Automatic Bandwidth Estimation Strategy for High-Quality Non-Parametric Modeling based Moving Object Detection

Here, a novel and efficient moving object detection strategy by non-parametric modeling is presented. Whereas the foreground is modeled by combining color and spatial information, the background model is constructed exclusively with color information, thus resulting in a great reduction of the computational and memory requirements. The estimation of the background and foreground covariance matrices, allows us to obtain compact moving regions while the number of false detections is reduced. Additionally, the application of a tracking strategy provides a priori knowledge about the spatial position of the moving objects, which improves the performance of the Bayesian classifier

Versatile Bayesian classifier for moving object detection by non-parametric background-foreground modeling

Along the recent years, several moving object detection strategies by non-parametric background-foreground modeling have been proposed. To combine both models and to obtain the probability of a pixel to belong to the foreground, these strategies make use of Bayesian classifiers. However, these classifiers do not allow to take advantage of additional prior information at different pixels. So, we propose a novel and efficient alternative Bayesian classifier that is suitable for this kind of strategies and that allows the use of whatever prior information. Additionally, we present an effective method to dynamically estimate prior probability from the result of a particle filter-based tracking strategy.

Piezoelectric sensing and non-parametric statistical signal processing for health monitoring of hysteretic dampers used in seismic-resistant structures

The paper proposes a new application of non-parametric statistical processing of signals recorded from vibration tests for damage detection and evaluation on I-section steel segments. The steel segments investigated constitute the energy dissipating part of a new type of hysteretic damper that is used for passive control of buildings and civil engineering structures subjected to earthquake-type dynamic loadings. Two I-section steel segments with different levels of damage were instrumented with piezoceramic sensors and subjected to controlled white noise random vibrations. The signals recorded during the tests were processed using two non-parametric methods (the power spectral density method and the frequency response function method) that had never previously been applied to hysteretic dampers. The appropriateness of these methods for quantifying the level of damage on the I-shape steel segments is validated experimentally. Based on the results of the random vibrations, the paper proposes a new index that predicts the level of damage and the proximity of failure of the hysteretic damper

