Belief functions contextual discounting and canonical decompositions

AbstractIn this article, the contextual discounting of a belief function, a classical discounting generalization, is extended and its particular link with the canonical disjunctive decomposition is highlighted. A general family of correction mechanisms allowing one to weaken the information provided by a source is then introduced, as well as the dual of this family allowing one to strengthen a belief function

Soluble markers of B cell activation suggest a role of B cells in the pathogenesis of systemic sclerosis-associated pulmonary arterial hypertension

IntroductionSoluble markers of B cell activation are interesting diagnostic and prognostic tools in autoimmune diseases. Data in systemic sclerosis (SSc) are scarce and few studies focused on their association with disease characteristics.Methods1. Serum levels of 14 B cell biomarkers (β2-microglobulin, rheumatoid factor (RF), immunoglobulins (Ig) G, IgA, IgM, BAFF, APRIL, soluble (s)TACI, sBCMA sCD21, sCD23, sCD25, sCD27, CXCL13) were measured in SSc patients and healthy controls (HC). 2. Associations between these biomarkers and SSc characteristics were assessed. 3. The pathophysiological relevance of identified associations was explored by studying protein production in B cell culture supernatant.ResultsIn a discovery panel of 80 SSc patients encompassing the broad spectrum of disease manifestations, we observed a higher frequency of RF positivity, and increased levels of β2-microglobulin, IgG and CXCL13 compared with HC. We found significant associations between several biomarkers and SSc characteristics related to disease phenotype, activity and severity. Especially, serum IgG levels were associated with pulmonary hypertension (PH); β2-microglobulin with Nt-pro-BNP and DLCO; and BAFF with peak tricuspid regurgitation velocity (TRV). In a validation cohort of limited cutaneous SSc patients without extensive ILD, we observed lower serum IgG levels, and higher β2-microglobulin, sBCMA, sCD23 and sCD27 levels in patients with pulmonary arterial hypertension (PAH). BAFF levels strongly correlated with Nt-pro-BNP levels, FVC/DLCO ratio and peak TRV in SSc-PAH patients. Cultured SSc B cells showed increased production of various angiogenic factors (angiogenin, angiopoietin-1, VEGFR-1, PDGF-AA, MMP-8, TIMP-1, L-selectin) and decreased production of angiopoietin-2 compared to HC.ConclusionSoluble markers of B cell activation could be relevant tools to assess organ involvements, activity and severity in SSc. Their associations with PAH could plead for a role of B cell activation in the pathogenesis of pulmonary microangiopathy. B cells may contribute to SSc vasculopathy through production of angiogenic mediators

Le sport à Condé-Sur-Noireau de la belle époque à la deuxième Guerre Mondiale (1890-1940)

Lefèvre Éric. Le sport à Condé-Sur-Noireau de la belle époque à la deuxième Guerre Mondiale (1890-1940). In: Annales de Normandie, 43ᵉ année, n°3, 1993. pp. 181-200

Corrigendum to “Belief functions contextual discounting and canonical decompositions” [International Journal of Approximate Reasoning 53 (2012) 146–158]

International audienceProposition 4 and Theorem 1 of the article "Belief Functions Contextual Discounting and Canonical Decompositions" [International Journal of Approximate Reasoning 53 (2012) 146-158] provide an erroneous result. We give here the true result with a correct proof

Methods using belief functions to manage imperfect information concerning events on the road in VANETs

International audienceDifferent models using belief functions are proposed and compared in this article to share and manage imperfect information about events on the road in vehicular networks. In an environment without infrastructure, the goal is to provide to driver the synthesis of the situation on the road from all acquired information. Different strategies are considered: discount or reinforce towards the absence of the event to take into account messages agings, keep the original messages or only the fusion results in vehicles databases, consider the world update, manage the spatiality of traffic jams by taking into account neighborhood. Methods are tested and compared using a Matlab TM simulator. Two strategies are introduced to tackle fog blankets spatiality; they are compared through an example

Hyperconnections and Hierarchical Representations for Grayscale and Multiband Image Processing

Abstract—Connections in image processing are an important notion that describes how pixels can be grouped together according to their spatial relationships and/or their gray level values. In recent years, several works were devoted to the development of new theories of connections among which hyper-connection (h-connection) is a very promising notion. This paper addresses two major issues of this theory. First, we propose a new axiomatic which ensures that every h-connection generates decompositions that are consistent for image processing and more precisely for the design of h-connected filters. Second, we develop a general framework to represent the decomposition of an image into h-connections as a tree which corresponds to the generalization of the connected component tree. Such trees are indeed an efficient and intuitive way to design attribute filters or to perform detection tasks based on qualitative or quantitative attributes. These theoretical developments are applied to a particular fuzzy h-connection and we test this new framework on several classical applications in image processing: segmentation, connected filtering, and document image binarization. The experiments confirm the suitability of the proposed approach: it is robust to noise and it provides an efficient framework to design selective filters. Index Terms—Edics: SMR-STM, Hyperconnection, hierarchical representation, connected operator, connected filter, document image binarization, mathematical morphology, connection, Max-Tree, image filtering, image segmentation.

0-1 Combinatorial Optimization Problems with Qualitative and Uncertain Profits

International audienceRecent works have studied 0-1 combinatorial optimization problems where profits of items are measured on a qualitative scale such as “low”, “medium” and “high”. In this study, we extend this body of work by allowing these profits to be both qualitative and uncertain. In the first step, we use probability theory to handle uncertainty. In the second step, we use evidence theory to handle uncertainty. We combine their approaches with approaches in decision making under uncertainty that utilize the Maximum Expected Utility principle and generalized Hurwicz criterion, to compare solutions. We show that under probabilistic uncertainty and a special case of evidential uncertainty where the focal sets are rectangles, the task of identifying the non-dominated solutions can be framed as solving a multi-objective version of the considered problem. This result mirrors that of the case of qualitative profits with no uncertainty

Optimization Under Severe Uncertainty: a Generalized Minimax Regret Approach for Problems with Linear Objectives

International audienceWe study a general optimization problem with an uncertain linear objective. We address the uncertainty using two models: belief functions and, more generally, capacities. In the former model, we use the generalized minimax regret criterion introduced by Yager, while in the latter one, we extend this criterion, to find optimal solutions. This paper identifies some tractable cases for the resulting problem. Furthermore, when focal sets of the considered belief functions are Cartesian products of intervals, we develop a 2-approximation method that mirrors the well-known midpoint scenario method used for minimax regret optimization problems with interval data

On Modelling and Solving the Shortest Path Problem with Evidential Weights

International audienceWe study the single source single destination shortest path problem in a graph where information about arc weights is modelled by a belief function. We consider three common criteria to compare paths with respect to their weights in this setting: generalized Hurwicz, strong dominance and weak dominance. We show that in the particular case where the focal sets of the belief function are Cartesian products of intervals, finding best, i.e., non-dominated, paths according to these criteria amounts to solving known variants of the deterministic shortest path problem, for which exact resolution algorithms exist

Le problème de tournées de véhicules avec des demandes évidentielles

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