A method for inferring inequalities for probability values applied to complex networks

In this article, we infer inequalities representing (upper) bounds for real quantities. The obtained bound is parametric and, hence, it might be applicable to special problems. Here, we apply it to probabilities assigned to complex networks. © 2015 Wiley Periodicals, Inc. Complexity 21: 113–115, 2016     

On oriented supersingular elliptic curves

We revisit theoretical background on OSIDH (Oriented Supersingular Isogeny Diffie-Hellman protocol), which is an isogeny-based key-exchange protocol proposed by Colò and Kohel at NutMiC 2019. We give a proof of a fundamental theorem for OSIDH. The theorem was stated by Colò and Kohel without proof. Furthermore, we consider parameters of OSIDH, give a sufficient condition on the parameters for the protocol to work, and estimate the size of the parameters for a certain security level.     

Sharp Dirac's theorem for DP‐critical graphs

Correspondence coloring, or DP‐coloring, is a generalization of list coloring introduced recently by Dvořák and Postle [11]. In this article, we establish a version of Dirac's theorem on the minimum number of edges in critical graphs [9] in the framework of DP‐colorings. A corollary of our main result answers a question posed by Kostochka and Stiebitz [15] on classifying list‐critical graphs that satisfy Dirac's bound with equality.     

Postman problems on series–parallel mixed graphs

Given a strongly connected, mixed graph with costs on its edges and arcs, the mixed postman problem is to find a minimum cost closed walk traversing its edges and arcs at least once. This problem is NP-hard even on planar graphs but is solvable in polynomial time on series–parallel graphs. We give $O\left(n{m}^2\right)$ dynamic programming algorithms for the mixed postman problem and other similar problems on series–parallel graphs with $n$ vertices and $m$ edges and arcs.     

Learning marginal AMP chain graphs under faithfulness revisited

Marginal AMP chain graphs are a recently introduced family of models that is based on graphs that may have undirected, directed and bidirected edges. They unify and generalize the AMP and the multivariate regression interpretations of chain graphs. In this paper, we present a constraint based algorithm for learning a marginal AMP chain graph from a probability distribution which is faithful to it. We show that the marginal AMP chain graph returned by our algorithm is a distinguished member of its Markov equivalence class. We also show that our algorithm performs well in practice. Finally, we show that the extension of Meek's conjecture to marginal AMP chain graphs does not hold, which compromises the development of efficient and correct score+search learning algorithms under assumptions weaker than faithfulness.