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Local cortical thinning links to resting-state disconnectivity in major depressive disorder

PSYCHOLOGICAL MEDICINE 2014年第10期44卷 2053-2065页

作者： van Tol, M. -J. Li, M. Metzger, C. D. Hailla, N. Horn, D. I. Li, W. Heinze, H. J. Bogerts, B. Steiner, J. He, H. Walter, M. Univ Groningen Univ Med Ctr Groningen NeuroImaging Ctr Groningen Netherlands Leibniz Inst Neurobiol Magdeburg Germany Ctr Behav Brain Sci Clin Affect Neuroimaging Lab Magdeburg Germany Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Otto Von Guericke Univ Dept Psychiat & Psychotherapy D-39118 Magdeburg Germany Otto Von Guericke Univ Dept Neurol D-39118 Magdeburg Germany

Background. Local structural and metabolic as well as inter-regional connectivity abnormalities have been implicated in the neuropathology of major depressive disorder (MDD). How local tissue properties affect intrinsic functional connectivity is, however, unclear. Using a cross-sectional, multi-modal imaging approach, we investigated the relationship between local cortical tissue abnormalities and intrinsic resting-state functional connectivity (RSFC) in MDD. Method. A total of 20 MDD in-patients and 20 healthy controls underwent magnetic resonance imaging at 3 T for structural and functional imaging. Whole-brain cortical thickness was calculated and compared between groups. Regions with reduced cortical thickness defined seeds for subsequent whole-brain RSFC analyses. Contributions of structural tissue abnormalities on inter-regional RSFC were explicitly investigated. Results. Lower cortical thickness was observed in MDD in the right dorsomedial prefrontal cortex (PFC), superior temporal gyrus/temporal pole, middle-posterior cingulate cortex, and dorsolateral PFC. No differences in local fractional amplitude of low-frequency fluctuations were observed. Lower thickness in patients' dorsomedial PFC further directly and selectively affected its RSFC with the precuneus, which was unaffected by symptom severity. No effects of cortical thickness in other regions showing abnormal thickness were observed to influence functional connectivity. Conclusions. Abnormal cortical thickness in the dorsomedial PFC in MDD patients was observed to selectively and directly affect its intrinsic connectivity with the precuneus in MDD patients independent of depression severity, thereby marking a potential vulnerability for maladaptive mood regulation. Future studies should include an unmedicated sample and replicate findings using independent component analysis to test for morphometric effects on network integrity.

关键词： Cortical thickness fractional amplitude of low-frequency fluctuations functional connectivity major depressive disorder multimodal imaging resting-state functional magnetic resonance imaging

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Prediction and evaluation of traffic states at signalized intersections

Prediction and evaluation of traffic states at signalized in...

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Ji-quan Shen Qing-jie Kong The School of Computer Science and Technology Henan Polytechnic University Jiaozuo Henan China State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781479960590

The parallel transportation system based on the method of ACP (Artificial systems, Computing experiments, Parallel control) will promote the level of city traffic intelligent decision and scientific management. A key problem in the system is how to design a computing experiment method to predict and evaluate the traffic state by real-time and accuracy. This paper introduces the discrete-time queuing model to analyze the traffic flow at the signalized intersection and gives the evaluation conditions of the traffic state. Then, the evaluation conditions are applied to judge the traffic state based on the prediction data of traffic flows from the grey model. Experiments show the method is effective and feasible.

关键词： Computational modeling Analytical models Educational institutions Personnel Predictive models

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Information Disclosure in Real-Time Bidding Advertising Markets

Information Disclosure in Real-Time Bidding Advertising Mark...

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IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)

作者： Li, Juanjuan Yuan, Yong Qin, Rui Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Shandong Peoples R China Chinese Acad Sci Inst Automat Beijing Engn Res Ctr Intelligent Syst & Technol Beijing 100190 Peoples R China

ISBN: (纸本)9781479960583

Real-time bidding (RTB) advertising is a fast-growing business model in online display advertising markets. Unlike the guaranteed display advertising, it utilizes programmatic instantaneous auction to price and sell ad inventory on a per-impression basis. RTB en bales advertisers to target audiences at demographic, psychographic or behavioral levels across a wide range of websites. In RTB markets, information about target audiences is usually disclosed to advertisers, while information about publishers (e.g. website ranking, advertising page) are typically not avaliable for advertisers, especially in real-time setting. This leads to a serious information asymmetry problem in RTB markets, which has significant influence on both advertisers' bidding strategies and publishers' revenues. In this paper, we study the information disclosure strategies of publishers in case when the disclosure may incur an extra cost. We address this information asymmetry problem by first formulating the RTB auction as an second-price sealed-bid game, then discussing equilibrium information disclosure strategies for publishers and also investigating advertisers' bidding strategies in the three information disclosure cases: all disclosed, non-disclosed, and partially disclosed. We find that non-disclosed and partially disclosed strategies may lead to an adverse selection effect, and high-quality publishers will be forced to quit the RTB markets.

