Performance of artificial neural networks and genetical evolved artificial neural networks unfolding techniques

With the Bonner spheres spectrometer neutron spectrum is obtained through an unfolding procedure. Monte Carlo methods, Regularization, Parametrization, Least-squares, and Maximum Entropy are some of the techniques utilized for unfolding. In the last decade methods based on Artificial Intelligence Technology have been used. Approaches based on Genetic Algorithms and Artificial Neural Networks have been developed in order to overcome the drawbacks of previous techniques. Nevertheless the advantages of Artificial Neural Networks still it has some drawbacks mainly in the design process of the network, vg the optimum selection of the architectural and learning ANN parameters. In recent years the use of hybrid technologies, combining Artificial Neural Networks and Genetic Algorithms, has been utilized to. In this work, several ANN topologies were trained and tested using Artificial Neural Networks and Genetically Evolved Artificial Neural Networks in the aim to unfold neutron spectra using the count rates of a Bonner sphere spectrometer. Here, a comparative study of both procedures has been carried out.

Super-Resolution in Respiratory Synchronized Positron Emission Tomography

Respiratory motion is a major source of reduced quality in positron emission tomography (PET). In order to minimize its effects, the use of respiratory synchronized acquisitions, leading to gated frames, has been suggested. Such frames, however, are of low signal-to-noise ratio (SNR) as they contain reduced statistics. Super-resolution (SR) techniques make use of the motion in a sequence of images in order to improve their quality. They aim at enhancing a low-resolution image belonging to a sequence of images representing different views of the same scene. In this work, a maximum a posteriori (MAP) super-resolution algorithm has been implemented and applied to respiratory gated PET images for motion compensation. An edge preserving Huber regularization term was used to ensure convergence. Motion fields were recovered using a B-spline based elastic registration algorithm. The performance of the SR algorithm was evaluated through the use of both simulated and clinical datasets by assessing image SNR, as well as the contrast, position and extent of the different lesions. Results were compared to summing the registered synchronized frames on both simulated and clinical datasets. The super-resolution image had higher SNR (by a factor of over 4 on average) and lesion contrast (by a factor of 2) than the single respiratory synchronized frame using the same reconstruction matrix size. In comparison to the motion corrected or the motion free images a similar SNR was obtained, while improvements of up to 20% in the recovered lesion size and contrast were measured. Finally, the recovered lesion locations on the SR images were systematically closer to the true simulated lesion positions. These observations concerning the SNR, lesion contrast and size were confirmed on two clinical datasets included in the study. In conclusion, the use of SR techniques applied to respiratory motion synchronized images lead to motion compensation combined with improved image SNR and contrast, without any increase in the overall acquisition times.

Regularized model learning in EDAs for continuous and multi-objective optimization

Probabilistic modeling is the de�ning characteristic of estimation of distribution algorithms (EDAs) which determines their behavior and performance in optimization. Regularization is a well-known statistical technique used for obtaining an improved model by reducing the generalization error of estimation, especially in high-dimensional problems. `1-regularization is a type of this technique with the appealing variable selection property which results in sparse model estimations. In this thesis, we study the use of regularization techniques for model learning in EDAs. Several methods for regularized model estimation in continuous domains based on a Gaussian distribution assumption are presented, and analyzed from di�erent aspects when used for optimization in a high-dimensional setting, where the population size of EDA has a logarithmic scale with respect to the number of variables. The optimization results obtained for a number of continuous problems with an increasing number of variables show that the proposed EDA based on regularized model estimation performs a more robust optimization, and is able to achieve signi�cantly better results for larger dimensions than other Gaussian-based EDAs. We also propose a method for learning a marginally factorized Gaussian Markov random �eld model using regularization techniques and a clustering algorithm. The experimental results show notable optimization performance on continuous additively decomposable problems when using this model estimation method. Our study also covers multi-objective optimization and we propose joint probabilistic modeling of variables and objectives in EDAs based on Bayesian networks, speci�cally models inspired from multi-dimensional Bayesian network classi�ers. It is shown that with this approach to modeling, two new types of relationships are encoded in the estimated models in addition to the variable relationships captured in other EDAs: objectivevariable and objective-objective relationships. An extensive experimental study shows the e�ectiveness of this approach for multi- and many-objective optimization. With the proposed joint variable-objective modeling, in addition to the Pareto set approximation, the algorithm is also able to obtain an estimation of the multi-objective problem structure. Finally, the study of multi-objective optimization based on joint probabilistic modeling is extended to noisy domains, where the noise in objective values is represented by intervals. A new version of the Pareto dominance relation for ordering the solutions in these problems, namely �-degree Pareto dominance, is introduced and its properties are analyzed. We show that the ranking methods based on this dominance relation can result in competitive performance of EDAs with respect to the quality of the approximated Pareto sets. This dominance relation is then used together with a method for joint probabilistic modeling based on `1-regularization for multi-objective feature subset selection in classi�cation, where six di�erent measures of accuracy are considered as objectives with interval values. The individual assessment of the proposed joint probabilistic modeling and solution ranking methods on datasets with small-medium dimensionality, when using two di�erent Bayesian classi�ers, shows that comparable or better Pareto sets of feature subsets are approximated in comparison to standard methods.

