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Recently, Dufrenois and Noyer proposed a one class Fisher’s linear discriminant to isolate normal data from outliers. In this paper, a kernelized version of their criterion is presented. Originally on the basis of an iterative optimization process, alternating between subspace selection and clustering, I show here that their criterion has an upper bound making these two problems independent. In particular, the estimation of the label vector is formulated as an unconstrained binary linear problem (UBLP) which can be solved using an iterative perturbation method. Once the label vector is estimated, an optimal projection subspace is obtained by solving a generalized eigenvalue problem. Like many other kernel methods, the performance of the proposed approach depends on the choice of the kernel. Constructed with a Gaussian kernel, I show that the proposed contrast measure is an efficient indicator for selecting an optimal kernel width. This property simplifies the model selection problem which is typically solved by costly (generalized) cross-validation procedures. Initialization, convergence analysis, and computational complexity are also discussed. Lastly, the proposed algorithm is compared with recent novelty detectors on synthetic and real data sets. [ABSTRACT FROM PUBLISHER]

The multivalued neuron with periodic activation function (MVN-P) was proposed by Aizenberg for solving classification problems. The boundaries between two distinct categories are crisply specified in MVN-P, which may result in slow convergence or being unable to converge at all in the learning process. In this paper, we propose a revised model of MVN-P based on the idea of unsharp boundaries. In this revised model, a fuzzy buffer is provided around a boundary between two distinct categories, allowing incorrect assignments with membership degree less than a threshold to be tolerated in the training phase. Genetic algorithms are applied to derive optimal values for the parameters involved in this model, alleviating the burden of setting them manually by the user. Besides, MVN-P has difficulties solving the classification problems having a large number of categories. A tree structure is developed to overcome these difficulties. Simulation results demonstrate the effectiveness of our proposed ideas. [ABSTRACT FROM AUTHOR]

Feature extraction is the first and most critical step in image classification. Most existing image classification methods use hand-crafted features, which are not adaptive for different image domains. In this paper, we develop an evolutionary learning methodology to automatically generate domain-adaptive global feature descriptors for image classification using multiobjective genetic programming (MOGP). In our architecture, a set of primitive 2-D operators are randomly combined to construct feature descriptors through the MOGP evolving and then evaluated by two objective fitness criteria, i.e., the classification error and the tree complexity. After the entire evolution procedure finishes, the best-so-far solution selected by the MOGP is regarded as the (near-)optimal feature descriptor obtained. To evaluate its performance, the proposed approach is systematically tested on the Caltech-101, the MIT urban and nature scene, the CMU PIE, and Jochen Triesch Static Hand Posture II data sets, respectively. Experimental results verify that our method significantly outperforms many state-of-the-art hand-designed features and two feature learning techniques in terms of classification accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a Gaussian classifier-based evolutionary strategy (GCES) to solve multimodal optimization problems. An evolutionary technique for them must answer two crucial questions to guarantee its success: how to distinguish among the different basins of attraction and how to safeguard the already discovered good-quality solutions including both global and local optima. In GCES, multimodal optimization problems are regarded as classification ones, and Gaussian mixture models are employed to save the locations and basins of already and presently identified local or global optima. A sequential estimation technique for the covariance of a Gaussian model is introduced into GCES. To best adjust the global step size, a strategy named top-ranked sample selection is introduced, and a classification method instead of a common but problematic radius-triggered manner is proposed. Experiments are performed on a series of benchmark test functions to compare GCES with the state-of-the-art multimodal optimization approaches. The results show that GCES is not only simple to program and understand, but also provides better and consistent performance. [ABSTRACT FROM AUTHOR]

In this paper, we study a class of risk-sensitive mean-field stochastic differential games. We show that under appropriate regularity conditions, the mean-field value of the stochastic differential game with exponentiated integral cost functional coincides with the value function satisfying a Hamilton –Jacobi– Bellman (HJB) equation with an additional quadratic term. We provide an explicit solution of the mean-field best response when the instantaneous cost functions are log-quadratic and the state dynamics are affine in the control. An equivalent mean-field risk-neutral problem is formulated and the corresponding mean-field equilibria are characterized in terms of backward-forward macroscopic McKean–Vlasov equations, Fokker–Planck–Kolmogorov equations, and HJB equations. We provide numerical examples on the mean field behavior to illustrate both linear and McKean–Vlasov dynamics. [ABSTRACT FROM PUBLISHER]

