Evolução de estruturas primordiais

Understanding the way in which large-scale structures, like galaxies, form remains one of the most challenging problems in cosmology today. The standard theory for the origin of these structures is that they grew by gravitational instability from small, perhaps quantum generated, °uctuations in the density of dark matter, baryons and photons over an uniform primordial Universe. After the recombination, the baryons began to fall into the pre-existing gravitational potential wells of the dark matter. In this dissertation a study is initially made of the primordial recombination era, the epoch of the formation of the neutral hydrogen atoms. Besides, we analyzed the evolution of the density contrast (of baryonic and dark matter), in clouds of dark matter with masses among 104M¯ ¡ 1010M¯. In particular, we take into account the several physical mechanisms that act in the baryonic component, during and after the recombination era. The analysis of the formation of these primordial objects was made in the context of three models of dark energy as background: Quintessence, ¤CDM(Cosmological Constant plus Cold Dark Matter) and Phantom. We show that the dark matter is the fundamental agent for the formation of the structures observed today. The dark energy has great importance at that epoch of its formation

Application of ARX neural networks to model the rate of penetration of petroleum wells drilling

Bit performance prediction has been a challenging problem for the petroleum industry. It is essential in cost reduction associated with well planning and drilling performance prediction, especially when rigs leasing rates tend to follow the projects-demand and barrel-price rises. A methodology to model and predict one of the drilling bit performance evaluator, the Rate of Penetration (ROP), is presented herein. As the parameters affecting the ROP are complex and their relationship not easily modeled, the application of a Neural Network is suggested. In the present work, a dynamic neural network, based on the Auto-Regressive with Extra Input Signals model, or ARX model, is used to approach the ROP modeling problem. The network was applied to a real oil offshore field data set, consisted of information from seven wells drilled with an equal-diameter bit.

Contribuições na modelagem dinâmica e no controle neural de um braço robótico com elos flexíveis

Em geral, estruturas espaciais e manipuladores robóticos leves têm uma característica similar e inerente que é a flexibilidade. Esta característica torna a dinâmica do sistema muito mais complexa e com maiores dificuldades para a análise de estabilidade e controle. Então, braços robóticos bastantes leves, com velocidade elevada e potencia limitada devem considerar o controle de vibração causada pela flexibilidade. Por este motivo, uma estratégia de controle é desejada não somente para o controle do modo rígido mas também que seja capaz de controlar os modos de vibração do braço robótico flexível. Também, redes neurais artificiais (RNA) são identificadas como uma subespecialidade de inteligência artificial. Constituem atualmente uma teoria para o estudo de fenômenos complexos e representam uma nova ferramenta na tecnologia de processamento de informação, por possuírem características como processamento paralelo, capacidade de aprendizagem, mapeamento não-linear e capacidade de generalização. Assim, neste estudo utilizam-se RNA na identificação e controle do braço robótico com elos flexíveis. Esta tese apresenta a modelagem dinâmica de braços robóticos com elos flexíveis, 1D no plano horizontal e 2D no plano vertical com ação da gravidade, respectivamente. Modelos dinâmicos reduzidos são obtidos pelo formalismo de Newton-Euler, e utiliza-se o método dos elementos finitos (MEF) na discretização dos deslocamentos elásticos baseado na teoria elementar da viga. Além disso, duas estratégias de controle têm sido desenvolvidas com a finalidade de eliminar as vibrações devido à flexibilidade do braço robótico com elos flexíveis. Primeiro, utilizase um controlador neural feedforward (NFF) na obtenção da dinâmica inversa do braço robótico flexível e o calculo do torque da junta. E segundo, para obter precisão no posicionamento... (Resumo completo, clicar acesso eletrônico abaixo)

Many-core Algorithms for Combinatorial Optimization

Combinatorial Optimization is becoming ever more crucial, in these days. From natural sciences to economics, passing through urban centers administration and personnel management, methodologies and algorithms with a strong theoretical background and a consolidated real-word effectiveness is more and more requested, in order to find, quickly, good solutions to complex strategical problems. Resource optimization is, nowadays, a fundamental ground for building the basements of successful projects. From the theoretical point of view, Combinatorial Optimization rests on stable and strong foundations, that allow researchers to face ever more challenging problems. However, from the application point of view, it seems that the rate of theoretical developments cannot cope with that enjoyed by modern hardware technologies, especially with reference to the one of processors industry. In this work we propose new parallel algorithms, designed for exploiting the new parallel architectures available on the market. We found that, exposing the inherent parallelism of some resolution techniques (like Dynamic Programming), the computational benefits are remarkable, lowering the execution times by more than an order of magnitude, and allowing to address instances with dimensions not possible before. We approached four Combinatorial Optimization’s notable problems: Packing Problem, Vehicle Routing Problem, Single Source Shortest Path Problem and a Network Design problem. For each of these problems we propose a collection of effective parallel solution algorithms, either for solving the full problem (Guillotine Cuts and SSSPP) or for enhancing a fundamental part of the solution method (VRP and ND). We endorse our claim by presenting computational results for all problems, either on standard benchmarks from the literature or, when possible, on data from real-world applications, where speed-ups of one order of magnitude are usually attained, not uncommonly scaling up to 40 X factors.

