Texte remanié de: Doctoral diss.--Bowling Green state university. / Contient l'oeuvre de Thomas Nashe.

Inaug-diss.--Greifswald. / Lebenslauf. "Literatur" 2 p. at end.

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Uncoordinated frequency hopping (UFH) has recently emerged as an effective mechanism to defend against jamming attacks. Existing research focuses on the optimal design of the hopping pattern, which implicitly assumes that the strategy of the attacker is fixed. In practice, the attacker might adjust its strategy to maximize its damage on the communication system. In this thesis, we study the design of optimal hopping pattern (the defense strategy) as long as the optimal jamming pattern (the attack strategy). In particular, we model the dynamic between the legitimate users and the attacker as a zero sum game, and study the property of this game. We show that when the legitimate users and the jammer can access only one channel at any time, the game has a unique Nash equilibrium. In the Nash equilibrium, the legitimate users and Eve will access or jam only a subset of channels that have good channel quality. Furthermore, the better the channel, the larger the probability that Eve will jam the channel and the smaller the probability the legitimate users will access this channel. We further extend the study to multiple access multiple jamming case and characterize the Nash equilibrium. We also give numerical results to illustrate the analytical results derived in this thesis.

Machinery investments represent a substantial portion of agribusiness firms’ costs. Because of high machinery costs, variable profit margins, and increasing competition, agribusiness managers continually seek methods to maintain profitability and manage risk. One relatively new method is jointly owning and sharing machinery. Contract design issues to enhance horizontal linkages between firms through machinery sharing are addressed. Specifically, costs and depreciation sharing between two firms entering into a joint machinery ownership contract are examined. Two, two-player models, a Nash equilibrium game theoretical model and an applied two-farm simulation model are used to determine impacts of machinery sharing on firms engaged in machinery sharing. The Nash equilibrium model determines theoretical optimal sharing rules for two generic firms. Using the Nash equilibrium model as the basis, the two-farm simulation model provides more specific insights into joint harvest machinery sharing. Both models include contractual components that are uniquely associated with machinery sharing. Contractual components include penalty payment structure for untimely machinery delivery and the percentages of shared costs paid and depreciation claimed paid by each firm. Harvesting windows for each farm and yield reductions associated with untimely machinery delivery are accounted for within the models. Machinery sharing can increase the NPV of after tax cash flows and potentially reduce risk. Sharing will, however, not occur if own marginal transaction costs and/or marginal penalty costs associated with untimely machinery delivery are too large. Further, if the marginal costs of sharing are small relative to own marginal net benefits, sharing will not occur. There are potential tradeoffs between the percentage of shared costs paid and the percentage of shared depreciation claimed depending on each farms’ specific tax deductions. Harvesting window overlaps help determine the viability of machinery sharing. Farms may be better off sharing larger, more efficient machinery than using smaller machinery even when harvest must be delayed. Percentages of shared costs, depreciation, and tax deductions have important tax implications that impact the after tax cash flows and should be considered when negotiating machinery sharing contracts.

Les problèmes d’investissements d’énergie sont difficiles à cause des incertitudes. Certaines incertitudes peuvent être modélisées par les probabilités. Mais il y a des problèmes difficiles tels que l'évolution de technologie et la pénalisation de CO2, délicats à modéliser par des probabilités. Aussi, les travaux sur l’optimisation des systèmes d’énergie est souvent déterministe. Cette thèse s’intéresse à appliquer l’optimisation bruitée aux systèmes d’énergie. Cette thèse se concentre sur trois parties principales: les études des méthodes pour gérer le bruit, y compris utiliser des méthodes de ré-échantillonnage pour améliorer la vitesse de convergence; les applications des méthodes de portefeuilles à l’optimisation bruitée dans le continu; les applications des méthodes de portefeuilles aux cas avec incertitudes pour la planification des investissements d’énergie et aux jeux, y compris l’utilisation de l’algorithme de bandit adversarial pour calculer l’équilibre de Nash d'un jeu matriciel à somme nulle et l’utilisation de “sparsity” pour accélérer le calcul de l’équilibre de Nash. / This manuscript concentrates in studying methods to handle the noise, including using resampling methods to improve the convergence rates and applying portfolio methods to cases with uncertainties (games, and noisy optimization in continuous domains).Part I will introduce the manuscript, then review the state of the art in noisy optimization, portfolio algorithm, multi-armed bandit algorithms and games.Part II concentrates on the work on noisy optimization:∙ Chapter 4 provides a generic algorithm for noisy optimization recovering most of the existing bounds in one single noisy optimization algorithm.∙ Chapter5 applies different resampling rules in evolution strategies for noisy optimization, without the assumption of variance vanishing in the neighborhood of the optimum, and shows mathematically log-log convergence results and studies experimentally the slope of this convergence.∙ Chapter 6 compares resampling rules used in the differential evolution algorithm for strongly noisy optimization. By mathematical analysis, a new rule is designed for choosing the number of resamplings, as a function of the dimension, and validate its efficiency compared to existing heuristics - though there is no clear improvement over other empirically derived rules.∙ Chapter 7 applies “common random numbers”, also known as pairing, to an intermediate case between black-box and white-box cases for improving the convergence.Part III is devoted to portfolio in adversarial problems:∙ Nash equilibria are cases in which combining pure strategies is necessary for designing optimal strategies. Two chapters are dedicated to the computation of Nash equilibria:– Chapter 9 investigates combinations of pure strategies, when a small set of pure strategies is concerned; basically, we get improved rates when the support of the Nash equilibrium is small.– Chapter 10 applies these results to a power system problem. This compares several bandit algorithms for Nash equilibria, defines parameter-free bandit algorithms, and shows the relevance of the sparsity approach dis- cussed in Chapter 9.∙ Then, two chapters are dedicated to portfolios of game methods:– Chapter 11 shows how to generate multiple policies, from a single one, when only one such policy is available. This kind of bootstrap (based on random seeds) generates many deterministic policies, and then combines them into one better policy. This has been tested on several games.– Chapter 12 extends chapter 11 by combining policies in a position-specific manner. In particular, we get a better asymptotic behavior than MCTS.Part IV is devoted to portfolios in noisy optimization:∙ Chapter 14 is devoted to portfolio of noisy optimization methods in continuous domains;∙ Chapter 15 proposed differential evolution as a tool for non- stationary bandit problems.

