Toward a General Framework for Information Fusion

National audienceDepending on the representation setting, different combination rules have been proposed for fusing information from distinct sources. Moreover in each setting, different sets of axioms that combination rules should satisfy have been advocated, thus justifying the existence of alternative rules (usually motivated by situations where the behavior of other rules was found unsatisfactory). These sets of axioms are usually purely considered in their own settings, without in-depth analysis of common properties essential for all the settings. This paper introduces core properties that, once properly instantiated, are meaningful in different representation settings ranging from logic to imprecise probabilities. The following representation settings are especially considered: classical set representation, possibility theory, and evidence theory, the latter encompassing the two other ones as special cases. This unified discussion of combination rules across different settings is expected to provide a fresh look on some old but basic issues in information fusion

Advanced learning in massive fusion databases : nonlinear regression, clustering, dimensionality reduction and information retrieval

The use of advanced data mining techniques in fusion experiments can help both in the progress of physical insight as well as in solving current engineering challenges in a fast track approach to the realization of fusion power. We present a research program concerning several important operations that are useful for detecting structures of interest in massive fusion databases. We consider measurement uncertainty through a probabilistic approach and we exploit useful information residing in the temporal structure of signals (e.g. the $D_\alpha$ signal for plasma regime identification), explicitly taking into account nonstationarity and transient behavior. Therefore we adopt a multiscale wavelet representation, modeling the wavelet coefficients through appropriate probability distributions. We integrate data from multiple diagnostics, optionally capturing signal dependencies by multivariate distributions. Our framework is concerned with the following tasks: for learning an in general nonlinear relation between physical variables and for extrapolation toward reactor-relevant conditions (e.g. confinement prediction); of objects (e.g. discharges) into physically meaningful groups; to uncover the fundamental degrees of freedom driving certain aspects of plasma behavior. In addition, this scheme is useful for data visualization and as a preprocessing step for various machine learning algorithms, in order to mitigate issues related to a high data dimensionality; by searching in a database for plasma conditions or phenomena that are similar to a given query. In order to accomplish our program, we employ the powerful language of information geometry, i.e. the study of probabilistic manifolds using differential geometry. In this work, we present the details of our mathematical framework and we show results of an example clustering application for plasma regime identification

(WP 2010-08) Neuroeconomics: Constructing Identity

This paper asks whether neuroeconomics will make instrumental use of neuroscience to adjudicate existing disputes in economics or be more seriously informed by neuroscience in ways that might transform economics. The paper pursues the question by asking how neuroscience constructs an understanding of individuals as whole persons. The body of the paper is devoted to examining two approaches: Don Ross’s neurocellular approach to neuroeconomics and Joseph Dumit’s cultural anthropological science organization approach. The accounts are used to identify boundaries on single individual explanations. With that space Andy Clark’s external scaffolding view and Nathaniel Wilcox’s socially distributed cognition view are employed

Learning to Rank Academic Experts in the DBLP Dataset

Expert finding is an information retrieval task that is concerned with the search for the most knowledgeable people with respect to a specific topic, and the search is based on documents that describe people's activities. The task involves taking a user query as input and returning a list of people who are sorted by their level of expertise with respect to the user query. Despite recent interest in the area, the current state-of-the-art techniques lack in principled approaches for optimally combining different sources of evidence. This article proposes two frameworks for combining multiple estimators of expertise. These estimators are derived from textual contents, from graph-structure of the citation patterns for the community of experts, and from profile information about the experts. More specifically, this article explores the use of supervised learning to rank methods, as well as rank aggregation approaches, for combing all of the estimators of expertise. Several supervised learning algorithms, which are representative of the pointwise, pairwise and listwise approaches, were tested, and various state-of-the-art data fusion techniques were also explored for the rank aggregation framework. Experiments that were performed on a dataset of academic publications from the Computer Science domain attest the adequacy of the proposed approaches.Comment: Expert Systems, 2013. arXiv admin note: text overlap with arXiv:1302.041