3,978 research outputs found

    Two novel evolutionary formulations of the graph coloring problem

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    We introduce two novel evolutionary formulations of the problem of coloring the nodes of a graph. The first formulation is based on the relationship that exists between a graph's chromatic number and its acyclic orientations. It views such orientations as individuals and evolves them with the aid of evolutionary operators that are very heavily based on the structure of the graph and its acyclic orientations. The second formulation, unlike the first one, does not tackle one graph at a time, but rather aims at evolving a `program' to color all graphs belonging to a class whose members all have the same number of nodes and other common attributes. The heuristics that result from these formulations have been tested on some of the Second DIMACS Implementation Challenge benchmark graphs, and have been found to be competitive when compared to the several other heuristics that have also been tested on those graphs.Comment: To appear in Journal of Combinatorial Optimizatio

    Modeling the input history of programs for improved instruction-memory performance

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    When a program is loaded into memory for execution, the relative position of its basic blocks is crucial, since loading basic blocks that are unlikely to be executed first places them high in the instruction-memory hierarchy only to be dislodged as the execution goes on. In this paper we study the use of Bayesian networks as models of the input history of a program. The main point is the creation of a probabilistic model that persists as the program is run on different inputs and at each new input refines its own parameters in order to reflect the program's input history more accurately. As the model is thus tuned, it causes basic blocks to be reordered so that, upon arrival of the next input for execution, loading the basic blocks into memory automatically takes into account the input history of the program. We report on extensive experiments, whose results demonstrate the efficacy of the overall approach in progressively lowering the execution times of a program on identical inputs placed randomly in a sequence of varied inputs. We provide results on selected SPEC CINT2000 programs and also evaluate our approach as compared to the gcc level-3 optimization and to Pettis-Hansen reordering

    Porokeratosis of Mibelli. A Case Report

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    As poroqueratoses resultam de uma hiper-proliferação clonal dos queratinócitos, encontrando-se pelo menos descritas seis formas clínicas, que partilham o achado da lamela cornóide no exame histopatológico. Os autores descrevem o caso de uma poroqueratose de Mibelli numa mulher de 27 anos, raça negra, com início na infância, eficazmente tratada com retinóide tópico, apresentando-se as manifestações típicas de uma dermatose pouco frequente e destacando-se a importância da histopatologia na confirmação do seu diagnóstico. A poroqueratose de Mibelli é uma dermatose crónica e progressiva, raramente com remissão espontânea. A sua evolução para neoplasia maligna, particularmente carcinoma espino-celular, pode ocorrer em cerca de 7% dos doentes, reforçando-se a importância de uma adequada vigilância

    Dynamic range of hypercubic stochastic excitable media

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    We study the response properties of d-dimensional hypercubic excitable networks to a stochastic stimulus. Each site, modelled either by a three-state stochastic susceptible-infected-recovered-susceptible system or by the probabilistic Greenberg-Hastings cellular automaton, is continuously and independently stimulated by an external Poisson rate h. The response function (mean density of active sites rho versus h) is obtained via simulations (for d=1, 2, 3, 4) and mean field approximations at the single-site and pair levels (for all d). In any dimension, the dynamic range of the response function is maximized precisely at the nonequilibrium phase transition to self-sustained activity, in agreement with a reasoning recently proposed. Moreover, the maximum dynamic range attained at a given dimension d is a decreasing function of d.Comment: 7 pages, 4 figure
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