Generating Scale-free Networks with Adjustable Clustering Coefficient Via Random Walks

This paper presents an algorithm for generating scale-free networks with adjustable clustering coefficient. The algorithm is based on a random walk procedure combined with a triangle generation scheme which takes into account genetic factors; this way, preferential attachment and clustering control are implemented using only local information. Simulations are presented which support the validity of the scheme, characterizing its tuning capabilities.

Percolation in soils at Different Bulk Densities: Porosity, Connectivity and Grey Threshold.

To improve percolation modelling on soils the geometrical properties of the pore space must be understood; this includes porosity, particle and pore size distribution and connectivity of the pores. A study was conducted with a soil at different bulk densities based on 3D grey images acquired by X-ray computed tomography. The objective was to analyze the effect in percolation of aspects of pore network geometry and discuss the influence of the grey threshold applied to the images. A model based on random walk algorithms was applied to the images, combining five bulk densities with up to six threshold values per density. This allowed for a dynamical perspective of soil structure in relation to water transport through the inclusion of percolation speed in the analyses. To evaluate separately connectivity and isolate the effect of the grey threshold, a critical value of 35% of porosity was selected for every density. This value was the smallest at which total-percolation walks appeared for the all images of the same porosity and may represent a situation of percolation comparable among bulks densities. This criterion avoided an arbitrary decision in grey thresholds. Besides, a random matrix simulation at 35% of porosity with real images was used to test the existence of pore connectivity as a consequence of a non-random soil structure.

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.

Brief Announcement: Node Sampling Using Centrifugal Random Walks.

We propose distributed algorithms for sampling networks based on a new class of random walks that we call Centrifugal Random Walks (CRW). A CRW is a random walk that starts at a source and always moves away from it. We propose CRW algorithms for connected networks with arbitrary probability distributions, and for grids and networks with regular concentric connectivity with distance based distributions. All CRW sampling algorithms select a node with the exact probability distribution, do not need warm-up, and end in a number of hops bounded by the network diameter.

Node sampling using random centrifugal walks

Sampling a network with a given probability distribution has been identified as a useful operation. In this paper we propose distributed algorithms for sampling networks, so that nodes are selected by a special node, called the source, with a given probability distribution. All these algorithms are based on a new class of random walks, that we call Random Centrifugal Walks (RCW). A RCW is a random walk that starts at the source and always moves away from it. Firstly, an algorithm to sample any connected network using RCW is proposed. The algorithm assumes that each node has a weight, so that the sampling process must select a node with a probability proportional to its weight. This algorithm requires a preprocessing phase before the sampling of nodes. In particular, a minimum diameter spanning tree (MDST) is created in the network, and then nodes weights are efficiently aggregated using the tree. The good news are that the preprocessing is done only once, regardless of the number of sources and the number of samples taken from the network. After that, every sample is done with a RCW whose length is bounded by the network diameter. Secondly, RCW algorithms that do not require preprocessing are proposed for grids and networks with regular concentric connectivity, for the case when the probability of selecting a node is a function of its distance to the source. The key features of the RCW algorithms (unlike previous Markovian approaches) are that (1) they do not need to warm-up (stabilize), (2) the sampling always finishes in a number of hops bounded by the network diameter, and (3) it selects a node with the exact probability distribution.

Modelos para el análisis dinámico y la predicción a corto plazo de los precios de la electricidad en mercados liberalizados