Contributions to Bayesian network learning with applications to neuroscience

Neuronal morphology is a key feature in the study of brain circuits, as it is highly related to information processing and functional identification. Neuronal morphology affects the process of integration of inputs from other neurons and determines the neurons which receive the output of the neurons. Different parts of the neurons can operate semi-independently according to the spatial location of the synaptic connections. As a result, there is considerable interest in the analysis of the microanatomy of nervous cells since it constitutes an excellent tool for better understanding cortical function. However, the morphologies, molecular features and electrophysiological properties of neuronal cells are extremely variable. Except for some special cases, this variability makes it hard to find a set of features that unambiguously define a neuronal type. In addition, there are distinct types of neurons in particular regions of the brain. This morphological variability makes the analysis and modeling of neuronal morphology a challenge. Uncertainty is a key feature in many complex real-world problems. Probability theory provides a framework for modeling and reasoning with uncertainty. Probabilistic graphical models combine statistical theory and graph theory to provide a tool for managing domains with uncertainty. In particular, we focus on Bayesian networks, the most commonly used probabilistic graphical model. In this dissertation, we design new methods for learning Bayesian networks and apply them to the problem of modeling and analyzing morphological data from neurons. The morphology of a neuron can be quantified using a number of measurements, e.g., the length of the dendrites and the axon, the number of bifurcations, the direction of the dendrites and the axon, etc. These measurements can be modeled as discrete or continuous data. The continuous data can be linear (e.g., the length or the width of a dendrite) or directional (e.g., the direction of the axon). These data may follow complex probability distributions and may not fit any known parametric distribution. Modeling this kind of problems using hybrid Bayesian networks with discrete, linear and directional variables poses a number of challenges regarding learning from data, inference, etc. In this dissertation, we propose a method for modeling and simulating basal dendritic trees from pyramidal neurons using Bayesian networks to capture the interactions between the variables in the problem domain. A complete set of variables is measured from the dendrites, and a learning algorithm is applied to find the structure and estimate the parameters of the probability distributions included in the Bayesian networks. Then, a simulation algorithm is used to build the virtual dendrites by sampling values from the Bayesian networks, and a thorough evaluation is performed to show the model’s ability to generate realistic dendrites. In this first approach, the variables are discretized so that discrete Bayesian networks can be learned and simulated. Then, we address the problem of learning hybrid Bayesian networks with different kinds of variables. Mixtures of polynomials have been proposed as a way of representing probability densities in hybrid Bayesian networks. We present a method for learning mixtures of polynomials approximations of one-dimensional, multidimensional and conditional probability densities from data. The method is based on basis spline interpolation, where a density is approximated as a linear combination of basis splines. The proposed algorithms are evaluated using artificial datasets. We also use the proposed methods as a non-parametric density estimation technique in Bayesian network classifiers. Next, we address the problem of including directional data in Bayesian networks. These data have some special properties that rule out the use of classical statistics. Therefore, different distributions and statistics, such as the univariate von Mises and the multivariate von Mises–Fisher distributions, should be used to deal with this kind of information. In particular, we extend the naive Bayes classifier to the case where the conditional probability distributions of the predictive variables given the class follow either of these distributions. We consider the simple scenario, where only directional predictive variables are used, and the hybrid case, where discrete, Gaussian and directional distributions are mixed. The classifier decision functions and their decision surfaces are studied at length. Artificial examples are used to illustrate the behavior of the classifiers. The proposed classifiers are empirically evaluated over real datasets. We also study the problem of interneuron classification. An extensive group of experts is asked to classify a set of neurons according to their most prominent anatomical features. A web application is developed to retrieve the experts’ classifications. We compute agreement measures to analyze the consensus between the experts when classifying the neurons. Using Bayesian networks and clustering algorithms on the resulting data, we investigate the suitability of the anatomical terms and neuron types commonly used in the literature. Additionally, we apply supervised learning approaches to automatically classify interneurons using the values of their morphological measurements. Then, a methodology for building a model which captures the opinions of all the experts is presented. First, one Bayesian network is learned for each expert, and we propose an algorithm for clustering Bayesian networks corresponding to experts with similar behaviors. Then, a Bayesian network which represents the opinions of each group of experts is induced. Finally, a consensus Bayesian multinet which models the opinions of the whole group of experts is built. A thorough analysis of the consensus model identifies different behaviors between the experts when classifying the interneurons in the experiment. A set of characterizing morphological traits for the neuronal types can be defined by performing inference in the Bayesian multinet. These findings are used to validate the model and to gain some insights into neuron morphology. Finally, we study a classification problem where the true class label of the training instances is not known. Instead, a set of class labels is available for each instance. This is inspired by the neuron classification problem, where a group of experts is asked to individually provide a class label for each instance. We propose a novel approach for learning Bayesian networks using count vectors which represent the number of experts who selected each class label for each instance. These Bayesian networks are evaluated using artificial datasets from supervised learning problems. Resumen La morfología neuronal es una característica clave en el estudio de los circuitos cerebrales, ya que está altamente relacionada con el procesado de información y con los roles funcionales. La morfología neuronal afecta al proceso de integración de las señales de entrada y determina las neuronas que reciben las salidas de otras neuronas. Las diferentes partes de la neurona pueden operar de forma semi-independiente