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A fundamental open question in the analysis of social networks was to understand the evolution between similarity and group social ties. In general, two groups are similar for two distinct reasons: first, they grow to change their behaviors to the same group due to social influence; second, they tend to merge a group due to similar behaviors, where a process often is termed selection by sociologists. It was important to understand why two groups could merge and what led to high similarities for members in a group, influence or selection. In this paper, the techniques for identifying and modeling interactions between social influence and selection for different groups were developed. Different similarities were computed in three phases where groups came into being, before or after according to the number of common edits in Wikipedia. Experimental results showed selection played a more important role in two group merging. [ABSTRACT FROM AUTHOR]

Abstract: This paper deals with the problem of fault detection for discrete-time Markovian jump linear systems (MJLS). Using an observer-based fault detection filter (FDF) as a residual generator, the design of the FDF is formulated as an optimization problem for maximizing stochastic H−/H∞ or H∞/H∞ performance index. With the aid of an operator optimization method, it is shown that a unified optimal solution can be derived by solving a coupled Riccati equation. Numerical examples are given to show the effectiveness of the proposed method. [Copyright &y& Elsevier]