We consider a continuum of phase oscillators on the circle interacting through an impulsive instantaneous coupling. In contrast with previous studies on related pulse-coupled models, the stability results obtained in the continuum limit are global. For the nonlinear transport equation governing the evolution of the oscillators, we propose (under technical assumptions) a global Lyapunov function which is induced by a total variation distance between quantile densities. The monotone time evolution of the Lyapunov function completely characterizes the dichotomic behavior of the oscillators: either the oscillators converge in finite time to a synchronous state or they asymptotically converge to an asynchronous state uniformly spread on the circle. The results of the present paper apply to popular phase oscillators models (e.g., the well-known leaky integrate-and-fire model) and show a strong parallel between the analysis of finite and infinite populations. In addition, they provide a novel approach for the (global) analysis of pulse-coupled oscillators. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to synthesize initial mean consensus behavior of a set of agents from the fundamental optimization principles of i) stochastic dynamic games, and ii) optimal control. In the stochastic dynamic game model each agent seeks to minimize its individual quadratic discounted cost function involving the mean of the states of all other agents. In this formulation we derive the limiting infinite population mean field equation system and explicitly compute its unique solution. The resulting mean field (MF) control strategies drive each agent to track the overall population's initial state distribution mean, and by applying these decentralized strategies, any finite population system reaches mean consensus asymptotically as time goes to infinity. Furthermore, these control laws possess an \varepsilonN-Nash equilibrium property where \varepsilonN goes to zero as the population size N goes to infinity. Finally, the analysis is extended to the case of random mean field couplings. In the social cooperative formulation the basic objective is to minimize a social cost as the sum of the individual cost functions containing mean field coupling. In this formulation we show that for any individual agent the decentralized mean field social (MF Social) control strategy is the same as the mean field Nash (MF Nash) equilibrium strategy. Hence $MF-Nash\ Controls\ UNash^N=\rm MF-Social\ Controls\ USoc^N.$On the other hand, the solution to the centralized LQR optimal control formulation yields the Standard Consensus (SC) algorithm whenever the graph representing the corresponding topology of the network is Completely Connected (CC). Hence $Cent.\ LQR\ Controls\ UCent^N=\rm SC-CC\ Controls\ USC^N.$Moreover, a system with centralized control laws reaches consensus on the initial state distribution mean as time and population size N go to infinity. Hence, asymptotically in time $\eqalignno&MF-Nash\ Controls\ UNash^N\!=\!\,\rm MF-Social\ Controls\ USoc^N\cr&\!=\!\,\rm Cent.\ \rm LQR\ Controls\ UCent^\infty \!=\! \,\rm SC-CC\ Controls\ USC^\infty.$Finally, the analysis is extended to the long time average (LTA) cost functions case. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a chaotic secure communication system is proposed by using the schemes of projective-lag and/or projective-anticipating synchronizations of coupled multidelay feedback systems and the modulation technique of synchronization-manifold shift keying. Further, the security of the proposed system is enhanced by using non-stationary dynamics in the master and/or non-stationary synchronization manifolds. The specific examples using modified Ikeda systems demonstrate and verify the effectiveness of the proposed system. [Copyright &y& Elsevier]

Abstract: In this paper a complete analysis of a chaotic function is provided. The chaotic map is a parametric one-dimensional non invertible piecewise linear (PWL) map which is used to generate binary sequences. Implementation, key management and security associated to this chaotic generator are discussed. Then, we propose to use this chaotic map and its sequences in a communication system. The proposed communication system relies on the CSK principle and allows to transmit a binary information by switching between orthogonal binary sequences. The system is then analyzed by providing the bit-error rates derivation both theoretically and by computer simulations. [Copyright &y& Elsevier]

Purpose - The aim is to analyse changes of mass distributed value hierarchies in the course of social transformations in Russia during the last 25 years. Design/methodology/approach - The article's approach is development of the model of value hierarchies within the framework of Batesonian thinking and analysis of secondary sociological and economic data on Russia. Findings - The value hierarchy model, built within the Batesonian approach, provides a framework for analysis of the behavioural alternative choice by social individuals. Crisis events in Russia in the 1990s were related to the demolition of behavioural contexts and mass scale learning-III of the adult population. New value hierarchy structures were subsequently formed in the early 2000s. Attention of social planners to the structure of mass-distributed value hierarchies and the need to learn in changing contexts could improve the efficiency of social transformation. Research limitations/implications - Research findings are limited by the use of secondary data. However, even in this case, the model of value hierarchies has been found a useful tool of sociological data interpretation. Practical implications - Need for changes in epistemological grounds that underlie the design of massive socioeconomic reforms. Originality/value - The value hierarchy model was developed as a contribution to the Batesonian tradition of epistemology. Insights were provided into value hierarchy changes as the key driver of chronic crises in Russia in the 1990s. Social scientists and social planners within the system thinking approach could benefit from this paper. [ABSTRACT FROM AUTHOR]

Purpose - To assess the recent influence of Gregory Bateson on publication within the field of psychotherapy. Design/methodology/approach - Literature survey of all accessible literature from 1996 to 2006, utilizing citation search engines for identification purposes followed by reading and evaluating the most relevant publications. Findings - Research that investigates Bateson's ideas is promising but scarce, and theoretical development that builds on his constructs is rare. Apart from frequency in citation, his contributions seem to be more durable than generative. Research limitations/implications - Owing to the volume of data, a complete survey of literature was impossible, focusing our work on psychotherapy for this paper. Other projects surveying fields such as management and sociology should be undertaken to assess Bateson's influence. Practical implications - Practice should be tied to theory-building and ongoing research rather than nostalgia, myopic commitment to ideals, or myth. Purposeful commitment to cybernetic theory and practice development is needed. Originality/value - Few literature reviews have attempted to conduct a survey this broad. It is believed this has been successful in addressing actual publication practices. [ABSTRACT FROM AUTHOR]