de acuerdo a la localización espacial de las conexiones sinápticas. Por tanto, existe un interés considerable en el análisis de la microanatomía de las células nerviosas, ya que constituye una excelente herramienta para comprender mejor el funcionamiento de la corteza cerebral. Sin embargo, las propiedades morfológicas, moleculares y electrofisiológicas de las células neuronales son extremadamente variables. Excepto en algunos casos especiales, esta variabilidad morfológica dificulta la definición de un conjunto de características que distingan claramente un tipo neuronal. Además, existen diferentes tipos de neuronas en regiones particulares del cerebro. La variabilidad neuronal hace que el análisis y el modelado de la morfología neuronal sean un importante reto científico. La incertidumbre es una propiedad clave en muchos problemas reales. La teoría de la probabilidad proporciona un marco para modelar y razonar bajo incertidumbre. Los modelos gráficos probabilísticos combinan la teoría estadística y la teoría de grafos con el objetivo de proporcionar una herramienta con la que trabajar bajo incertidumbre. En particular, nos centraremos en las redes bayesianas, el modelo más utilizado dentro de los modelos gráficos probabilísticos. En esta tesis hemos diseñado nuevos métodos para aprender redes bayesianas, inspirados por y aplicados al problema del modelado y análisis de datos morfológicos de neuronas. La morfología de una neurona puede ser cuantificada usando una serie de medidas, por ejemplo, la longitud de las dendritas y el axón, el número de bifurcaciones, la dirección de las dendritas y el axón, etc. Estas medidas pueden ser modeladas como datos continuos o discretos. A su vez, los datos continuos pueden ser lineales (por ejemplo, la longitud o la anchura de una dendrita) o direccionales (por ejemplo, la dirección del axón). Estos datos pueden llegar a seguir distribuciones de probabilidad muy complejas y pueden no ajustarse a ninguna distribución paramétrica conocida. El modelado de este tipo de problemas con redes bayesianas híbridas incluyendo variables discretas, lineales y direccionales presenta una serie de retos en relación al aprendizaje a partir de datos, la inferencia, etc. En esta tesis se propone un método para modelar y simular árboles dendríticos basales de neuronas piramidales usando redes bayesianas para capturar las interacciones entre las variables del problema. Para ello, se mide un amplio conjunto de variables de las dendritas y se aplica un algoritmo de aprendizaje con el que se aprende la estructura y se estiman los parámetros de las distribuciones de probabilidad que constituyen las redes bayesianas. Después, se usa un algoritmo de simulación para construir dendritas virtuales mediante el muestreo de valores de las redes bayesianas. Finalmente, se lleva a cabo una profunda evaluaci ón para verificar la capacidad del modelo a la hora de generar dendritas realistas. En esta primera aproximación, las variables fueron discretizadas para poder aprender y muestrear las redes bayesianas. A continuación, se aborda el problema del aprendizaje de redes bayesianas con diferentes tipos de variables. Las mixturas de polinomios constituyen un método para representar densidades de probabilidad en redes bayesianas híbridas. Presentamos un método para aprender aproximaciones de densidades unidimensionales, multidimensionales y condicionales a partir de datos utilizando mixturas de polinomios. El método se basa en interpolación con splines, que aproxima una densidad como una combinación lineal de splines. Los algoritmos propuestos se evalúan utilizando bases de datos artificiales. Además, las mixturas de polinomios son utilizadas como un método no paramétrico de estimación de densidades para clasificadores basados en redes bayesianas. Después, se estudia el problema de incluir información direccional en redes bayesianas. Este tipo de datos presenta una serie de características especiales que impiden el uso de las técnicas estadísticas clásicas. Por ello, para manejar este tipo de información se deben usar estadísticos y distribuciones de probabilidad específicos, como la distribución univariante von Mises y la distribución multivariante von Mises–Fisher. En concreto, en esta tesis extendemos el clasificador naive Bayes al caso en el que las distribuciones de probabilidad condicionada de las variables predictoras dada la clase siguen alguna de estas distribuciones. Se estudia el caso base, en el que sólo se utilizan variables direccionales, y el caso híbrido, en el que variables discretas, lineales y direccionales aparecen mezcladas. También se estudian los clasificadores desde un punto de vista teórico, derivando sus funciones de decisión y las superficies de decisión asociadas. El comportamiento de los clasificadores se ilustra utilizando bases de datos artificiales. Además, los clasificadores son evaluados empíricamente utilizando bases de datos reales. También se estudia el problema de la clasificación de interneuronas. Desarrollamos una aplicación web que permite a un grupo de expertos clasificar un conjunto de neuronas de acuerdo a sus características morfológicas más destacadas. Se utilizan medidas de concordancia para analizar el consenso entre los expertos a la hora de clasificar las neuronas. Se investiga la idoneidad de los términos anatómicos y de los tipos neuronales utilizados frecuentemente en la literatura a través del análisis de redes bayesianas y la aplicación de algoritmos de clustering. Además, se aplican técnicas de aprendizaje supervisado con el objetivo de clasificar de forma automática las interneuronas a partir de sus valores morfológicos. A continuación, se presenta una metodología para construir un modelo que captura las opiniones de todos los expertos. Primero, se genera una red bayesiana para cada experto y se propone un algoritmo para agrupar las redes bayesianas que se corresponden con expertos con comportamientos similares. Después, se induce una red bayesiana que modela la opinión de cada grupo de expertos. Por último, se construye una multired bayesiana que modela las opiniones del conjunto completo de expertos. El análisis del modelo consensuado permite identificar diferentes comportamientos entre los expertos a la hora de clasificar las neuronas. Además, permite extraer un conjunto de características morfológicas relevantes para cada uno de los tipos neuronales mediante inferencia con la multired bayesiana. Estos descubrimientos se utilizan para validar el modelo y constituyen información relevante acerca de la morfología neuronal. Por último, se estudia un problema de clasificación en el que la etiqueta de clase de los datos de entrenamiento es incierta. En cambio, disponemos de un conjunto de etiquetas para cada instancia. Este problema está inspirado en el problema de la clasificación de neuronas, en el que un grupo de expertos proporciona una etiqueta de clase para cada instancia de manera individual. Se propone un método para aprender redes bayesianas utilizando vectores de cuentas, que representan el número de expertos que seleccionan cada etiqueta de clase para cada instancia. Estas redes bayesianas se evalúan utilizando bases de datos artificiales de problemas de aprendizaje supervisado.

