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On rainbowness of semiregular polyhedra

Stanislav Jendroľ, Štefan Schrötter (2008)

Czechoslovak Mathematical Journal

We introduce the rainbowness of a polyhedron as the minimum number k such that any colouring of vertices of the polyhedron using at least k colours involves a face all vertices of which have different colours. We determine the rainbowness of Platonic solids, prisms, antiprisms and ten Archimedean solids. For the remaining three Archimedean solids this parameter is estimated.

On reduced pairs of bounded closed convex sets.

Jerzy Grzybowski, Ryszard Urbanski (2003)

Revista Matemática Complutense

In this paper certain criteria for reduced pairs of bounded closed convex set are presented. Some examples of reduced and not reduced pairs are enclosed.

On set covariance and three-point test sets

Jan Rataj (2004)

Czechoslovak Mathematical Journal

The information contained in the measure of all shifts of two or three given points contained in an observed compact subset of d is studied. In particular, the connection of the first order directional derivatives of the described characteristic with the oriented and the unoriented normal measure of a set representable as a finite union of sets with positive reach is established. For smooth convex bodies with positive curvatures, the second and the third order directional derivatives of the characteristic...

On smooth points of boundaries of open sets

S. Rolewicz (2009)

Studia Mathematica

The notions of smooth points of the boundary of an open set and α(·) intrinsically paraconvex sets are introduced. It is shown that for an α(·) intrinsically paraconvex open set the set of smooth points is a dense G δ -set of the boundary.

On solution sets of information inequalities

Nihat Ay, Walter Wenzel (2012)

Kybernetika

We investigate solution sets of a special kind of linear inequality systems. In particular, we derive characterizations of these sets in terms of minimal solution sets. The studied inequalities emerge as information inequalities in the context of Bayesian networks. This allows to deduce structural properties of Bayesian networks, which is important within causal inference.

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