Adefovir, an acyclic nucleotide reverse transcriptase inhibitor used to treat hepatitis B viral infection, is primarily eliminated renally through cooperation of glomerular filtration with active tubular transport. Nonalcoholic steatohepatitis is a variable in drug disposition, yet the impact on renal transport processes has yet to be fully understood. The goal of this study was to determine the effect of nonalcoholic steatohepatitis on the pharmacokinetics of adefovir in rats given a control or methionine and choline deficient diet to induce nonalcoholic steatohepatitis.

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Würzburg, Univ., Diss., 2009.

Ingeniero Civil Matemático / El objetivo principal de este trabajo de memoria de título es estudiar la unicidad del Equilibrio de Nash para el mecanismo de asignación proporcional aplicado a Redes de Congestión, descrito por Johari y Tsitsiklis, donde lo que se busca repartir es la capacidad de los arcos de la red entre varios agentes interesados, cada uno de los cuales cuenta con un conjunto de caminos en la red a través de los que pretende enviar flujo. En dicho trabajo, se muestra que el equilibrio es único para el caso en que la red es en realidad un solo arco, pero se deja como problema abierto la unicidad en una red general. Aportar al conocimiento sobre la unicidad del equilibrio en configuraciones más generales, es la principal motivación del trabajo desarrollado. En esta memoria se aborda el problema estudiando la unicidad desde casos particulares a situaciones más generales, obteniendo como resultado principal que el equilibrio es único para el caso de una red con arcos de distintas capacidades, y donde los jugadores están interesados cada uno en un solo camino (esto último se denomina \emph{Fixed Routing}). Para algunos casos particulares incluso fue posible explicitar las estrategias que definen el único equilibrio. El caso más general -donde cada jugador está interesado en varios caminos en la red- continúa como problema abierto, sin embargo se muestran aquí algunos contraejemplos a otras nociones de unicidad que se pierden en aquel caso. Como objetivo secundario, el trabajo desarrollado en el marco de esta memoria busca dar una nueva demostración de que el Precio de la Anarquía del juego en una red general es $3/4$, aplicando la técnica para el caso de un solo arco descrita por Correa et al. Dicho objetivo se logra, en primer lugar para el caso de \emph{Fixed Routing} y una red con la misma capacidad en todos los arcos, y también para el caso en que los jugadores tienen todos el mismo camino de interés, y las capacidades de los arcos son distintas. Por la experiencia adquirida durante el desarrollo del trabajo, es posible intuir que este es el caso más general en que se puede aplicar la técnica sin modificarla. Por último, se define para el caso de un solo arco, una versión secuencial del mecanismo de asignación proporcional, donde los jugadores no actúan simultáneamente sino que van llegando en orden. Para el caso de dos jugadores, se muestra explícitamente cuál es el único Equilibrio Perfecto en Subjuegos y se obtiene que el Precio de la Anarquía secuencial asociado es $0.875$. Este resultado coincide -tanto en la asignación que entrega el equilibrio, como en la eficiencia del mismo- con el mecanismo \emph{óptimo} para dos jugadores definido por Sanghavi y Hajek. Para el caso de tres jugadores no es posible encontrar analíticamente equilibrios, pero sí se encuentran relaciones implícitas entre las estrategias de los jugadores que permiten hallar numéricamente dos candidatos a equilibrio.