Non-destructive measurement techniques for taper equation development: a study case in the Spanish Northern Iberian Range

In recent years, the technology for measuring the diameter and height of standing trees has improved significantly. These enhancements allow estimation of the volume of standing trees using stem taper equations, which traditionally have been constructed with data from felled trees, in an accurate and economically feasible way. A nondestructive method was evaluated with data from 38 pines and was validated with data from another 38 pines, both in the Northern Iberian Range (Spain). The electronic dendrometer Criterion RD1000 (Laser Technology Inc.) and the laser hypsometer TruPulse (Laser Technology Inc.) were used due to their accuracy and interoperability. The methodology was valid (unbiased and precise) measuring from a distance similar to the height of the tree. In this distance, statistical criteria and plots based on the residuals showed no clear advantage in volume estimation with models fitted with data from destructive methods against models fitted with data from the proposed non-destructive technique. This methodology can be considered useful for individual volume estimation and for developing taper equations.

Conditional density approximations with mixtures of polynomials

Mixtures of polynomials (MoPs) are a non-parametric density estimation technique especially designed for hybrid Bayesian networks with continuous and discrete variables. Algorithms to learn one- and multi-dimensional (marginal) MoPs from data have recently been proposed. In this paper we introduce two methods for learning MoP approximations of conditional densities from data. Both approaches are based on learning MoP approximations of the joint density and the marginal density of the conditioning variables, but they differ as to how the MoP approximation of the quotient of the two densities is found. We illustrate and study the methods using data sampled from known parametric distributions, and we demonstrate their applicability by learning models based on real neuroscience data. Finally, we compare the performance of the proposed methods with an approach for learning mixtures of truncated basis functions (MoTBFs). The empirical results show that the proposed methods generally yield models that are comparable to or significantly better than those found using the MoTBF-based method.

Desarrollo de un método estadístico para la generación de escenarios climáticos de alta resolución a partir de simulaciones de baja resolución de Modelos de Circulación General: aplicación a España