Bases metodológicas para un sistema de apoyo a la selección de especies en la restauración de la vegetación

RESUMEN El apoyo a la selección de especies a la restauración de la vegetación en España en los últimos 40 años se ha basado fundamentalmente en modelos de distribución de especies, también llamados modelos de nicho ecológico, que estiman la probabilidad de presencia de las especies en función de las condiciones del medio físico (clima, suelo, etc.). Con esta tesis se ha intentado contribuir a la mejora de la capacidad predictiva de los modelos introduciendo algunas propuestas metodológicas adaptadas a los datos disponibles actualmente en España y enfocadas al uso de los modelos en la selección de especies. No siempre se dispone de datos a una resolución espacial adecuada para la escala de los proyectos de restauración de la vegetación. Sin embrago es habitual contar con datos de baja resolución espacial para casi todas las especies vegetales presentes en España. Se propone un método de recalibración que actualiza un modelo de regresión logística de baja resolución espacial con una nueva muestra de alta resolución espacial. El método permite obtener predicciones de calidad aceptable con muestras relativamente pequeñas (25 presencias de la especie) frente a las muestras mucho mayores (más de 100 presencias) que requería una estrategia de modelización convencional que no usara el modelo previo. La selección del método estadístico puede influir decisivamente en la capacidad predictiva de los modelos y por esa razón la comparación de métodos ha recibido mucha atención en la última década. Los estudios previos consideraban a la regresión logística como un método inferior a técnicas más modernas como las de máxima entropía. Los resultados de la tesis demuestran que esa diferencia observada se debe a que los modelos de máxima entropía incluyen técnicas de regularización y la versión de la regresión logística usada en las comparaciones no. Una vez incorporada la regularización a la regresión logística usando penalización, las diferencias en cuanto a capacidad predictiva desaparecen. La regresión logística penalizada es, por tanto, una alternativa más para el ajuste de modelos de distribución de especies y está a la altura de los métodos modernos con mejor capacidad predictiva como los de máxima entropía. A menudo, los modelos de distribución de especies no incluyen variables relativas al suelo debido a que no es habitual que se disponga de mediciones directas de sus propiedades físicas o químicas. La incorporación de datos de baja resolución espacial proveniente de mapas de suelo nacionales o continentales podría ser una alternativa. Los resultados de esta tesis sugieren que los modelos de distribución de especies de alta resolución espacial mejoran de forma ligera pero estadísticamente significativa su capacidad predictiva cuando se incorporan variables relativas al suelo procedente de mapas de baja resolución espacial. La validación es una de las etapas fundamentales del desarrollo de cualquier modelo empírico como los modelos de distribución de especies. Lo habitual es validar los modelos evaluando su capacidad predictiva especie a especie, es decir, comparando en un conjunto de localidades la presencia o ausencia observada de la especie con las predicciones del modelo. Este tipo de evaluación no responde a una cuestión clave en la restauración de la vegetación ¿cuales son las n especies más idóneas para el lugar a restaurar? Se ha propuesto un método de evaluación de modelos adaptado a esta cuestión que consiste en estimar la capacidad de un conjunto de modelos para discriminar entre las especies presentes y ausentes de un lugar concreto. El método se ha aplicado con éxito a la validación de 188 modelos de distribución de especies leñosas orientados a la selección de especies para la restauración de la vegetación en España. Las mejoras metodológicas propuestas permiten mejorar la capacidad predictiva de los modelos de distribución de especies aplicados a la selección de especies en la restauración de la vegetación y también permiten ampliar el número de especies para las que se puede contar con un modelo que apoye la toma de decisiones. SUMMARY During the last 40 years, decision support tools for plant species selection in ecological restoration in Spain have been based on species distribution models (also called ecological niche models), that estimate the probability of occurrence of the species as a function of environmental predictors (e.g., climate, soil). In this Thesis some methodological improvements are proposed to contribute to a better predictive performance of such models, given the current data available in Spain and focusing in the application of the models to selection of species for ecological restoration. Fine grained species distribution data are required to train models to be used at the scale of the ecological restoration projects, but this kind of data are not always available for every species. On the other hand, coarse grained data are available for almost every species in Spain. A recalibration method is proposed that updates a coarse grained logistic regression model using a new fine grained updating sample. The method allows obtaining acceptable predictive performance with reasonably small updating sample (25 occurrences of the species), in contrast with the much larger samples (more than 100 occurrences) required for a conventional modeling approach that discards the coarse grained data. The choice of the statistical method may have a dramatic effect on model performance, therefore comparisons of methods have received much interest in the last decade. Previous studies have shown a poorer performance of the logistic regression compared to novel methods like maximum entropy models. The results of this Thesis show that the observed difference is caused by the fact that maximum entropy models include regularization techniques and the versions of logistic regression compared do not. Once regularization has been added to the logistic regression using a penalization procedure, the differences in model performance disappear. Therefore, penalized logistic regression may be considered one of the best performing methods to model species distributions. Usually, species distribution models do not consider soil related predictors because direct measurements of the chemical or physical properties are often lacking. The inclusion of coarse grained soil data from national or continental soil maps could be a reasonable alternative. The results of this Thesis suggest that the performance of the models slightly increase after including soil predictors form coarse grained soil maps. Model validation is a key stage of the development of empirical models, such as species distribution models. The usual way of validating is based on the evaluation of model performance for each species separately, i.e., comparing observed species presences or absence to predicted probabilities in a set of sites. This kind of evaluation is not informative for a common question in ecological restoration projects: which n species are the most suitable for the environment of the site to be restored? A method has been proposed to address this question that estimates the ability of a set of models to discriminate among present and absent species in a evaluation site. The method has been successfully applied to the validation of 188 species distribution models used to support decisions on species selection for ecological restoration in Spain. The proposed methodological approaches improve the predictive performance of the predictive models applied to species selection in ecological restoration and increase the number of species for which a model that supports decisions can be fitted.