Immunohistochemical localization of osteoclastogenic cell mediators in feline tooth resorption and healthy teeth

Tooth resorption is among the most common and most challenging problems in feline dentistry It is a progressive disease eventually leading to tooth loss and often root replacement. The etiology of moth resorption remains obscure and to date no effective therapeutic approach is known. The present study is aimed at assessing the reliability of radiographic imaging and addressing the possible involvement of receptor activator of NF kappa B (RANK), its ligand (RANKL), and osteoprotegerin (OPG) in the process of tooth resorption. Teeth from 8 cats were investigated by means of radiographs and paraffin sections followed by immunolabeling. Six cats were diagnosed with tooth resorption based on histopathologic and radiographic findings. Samples were classified according to a four-stage diagnostic system. Radiologic assessment of tooth resorption correlated very strongly with histopathologic findings. Tooth resorption was accompanied by a strong staining with all three antibodies used, especially with anti-RANK and anti-RANKL antibodies. The presence of OPG and RANKL at the resorption site is indicative of repair attempts by fibroblasts and stromal cells. These findings should be extended by further investigations in order to elucidate the pathophysiologic processes underlying tooth resorption that might lead to prophylactic and/or therapeutic measures. J Vet Dent 27(2); 75 - 83, 2010

Rendering Falling Leaves on Graphics Hardware

There is a growing interest in simulating natural phenomena in computer graphics applications. Animating natural scenes in real time is one of the most challenging problems due to the inherent complexity of their structure, formed by millions of geometric entities, and the interactions that happen within. An example of natural scenario that is needed for games or simulation programs are forests. Forests are difficult to render because the huge amount of geometric entities and the large amount of detail to be represented. Moreover, the interactions between the objects (grass, leaves) and external forces such as wind are complex to model. In this paper we concentrate in the rendering of falling leaves at low cost. We present a technique that exploits graphics hardware in order to render thousands of leaves with different falling paths in real time and low memory requirements.

Ultracold Quantum Gases and Lattice Systems: Quantum Simulation of Lattice Gauge Theories

Abelian and non-Abelian gauge theories are of central importance in many areas of physics. In condensed matter physics, AbelianU(1) lattice gauge theories arise in the description of certain quantum spin liquids. In quantum information theory, Kitaev’s toric code is a Z(2) lattice gauge theory. In particle physics, Quantum Chromodynamics (QCD), the non-Abelian SU(3) gauge theory of the strong interactions between quarks and gluons, is nonperturbatively regularized on a lattice. Quantum link models extend the concept of lattice gauge theories beyond the Wilson formulation, and are well suited for both digital and analog quantum simulation using ultracold atomic gases in optical lattices. Since quantum simulators do not suffer from the notorious sign problem, they open the door to studies of the real-time evolution of strongly coupled quantum systems, which are impossible with classical simulation methods. A plethora of interesting lattice gauge theories suggests itself for quantum simulation, which should allow us to address very challenging problems, ranging from confinement and deconfinement, or chiral symmetry breaking and its restoration at finite baryon density, to color superconductivity and the real-time evolution of heavy-ion collisions, first in simpler model gauge theories and ultimately in QCD.

Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations

Multi-objective optimization algorithms aim at finding Pareto-optimal solutions. Recovering Pareto fronts or Pareto sets from a limited number of function evaluations are challenging problems. A popular approach in the case of expensive-to-evaluate functions is to appeal to metamodels. Kriging has been shown efficient as a base for sequential multi-objective optimization, notably through infill sampling criteria balancing exploitation and exploration such as the Expected Hypervolume Improvement. Here we consider Kriging metamodels not only for selecting new points, but as a tool for estimating the whole Pareto front and quantifying how much uncertainty remains on it at any stage of Kriging-based multi-objective optimization algorithms. Our approach relies on the Gaussian process interpretation of Kriging, and bases upon conditional simulations. Using concepts from random set theory, we propose to adapt the Vorob’ev expectation and deviation to capture the variability of the set of non-dominated points. Numerical experiments illustrate the potential of the proposed workflow, and it is shown on examples how Gaussian process simulations and the estimated Vorob’ev deviation can be used to monitor the ability of Kriging-based multi-objective optimization algorithms to accurately learn the Pareto front.