Esta tesis doctoral presenta el desarrollo, verificación y aplicación de un método original de regionalización estadística para generar escenarios locales de clima futuro de temperatura y precipitación diarias, que combina dos pasos. El primer paso es un método de análogos: los "n" días cuya configuración atmosférica de baja resolución es más parecida a la del día problema, se seleccionan de un banco de datos de referencia del pasado. En el segundo paso, se realiza un análisis de regresión múltiple sobre los "n" días más análogos para la temperatura, mientras que para la precipitación se utiliza la distribución de probabilidad de esos "n" días análogos para obtener la estima de precipitación. La verificación de este método se ha llevado a cabo para la España peninsular y las Islas Baleares. Los resultados muestran unas buenas prestaciones para temperatura (BIAS cerca de 0.1ºC y media de errores absolutos alrededor de 1.9ºC); y unas prestaciones aceptables para la precipitación (BIAS razonablemente bajo con una media de -18%; error medio absoluto menor que para una simulación de referencia (la persistencia); y una distribución de probabilidad simulada similar a la observada según dos test no-paramétricos de similitud). Para mostrar la aplicabilidad de la metodología desarrollada, se ha aplicado en detalle en un caso de estudio. El método se aplicó a cuatro modelos climáticos bajo diferentes escenarios futuros de emisiones de gases de efecto invernadero, para la región de Aragón, produciendo así proyecciones futuras de precipitación y temperaturas máximas y mínimas diarias. La fiabilidad de la técnica de regionalización fue evaluada de nuevo para el caso de estudio mediante un proceso de verificación. Para determinar la capacidad de los modelos climáticos para simular el clima real, sus simulaciones del pasado (la denominada salida 20C3M) se regionalizaron y luego se compararon con el clima observado (los resultados son bastante robustos para la temperatura y menos concluyentes para la precipitación). Las proyecciones futuras a escala local presentan un aumento significativo durante todo el siglo XXI de las temperaturas máximas y mínimas para todos los futuros escenarios de emisiones considerados. Las simulaciones de precipitación presentan mayores incertidumbres. Además, la aplicabilidad práctica del método se demostró también mediante su utilización para producir escenarios climáticos futuros para otros casos de estudio en los distintos sectores y regiones del mundo. Se ha prestado especial atención a una aplicación en Centroamérica, una región que ya está sufriendo importantes impactos del cambio climático y que tiene un clima muy diferente. ABSTRACT This doctoral thesis presents the development, verification and application of an original downscaling method for daily temperature and precipitation, which combines two statistical approaches. The first step is an analogue approach: the “n” days most similar to the day to be downscaled are selected. In the second step, a multiple regression analysis using the “n” most analogous days is performed for temperature, whereas for precipitation the probability distribution of the “n” analogous days is used to obtain the amount of precipitation. Verification of this method has been carried out for the Spanish Iberian Peninsula and the Balearic Islands. Results show good performance for temperature (BIAS close to 0.1ºC and Mean Absolute Errors around 1.9ºC); and an acceptable skill for precipitation (reasonably low BIAS with a mean of - 18%, Mean Absolute Error lower than for a reference simulation, i.e. persistence, and a well-simulated probability distribution according to two non-parametric tests of similarity). To show the applicability of the method, a study case has been analyzed. The method was applied to four climate models under different future emission scenarios for the region of Aragón, thus producing future projections of daily precipitation and maximum and minimum temperatures. The reliability of the downscaling technique was re-assessed for the study case by a verification process. To determine the ability of the climate models to simulate the real climate, their simulations of the past (the 20C3M output) were downscaled and then compared with the observed climate – the results are quite robust for temperature and less conclusive for the precipitation. The downscaled future projections exhibit a significant increase during the entire 21st century of the maximum and minimum temperatures for all the considered future emission scenarios. Precipitation simulations exhibit greater uncertainties. Furthermore, the practical applicability of the method was demonstrated also by using it to produce future climate scenarios for some other study cases in different sectors and regions of the world. Special attention was paid to an application of the method in Central America, a region that is already suffering from significant climate change impacts and that has a very different climate from others where the method was previously applied.

Non contact inspection of the fatigue damage state of carbon fiber reinforced polymer by optical surface roughness measurements

This work presents the evaluation of a new non-contact technique to assess the fatigue damage state of CFRP structures by measuring surface roughness parameters. Surface roughness and stiffness degradation have been measured in CFRP coupons cycled with constant amplitude loads, and a Pearson?s correlation of 0.79 was obtained between both variables. Results suggest that changes on the surface roughness measured in strategic zones of components made of the evaluated CFRP, could be indicative of the level of damage due to fatigue loads. This methodology could be useful for other FRP due to similarities in the fatigue damage process.

