Directional Convexity Measure for Binary Tomography

A Measure of Directional Convexity Inspired by Binary Tomography

Learning Connectedness and Convexity of Binary Images from Their Projections

In this paper we investigate the retrieval of geometrical information (especially, convexity and connectedness) of binary images from their projections which can be useful in binary tomography to facilitate the task of reconstruction. Supposing that the projections are the features of the images, we study how decision trees, neural networks, and nearest neighbor learning algorithms perform in c...

Algorithm for Reconstructing 3D-Binary Matrix with Periodicity Constraints from Two Projections

We study the problem of reconstructing a three dimensional binary matrices whose interiors are only accessible through few projections. Such question is prominently motivated by the demand in material science for developing tool for reconstruction of crystalline structures from their images obtained by high-resolution transmission electron microscopy. Various approaches have been suggested to r...

An algorithm for detecting Directional Quasi–Convexity

Directional Quasi–Convexity (DQC) is a sufficient condition for Nekhoroshev stability, that is, stability for finite but very long times, of elliptic equilibria of Hamiltonian systems. The numerical detection of DQC is elementary for system with three degrees of freedom. In this article, we propose a recursive algorithm to test DQC in any number n ≥ 4 of degrees of freedom.

Strong Convexity and Directional Derivatives of Marginal Values in Two{stage Stochastic Programming

Two-stage stochastic programs with random right-hand side are considered. Veriiable suucient conditions for the existence of second-order directional derivatives of marginal values are presented. The central role of the strong convexity of the expected recourse function as well as of a Lipschitz stability result for optimal sets is emphasized.