Guiding functional connectivity estimation by structural connectivity in MEG: an application to discrimination of mild cognitive impaired conditions

Whole brain resting state connectivity is a promising biomarker that might help to obtain an early diagnosis in many neurological diseases, such as dementia. Inferring resting-state connectivity is often based on correlations, which are sensitive to indirect connections, leading to an inaccurate representation of the real backbone of the network. The precision matrix is a better representation for whole brain connectivity, as it considers only direct connections. The network structure can be estimated using the graphical lasso (GL), which achieves sparsity through l1-regularization on the precision matrix. In this paper, we propose a structural connectivity adaptive version of the GL, where weaker anatomical connections are represented as stronger penalties on the corre- sponding functional connections. We applied beamformer source reconstruction to the resting state MEG record- ings of 81 subjects, where 29 were healthy controls, 22 were single-domain amnestic Mild Cognitive Impaired (MCI), and 30 were multiple-domain amnestic MCI. An atlas-based anatomical parcellation of 66 regions was ob- tained for each subject, and time series were assigned to each of the regions. The fiber densities between the re- gions, obtained with deterministic tractography from diffusion-weighted MRI, were used to define the anatomical connectivity. Precision matrices were obtained with the region specific time series in five different frequency bands. We compared our method with the traditional GL and a functional adaptive version of the GL, in terms of log-likelihood and classification accuracies between the three groups. We conclude that introduc- ing an anatomical prior improves the expressivity of the model and, in most cases, leads to a better classification between groups.

Análisis mediante elementos finitos del comportamiento de losas de hormigón armado sometidas a explosiones