Estimadores bayesianos de la fiabilidad con muestreo censurado

El estudio de la fiabilidad de componentes y sistemas tiene gran importancia en diversos campos de la ingenieria, y muy concretamente en el de la informatica. Al analizar la duracion de los elementos de la muestra hay que tener en cuenta los elementos que no fallan en el tiempo que dure el experimento, o bien los que fallen por causas distintas a la que es objeto de estudio. Por ello surgen nuevos tipos de muestreo que contemplan estos casos. El mas general de ellos, el muestreo censurado, es el que consideramos en nuestro trabajo. En este muestreo tanto el tiempo hasta que falla el componente como el tiempo de censura son variables aleatorias. Con la hipotesis de que ambos tiempos se distribuyen exponencialmente, el profesor Hurt estudio el comportamiento asintotico del estimador de maxima verosimilitud de la funcion de fiabilidad. En principio parece interesante utilizar metodos Bayesianos en el estudio de la fiabilidad porque incorporan al analisis la informacion a priori de la que se dispone normalmente en problemas reales. Por ello hemos considerado dos estimadores Bayesianos de la fiabilidad de una distribucion exponencial que son la media y la moda de la distribucion a posteriori. Hemos calculado la expansion asint6tica de la media, varianza y error cuadratico medio de ambos estimadores cuando la distribuci6n de censura es exponencial. Hemos obtenido tambien la distribucion asintotica de los estimadores para el caso m3s general de que la distribucion de censura sea de Weibull. Dos tipos de intervalos de confianza para muestras grandes se han propuesto para cada estimador. Los resultados se han comparado con los del estimador de maxima verosimilitud, y con los de dos estimadores no parametricos: limite producto y Bayesiano, resultando un comportamiento superior por parte de uno de nuestros estimadores. Finalmente nemos comprobado mediante simulacion que nuestros estimadores son robustos frente a la supuesta distribuci6n de censura, y que uno de los intervalos de confianza propuestos es valido con muestras pequenas. Este estudio ha servido tambien para confirmar el mejor comportamiento de uno de nuestros estimadores. SETTING OUT AND SUMMARY OF THE THESIS When we study the lifetime of components it's necessary to take into account the elements that don't fail during the experiment, or those that fail by reasons which are desirable to exclude from consideration. The model of random censorship is very usefull for analysing these data. In this model the time to failure and the time censor are random variables. We obtain two Bayes estimators of the reliability function of an exponential distribution based on randomly censored data. We have calculated the asymptotic expansion of the mean, variance and mean square error of both estimators, when the censor's distribution is exponential. We have obtained also the asymptotic distribution of the estimators for the more general case of censor's Weibull distribution. Two large-sample confidence bands have been proposed for each estimator. The results have been compared with those of the maximum likelihood estimator, and with those of two non parametric estimators: Product-limit and Bayesian. One of our estimators has the best behaviour. Finally we have shown by simulation, that our estimators are robust against the assumed censor's distribution, and that one of our intervals does well in small sample situation.

Segmentación de Secuencias de Vídeo: análisis y desarrollo de estrategias para la detección de cambios de toma y para la detección de objetos móviles

The important technological advances experienced along the last years have resulted in an important demand for new and efficient computer vision applications. On the one hand, the increasing use of video editing software has given rise to a necessity for faster and more efficient editing tools that, in a first step, perform a temporal segmentation in shots. On the other hand, the number of electronic devices with integrated cameras has grown enormously. These devices require new, fast, and efficient computer vision applications that include moving object detection strategies. In this dissertation, we propose a temporal segmentation strategy and several moving object detection strategies, which are suitable for the last generation of computer vision applications requiring both low computational cost and high quality results. First, a novel real-time high-quality shot detection strategy is proposed. While abrupt transitions are detected through a very fast pixel-based analysis, gradual transitions are obtained from an efficient edge-based analysis. Both analyses are reinforced with a motion analysis that allows to detect and discard false detections. This analysis is carried out exclusively over a reduced amount of candidate transitions, thus maintaining the computational requirements. On the other hand, a moving object detection strategy, which is based on the popular Mixture of Gaussians method, is proposed. This strategy, taking into account the recent history of each image pixel, adapts dynamically the amount of Gaussians that are required to model its variations. As a result, we improve significantly the computational efficiency with respect to other similar methods and, additionally, we reduce the influence of the used parameters in the results. Alternatively, in order to improve the quality of the results in complex scenarios containing dynamic backgrounds, we propose different non-parametric based moving object detection strategies that model both background and foreground. To obtain high quality results regardless of the characteristics of the analyzed sequence we dynamically estimate the most adequate bandwidth matrices for the kernels that are used in the background and foreground modeling. Moreover, the application of a particle filter allows to update the spatial information and provides a priori knowledge about the areas to analyze in the following images, enabling an important reduction in the computational requirements and improving the segmentation results. Additionally, we propose the use of an innovative combination of chromaticity and gradients that allows to reduce the influence of shadows and reflects in the detections.

Comparative analysis of meta-analysis methods: when to use which?

Background: Several meta-analysis methods can be used to quantitatively combine the results of a group of experiments, including the weighted mean difference, statistical vote counting, the parametric response ratio and the non-parametric response ratio. The software engineering community has focused on the weighted mean difference method. However, other meta-analysis methods have distinct strengths, such as being able to be used when variances are not reported. There are as yet no guidelines to indicate which method is best for use in each case. Aim: Compile a set of rules that SE researchers can use to ascertain which aggregation method is best for use in the synthesis phase of a systematic review. Method: Monte Carlo simulation varying the number of experiments in the meta analyses, the number of subjects that they include, their variance and effect size. We empirically calculated the reliability and statistical power in each case Results: WMD is generally reliable if the variance is low, whereas its power depends on the effect size and number of subjects per meta-analysis; the reliability of RR is generally unaffected by changes in variance, but it does require more subjects than WMD to be powerful; NPRR is the most reliable method, but it is not very powerful; SVC behaves well when the effect size is moderate, but is less reliable with other effect sizes. Detailed tables of results are annexed. Conclusions: Before undertaking statistical aggregation in software engineering, it is worthwhile checking whether there is any appreciable difference in the reliability and power of the methods. If there is, software engineers should select the method that optimizes both parameters.