Áreas rurales marginales en las economías regionales argentinas : problemas y alternativas

El tema de las economías regionales en Argentina, y específicamente el de sus áreas rurales marginales, representan cuestiones pendientes en el marco de los estudios geográficos nacionales y, a la vez, constituyen problemáticas desafiantes para su comprensión por la complejidad de las situaciones socioeconómicas y políticas que experimentan. Este trabajo retoma el tema de las áreas marginales en general, considerada en trabajos anteriores, y aporta una serie de reflexiones que profundizan la caracterización de las áreas rurales marginales en particular. Este fue el paso previo para analizar el complejo proceso que han experimentado las economías regionales extrapampeanas más aisladas del modelo centro-litoral del país. Se ahondan las evoluciones e impactos de la marginalidad en las economías regionales y se procura adaptar los procesos temporales y territoriales considerados, en un área rural marginal de la Patagonia Meridional. Por último, se presentan posibles alternativas de solución a las problemáticas de estas regiones.

Parallelizing irregular and pointer-based computations automatically: perspectives from logic and constraint programming

Irregular computations pose sorne of the most interesting and challenging problems in automatic parallelization. Irregularity appears in certain kinds of numerical problems and is pervasive in symbolic applications. Such computations often use dynamic data structures, which make heavy use of pointers. This complicates all the steps of a parallelizing compiler, from independence detection to task partitioning and placement. Starting in the mid 80s there has been significant progress in the development of parallelizing compilers for logic pro­gramming (and more recently, constraint programming) resulting in quite capable paralle­lizers. The typical applications of these paradigms frequently involve irregular computations, and make heavy use of dynamic data structures with pointers, since logical variables represent in practice a well-behaved form of pointers. This arguably makes the techniques used in these compilers potentially interesting. In this paper, we introduce in a tutoríal way, sorne of the problems faced by parallelizing compilers for logic and constraint programs and provide pointers to sorne of the significant progress made in the area. In particular, this work has resulted in a series of achievements in the areas of inter-procedural pointer aliasing analysis for independence detection, cost models and cost analysis, cactus-stack memory management, techniques for managing speculative and irregular computations through task granularity control and dynamic task allocation such as work-stealing schedulers), etc.

Automatic parallelization of irregular and pointer-based computations: perspectives from logic and constraint programming

Irregular computations pose some of the most interesting and challenging problems in automatic parallelization. Irregularity appears in certain kinds of numerical problems and is pervasive in symbolic applications. Such computations often use dynamic data structures which make heavy use of pointers. This complicates all the steps of a parallelizing compiler, from independence detection to task partitioning and placement. In the past decade there has been significant progress in the development of parallelizing compilers for logic programming and, more recently, constraint programming. The typical applications of these paradigms frequently involve irregular computations, which arguably makes the techniques used in these compilers potentially interesting. In this paper we introduce in a tutorial way some of the problems faced by parallelizing compilers for logic and constraint programs. These include the need for inter-procedural pointer aliasing analysis for independence detection and having to manage speculative and irregular computations through task granularity control and dynamic task allocation. We also provide pointers to some of the progress made in these áreas. In the associated talk we demónstrate representatives of several generations of these parallelizing compilers.

An approach to a model of the visual cortex: disorders of vision

One of the most challenging problems that must be solved by any theoretical model purporting to explain the competence of the human brain for relational tasks is the one related with the analysis and representation of the internal structure in an extended spatial layout of múltiple objects. In this way, some of the problems are related with specific aims as how can we extract and represent spatial relationships among objects, how can we represent the movement of a selected object and so on. The main objective of this paper is the study of some plausible brain structures that can provide answers in these problems. Moreover, in order to achieve a more concrete knowledge, our study will be focused on the response of the retinal layers for optical information processing and how this information can be processed in the first cortex layers. The model to be reported is just a first trial and some major additions are needed to complete the whole vision process.

