Abstract: Mobile ad hoc networks (MANETs) and wireless sensor networks (WSNs) are two recently-developed technologies that uniquely function without xed infrastructure support, and sense at scales, resolutions, and durations previously not possible. While both offer great potential in many applications, developing software for these types of networks is extremely dffcult, preventing their wide-spread use. Three primary challenges are (1) the high level of dynamics within the network in terms of changing wireless links and node hardware con gurations, (2) the wide variety of hardware present in these networks, and (3) the extremely limited computational and energy resources available. Until now, the burden of handling these issues was put on the software application developer. This dissertation presents three novel programming models and middleware systems that address these challenges: Limone, Agilla, and Servilla. Limone reliably handles high levels of dynamics within MANETs. It does this through lightweight coordination primitives that make minimal assumptions about network connectivity. Agilla enables self-adaptive WSN applications via the integration of mobile agent and tuple space programming models, which is critical given the continuously changing network. It is the rst system to successfully demonstrate the feasibility of using mobile agents and tuple spaces within

Abstract. The areal modeling of the extremes of a natural process such as rainfall or temperature is important in environmental statistics; for example, understanding extreme areal rainfall is crucial in flood protection. This ar-ticle reviews recent progress in the statistical modeling of spatial extremes, starting with sketches of the necessary elements of extreme value statistics and geostatistics. The main types of statistical models thus far proposed, based on latent variables, on copulas and on spatial max-stable processes, are described and then are compared by application to a data set on rain-fall in Switzerland. Whereas latent variable modeling allows a better fit to marginal distributions, it fits the joint distributions of extremes poorly, so appropriately-chosen copula or max-stable models seem essential for suc-cessful spatial modeling of extremes.

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Corporate Governance determinants of voluntary disclosure and its effects on information asymmetry: an analysis for Iberian Peninsula listed companies.

ii iv vTo my parents vi vii Acknowledgements I would first and foremost extend my whole-hearted gratitude to my advisors, An-drea Arpaci-Dusseau and Remzi Arpaci-Dusseau. Andrea and Remzi are the reason that I had the opportunity for this exceptional Ph.D. journey. To this day, I still re-member the moment when they took me as their student and the joy and hope in my heart. Andrea and Remzi have showed me what systems research is like and how much fun and challenging it can be. Before this journey with them, I had always liked and believed in the beauty of mathematics and theory. My initial interest in systems research happened when I took Remzi’s Advanced Operating Systems

Abstract. We review theoretical models of individual motility as well as collective dynamics and pattern formation of active particles. We focus on simple models of active dynamics with a particular emphasis on nonlinear and stochastic dynamics of such self-propelled entities in the framework of statistical mechanics. Examples of such active units in complex physico-chemical and biological systems are chemically pow-ered nano-rods, localized patterns in reaction-diffusion system, motile cells or macroscopic animals. Based on the description of individual motion of point-like active particles by stochastic differential equations, we discuss different velocity-dependent friction functions, the impact of various types of fluctuations and calculate characteristic observ-ables such as stationary velocity distributions or diffusion coefficients. Finally, we consider not only the free and confined individual active dynamics but also different types of interaction between active par-ticles. The resulting collective dynamical behavior of large assemblies and aggregates of active units is discussed and an overview over some recent results on spatiotemporal pattern formation in such systems is given. 1

)SC of [h~ rcrm: as providing a specially constructed space in which the parties can engage in the process of exploration, observation, and analysis and f.lshion new products (hat can be exported into the political arena, \'I;'orkshoJls thus have a duni purpose. They arc designed. firsr, (0

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Parametric max-stable processes are increasingly used to model spatial extremes. Since the dependence structure is specified for block maxima, the data used for inference are block maxima from all sites. To improve the estimation efficiency, we propose a two-step approach with composite likelihood that utilizes site-wise daily records in addition to block maxima. Besides the parameter estimation, there is no formal model checking and diagnosis method for spatial extremes modeling yet. Model diagnosis in practice has been informal and mostly based on visual checking tools such as residual plot and quantile-quantile plot. We proposed a goodness-of-fit test for max-stable processes based on the comparison between a nonparametric and a parametric estimator of the corresponding unknown multivariate Pickands dependence function. The proposed two-step procedure separates the estimation of marginal parameters and dependence parameters into two steps. The first step estimates the marginal pa-rameters with an independence Likelihood by ignoring the spatial dependence. Given ithe marginal parameter estimates, the second step estimates the dependence parameters