Se presenta en este trabajo una investigación sobre el comportamiento de losas de hormigón armado sometidas a explosiones y la simulación numérica de dicho fenómeno mediante el método de los elementos finitos. El trabajo aborda el estudio de la respuesta de dichos elementos estructurales por comparación entre los resultados obtenidos en ensayos reales a escala 1:1 y los cálculos realizados mediante modelos de ordenador. Este procedimiento permite verificar la idoneidad, o no, de estos últimos. Se expone en primer lugar el comportamiento mecánico de los modelos de material que son susceptibles de emplearse en la simulación de estructuras mediante el software empleado en la presente investigación, así como las diferentes formas de aplicar cargas explosivas en estructuras modeladas mediante el método de los Elementos Finitos, razonándose en ambos casos la elección llevada a cabo. Posteriormente, se describen los ensayos experimentales disponibles, que tuvieron lugar en las instalaciones del Laboratorio de Balística de Efectos, perteneciente al Instituto Tecnológico de la Marañosa (ITM), de Madrid, para estudiar el comportamiento de losas de hormigón armado a escala 1:1 sometidas a explosiones reales. Se ha propuesto un método de interpretación del nivel de daño en las losas mediante el martillo de Schmidt, que posteriormente permitirá comparar resultados con los modelos de ordenador realizados. Asimismo, se propone un método analítico para la determinación del tamaño óptimo de la malla en las simulaciones realizadas, basado en la distribución de la energía interna del sistema. Es conocido que el comportamiento de los modelos pueden verse fuertemente influenciados por el mallado empleado. Según el mallado sea “grosero” o “fino” el fallo puede no alcanzarse o hacerlo de forma prematura, o excesiva, respectivamente. Es más, algunos modelos de material contemplan una “regularización” del tamaño de la malla, pero en la presente investigación se evidencia que dicho procedimiento tiene un rango de validez limitado, incluso se determina un entorno óptimo de valores. Finalmente, se han elaborado los modelos numéricos con el software comercial LS-DYNA, contemplando todos los aspectos reseñados en los párrafos anteriores, procediendo a realizar una comparación de los resultados obtenidos en las simulaciones con los procedidos en los ensayos reales a escala 1:1, observando que existe una muy buena correlación entre ambas situaciones que evidencian que el procedimiento propuesto en la investigación es de todo punto adecuado para la simulación de losas de hormigón armado sometidas a explosiones. ABSTRACT This doctoral thesis presents an investigation on the behavior of reinforced concrete slabs subjected to explosions along with the numerical simulation of this phenomenon by the finite elements method. The work involves the study of the response of these structural elements by comparing the results of field tests at full scale and the calculations performed by the computer model. This procedure allows to verify the appropriateness or not of the latter. Firstly, the mechanical behavior of the material models that are likely to be used in the modelling of structures is explained. In addition, different ways of choosing explosive charges when conducting finite element methods are analyzed and discussed. Secondly, several experimental tests, which took place at the Laboratorio de Balística de Efectos at the Instituto Tecnológico de la Marañosa (ITM), in Madrid, are described in order to study the behavior of these reinforced concrete slabs. A method for the description of the slab damage level by the Schmidt hammer is proposed, which will make possible to compare the modelling results extracted from the computation experiments. Furthermore, an analytical method for determining the optimal mesh size to be used in the simulations is proposed. It is well known that the behavior of the models can be strongly influenced by the mesh size used. According to this, when modifiying the meshing density the damaged cannot be reached or do it prematurely, or excessive, respectively. Moreover, some material models include a regularization of the mesh size, but the present investigation evidenced that this procedure has a limited range of validity, even an optimal environment values are determined. The method proposed is based on the distribution of the internal energy of the system. Finally, several expecific numerical models have been performed by using LS-DYNA commercial software, considering all the aspects listed in the preceding paragraphs. Comparisons of the results extracted from the simulations and full scale experiments were carried out, noting that there exists a very good correlation between both of them. This fact demonstrates that the proposed research procedure is highly suitable for the modelling of reinforced concrete slabs subjected to blast loading.

