Surrogate modeling approximation using a mixture of experts based on EM joint estimation

An automatic method to combine several local surrogate models is presented. This method is intended to build accurate and smooth approximation of discontinuous functions that are to be used in structural optimization problems. It strongly relies on the Expectation−Maximization (EM) algorithm for Gaussian mixture models (GMM). To the end of regression, the inputs are clustered together with their output values by means of parameter estimation of the joint distribution. A local expert is then built (linear, quadratic, artificial neural network, moving least squares) on each cluster. Lastly, the local experts are combined using the Gaussian mixture model parameters found by the EM algorithm to obtain a global model. This method is tested over both mathematical test cases and an engineering optimization problem from aeronautics and is found to improve the accuracy of the approximation.     

Problematic Demography: Representations of Population Ageing in the Swedish Daily Press

The ageing of populations has been a topic of discussion during the last few decades, but how is this subject represented in the media, and what images of old age are produced? In this article we present the results of a quantitative content analysis that investigates how the concept of population ageing has been represented in Swedish local and national daily news press between 1988 and 2009, and the old age positions that are offered in these representations. We also use discourse analysis in order to qualitatively examine the ways in which the concept of population ageing is articulated in these news press articles and the old age positions that are thereby constituted. The results show that the concept is constituted as a naturalised expert concept, and is primarily used in order to contextualise articles about future political and economical difficulties or even crises. By articulating population ageing with both political policies, political economy and older people??s (as a group) reported need for care and services, population ageing was constituted as a political economic concern rather than a problem for the aged individual.     

Shifts in sensory dominance between various stages of user-product interactions

In the area of product design, sensory dominance can be defined as the relative importance of different sensory modalities for product experience. It is often assumed that vision dominates the other senses. In the present study, we asked 243 participants to describe their experiences with consumer products in various situations: while buying a product, after the first week, the first month, and the first year of usage. The data suggest that the dominant sensory modality depends on the period of product usage. At the moment of buying, vision is the most important modality, but during the usage the other sensory modalities gain importance. The roles of the different modalities during usage are product-dependent. Averaged over 93 products analyzed in this study, after one month of usage touch becomes more important than vision, and after one year vision, touch and audition appear to be equally important. We conclude that to create a long-lasting positive product experience, designers need to consider user-product interaction at different stages of product usage and to determine which sensory modality dominates product experience at each stage.     

浅议以太网基本性能测试指标及相应的测试方法

本文就目前国际上对以太网基本性能测量的指标进行整理和分析并给出了相应的测试方法。     

Complexity issues in color-preserving graph embeddings

In the context of comparative analysis of protein–protein interaction graphs, we use a graph-based formalism to detect the preservation of a given protein complex (pattern graph) in the protein–protein interaction graph (target graph) of another species with respect to (w.r.t.) orthologous proteins. We give an efficient exponential-time randomized algorithm in case the occurrence of the pattern graph in the target graph is required to be exact. For approximate occurrences, we prove a tight inapproximability result and give four approximation algorithms that deal with bounded degree graphs, small ortholog numbers, linear forests and very simple yet hard instances, respectively.     

Stability of a class of discrete-time nonlinear recursive observers

This work provides a framework for nominal and robust stability analysis for a class of discrete-time nonlinear recursive observers (DNRO). Given that the system has linear output mapping, local observability and Jacobian matrices satisfying certain conditions, the nominal and robust stability of the DNRO is defined by the property of estimation error dynamics and is analyzed using Lyapunov theory. Moreover, a simultaneous state and parameter estimation scheme is shown to be Input-to-State Stable (ISS), and adaptively reduce plant-model mismatch on-line. Three design strategies of the DNRO that satisfy the stability results are given as examples, including the widely used extended Kalman filter, extended Luenberger observer, and the fixed gain observer.     

Privacy policies for shared content in social network sites

Social networking is one of the major technological phenomena of the Web 2.0, with hundreds of millions of subscribed users. Social networks enable a form of self-expression for users and help them to socialize and share content with other users. In spite of the fact that content sharing represents one of the prominent features of existing Social network sites, they do not provide any mechanisms for collective management of privacy settings for shared content. In this paper, using game theory, we model the problem of collective enforcement of privacy policies on shared data. In particular, we propose a solution that offers automated ways to share images based on an extended notion of content ownership. Building upon the Clarke-Tax mechanism, we describe a simple mechanism that promotes truthfulness and that rewards users who promote co-ownership. Our approach enables social network users to compose friendship based policies based on distances from an agreed upon central user selected using several social networks metrics. We integrate our design with inference techniques that free the users from the burden of manually selecting privacy preferences for each picture. To the best of our knowledge, this is the first time such a privacy protection mechanism for social networking has been proposed. We also extend our mechanism so as to support collective enforcement across multiple social network sites. In the paper, we also show a proof-of-concept application, which we implemented in the context of Facebook, one of today’s most popular social networks. Through our implementation, we show the feasibility of such approach and show that it can be implemented with a minimal increase in overhead to end-users. We complete our analysis by conducting a user study to investigate users’ understanding of co-ownership, usefulness and understanding of our approach. Users responded favorably to the approach, indicating a general understanding of co-ownership and the auction, and found the approach to be both useful and fair.