Abstract: Applying the sliding mode observer strategy, this paper proposes a new observer-based synchronization scheme in the drive-response framework. If a chaotic system (the driving system) satisfies strictly positive real condition, a sliding mode observer (the response system) can be constructed to synchronize the driving system globally. Further, the hidden message can be approximately recovered by the equivalent control signal in the sense of least mean square. Theoretical analysis and numerical simulation show the effectiveness of the proposed scheme. [Copyright &y& Elsevier]

Random device mismatch that arises as a result of scaling of the CMOS (complementary metal-oxide semi-conductor) technology into the deep submicrometer regime degrades the accuracy of analog circuits. Methods to combat this increase the complexity of design. We have developed a novel neuromorphic system called a trainable analog block (TAB), which exploits device mismatch as a means for random projections of the input to a higher dimensional space. The TAB framework is inspired by the principles of neural population coding operating in the biological nervous system. Three neuronal layers, namely input, hidden, and output, constitute the TAB framework, with the number of hidden layer neurons far exceeding the input layer neurons. Here, we present measurement results of the first prototype TAB chip built using a 65 nm process technology and show its learning capability for various regression tasks. Our TAB chip is tolerant to inherent randomness and variability arising due to the fabrication process. Additionally, we characterize each neuron and discuss the statistical variability of its tuning curve that arises due to random device mismatch, a desirable property for the learning capability of the TAB. We also discuss the effect of the number of hidden neurons and the resolution of output weights on the accuracy of the learning capability of the TAB. We show that the TAB is a low power system—the power dissipation in the TAB with 456 neuron blocks is 1.38 \mu\textW. [ABSTRACT FROM AUTHOR]

This article presents views of Professor Linda J. Waite, on the benefits of marriage, on the basis of her paper at the Population Association of America's annual conference, "Does Marriage Matter?" As she has discovered, marriage is good for a person. Married people have more money, more sex, more satisfaction and, longer lives than those who are unmarried. Certainly there is no need for sociology to tell that two people pooling their resources and facing life together can reap benefits closed to those who are single or who live together but keep their resources separate. This would probably be true in any society, but is particularly the case in the U.S., where for millions of people, marriage is the only social-welfare system.

The incipience of synchrony in a diverse population of phase oscillators with non-identical interactions is an intriguing phenomenon. We study frequency synchronization of such oscillators composing networks with arbitrary topology in the context of the Kuramoto model and we show that its synchronization manifold is exponentially stable when the coupling has certain properties. Several example systems with periodic linear, cubic and sinusoidal coupling functions were examined, some including frustration and external fields. The numerical results confirmed the analytic findings and revealed some other interesting occurrences, like phase clustering in a synchronized network of strongly coupled oscillators. We also analyze the effects of the topology by considering random weighted networks. [ABSTRACT FROM AUTHOR]

In this work, a novel genetic algorithm (GA) is proposed for the design of multiplierless linear phase finite impulse response (FIR) filters. The filters under consideration are of high order and wide coefficient wordlength. Both the single-stage and cascade form are considered. In a practical filter design problem, when the filter specification is stringent, requiring high filter order and wide coefficient wordlength, GAs often fail to find feasible solutions, because the discrete search space thus constructed is huge and the majority of the solution candidates therein can not meet the specification. In the proposed GA, the discrete search space is partitioned into smaller ones. Each small space is constructed surrounding a base discrete coefficient set which is obtained by a proposed greedy algorithm. The partition of the search space increases the chances for the GA to find feasible solutions, but does not sacrifice the coverage of the search. The proposed GA applies to the design of single-stage filters. When a cascade form filter is designed, for each single-stage filter meeting the filter specification generated during the course of GA, an integer polynomial factorization is applied. Design examples show that the proposed GA significantly outperforms existing algorithms dealing with the similar problems in terms of design time, and the hardware cost is saved in most cases. [ABSTRACT FROM PUBLISHER]

The article focuses on the concepts depicted in the social science fiction television series "The Wire" which has made it the subject of a study during a conference to be held at the University of York, England on November 26-27, 2009. It says that the series has attracted several debates, papers and theses on sociology, economy and political science because of its theme on diverse issues such as school systems, the impact of dying industries on communities and the parallelism between life on the streets of Baltimore and certain parts of Great Britain.

In this editorial the author comments on a decline in paper use by office workers in the United States, a trend that began in 2001. This development is taken as an indication that discarded technological ideas, in this case the notion of a "paperless office," can be reconstituted and put to use if influenced by sociological and research and development factors.