Computer Science

Latin squares and hypercubes are combinatorial designs with several applications in statistics, cryptography and coding theory. In this paper, we generalize a construction of Latin squares based on bipermutive cellular automata (CA) to the case of Latin hypercubes of dimension k>2 . In particular, we prove that linear bipermutive CA (LBCA) yielding Latin hypercubes of dimension k>2 are defined by sequences of invertible Toeplitz matrices with partially overlapping coefficients, which can be described by a specific kind of regular de Bruijn graph induced by the support of the determinant function. Further, we derive the number of k -dimensional Latin hypercubes generated by LBCA by counting the number of paths of length k−3 on this de Bruijn graph.

In this paper, we present two deterministic leader election algorithms for programmable matter on the face-centered cubic grid. The face-centered cubic grid is a 3-dimensional 12-regular infinite grid that represents an optimal way to pack spheres (i.e., spherical particles or modules in the context of the programmable matter) in the 3-dimensional space. While the first leader election algorithm requires a strong hypothesis about the initial configuration of the particles and no hypothesis on the system configurations that the particles are forming, the second one requires fewer hypothesis about the initial configuration of the particles but does not work for all possible particles' arrangement. We also describe a way to compute and assign-local identifiers to the particles in this grid with a memory space not dependent on the number of particles. A-local identifier is a variable assigned to each particle in such a way that particles at distance at most each have a different identifier.

Lexicographic Depth First Search (LexDFS) is a special variant of a Depth First Search (DFS), which was introduced by Corneil and Krueger in 2008. While this search has been used in various applications, in contrast to other graph searches, no general linear time implementation is known to date. In 2014, Köhler and Mouatadid achieved linear running time to compute some special LexDFS orders for cocomparability graphs. In this paper, we present a linear time implementation of LexDFS for chordal graphs. Our algorithm is able to find any LexDFS order for this graph class. To the best of our knowledge this is the first unrestricted linear time implementation of LexDFS on a non-trivial graph class. In the algorithm we use a search tree computed by Lexicographic Breadth First Search (LexBFS).

Let k and d be such that k≥d+2 . Consider two k -colorings of a d -degenerate graph G . Can we transform one into the other by recoloring one vertex at each step while maintaining a proper coloring at any step? Cereceda et al. answered that question in the affirmative, and exhibited a recolouring sequence of exponential length. If k=d+2 , we know that there exists graphs for which a quadratic number of recolorings is needed. And when k=2d+2 , there always exists a linear transformation. In this paper, we prove that, as long as k≥d+4 , there exists a transformation of length at most f(Δ)⋅n between any pair of k -colorings of chordal graphs (where Δ denotes the maximum degree of the graph). The proof is constructive and provides a linear time algorithm that, given two k -colorings c 1 , c 2 computes a linear transformation between c 1 and c 2 .

Given a graph G and an integer k , a token addition and removal ({\sf TAR} for short) reconfiguration sequence between two dominating sets D s and D t of size at most k is a sequence S=⟨ D 0 = D s , D 1 …, D ℓ = D t ⟩ of dominating sets of G such that any two consecutive dominating sets differ by the addition or deletion of one vertex, and no dominating set has size bigger than k . We first improve a result of Haas and Seyffarth, by showing that if k=Γ(G)+α(G)−1 (where Γ(G) is the maximum size of a minimal dominating set and α(G) the maximum size of an independent set), then there exists a linear {\sf TAR} reconfiguration sequence between any pair of dominating sets. We then improve these results on several graph classes by showing that the same holds for K ℓ -minor free graph as long as k≥Γ(G)+O(ℓ logℓ − − − − √ ) and for planar graphs whenever k≥Γ(G)+3 . Finally, we show that if k=Γ(G)+tw(G)+1 , then there also exists a linear transformation between any pair of dominating sets.

A linear-interval order is the intersection of a linear order and an interval order. For this class of orders, several structural results have been known. This paper introduces a new subclass of linear-interval orders. We call a partial order a \emph{linear-semiorder} if it is the intersection of a linear order and a semiorder. We show a characterization and a polynomial-time recognition algorithm for linear-semiorders. We also prove that being a linear-semiorder is a comparability invariant, showing that incomparability graphs of linear-semiorders can be recognized in polynomial time.

Minimum resolution set and associated metric dimension provide the basis for unique and systematic labeling of nodes of a graph using distances to a set of landmarks. Such a distance vector set, however, may not be unique to the graph and does not allow for its exact construction. The concept of construction set is presented, which facilitates the unique representation of nodes and the graph as well as its exact construction. Link dimension is the minimum number of landmarks in a construction set. Results presented include necessary conditions for a set of landmarks to be a construction set, bounds for link dimension, and guidelines for transforming a resolution set to a construction set.

Exponential family Random Graph Models (ERGMs) can be viewed as expressing a probability distribution on graphs arising from the action of competing social forces that make ties more or less likely, depending on the state of the rest of the graph. Such forces often lead to a complex pattern of dependence among edges, with non-trivial large-scale structures emerging from relatively simple local mechanisms. While this provides a powerful tool for probing macro-micro connections, much remains to be understood about how local forces shape global outcomes. One simple question of this type is that of the conditions needed for social forces to stabilize a particular structure. We refer to this property as local stability and seek a general means of identifying the set of parameters under which a target graph is locally stable with respect to a set of alternatives. Here, we provide a complete characterization of the region of the parameter space inducing local stability, showing it to be the interior of a convex cone whose faces can be derived from the change-scores of the sufficient statistics vis-a-vis the alternative structures. As we show, local stability is a necessary but not sufficient condition for more general notions of stability, the latter of which can be explored more efficiently by using the ``stable cone'' within the parameter space as a starting point. In addition, we show how local stability can be used to determine whether a fitted model implies that an observed structure would be expected to arise primarily from the action of social forces, versus by merit of the model permitting a large number of high probability structures, of which the observed structure is one. We also use our approach to identify the dyads within a given structure that are the least stable, and hence predicted to have the highest probability of changing over time.

Issues concerning intelligent data analysis occurring in machine learning are investigated. A scheme for synthesizing correct supervised classification procedures is proposed. These procedures are focused on specifying partial order relations on sets of feature values; they are based on a generalization of the classical concepts of logical classification. It is shown that learning the correct logical classifier requires an intractable discrete problem to be solved. This is the dualization problem over products of partially ordered sets. The matrix formulation of this problem is given. The effectiveness of the proposed approach to the supervised classification problem is illustrated on model and real-life data.

While justifying that independence is a canonic coupling, the authors show the existence of a second equilibrium to reduce the information conveyed from the margins to the joined distribution: the so-called indetermination. They use this null information property to apply indetermination to graph clustering. Furthermore, they break down a drawing under indetermination to emphasis it is the best construction to reduce couple matchings, meaning, the expected number of equal couples drawn in a row. Using this property, they notice that indetermination appears in two problems (Guessing and Task Partitioning) where couple matchings reduction is a key objective.