Moving Object Detection for Real-Time High-Quality Lightweight Applications on Smart Cameras

Here, a novel and efficient strategy for moving object detection by non-parametric modeling on smart cameras is presented. Whereas the background is modeled using only color information, the foreground model combines color and spatial information. The application of a particle filter allows the update of the spatial information and provides a priori information about the areas to analyze in the following images, enabling an important reduction in the computational requirements and improving the segmentation results

De la ciudad en vibración al ser resonante: Una investigación a propósito de los conciertos de campanas de Llorenç Barber

Este trabajo tiene la vocación de dibujar el mapa de los dominios de oportunidad que hay a disposición de un arquitecto, al imaginar el aspecto sonoro de la ciudad en relación con la campana. Muchas de las dimensiones señaladas, tanto conocidas como desconocidas, son sencillamente despertadas al estudio de lo que un diseñador del espacio sonoro, como Llorenç Barber, viene a decirnos, desde una disciplina aparentemente contactada, pero muchas veces paralela a la arquitectónica. Tomando los conciertos de campanas celebrados hasta la fecha como puestas en práctica real de la condición instrumental de más de doscientas ciudades de todo el mundo, el estudio analiza los hallazgos, éxitos y fracasos, obtenidos a lo largo de estos últimos veinticinco años de experiencia, como resultados explícitos de un trabajo sonoro en la ciudad, probado, contrastado y afinado, del que extraer indicaciones concretas susceptibles de ser aplicadas en el diseño del espacio urbano. Mediante un análisis triangular de vértices definidos: emisor-campana, medio-ciudad, receptor-ciudadano; la investigación aborda los aspectos relativos a la producción y recepción del fenómeno sonoro generado por la campana en la ciudad. En relación con la producción, una parte del trabajo se dedica al estudio de los efectos acústicos observados, ordenado por escalas graduales en las que se produce una distribución del sonido susceptible de diseño: el vaso de la campana, la sala de campanas, el campanario, el cuerpo del edificio, la ciudad y el territorio. A modo de cajas de resonancia y leídas como muñecas rusas, unas dentro de otras, los espacios identificados muestran cualidades sonoras específicas, definidas tanto por sus condiciones geométricas, formales, constructivas o de uso; como por las correspondientes a las subsiguientes cajas que alojan. A fin de esclarecer la influencia de tales cuestiones en el aspecto sonoro de la ciudad, se propone un modelo ordenado de conexión y desconexión de escalas, utilizando una paramétrica puramente técnica creada ex profeso, junto con variables metodológicas más tradicionales. Al mismo tiempo, y tratando de esclarecer cómo, al ser puesta en vibración, la ciudad es aprehendida, disfrutada y rememorada por el ciudadano-oyente, otra parte del trabajo se dedica al estudio de los aspectos relativos a su recepción aural en deambulación compartida. En este caso la investigación se propone reclamar otras dimensiones más subversivas que, si bien escapan a los modos habituales de trabajar del arquitecto, se revelan intensamente en la experiencia plurifocal, multiplicando los efectos a considerar: efectos funcionales y significativos, de reconocimiento, integración y pertenencia a un cuerpo territorial y social de coordenadas históricas y geográficas de nuevo significadas; efectos perceptuales de inmersión, ubicuidad, temporalidad o inestabilidad; efectos estéticos, de rememoración, interpretación simbólica y recreación poética; e incluso efectos políticos, descubriendo un espacio urbano en continua regeneración, lugar para la exposición en su doble acepción, para la exhibición y el peligro, o como contenedor situacional del más profundo sentido ciudadano. Para afinar la relevancia de lo obtenido en cada una de las dimensiones señaladas, el trabajo se articula en tres aproximaciones graduales: el corpus general de los conciertos celebrados hasta la fecha; los conciertos celebrados en España; los tres conciertos para Madrid: Magna Mater (1991), Festi Clamores (2000) y Aurea Catena (2007). Si bien el modelo propuesto nace a la luz de los conciertos de campanas de Llorenç Barber, a escala de una ciudad entera y con una intención compositiva individual, se entiende que sería útil para el uso ordenado de cualquier profesional interesado en el aspecto sonoro de la ciudad, faceta escasamente atendida, dicho sea de paso, desde la disciplina arquitectónica. ABSTRACT This work has the vocation of drawing out the numerous opportunities an architect has at his disposal, upon imagining the sonorous aspect of the city in relation to the bell. Many of the dimensions indicated, both known and unknown, are just awakened to the study of what a sound space designer, as Llorenç Barber, comes to tell us, from a discipline apparently contacted, but often parallel to the architecture. Taking the bell concerts held so far as actual implementation of the instrumental condition of over two hundred cities around the world, the study analyzes the findings, successes and failures, obtained over the last twenty years of experience, as explicit results of a sound work in the city, tested, verified and refined, from which to extract specific indications that can be applied in the design of urban space. By triangular analyzing of defined vertices: sender-bell, half-city, receptor-citizen; the research addresses issues relating to the production and reception of sound phenomenon generated by the bell in the city. In relation to production, part of the work is devoted to the study of observed acoustic effects, ordered gradual scale which produces a distribution of sound capable of design: the glass of the bell, the bell room, the bell tower and the body of the building, the city and territory. By way of sounding boards and read as if they were Russian dolls, one inside the other, the show spaces identified specific sound qualities, defined both for their geometric, formal, constructive use, such as those for hosting the subsequent boxes. In order to clarify the influence of such issues in the sound aspect of the city, we propose an ordered pattern of connection and disconnection of scales, using a purely parametric technique created on purpose, along with more traditional methodological variables. At the same time, and trying to clarify how, when set in vibration, the city is apprehended, enjoyed and remembered for the citizen-listener, another part of the work is devoted to the study of aspects of aural reception in shared ambulation. In this case the research aims to claim more subversive than other dimensions, but beyond the usual ways in which an architect works, the experience reveals intensely plurifocal multiplying effects to consider: functional effects and significant recognition and integration belonging to a body of territorial and social historical and geographical coordinates of new meaning and perceptual effects of immersion, ubiquity, timeliness or instability; aesthetic effects of recall, interpretation and recreation of symbolic poetic; and even political effects, revealing a continuous urban space regeneration site for the exhibition in its double meaning, for display and danger, or as a citizen sense container. To sharpen the relevance of what was obtained in each of the dimensions mentioned, the work is divided into three incremental approaches: the general corpus of the concerts held so far, the concerts in Spain, the three concerts for Madrid: Magna Mater (1991), Festi Clamores (2000) and Aurea Catena (2007). While the proposed model comes in the light of the bells concert Llorenç Barber, the scale of a whole city and individual compositional intent, it is understood that it would be useful for the orderly use of any professional interested in the sound aspect of the city, an aspect sparsely attended, incidentally, from the architectural discipline.

