in this paper, we deal with fuzzy random variables for inputs andoutputs in data envelopment analysis (dea). these variables are considered as fuzzyrandom flat lr numbers with known distribution. the problem is to find a method forconverting the imprecise chance-constrained dea model into a crisp one. this can bedone by first, defuzzification of imprecise probability by constructing a suitablem...

The starting point of the paper is the (well-known)observation that the “classical” Rough Set Theory as introduced by PAWLAK is equivalent to the S5 Propositional Modal Logic where the reachability relation is an equivalence relation. By replacing this equivalence relation by an arbitrary binary relation (satisfying certain properties, for instance, reflexivity and transitivity) we shall obtain...

Accurate uncertainty estimation is a critical need for the medical imaging community. A variety of methods have been proposed, all direct extensions classification estimations techniques. The independent pixel-wise estimates, often based on probabilistic interpretation neural networks, do not take into account anatomical prior knowledge and consequently provide sub-optimal results to many segme...

Since 1975 the Bell Labs C Machine Project has designed several computer architectures to support the C programming language1 efficiently. These designs have evolved into the current C Machine instruction set architecture. The CRISP Microprocessor is one particular implementation of that architecture. Since we wish to describe the capabilities of a particular implementation in this paper, we wi...

This brief describes neural modeling fields (NMFs) for object perception, a bio-inspired paradigm. We discuss previous difficulties in object perception algorithms encountered since the 1950s, and describe how NMF overcomes these difficulties. NMF mechanisms are compared to recent experimental neuroimaging observations, which have demonstrated that initial top-down signals are vague and during ...