High Fidelity Models for Near-Earth Object Dynamics

Motivado por los últimos hallazgos realizados gracias a los recientes avances tecnológicos y misiones espaciales, el estudio de los asteroides ha despertado el interés de la comunidad científica. Tal es así que las misiones a asteroides han proliferado en los últimos años (Hayabusa, Dawn, OSIRIX-REx, ARM, AIMS-DART, ...) incentivadas por su enorme interés científico. Los asteroides son constituyentes fundamentales en la evolución del Sistema Solar, son además grandes concentraciones de valiosos recursos naturales, y también pueden considerarse como objectivos estratégicos para la futura exploración espacial. Desde hace tiempo se viene especulando con la posibilidad de capturar objetos próximos a la Tierra (NEOs en su acrónimo anglosajón) y acercarlos a nuestro planeta, permitiendo así un acceso asequible a los mismos para estudiarlos in-situ, explotar sus recursos u otras finalidades. Por otro lado, las asteroides se consideran con frecuencia como posibles peligros de magnitud planetaria, ya que impactos de estos objetos con la Tierra suceden constantemente, y un asteroide suficientemente grande podría desencadenar eventos catastróficos. Pese a la gravedad de tales acontecimientos, lo cierto es que son ciertamente difíciles de predecir. De hecho, los ricos aspectos dinámicos de los asteroides, su modelado complejo y las incertidumbres observaciones hacen que predecir su posición futura con la precisión necesaria sea todo un reto. Este hecho se hace más relevante cuando los asteroides sufren encuentros próximos con la Tierra, y más aún cuando estos son recurrentes. En tales situaciones en las cuales fuera necesario tomar medidas para mitigar este tipo de riesgos, saber estimar con precisión sus trayectorias y probabilidades de colisión es de una importancia vital. Por ello, se necesitan herramientas avanzadas para modelar su dinámica y predecir sus órbitas con precisión, y son también necesarios nuevos conceptos tecnológicos para manipular sus órbitas llegado el caso. El objetivo de esta Tesis es proporcionar nuevos métodos, técnicas y soluciones para abordar estos retos. Las contribuciones de esta Tesis se engloban en dos áreas: una dedicada a la propagación numérica de asteroides, y otra a conceptos de deflexión y captura de asteroides. Por lo tanto, la primera parte de este documento presenta novedosos avances de apliación a la propagación dinámica de alta precisión de NEOs empleando métodos de regularización y perturbaciones, con especial énfasis en el método DROMO, mientras que la segunda parte expone ideas innovadoras para la captura de asteroides y comenta el uso del “ion beam shepherd” (IBS) como tecnología para deflectarlos. Abstract Driven by the latest discoveries enabled by recent technological advances and space missions, the study of asteroids has awakened the interest of the scientific community. In fact, asteroid missions have become very popular in the recent years (Hayabusa, Dawn, OSIRIX-REx, ARM, AIMS-DART, ...) motivated by their outstanding scientific interest. Asteroids are fundamental constituents in the evolution of the Solar System, can be seen as vast concentrations of valuable natural resources, and are also considered as strategic targets for the future of space exploration. For long it has been hypothesized with the possibility of capturing small near-Earth asteroids and delivering them to the vicinity of the Earth in order to allow an affordable access to them for in-situ science, resource utilization and other purposes. On the other side of the balance, asteroids are often seen as potential planetary hazards, since impacts with the Earth happen all the time, and eventually an asteroid large enough could trigger catastrophic events. In spite of the severity of such occurrences, they are also utterly hard to predict. In fact, the rich dynamical aspects of asteroids, their complex modeling and observational uncertainties make exceptionally challenging to predict their future position accurately enough. This becomes particularly relevant when asteroids exhibit close encounters with the Earth, and more so when these happen recurrently. In such situations, where mitigation measures may need to be taken, it is of paramount importance to be able to accurately estimate their trajectories and collision probabilities. As a consequence, advanced tools are needed to model their dynamics and accurately predict their orbits, as well as new technological concepts to manipulate their orbits if necessary. The goal of this Thesis is to provide new methods, techniques and solutions to address these challenges. The contributions of this Thesis fall into two areas: one devoted to the numerical propagation of asteroids, and another to asteroid deflection and capture concepts. Hence, the first part of the dissertation presents novel advances applicable to the high accuracy dynamical propagation of near-Earth asteroids using regularization and perturbations techniques, with a special emphasis in the DROMO method, whereas the second part exposes pioneering ideas for asteroid retrieval missions and discusses the use of an “ion beam shepherd” (IBS) for asteroid deflection purposes.

Caracterización y simulación de arborizaciones dentríticas con redes bayesianas incluyendo variables angulares

El funcionamiento interno del cerebro es todavía hoy en día un misterio, siendo su comprensión uno de los principales desafíos a los que se enfrenta la ciencia moderna. El córtex cerebral es el área del cerebro donde tienen lugar los procesos cerebrales de más alto nivel, cómo la imaginación, el juicio o el pensamiento abstracto. Las neuronas piramidales, un tipo específico de neurona, suponen cerca del 80% de los cerca de los 10.000 millones de que componen el córtex cerebral, haciendo de ellas un objetivo principal en el estudio del funcionamiento del cerebro. La morfología neuronal, y más específicamente la morfología dendrítica, determina cómo estas procesan la información y los patrones de conexión entre neuronas, siendo los modelos computacionales herramientas imprescindibles para el estudio de su rol en el funcionamiento del cerebro. En este trabajo hemos creado un modelo computacional, con más de 50 variables relativas a la morfología dendrítica, capaz de simular el crecimiento de arborizaciones dendríticas basales completas a partir de reconstrucciones de neuronas piramidales reales, abarcando desde el número de dendritas hasta el crecimiento los los árboles dendríticos. A diferencia de los trabajos anteriores, nuestro modelo basado en redes Bayesianas contempla la arborización dendrítica en su conjunto, teniendo en cuenta las interacciones entre dendritas y detectando de forma automática las relaciones entre las variables morfológicas que caracterizan la arborización. Además, el análisis de las redes Bayesianas puede ayudar a identificar relaciones hasta ahora desconocidas entre variables morfológicas. Motivado por el estudio de la orientación de las dendritas basales, en este trabajo se introduce una regularización L1 generalizada, aplicada al aprendizaje de la distribución von Mises multivariante, una de las principales distribuciones de probabilidad direccional multivariante. También se propone una distancia circular multivariante que puede utilizarse para estimar la divergencia de Kullback-Leibler entre dos muestras de datos circulares. Comparamos los modelos con y sin regularizaci ón en el estudio de la orientación de la dendritas basales en neuronas humanas, comprobando que, en general, el modelo regularizado obtiene mejores resultados. El muestreo, ajuste y representación de la distribución von Mises multivariante se implementa en un nuevo paquete de R denominado mvCircular.---ABSTRACT---The inner workings of the brain are, as of today, a mystery. To understand the brain is one of the main challenges faced by current science. The cerebral cortex is the region of the brain where all superior brain processes, like imagination, judge and abstract reasoning take place. Pyramidal neurons, a specific type of neurons, constitute approximately the 80% of the more than 10.000 million neurons that compound the cerebral cortex. It makes the study of the pyramidal neurons crucial in order to understand how the brain works. Neuron morphology, and specifically the dendritic morphology, determines how the information is processed in the neurons, as well as the connection patterns among neurons. Computational models are one of the main tools for studying dendritic morphology and its role in the brain function. We have built a computational model that contains more than 50 morphological variables of the dendritic arborizations. This model is able to simulate the growth of complete dendritic arborizations from real neuron reconstructions, starting with the number of basal dendrites, and ending modeling the growth of dendritic trees. One of the main diferences between our approach, mainly based on the use of Bayesian networks, and other models in the state of the art is that we model the whole dendritic arborization instead of focusing on individual trees, which makes us able to take into account the interactions between dendrites and to automatically detect relationships between the morphologic variables that characterize the arborization. Moreover, the posterior analysis of the relationships in the model can help to identify new relations between morphological variables. Motivated by the study of the basal dendrites orientation, a generalized L1 regularization applied to the multivariate von Mises distribution, one of the most used distributions in multivariate directional statistics, is also introduced in this work. We also propose a circular multivariate distance that can be used to estimate the Kullback-Leibler divergence between two circular data samples. We compare the regularized and unregularized models on basal dendrites orientation of human neurons and prove that regularized model achieves better results than non regularized von Mises model. Sampling, fitting and plotting functions for the multivariate von Mises are implemented in a new R packaged called mvCircular.

