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Il ritorno dei Classici nell’Umanesimo: Studi in memoria di Gianvito Resta. Gabriella Albanese, Claudio Ciociola, Mariarosa Cortesi, and Claudia Villa, eds. With Paolo Pontari. Florence: SISMEL Edizioni del Galluzzo, 2015. xxxii + 700 pp. €75.

Proefschrift Universiteit Twente, Enschede. / Met lit. opg. - Met samenvatting in het Nederlands.

Diss. (sammanfattning) Umeå : Univ., 2004. / Härtill 4 uppsatser.

<p> We consider a one dimensional random walk in a random environment (RWRE) with a positive speed lim<i>_n</i>_{&rarr;&infin;} (<i>X_n/</i>) = &upsi;_&alpha; > 0. Gantert and Zeitouni showed that if the environment has both positive and negative local drifts then the quenched slowdown probabilities <i>P</i>_&omega;(<i>X_n</i> &lt; <i>xn</i>) with <i> x</i>&isin; (0,&upsi;_&alpha;) decay approximately like exp{-<i> n</i>^1-1/<i>^s</i>} for a deterministic <i> s</i> > 1. More precisely, they showed that <i>n</i>^-&gamma; log <i>P</i>_&omega;(<i>X_n</i> &lt; <i>xn</i>) converges to 0 or -&infin; depending on whether &gamma; > 1 - 1/<i>s</i> or &gamma; &lt; 1 - 1/<i> s</i>. In this paper, we improve on this by showing that <i>n</i>^-1+1/<i>^s</i> log <i>P</i>_&omega;(X_n &lt; <i>xn</i>) oscillates between 0 and -&infin; , almost surely.</p>

Magíster en Economía Aplicada. Ingeniero Civil Matemático / El objetivo de este trabajo es estudiar el problema de asignación escolar como uno de asignación probabilística y poder entender como diversos mecanismos de asignación escolar se desempeñan en términos de las probabilidades que le asignan a los alumnos de poder acceder a los colegios. Para éste fin se asume que el planificador central determina una función que les permite generar preferencias sobre loterías desde preferencias ordinales por los colegios, estás funciones se denominan extensiones. Se elabora una nueva noción de equidad (estabilidad) la cual generaliza nociones previas tanto en la literatura de asignación escolar como en la de asignación probabilística. El resultado principal de éste trabajo corresponde a la caracterización, bajo supuestos razonables en las preferencias, del conjunto de asignaciones probabilísticas estables. También se desarrollan nuevos resultados de existencia de asignaciones probabilísticas estables y eficientes, se presentan resultados de mecanismos probabilísticos compatibles en incentivos y se evalúan los mecanismos de asignación escolar Boston, Deferred Acceptance, Top Trading Cycles y Fraction Deferred Acceptance en términos de eficiencia, estabilidad e incentivos. / Este trabajo ha sido parcialmente financiado por MIPP

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No description available.

Parameterized NP-hard problems are NP-hard problems that are associated with special variables called parameters. One example of the problem is to find simple paths of length k in a graph, where the integer k is the parameter. We call this problem the p-path problem. The p-path problem is the parameterized version of the well-known NP-complete problem - the longest simple path problem. There are two main reasons why we study parameterized NP-hard problems. First, many application problems are naturally associated with certain parameters. Hence we need to solve these parameterized NP-hard problems. Second, if parameters take only small values, we can take advantage of these parameters to design very effective algorithms. If a parameterized NP-hard problem can be solved by an algorithm of running time in form of f(k)nO(1), where k is the parameter, f(k) is independent of n, and n is the input size of the problem instance, we say that this parameterized NP-hard problem is fixed parameter tractable (FPT). If a problem is FPT and the parameter takes only small values, the problem can be solved efficiently (it can be solved almost in polynomial time). In this dissertation, first, we introduce several techniques that can be used to design efficient algorithms for parameterized NP-hard problems. These techniques include branch and bound, divide and conquer, color coding and dynamic programming, iterative compression, iterative expansion and kernelization. Then we present our results about how to use these techniques to solve parameterized NP-hard problems, such as the p-path problem and the pd-feedback vertex set problem. Especially, we designed the first algorithm of running time in form of f(k)nO(1) for the pd-feedback vertex set problem. Thus solved an outstanding open problem, i.e. if the pd-feedback vertex set problem is FPT. Finally, we will introduce how to use parameterized algorithm techniques to solve the signaling pathway problem and the motif finding problem from bioinformatics.