关键词： real-time bidding information disclosure asymmetric information adverse selection

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Interventions of traffic flow for intersection based on interval type-2 fuzzy sets

Interventions of traffic flow for intersection based on inte...

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International Conference on Informative and Cybernetics for Computational Social systems (ICCSS)

作者： Xuan Li Hong Mo FengHua Zhu College Of Electrical And Information Engineering Changsha University Of Science And Technology Changsha China State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781479947515

Assessment and analysis of the intersection status can help to make the right choice of interventions, it's can bring convenience to solve the traffic congestion. In this paper, interval type-2 fuzzy sets is applied in the analysis of the intersection state and congestion intervention, dynamic fuzzy comprehensive evaluation and footprint of uncertainty are used to assess the intersection condition. Combined with the example of intersection congestion, through corresponding intervention measures to improve the intersections crowded conditions. Thus the linguistic dynamic orbits of road status figured out.

关键词： Fuzzy sets Pragmatics Extraterrestrial measurements Manganese Orbits Uncertainty Vehicle dynamics

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The identifier-based relative position estimation for leader-follower robotic system

The identifier-based relative position estimation for leader...

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IEEE International Conference on Mechatronics and Automation

作者： Lingyi Xu Zhiqiang Cao Peng Zhao Yixin Yin State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9781479939800

A monocular vision measurement approach is presented for leader-follower robotic system to estimate the position of the leader robot relative to the follower robot. The color identifier with green and red blocks is attached on the back of the leader robot, and it may be captured by the camera mounted on the follower robot. A coarse-to-fine searching method is given to extract the center point of the identifier. The parameters of the camera with the combination of the intrinsic and extrinsic ones are calibrated via least square method with given relative positions and the corresponding image coordinates of the identifier's center. The parameters are further optimized with Levenberg-Marquardt method. Then the position of the leader robot relative to the follower robot can be estimated only with single feature point. The experimental results verify the validity of the given approach.

关键词： Robot kinematics Cameras Feature extraction Robot vision systems Position measurement Image color analysis

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Dimensionality reduction: An interpretation from manifold regularization perspective

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INFORMATION SCIENCES 2014年 277卷 694-714页

作者： Fan, Mingyu Gu, Nannan Qiao, Hong Zhang, Bo Wenzhou Univ Coll Math & Informat Sci Wenzhou 325035 Zhejiang Peoples R China Capital Univ Econ & Business Sch Stat Beijing 100070 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci AMSS LSEC Beijing 100190 Peoples R China Chinese Acad Sci AMSS Inst Appl Math Beijing 100190 Peoples R China

In this paper, we propose to unify various dimensionality reduction algorithms by interpreting the Manifold Regularization (MR) framework in a new way. Although the MR framework was originally proposed for learning, we utilize it to give a unified treatment for many dimensionality reduction algorithms from linear to nonlinear, supervised to unsupervised, and single class to multi-class approaches. In addition, the framework can provide a general platform to design new dimensionality reduction algorithms. The framework is expressed in the form of a regularized fitting problem in a Reproducing Kernel Hilbert Space. It consists of one error part and two regularization terms: the complexity term and the smoothness term. The error part measures the difference between the estimated (low-dimensional) data distribution and the true (high-dimensional) data distribution or the difference between the estimated and targeted low-dimensional representations of data, the complexity term is a measurement of the complexity of the feature mapping for dimensionality reduction, and the smoothness term reflects the intrinsic structure of data. Based on the framework, we propose a Manifold Regularized Kernel Least Squares (MR-KLS) method which can efficiently learn an explicit feature mapping (in the semi-supervised sense). Experiments show that our approach is effective for out-of-sample extrapolation. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Dimensionality reduction Manifold regularization Feature mapping Manifold learning Out-of-sample extrapolation

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Salient Local Binary Pattern for Ground-Based Cloud Classification

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Acta meteorologica Sinica 2013年第2期27卷 211-220页

作者：刘爽王春恒肖柏华张重邵允学 State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences

Ground-based cloud classification is challenging due to extreme variations in the appearance of clouds under different atmospheric conditions. Texture classification techniques have recently been introduced to deal with this issue. A novel texture descriptor, the salient local binary pattern （SLBP）, is proposed for ground-based cloud classification. The SLBP takes advantage of the most frequently occurring patterns （the salient patterns） to capture descriptive information. This feature makes the SLBP robust to noise. Experimental results using ground-based cloud images demonstrate that the proposed method can achieve better results than current state-of-the-art methods.