Los programas de regularización urbana en Brasil y su adecuación a los nuevos paradigmas urbanos tras el "Estatuto da Cidade": un estudio de casos en la ciudad de Porto Alegre, Rio Grande do Sul

En América Latina, y en Brasil en particular, las ocupaciones informales de la tierra urbana se tornaran un fenómeno generalizado en todas las ciudades, hecho que evidencio una serie de problemas urbanos y de ineficiencia en el proveimiento de los derechos básicos de los ciudadanos, principalmente el derecho a la morada digna, con eso, trajo la necesidad de priorización de política publicas curativas, como los programas de regularización urbana, cuyo objetivo es la inserción de las ocupaciones informales en la ciudad formal, con todos los impactos que eso genera: urbanísticos, legales, sociales y económicos. La ley federal intitulado Estatuto da Cidade (EC), reglamentada en 2001, es entendida como un avanzo jurídico por justamente intentar contrabalancear ese contexto, trayendo una serie de principios e instrumentos que buscan garantizar la función social de la propiedad y de la ciudad. Esa nueva lógica, en la teoría, tiene que ser la base de todas las políticas urbanas del país. Con eso, esa tesis tiene como objetivo evaluar si, realmente, los programas de regularización urbana desarrollados en Brasil cumplen con los dictámenes de dicha legislación. Para eso, fue elegido la metodología del estudio de caso, que fue desarrollado en la ciudad de Porto Alegre, capital del Rio Grande do Sul. Primero fue analizado el Estatuto da Cidade, para la definición de los principios de evaluación, luego, fue propuesto un sistema de evaluación, que fue aplicado en los dos casos estudiados: un anterior a la promulgación del EC, el Condominio dos Anjos, que serbio como parámetro referencial, y otro desarrollado tras la promulgación de dicha legislación, el Programa Integrado Entrada da Cidade (PIEC). Tras los análisis, se puede concluir que la legislación federal efectivamente no ha tenido el reflejo necesario, como conclusiones principales se puede citar: que la legislación municipal de Porto Alegre desde la década 90 ya tenía avances considerables, incluso algunos sirvieron de ejemplo en la elaboración del EC, luego, eso puede explicar el bajo impacto percibido; y que el principal fiscalizador y delineador de la política urbana es el financiador del programa, luego, muchas estrategias y dibujos proyectuales dependen de la línea de dicha financiación. ABSTRACT In Latin America, and Brazil in particular, informal urban land occupations pervasive be turned into all cities, a fact evidenced a series of urban problems and inefficiency to provide the basic rights of citizens, mainly the right to a decent housing, with that, brought the need for prioritization of public policy, such as urban regularization programs, aimed at the inclusion of informal occupations in the formal city, with all the impacts that generates: urban, legal, social and economic. Federal law entitled Estatuto da Cidade (EC), regulated in 2001, is understood as a legal advanced for just try to counterbalance this context, bringing a number of principles and instruments that seek to guarantee the social function of property and the city. This new logic, in theory, has to be the basis of all urban policies of the country. With that, this thesis aims to assess whether urban regularization programs developed in Brazil, actually, comply with the dictates of that legislation. For that, it was elected the methodology of the case study, which was developed in the city of Porto Alegre, capital of Rio Grande do Sul, Brazil. It was first analyzed the EC, for defining the principles for evaluation, then, was proposed an evaluation system, which was applied in two case studies: one before the promulgation of the EC, the Condominio dos Anjos, which used as a reference parameter, and another developed following the enactment of this legislation, the Program Integrate Entrada da Cidade (PIEC). After the analysis, it can be concluded that the federal legislation has not actually had the reflection necessary, main conclusions can be cited: the municipal legislation in Porto Alegre, since the early 90s, had considerable progress, including some served as an example in developing the EC, then, that may explain the low perceived impact; the principal auditor and eyeliner urban policy is the founder of the program, of course, many strategies and project drawings depend on the line of financing.

