Performance evaluation of fuzzy-based fusion rules for tracking applications

International audienceThe objective of this paper is to present and to evaluate the performance of particular fusion rules based on fuzzy T-Conorm/T-Norm operators for two tracking applications: (1) Tracking object's type changes, supporting the process of objects' identication (e.g. ghter against cargo, friendly aircraft against hostile ones), which, consequently is essential for improving the quality of generalized data association for targets' track- ing; (2) Alarms identication and prioritization in terms of degree of danger relating to a set of a priori dened, out of the ordinary dangerous directions. The aim is to present and demonstrate the ability of these rules to assure coherent and stable way for identication and to improve decision-making process in a temporal way. A com- parison with performance of Dezert-Smarandache Theory based Proportional Con ict Redistribution rule no.5 and Dempster's rule is also provided

Why Dempster's fusion rule is not a generalization of Bayes fusion rule

International audienceIn this paper, we analyze Bayes fusion rule in details from a fusion standpoint, as well as the emblematic Dempster's rule of combination introduced by Shafer in his Mathematical Theory of evidence based on belief functions. We propose a new interesting formulation of Bayes rule and point out some of its properties. A deep analysis of the compatibility of Dempster's fusion rule with Bayes fusion rule is done. We show that Dempster's rule is compatible with Bayes fusion rule only in the very particular case where the basic belief assignments (bba's) to combine are Bayesian, and when the prior information is modeled either by a uniform probability measure, or by a vacuous bba. We show clearly that Dempster's rule becomes incompatible with Bayes rule in the more general case where the prior is truly informative (not uniform, nor vacuous). Consequently, this paper proves that Dempster's rule is not a generalization of Bayes fusion rule

Why Dempster’s rule doesn’t behave as Bayes rule with informative priors

International audienceIn this paper, we analyze Bayes fusion rule in details from a fusion standpoint, as well as the emblematic Dempster’s rule of combination introduced by Shafer in his Mathematical Theory of evidence based on belief functions. We propose a new interesting formulation of Bayes rule and point out some of its properties. A deep analysis of the compatibility of Dempster’s fusion rule with Bayes fusion rule is done. Our analysis proves clearly that Dempster’s rule of combination does not behave as Bayes fusion rule in general, because these methods deal very differently with the prior information when it is really informative (not uniform). Only in the very particular case where the basic belief assignments to combine are Bayesian and when the prior information is uniform (or vacuous), Dempster’s rule remains consistent with Bayes fusion rule. In more general cases, Dempster’s rule is incompatible with Bayes rule and it is not a generalization of Bayes fusion rule

Trial of Fingolimod versus Interferon Beta-1a in Pediatric Multiple Sclerosis

BACKGROUND: Treatment of patients younger than 18 years of age with multiple sclerosis has not been adequately examined in randomized trials. We compared fingolimod with interferon beta-1a in this population. METHODS: In this phase 3 trial, we randomly assigned patients 10 to 17 years of age with relapsing multiple sclerosis in a 1:1 ratio to receive oral fingolimod at a dose of 0.5 mg per day (0.25 mg per day for patients with a body weight of ≤40 kg) or intramuscular interferon beta-1a at a dose of 30 μg per week for up to 2 years. The primary end point was the annualized relapse rate. RESULTS: Of a total of 215 patients, 107 were assigned to fingolimod and 108 to interferon beta-1a. The mean age of the patients was 15.3 years. Among all patients, there was a mean of 2.4 relapses during the preceding 2 years. The adjusted annualized relapse rate was 0.12 with fingolimod and 0.67 with interferon beta-1a (absolute difference, 0.55 relapses; relative difference, 82%; P<0.001). The key secondary end point of the annualized rate of new or newly enlarged lesions on T2-weighted magnetic resonance imaging (MRI) was 4.39 with fingolimod and 9.27 with interferon beta-1a (absolute difference, 4.88 lesions; relative difference, 53%; P<0.001). Adverse events, excluding relapses of multiple sclerosis, occurred in 88.8% of patients who received fingolimod and 95.3% of those who received interferon beta-1a. Serious adverse events occurred in 18 patients (16.8%) in the fingolimod group and included seizures (in 4 patients), infection (in 4 patients), and leukopenia (in 2 patients). Serious adverse events occurred in 7 patients (6.5%) in the interferon beta-1a group and included infection (in 2 patients) and supraventricular tachycardia (in 1 patient). CONCLUSIONS: Among pediatric patients with relapsing multiple sclerosis, fingolimod was associated with a lower rate of relapse and less accumulation of lesions on MRI over a 2-year period than interferon beta-1a but was associated with a higher rate of serious adverse events. Longer studies are required to determine the durability and safety of fingolimod in pediatric multiple sclerosis. (Funded by Novartis Pharma; PARADIGMS ClinicalTrials.gov number, NCT01892722 .)