Contribuciones al análisis de problemas supercomplejos de toma de decisiones

Los fundamentos de la Teoría de la Decisión Bayesiana proporcionan un marco coherente en el que se pueden resolver los problemas de toma de decisiones. La creciente disponibilidad de ordenadores potentes está llevando a tratar problemas cada vez más complejos con numerosas fuentes de incertidumbre multidimensionales; varios objetivos conflictivos; preferencias, metas y creencias cambiantes en el tiempo y distintos grupos afectados por las decisiones. Estos factores, a su vez, exigen mejores herramientas de representación de problemas; imponen fuertes restricciones cognitivas sobre los decisores y conllevan difíciles problemas computacionales. Esta tesis tratará estos tres aspectos. En el Capítulo 1, proporcionamos una revisión crítica de los principales métodos gráficos de representación y resolución de problemas, concluyendo con algunas recomendaciones fundamentales y generalizaciones. Nuestro segundo comentario nos lleva a estudiar tales métodos cuando sólo disponemos de información parcial sobre las preferencias y creencias del decisor. En el Capítulo 2, estudiamos este problema cuando empleamos diagramas de influencia (DI). Damos un algoritmo para calcular las soluciones no dominadas en un DI y analizamos varios conceptos de solución ad hoc. El último aspecto se estudia en los Capítulos 3 y 4. Motivado por una aplicación de gestión de embalses, introducimos un método heurístico para resolver problemas de decisión secuenciales. Como muestra resultados muy buenos, extendemos la idea a problemas secuenciales generales y cuantificamos su bondad. Exploramos después en varias direcciones la aplicación de métodos de simulación al Análisis de Decisiones. Introducimos primero métodos de Monte Cario para aproximar el conjunto no dominado en problemas continuos. Después, proporcionamos un método de Monte Cario basado en cadenas de Markov para problemas con información completa con estructura general: las decisiones y las variables aleatorias pueden ser continuas, y la función de utilidad puede ser arbitraria. Nuestro esquema es aplicable a muchos problemas modelizados como DI. Finalizamos con un capítulo de conclusiones y problemas abiertos.---ABSTRACT---The foundations of Bayesian Decisión Theory provide a coherent framework in which decisión making problems may be solved. With the advent of powerful computers and given the many challenging problems we face, we are gradually attempting to solve more and more complex decisión making problems with high and multidimensional uncertainty, múltiple objectives, influence of time over decisión tasks and influence over many groups. These complexity factors demand better representation tools for decisión making problems; place strong cognitive demands on the decison maker judgements; and lead to involved computational problems. This thesis will deal with these three topics. In recent years, many representation tools have been developed for decisión making problems. In Chapter 1, we provide a critical review of most of them and conclude with recommendations and generalisations. Given our second query, we could wonder how may we deal with those representation tools when there is only partial information. In Chapter 2, we find out how to deal with such a problem when it is structured as an influence diagram (ID). We give an algorithm to compute nondominated solutions in ID's and analyse several ad hoc solution concepts.- The last issue is studied in Chapters 3 and 4. In a reservoir management case study, we have introduced a heuristic method for solving sequential decisión making problems. Since it shows very good performance, we extend the idea to general problems and quantify its goodness. We explore then in several directions the application of simulation based methods to Decisión Analysis. We first introduce Monte Cario methods to approximate the nondominated set in continuous problems. Then, we provide a Monte Cario Markov Chain method for problems under total information with general structure: decisions and random variables may be continuous, and the utility function may be arbitrary. Our scheme is applicable to many problems modeled as IDs. We conclude with discussions and several open problems.

Neuronanatomy, neurology and Bayesian networks

Bayesian networks are data mining models with clear semantics and a sound theoretical foundation. In this keynote talk we will pinpoint a number of neuroscience problems that can be addressed using Bayesian networks. In neuroanatomy, we will show computer simulation models of dendritic trees and classification of neuron types, both based on morphological features. In neurology, we will present the search for genetic biomarkers in Alzheimer's disease and the prediction of health-related quality of life in Parkinson's disease. Most of these challenging problems posed by neuroscience involve new Bayesian network designs that can cope with multiple class variables, small sample sizes, or labels annotated by several experts.

On cortical coding of vocal communication sounds in primates

Understanding how the brain processes vocal communication sounds is one of the most challenging problems in neuroscience. Our understanding of how the cortex accomplishes this unique task should greatly facilitate our understanding of cortical mechanisms in general. Perception of species-specific communication sounds is an important aspect of the auditory behavior of many animal species and is crucial for their social interactions, reproductive success, and survival. The principles of neural representations of these behaviorally important sounds in the cerebral cortex have direct implications for the neural mechanisms underlying human speech perception. Our progress in this area has been relatively slow, compared with our understanding of other auditory functions such as echolocation and sound localization. This article discusses previous and current studies in this field, with emphasis on nonhuman primates, and proposes a conceptual platform to further our exploration of this frontier. It is argued that the prerequisite condition for understanding cortical mechanisms underlying communication sound perception and production is an appropriate animal model. Three issues are central to this work: (i) neural encoding of statistical structure of communication sounds, (ii) the role of behavioral relevance in shaping cortical representations, and (iii) sensory–motor interactions between vocal production and perception systems.