Generalized flows, intrinsic stochasticity, and turbulent transport

The study of passive scalar transport in a turbulent velocity field leads naturally to the notion of generalized flows, which are families of probability distributions on the space of solutions to the associated ordinary differential equations which no longer satisfy the uniqueness theorem for ordinary differential equations. Two most natural regularizations of this problem, namely the regularization via adding small molecular diffusion and the regularization via smoothing out the velocity field, are considered. White-in-time random velocity fields are used as an example to examine the variety of phenomena that take place when the velocity field is not spatially regular. Three different regimes, characterized by their degrees of compressibility, are isolated in the parameter space. In the regime of intermediate compressibility, the two different regularizations give rise to two different scaling behaviors for the structure functions of the passive scalar. Physically, this means that the scaling depends on Prandtl number. In the other two regimes, the two different regularizations give rise to the same generalized flows even though the sense of convergence can be very different. The “one force, one solution” principle is established for the scalar field in the weakly compressible regime, and for the difference of the scalar in the strongly compressible regime, which is the regime of inverse cascade. Existence and uniqueness of an invariant measure are also proved in these regimes when the transport equation is suitably forced. Finally incomplete self similarity in the sense of Barenblatt and Chorin is established.

Grass genomes

For the most part, studies of grass genome structure have been limited to the generation of whole-genome genetic maps or the fine structure and sequence analysis of single genes or gene clusters. We have investigated large contiguous segments of the genomes of maize, sorghum, and rice, primarily focusing on intergenic spaces. Our data indicate that much (>50%) of the maize genome is composed of interspersed repetitive DNAs, primarily nested retrotransposons that insert between genes. These retroelements are less abundant in smaller genome plants, including rice and sorghum. Although 5- to 200-kb blocks of methylated, presumably heterochromatic, retrotransposons flank most maize genes, rice and sorghum genes are often adjacent. Similar genes are commonly found in the same relative chromosomal locations and orientations in each of these three species, although there are numerous exceptions to this collinearity (i.e., rearrangements) that can be detected at the levels of both the recombinational map and cloned DNA. Evolutionarily conserved sequences are largely confined to genes and their regulatory elements. Our results indicate that a knowledge of grass genome structure will be a useful tool for gene discovery and isolation, but the general rules and biological significance of grass genome organization remain to be determined. Moreover, the nature and frequency of exceptions to the general patterns of grass genome structure and collinearity are still largely unknown and will require extensive further investigation.

Slip complexity in earthquake fault models.

We summarize studies of earthquake fault models that give rise to slip complexities like those in natural earthquakes. For models of smooth faults between elastically deformable continua, it is critical that the friction laws involve a characteristic distance for slip weakening or evolution of surface state. That results in a finite nucleation size, or coherent slip patch size, h*. Models of smooth faults, using numerical cell size properly small compared to h*, show periodic response or complex and apparently chaotic histories of large events but have not been found to show small event complexity like the self-similar (power law) Gutenberg-Richter frequency-size statistics. This conclusion is supported in the present paper by fully inertial elastodynamic modeling of earthquake sequences. In contrast, some models of locally heterogeneous faults with quasi-independent fault segments, represented approximately by simulations with cell size larger than h* so that the model becomes "inherently discrete," do show small event complexity of the Gutenberg-Richter type. Models based on classical friction laws without a weakening length scale or for which the numerical procedure imposes an abrupt strength drop at the onset of slip have h* = 0 and hence always fall into the inherently discrete class. We suggest that the small-event complexity that some such models show will not survive regularization of the constitutive description, by inclusion of an appropriate length scale leading to a finite h*, and a corresponding reduction of numerical grid size.