En esta tesis se va a describir y aplicar de forma novedosa la técnica del alisado exponencial multivariante a la predicción a corto plazo, a un día vista, de los precios horarios de la electricidad, un problema que se está estudiando intensivamente en la literatura estadística y económica reciente. Se van a demostrar ciertas propiedades interesantes del alisado exponencial multivariante que permiten reducir el número de parámetros para caracterizar la serie temporal y que al mismo tiempo permiten realizar un análisis dinámico factorial de la serie de precios horarios de la electricidad. En particular, este proceso multivariante de elevada dimensión se estimará descomponiéndolo en un número reducido de procesos univariantes independientes de alisado exponencial caracterizado cada uno por un solo parámetro de suavizado que variará entre cero (proceso de ruido blanco) y uno (paseo aleatorio). Para ello, se utilizará la formulación en el espacio de los estados para la estimación del modelo, ya que ello permite conectar esa secuencia de modelos univariantes más eficientes con el modelo multivariante. De manera novedosa, las relaciones entre los dos modelos se obtienen a partir de un simple tratamiento algebraico sin requerir la aplicación del filtro de Kalman. De este modo, se podrán analizar y poner al descubierto las razones últimas de la dinámica de precios de la electricidad. Por otra parte, la vertiente práctica de esta metodología se pondrá de manifiesto con su aplicación práctica a ciertos mercados eléctricos spot, tales como Omel, Powernext y Nord Pool. En los citados mercados se caracterizará la evolución de los precios horarios y se establecerán sus predicciones comparándolas con las de otras técnicas de predicción. ABSTRACT This thesis describes and applies the multivariate exponential smoothing technique to the day-ahead forecast of the hourly prices of electricity in a whole new way. This problem is being studied intensively in recent statistics and economics literature. It will start by demonstrating some interesting properties of the multivariate exponential smoothing that reduce drastically the number of parameters to characterize the time series and that at the same time allow a dynamic factor analysis of the hourly prices of electricity series. In particular this very complex multivariate process of dimension 24 will be estimated by decomposing a very reduced number of univariate independent of exponentially smoothing processes each characterized by a single smoothing parameter that varies between zero (white noise process) and one (random walk). To this end, the formulation is used in the state space model for the estimation, since this connects the sequence of efficient univariate models to the multivariate model. Through a novel way, relations between the two models are obtained from a simple algebraic treatment without applying the Kalman filter. Thus, we will analyze and expose the ultimate reasons for the dynamics of the electricity price. Moreover, the practical aspect of this methodology will be shown by applying this new technique to certain electricity spot markets such as Omel, Powernext and Nord Pool. In those markets the behavior of prices will be characterized, their predictions will be formulated and the results will be compared with those of other forecasting techniques.

Topology of a percolating soil pore network.

A connectivity function defined by the 3D-Euler number, is a topological indicator and can be related to hydraulic properties (Vogel and Roth, 2001). This study aims to develop connectivity Euler indexes as indicators of the ability of soils for fluid percolation. The starting point was a 3D grey image acquired by X-ray computed tomography of a soil at bulk density of 1.2 mg cm-3. This image was used in the simulation of 40000 particles following a directed random walk algorithms with 7 binarization thresholds. These data consisted of 7 files containing the simulated end points of the 40000 random walks, obtained in Ruiz-Ramos et al. (2010). MATLAB software was used for computing the frequency matrix of the number of particles arriving at every end point of the random walks and their 3D representation.

MBIS: Multivariate Bayesian Image Segmentation Tool

We present MBIS (Multivariate Bayesian Image Segmentation tool), a clustering tool based on the mixture of multivariate normal distributions model. MBIS supports multi-channel bias field correction based on a B-spline model. A second methodological novelty is the inclusion of graph-cuts optimization for the stationary anisotropic hidden Markov random field model. Along with MBIS, we release an evaluation framework that contains three different experiments on multi-site data. We first validate the accuracy of segmentation and the estimated bias field for each channel. MBIS outperforms a widely used segmentation tool in a cross-comparison evaluation. The second experiment demonstrates the robustness of results on atlas-free segmentation of two image sets from scan-rescan protocols on 21 healthy subjects. Multivariate segmentation is more replicable than the monospectral counterpart on T1-weighted images. Finally, we provide a third experiment to illustrate how MBIS can be used in a large-scale study of tissue volume change with increasing age in 584 healthy subjects. This last result is meaningful as multivariate segmentation performs robustly without the need for prior knowledge.

Systematic analysis of direct-drive baseline designs for shock ignition with the Laser MégaJoule

We present direct-drive target design studies for the laser mégajoule using two distinct initial aspect ratios (A = 34 and A = 5). Laser pulse shapes are optimized by a random walk method and drive power variations are used to cover a wide variety of implosion velocities between 260 km/s and 365 km/s. For selected implosion velocities and for each initial aspect ratio, scaled-target families are built in order to find self-ignition threshold. High-gain shock ignition is also investigated in the context of Laser MégaJoule for marginally igniting targets below their own self-ignition threshold.

