and impressive applicability of mathematics in the natural sciences. Quineans hold that mathematics is conIlrDled by these applications, that only that part of mathematics with application is justified. Defenders of pure mathematics naturally disagree, finding justification for unapplied mathematics in various places, perhaps in some largely unspecified aesthetic virtues or in some philosophy o...

O Abstract Agents are automating many tasks that used to be done by people. As agents begin to address more complex domains of human life, they will need to develop an understanding of the dominance hierarchies that people understand intuitively. We present examples of species with social hierarchies that occur in the natural world, in particular the gray wolf (Canis lupus) and the chimpanzee (...

We consider a distributionally robust optimization problem where the ambiguity set of probability distributions is characterized by a tractable conic representable support set and expectation constraints. Specifically, we propose and motivate a new class of infinitely constrained ambiguity sets in which the number of expectation constraints could potentially be infinite. We show how the infinit...

The separated electromagnetic responses RL(q, ω) and RT (q, ω) for inclusive electron scattering off nuclei are studied within a functional scheme.

The aim of this paper is to introduce and study set- valued homomorphism on lattices and T-rough lattice with respect to a sublattice. This paper deals with T-rough set approach on the lattice theory. The result of this study contributes to, T-rough fuzzy set and approximation theory and proved in several papers. Keywords: approximation space; lattice; prime ideal; rough ideal; T-rough set; set...

This tutorial is a survey on rough set theory and some of its applications in Knowledge Discovery from Databases (KDD). It will also cover the practice guide to analysis of different real life problems using rough set methods as well as the presentation of Rough Set Exploration System (RSES) what can be treated as a preliminary material for the main conference and associated workshops.

This paper proposes an approach to training rough set models using Bayesian framework trained using Markov Chain Monte Carlo (MCMC) method. The prior probabilities are constructed from the prior knowledge that good rough set models have fewer rules. Markov Chain Monte Carlo sampling is conducted through sampling in the rough set granule space and Metropolis algorithm is used as an acceptance cr...

In this paper, we discuss an attainable moment set (AMS) optimization methodology considering a system’s required (RMS). The AMS describes the achievable moments from system, given its input limits. An RMS, like AMS, is convex in space, describing for system to meet designed mission profile and disturbance rejection requirements. Given configuration of requirements, derived proposed maximizes c...