Aphasia diagnosis is a challenging medical diagnostic task due to the linguistic uncertainty and vagueness, large number of measurements with imprecision, inconsistencies in the definition of Aphasic syndromes, natural diversity and subjectivity in test objects as well as in options of experts who diagnose the disease. In this paper we present a new self-organized multi agent system that diagno...

We introduce the concept of logical full abstraction, generalising the usual equational notion. We consider the language PCF and two extensions with “parallel” operations. The main result is that, for standard interpretations, logical full abstraction is equivalent to equational full abstraction together with universality; the proof involves constructing enumeration operators. We also consider ...

The top-k ranking is based on some scoring function in deterministic applications. However, in uncertain applications, such a clean definition does not exist, since the process of reporting a tuple in a top-k answer does not depend only on its score but also on its membership probability. This work introduces an approach to processing top-k queries based on statistical information extraction mo...

[1] A persistent feature of empirical climate sensitivity estimates is their heavy tailed probability distribution indicating a sizeable probability of high sensitivities. Previous studies make general claims that this upper heavy tail is an unavoidable feature of (i) the Earth system, or of (ii) limitations in our observational capabilities. Here we show that reducing the uncertainty about (i)...

the square map is one of the functions used in cryptography. for instance, the square map is used in rabin encryption scheme, block cipher rc6 and stream cipher rabbit, in different forms. in this paper, we study statistical properties of the output of the square map as a vectorial boolean function. we obtain the joint probability distribution of arbitrary number of the upper and the lower bits...

We prove that the multivariate standard normal probability distribution function is concave for large argument values. The method of proof allows for the derivation of similar statements for other types of multivariate probability distribution functions too. The result has important application, e.g., in probabilistic constrained stochastic programming problems.