Empirical analysis of traffic volume for the application of the options theory to highway concessions

*************************************************************************************** EL WCTR es un Congreso de reconocido prestigio internacional en el ámbito de la investigación del transporte que hasta el 2010 publicaba sus libros de abstracts con ISBN. Por ello consideramos que debería seguir teníendose en cuenta para los indicadores de calidad ******************************************************************************************* Investment projects in the field of transportation infrastructures have a high degree of uncertainty and require an important amount of resources. In highway concessions in particular, the calculation of the Net Present Value (NPV) of the project by means of the discount of cash flows, may lead to erroneous results when the project incorporates certain flexibility. In these cases, the theory of real options is an alternative tool for the valuation of concessions. When the variable that generates uncertainty (in our case, the traffic) follows a random walk (or Geometric Brownian Motion), we can calculate the value of the options embedded in the contract starting directly from the process followed by that variable. This procedure notably simplifies the calculation method. In order to test the hypothesis of the evolution of traffic as a Geometric Brownian Motion, we have used the available series of traffic in Spanish highways, and we have applied the Augmented Dickey-Fuller approach, which is the most widely used test for this kind of study. The main result of the analysis is that we cannot reject the hypothesis that traffic follows a Geometric Brownian Motion in the majority of both toll highways and free highways in Spain.

Orthogonal MCMC algorithms

Monte Carlo (MC) methods are widely used in signal processing, machine learning and stochastic optimization. A well-known class of MC methods are Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce a novel parallel interacting MCMC scheme, where the parallel chains share information using another MCMC technique working on the entire population of current states. These parallel ?vertical? chains are led by random-walk proposals, whereas the ?horizontal? MCMC uses a independent proposal, which can be easily adapted by making use of all the generated samples. Numerical results show the advantages of the proposed sampling scheme in terms of mean absolute error, as well as robustness w.r.t. to initial values and parameter choice.

Diversidad funcional y diversidad filogenética en los bosques secos del sur del Ecuador