Designing Regularizers and Architectures for Recurrent Neural Networks

Cette thèse contribue a la recherche vers l'intelligence artificielle en utilisant des méthodes connexionnistes. Les réseaux de neurones récurrents sont un ensemble de modèles séquentiels de plus en plus populaires capable en principe d'apprendre des algorithmes arbitraires. Ces modèles effectuent un apprentissage en profondeur, un type d'apprentissage machine. Sa généralité et son succès empirique en font un sujet intéressant pour la recherche et un outil prometteur pour la création de l'intelligence artificielle plus générale. Le premier chapitre de cette thèse donne un bref aperçu des sujets de fonds: l'intelligence artificielle, l'apprentissage machine, l'apprentissage en profondeur et les réseaux de neurones récurrents. Les trois chapitres suivants couvrent ces sujets de manière de plus en plus spécifiques. Enfin, nous présentons quelques contributions apportées aux réseaux de neurones récurrents. Le chapitre \ref{arxiv1} présente nos travaux de régularisation des réseaux de neurones récurrents. La régularisation vise à améliorer la capacité de généralisation du modèle, et joue un role clé dans la performance de plusieurs applications des réseaux de neurones récurrents, en particulier en reconnaissance vocale. Notre approche donne l'état de l'art sur TIMIT, un benchmark standard pour cette tâche. Le chapitre \ref{cpgp} présente une seconde ligne de travail, toujours en cours, qui explore une nouvelle architecture pour les réseaux de neurones récurrents. Les réseaux de neurones récurrents maintiennent un état caché qui représente leurs observations antérieures. L'idée de ce travail est de coder certaines dynamiques abstraites dans l'état caché, donnant au réseau une manière naturelle d'encoder des tendances cohérentes de l'état de son environnement. Notre travail est fondé sur un modèle existant; nous décrivons ce travail et nos contributions avec notamment une expérience préliminaire.

A Clearer Picture of Total Variation Blind Deconvolution

Blind deconvolution is the problem of recovering a sharp image and a blur kernel from a noisy blurry image. Recently, there has been a significant effort on understanding the basic mechanisms to solve blind deconvolution. While this effort resulted in the deployment of effective algorithms, the theoretical findings generated contrasting views on why these approaches worked. On the one hand, one could observe experimentally that alternating energy minimization algorithms converge to the desired solution. On the other hand, it has been shown that such alternating minimization algorithms should fail to converge and one should instead use a so-called Variational Bayes approach. To clarify this conundrum, recent work showed that a good image and blur prior is instead what makes a blind deconvolution algorithm work. Unfortunately, this analysis did not apply to algorithms based on total variation regularization. In this manuscript, we provide both analysis and experiments to get a clearer picture of blind deconvolution. Our analysis reveals the very reason why an algorithm based on total variation works. We also introduce an implementation of this algorithm and show that, in spite of its extreme simplicity, it is very robust and achieves a performance comparable to the top performing algorithms.

Designing Regularizers and Architectures for Recurrent Neural Networks

Cette thèse contribue a la recherche vers l'intelligence artificielle en utilisant des méthodes connexionnistes. Les réseaux de neurones récurrents sont un ensemble de modèles séquentiels de plus en plus populaires capable en principe d'apprendre des algorithmes arbitraires. Ces modèles effectuent un apprentissage en profondeur, un type d'apprentissage machine. Sa généralité et son succès empirique en font un sujet intéressant pour la recherche et un outil prometteur pour la création de l'intelligence artificielle plus générale. Le premier chapitre de cette thèse donne un bref aperçu des sujets de fonds: l'intelligence artificielle, l'apprentissage machine, l'apprentissage en profondeur et les réseaux de neurones récurrents. Les trois chapitres suivants couvrent ces sujets de manière de plus en plus spécifiques. Enfin, nous présentons quelques contributions apportées aux réseaux de neurones récurrents. Le chapitre \ref{arxiv1} présente nos travaux de régularisation des réseaux de neurones récurrents. La régularisation vise à améliorer la capacité de généralisation du modèle, et joue un role clé dans la performance de plusieurs applications des réseaux de neurones récurrents, en particulier en reconnaissance vocale. Notre approche donne l'état de l'art sur TIMIT, un benchmark standard pour cette tâche. Le chapitre \ref{cpgp} présente une seconde ligne de travail, toujours en cours, qui explore une nouvelle architecture pour les réseaux de neurones récurrents. Les réseaux de neurones récurrents maintiennent un état caché qui représente leurs observations antérieures. L'idée de ce travail est de coder certaines dynamiques abstraites dans l'état caché, donnant au réseau une manière naturelle d'encoder des tendances cohérentes de l'état de son environnement. Notre travail est fondé sur un modèle existant; nous décrivons ce travail et nos contributions avec notamment une expérience préliminaire.