based on an AND-OR graph (AOG) in this paper. Our method includes three steps: constructing an AOG for representing vehicle objects in the congested traffic condition;training parameters in the AOG;and, finally, detecting vehicles using bottom-up inference. In AOG construction, sophisticated vehicle feature selection avoids using the easily occluded vehicle components but takes highly visible components into account. The vehicles are well represented by these selected vehicle features in the presence of a congested condition with serious vehicle occlusion. Furthermore, a hierarchical decomposition of the vehicle representation is proposed during AOG construction to further reduce the impact of vehicle occlusion. After AOG construction, all parameters in the AOG are manually learned from the training images or set and further applied to the bottom-up vehicle inference. There are two innovations of our method, i.e., the usage of the AOG in vehicle detection under congested traffic conditions and the special vehicle feature selection for vehicle representation. To fully test our method, we have done a quantitative experiment under a variety of traffic conditions, a contrast experiment, and several experiments on congested conditions. The experimental results illustrate that our method can effectively deal with various vehicle poses, vehicle shapes, and time-of-day and weather conditions. In particular, our approach performs well in congested traffic conditions with serious vehicle occlusion.

关键词： Active basis model (ABM) AND-OR graph (AOG) bottom-up inference maximally stable extremal region (MSER) vehicle detection

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