El concepto tradicional de reglas de ensamblaje refleja la idea de que las especies no co-ocurren al azar sino que están restringidos en su co-ocurrencia por la competencia interespecífica o por un filtrado ambiental. En está tesis abordé la importancia de los procesos que determinan el ensamble de la comunidad en la estructuración de los Bosques Secos en el Sur del Ecuador. Este estudio se realizó en la región biogeográfica Tumbesina, donde se encuentra la mayor concentración de bosques secos tropicales bien conservados del sur de Ecuador, y que constituyen una de las áreas de endemismo más importantes del mundo. El clima se caracteriza por una estación seca que va desde mayo a diciembre y una estación lluviosa de enero a abril, su temperatura anual varía entre 20°C y 26°C y una precipitación promedio anual entre 300 y 700 mm. Mi primer tema fue orientado a evaluar si la distribución de los rasgos funcionales a nivel comunitario es compatible con la existencia de un filtro ambiental (filtrado del hábitat) o con la existencia de un proceso de limitación de la semejanza funcional impuesta por la competencia inter-específica entre 58 especies de plantas leñosas repartidas en 109 parcelas (10x50m). Para ello, se analizó la distribución de los valores de cinco rasgos funcionales (altura máxima, densidad de la madera, área foliar específica, tamaño de la hoja y de masa de la semilla), resumida mediante varios estadísticos (rango, varianza, kurtosis y la desviación estándar de la distribución de distancias funcionales a la especies más próxima) y se comparó con la distribución esperada bajo un modelo nulo con ausencia de competencia. Los resultados obtenidos apoyan que tanto el filtrado ambiental como la limitación a la semejanza afectan el ensamble de las comunidades vegetales de los bosques secos Tumbesinos. Un segundo tema fue identificar si la diversidad funcional está condicionada por los gradientes ambientales, y en concreto si disminuye en los ambientes más estresantes a causa del filtrado ambiental, y si por el contrario aumenta en los ambientes más benignos donde la competencia se vuelve más importante, teniendo en cuenta las posibles modificaciones a este patrón general a causa de las interacciones de facilitación. Para abordar este estudio analizamos tanto las variaciones en la diversidad funcional (respecto a los de los cinco rasgos funcionales empleados en el primer capítulo de la tesis) como las variaciones de diversidad filogenética a lo largo de un gradiente de estrés climático en los bosques tumbesinos, y se contrastaron frente a las diversidades esperadas bajo un modelo de ensamblaje completamente aleatorio de la comunidad. Los análisis mostraron que tan sólo la diversidad de tamaños foliares siguió el patrón de variación esperado, disminuyendo a medida que aumentó el estrés abiótico mientras que ni el resto de rasgos funcionales ni la diversidad funcional multivariada ni la diversidad filogenética mostraron una variación significativa a lo largo del gradiente ambiental. Un tercer tema fue evaluar si los procesos que organizan la estructura funcional de la comunidad operan a diferentes escalas espaciales. Para ello cartografié todos los árboles y arbustos de más de 5 cm de diámetro en una parcela de 9 Ha de bosque seco y caractericé funcionalmente todas las especies. Dicha parcela fue dividida en subparcelas de diferente tamaño, obteniéndose subparcelas a seis escalas espaciales distintas. Los resultados muestran agregación de estrategias funcionales semejantes a escalas pequeñas, lo que sugiere la existencia bien de filtros ambientales actuando a escala fina o bien de procesos competitivos que igualan la estrategia óptima a dichas escalas. Finalmente con la misma información de la parcela permanente de 9 Ha. Nos propusimos evaluar el efecto y comportamiento de las especies respecto a la organización de la diversidad taxonómica, funcional y filogenética. Para ello utilicé tres funciones sumario espaciales: ISAR- para el nivel taxonómico, IFDAR para el nivel funcional y IPSVAR para el nivel filogenética y las contrastamos frente a modelos nulos que describen la distribución espacial de las especies individuales. Los resultados mostraron que en todas las escalas espaciales consideradas para ISAR, IFDAR y IPSVAR, la mayoría de las especies se comportaron como neutras, es decir, que están rodeados por la riqueza de diversidad semejante a la esperada. Sin embargo, algunas especies aparecieron como acumuladoras de diversidad funcional y filogenética, lo que sugiere su implicación en procesos competitivos de limitación de la semejanza. Una pequeña proporción de las especies apareció como repelente de la diversidad funcional y filogenética, lo que sugiere su implicación en un proceso de filtrado de hábitat. En este estudio pone de relieve cómo el análisis de las dimensiones alternativas de la biodiversidad, como la diversidad funcional y filogenética, puede ayudarnos a entender la co-ocurrencia de especies en diversos ensambles de comunidad. Todos los resultados de este estudio aportan nuevas evidencias de los procesos de ensamblaje de la comunidad de los Bosques Estacionalmente secos y como las variables ambientales y la competencia juegan un papel importante en la estructuración de la comunidad. ABSTRACT The traditional concept of the rules assembly for species communities reflects the idea that species do not co-occur at random but are restricted in their co-occurrence by interspecific competition or an environmental filter. In this thesis, I addressed the importance of the se processes in the assembly of plant communities in the dry forests of southern Ecuador. This study was conducted in the biogeographic region of Tumbesina has the largest concentration of well-conserved tropical dry forests of southern Ecuador, and is recognized as one of the most important areas of endemism in the world. The climate is characterized by a dry season from May to December and a rainy season from January to April. The annual temperature varies between 20 ° C and 26 ° C and an average annual rainfall between 300 and 700 mm. I first assessed whether the distribution of functional traits at the level of the community is compatible with the existence of an environmental filter (imposed by habitat) or the existence of a limitation on functional similarity imposed by interspecific competition. This analysis was conducted for 58 species of woody plants spread over 109 plots of 10 x 50 m. Specifically, I compared the distribution of values of five functional traits (maximum height, wood density, specific leaf area, leaf size and mass of the seed), via selected statistical properties (range, variance, kurtosis and analyzed the standard deviation of the distribution of the closest functional species) distances and compared with a expected distribution under a null model of no competition. The results support that both environmental filtering and a limitation on trait similarity affect the assembly of plant communities in dry forests Tumbesina. My second chapter evaluated whether variation in functional diversity is conditioned by environmental gradients. In particular, I tested whether it decreases in the most stressful environments because of environmental filters, or if, on the contrary, functional diversity is greater in more benign environments where competition becomes more important (notwithstanding possible changes to this general pattern due to facilitation). To address this theme I analyzed changes in both the functional diversity (maximum height, wood density, specific leaf area, leaf size and mass of the seed) and the phylogenetic diversity, along a gradient of climatic stress in Tumbes forests. The observed patterns of variation were contrasted against the diversity expected under a completely random null model of community assembly. Only the diversity of leaf sizes followed the hypothesis decreasing in as trait variation abiotic stress increased, while the other functional traits multivariate functional diversity and phylogenetic diversity no showed significant variation along the environmental gradient. The third theme assess whether the processes that organize the functional structure of the community operate at different spatial scales. To do this I mapped all the trees and shrubs of more than 5 cm in diameter within a plot of 9 hectares of dry forest and functionally classified each species. The plot was divided into subplots of different sizes, obtaining subplots of six different spatial scales. I found aggregation of similar functional strategies at small scales, which may indicate the existence of environmental filters or competitive processes that correspond to the optimal strategy for these fine scales. Finally, with the same information from the permanent plot of 9 ha, I evaluated the effect and behavior of individual species on the organization of the taxonomic, functional and phylogenetic diversity. The analysis comprised three spatial summary functions: ISAR- for taxonomic level analysis, IFDAR for functional level analysis, and IPSVAR for phylogenetic level analysis, in each case the pattern of diversity was contrasted against null models that randomly reallocate describe the spatial distribution of individual species and their traits. For all spatial scales considering ISAR, IFDAR and IPSVAR, most species behaved as neutral, i.e. they are surrounded by the diversity of other traits similar to that expected under a null model. However, some species appeared as accumulator of functional and phylogenetic diversity, suggesting that they may play a role in competitive processes that limiting similarity. A small proportion of the species appeared as repellent of functional and phylogenetic diversity, suggesting their involvement in a process of habitat filtering. These analysis highlights that the analysis of alternative dimensions of biodiversity, such as functional and phylogenetic diversity, can help us understand the co-occurrence of species in the assembly of biotic communities. All results of this study provide further evidence of the processes of assembly of the community of the seasonally dry forests as environmental variables and competition play an important role in structuring the community.