关键词： salient local binary pattern local binary pattern ground-based cloud classification

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Exploring Opinion Dynamics in Security-Related Microblog Data

Exploring Opinion Dynamics in Security-Related Microblog Dat...

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IEEE Joint Intelligence and Security Informatics Conference (JISIC)

作者： Yuhao Zhang Wenji Mao Daniel Zeng Ning Zhao Xiuguo Bao State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Department of Management Information Systems University of Arizona Tucson AZ USA Beijing Police Department CNCERT/CC Beijing China

Web social media has become one of the major channels for people to express their opinions, share their feelings and communicate with others. Public opinions often ebb and flow with time due to the occurrence of social events and mutual influence of people on certain topics. The dynamic change of public opinions reflects the evolvement and trend of public attitudes and can facilitate many security-related applications. In this paper, we explore the modeling and detection of opinion dynamics on a specific topic based on textual social media data. We first define three measures to provide a thorough description of opinion dynamics, and identify the key factors that influence opinion changes, namely sentiment, social influence and dynamic factors. We then develop the computational method to capture opinion dynamics in security-related data. A preliminary empirical study is conducted based on the data from Weibo, one of the most popular microblog sites in China. The experimental results show the effectiveness of our method in modeling and predicting opinion dynamics.

关键词： Time measurement Media Logistics Data models Predictive models Learning systems Support vector machines

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On controllability of Neuronal Networks With Constraints on the Average of control Gains

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IEEE TRANSACTIONS ON CYBERNETICS 2014年第12期44卷 2670-2681页

作者： Tang, Yang Wang, Zidong Gao, Huijun Qiao, Hong Kurths, Juergen Potsdam Inst Climate Impact Res D-14473 Potsdam Germany Humboldt Univ Inst Phys D-12489 Berlin Germany Brunel Univ Dept Comp Sci Uxbridge UB8 3PH Middx England King Abdulaziz Univ Fac Engn Jeddah 21589 Saudi Arabia Harbin Inst Technol Res Inst Intelligent Control & Syst Harbin 150080 Peoples R China King Abdulaziz Univ Jeddah 21589 Saudi Arabia Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Aberdeen Inst Complex Syst & Math Biol Aberdeen AB24 3UE Scotland

control gains play an important role in the control of a natural or a technical system since they reflect how much resource is required to optimize a certain control objective. This paper is concerned with the controllability of neuronal networks with constraints on the average value of the control gains injected in driver nodes, which are in accordance with engineering and biological backgrounds. In order to deal with the constraints on control gains, the controllability problem is transformed into a constrained optimization problem (COP). The introduction of the constraints on the control gains unavoidably leads to substantial difficulty in finding feasible as well as refining solutions. As such, a modified dynamic hybrid framework (MDyHF) is developed to solve this COP, based on an adaptive differential evolution and the concept of Pareto dominance. By comparing with statistical methods and several recently reported constrained optimization evolutionary algorithms (COEAs), we show that our proposed MDyHF is competitive and promising in studying the controllability of neuronal networks. Based on the MDyHF, we proceed to show the controlling regions under different levels of constraints. It is revealed that we should allocate the control gains economically when strong constraints are considered. In addition, it is found that as the constraints become more restrictive, the driver nodes are more likely to be selected from the nodes with a large degree. The results and methods presented in this paper will provide useful insights into developing new techniques to control a realistic complex network efficiently.

关键词： complex networks controllability evolutionary algorithms multiagent systems neural networks synchronization/consensus

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Behavior modeling of internet water army in online forums 19

Behavior modeling of internet water army in online forums

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19th IFAC World Congress on International Federation of Automatic control, IFAC 2014

作者： Zeng, Ke Wang, Xiao Zhang, Qingpeng Zhang, Xinzhan Wang, Fei-Yue School of Electronic and Information Engineering Xi'an Jiaotong University Xi'an710049 China State Key Laboratory of Management and Control of Complex Systems Institute of Automation Chinese Academy of Science Beijing100190 China Tetherless World Constellation Rensselaer Polytechnic Institute TroyNY12180 United States Software Engineering Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)9783902823625

The behavior patterns and strategies of Internet Water Army in online forums are investigated in this paper. Internet Water Army focuses on the controlling and steering of cyber collective opinions, and adjusts their behavior according to two principles: to avoid being exposed and to increase the ability to exert influence. To study how the ability of Internet Water Army to exert influence, we construct a multi-agent system with coevolution of topics and cyber collective behaviors and design the behavior patterns and strategies of Internet Water Army. Based on synthetic data and real data, we find that Internet Water Army dynamically adjusts their behavior strategy to maximize their influence and the effectiveness of strategy of Internet Water Army is closely related to the features of the users. Our work sheds insight on the design of viral marketing mechanism in e-commerce systems as well as on guiding collective behaviors in social media. © IFAC.

关键词： Social networking (online)

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