Search results for "General Computer Science"
showing 10 items of 895 documents
Heuristics for the Constrained Incremental Graph Drawing Problem
2019
Abstract Visualization of information is a relevant topic in Computer Science, where graphs have become a standard representation model, and graph drawing is now a well-established area. Within this context, edge crossing minimization is a widely studied problem given its importance in obtaining readable representations of graphs. In this paper, we focus on the so-called incremental graph drawing problem, in which we try to preserve the user’s mental map when obtaining successive drawings of the same graph. In particular, we minimize the number of edge crossings while satisfying some constraints required to preserve the position of vertices with respect to previous drawings. We propose heur…
GPU-accelerated exhaustive search for third-order epistatic interactions in case–control studies
2015
This is a post-peer-review, pre-copyedit version of an article published in Journal of Computational Science. The final authenticated version is available online at: https://doi.org/10.1016/j.jocs.2015.04.001 [Abstract] Interest in discovering combinations of genetic markers from case–control studies, such as Genome Wide Association Studies (GWAS), that are strongly associated to diseases has increased in recent years. Detecting epistasis, i.e. interactions among k markers (k ≥ 2), is an important but time consuming operation since statistical computations have to be performed for each k-tuple of measured markers. Efficient exhaustive methods have been proposed for k = 2, but exhaustive thi…
An Approximate Determinization Algorithm for Weighted Finite-State Automata
2001
Nondeterministic weighted finite-state automata are a key abstraction in automatic speech recognition systems. The efficiency of automatic speech recognition depends directly on the sizes of these automata and the degree of nondeterminism present, so recent research has studied ways to determinize and minimize them, using analogues of classical automata determinization and minimization. Although, as we describe here, determinization can in the worst case cause poly-exponential blowup in the number of states of a weighted finite-state automaton, in practice it is remarkably successful. In extensive experiments in automatic speech recognition systems, deterministic weighted finite-state autom…
Transition Function Complexity of Finite Automata
2019
State complexity of finite automata in some cases gives the same complexity value for automata which intuitively seem to have completely different complexities. In this paper we consider a new measure of descriptional complexity of finite automata -- BC-complexity. Comparison of it with the state complexity is carried out here as well as some interesting minimization properties are discussed. It is shown that minimization of the number of states can lead to a superpolynomial increase of BC-complexity.
How to simulate free will in a computational device
1999
Since we believe that human brain is not a purely deterministic device merely reacting to the environment but rather it is capable to a free will, Theoretical Computer Science has also tried to develop a system of notions generalizing determinism. Nondeterministic and probabilistic algorithms were the first generalizations. Nondeterministic machines constitute an important part of the Theory of Computation. Nondeterminism is a useful way to describe possible choices. In real life there are many regulations restricting our behavior. These regulations nearly always leave some freedom for us how to react. Such regulations are best described in terms of nondeterministic algorithms. Nondetermini…
Distributed Consensus in Noncooperative Inventory Games
2009
This paper deals with repeated nonsymmetric congestion games in which the players cannot observe their payoffs at each stage. Examples of applications come from sharing facilities by multiple users. We show that these games present a unique Pareto optimal Nash equilibrium that dominates all other Nash equilibria and consequently it is also the social optimum among all equilibria, as it minimizes the sum of all the players’ costs. We assume that the players adopt a best response strategy. At each stage, they construct their belief concerning others probable behavior, and then, simultaneously make a decision by optimizing their payoff based on their beliefs. Within this context, we provide a …
Some decisional problems on rational relations
1997
Abstract In this paper we prove that the problem of deciding whether a deterministic rational relation is star-free is recursively solvable, although the same problem for any rational relation is undecidable. We also prove that a rational relation is star-free if and only if it is aperiodic and deterministic.
Multiobjective GRASP with Path Relinking
2015
In this paper we review and propose different adaptations of the GRASP metaheuristic to solve multiobjective combinatorial optimization problems. In particular, we describe several alternatives to specialize the construction and improvement components of GRASP when two or more objectives are considered. GRASP has been successfully coupled with Path Relinking for single-objective optimization. Moreover, we propose different hybridizations of GRASP and Path Relinking for multiobjective optimization. We apply the proposed GRASP with Path Relinking variants to two combinatorial optimization problems, the biobjective orienteering problem and the biobjective path dissimilarity problem. We report …
On the vibrations of a mechanically based non-local beam model
2012
The vibration problem of a Timoshenko non-local beam is addressed. The beam model involves assuming that the equilibrium of each volume element is attained due to contact forces and long-range body forces exerted, respectively, by adjacent and non-adjacent volume elements. The contact forces result in the classical Cauchy stress tensor while the long-range forces are taken as depending on the product of the interacting volume elements and on their relative displacement through a material-dependent distance-decaying function. To derive the motion equations and the related mechanical boundary conditions, the Hamilton's principle is applied The vibration problem of a Timoshenko non-local beam …
Multilayer neural networks: an experimental evaluation of on-line training methods
2004
Artificial neural networks (ANN) are inspired by the structure of biological neural networks and their ability to integrate knowledge and learning. In ANN training, the objective is to minimize the error over the training set. The most popular method for training these networks is back propagation, a gradient descent technique. Other non-linear optimization methods such as conjugate directions set or conjugate gradient have also been used for this purpose. Recently, metaheuristics such as simulated annealing, genetic algorithms or tabu search have been also adapted to this context.There are situations in which the necessary training data are being generated in real time and, an extensive tr…