Methodology for assessing regional economic impacts of charging HGVs in Spain: an integrated approach through a random utility based multiregional input-output and a road transportation network model

An integrated approach composed of a random utility-based multiregional input-output model and a road transport network model was developed for evaluating the application of a fee to heavy-goods vehicles (HGVs) in Spain. For this purpose, a distance-based charge scenario (in euros per vehicle kilometer) for HGVs was evaluated for a selected motorway network in Spain. Although the aim of this charging policy was to increase the efficiency of transport, the approach strongly identified direct and indirect impacts on the regional economy. Estimates of the magnitude and extent of indirect effects on aggregated macroeconomic indicators (employment and gross domestic product) are provided. The macroeconomic effects of the charging policy were found to be positive for some regions and negative for other regions.

Refined second law of thermodynamics for fast random processes

We establish a refined version of the Second Law of Thermodynamics for Langevin stochastic processes describing mesoscopic systems driven by conservative or non-conservative forces and interacting with thermal noise. The refinement is based on the Monge-Kantorovich optimal mass transport and becomes relevant for processes far from quasi-stationary regime. General discussion is illustrated by numerical analysis of the optimal memory erasure protocol for a model for micron-size particle manipulated by optical tweezers.

A constitutive model for the in-plane mechanical behavior of nonwoven fabrics

A constitutive model is presented for the in-plane mechanical behavior of nonwoven fabrics. The model is developed within the context of the finite element method and provides the constitutive response for a mesodomain of the fabric corresponding to the area associated to a finite element. The model is built upon the ensemble of three blocks, namely fabric, fibers and damage. The continuum tensorial formulation of the fabric response rigorously takes into account the effect of fiber rotation for large strains and includes the nonlinear fiber behavior. In addition, the various damage mechanisms experimentally observed (bond and fiber fracture, interfiber friction and fiber pull-out) are included in a phenomenological way and the random nature of these materials is also taken into account by means of a Monte Carlo lottery to determine the damage thresholds. The model results are validated with recent experimental results on the tensile response of smooth and notched specimens of a polypropylene nonwoven fabric.