Ensemble transcript interaction networks: A case study on Alzheimer's disease

Systems biology techniques are a topic of recent interest within the neurological field. Computational intelligence (CI) addresses this holistic perspective by means of consensus or ensemble techniques ultimately capable of uncovering new and relevant findings. In this paper, we propose the application of a CI approach based on ensemble Bayesian network classifiers and multivariate feature subset selection to induce probabilistic dependences that could match or unveil biological relationships. The research focuses on the analysis of high-throughput Alzheimer's disease (AD) transcript profiling. The analysis is conducted from two perspectives. First, we compare the expression profiles of hippocampus subregion entorhinal cortex (EC) samples of AD patients and controls. Second, we use the ensemble approach to study four types of samples: EC and dentate gyrus (DG) samples from both patients and controls. Results disclose transcript interaction networks with remarkable structures and genes not directly related to AD by previous studies. The ensemble is able to identify a variety of transcripts that play key roles in other neurological pathologies. Classical statistical assessment by means of non-parametric tests confirms the relevance of the majority of the transcripts. The ensemble approach pinpoints key metabolic mechanisms that could lead to new findings in the pathogenesis and development of AD

Evolution and treatment of vitamin B12 deficiency as a risk factor for (cognitive and functional) neurodegenerative diseases in institutionalized elderly = Evolución y tratamiento de la deficiencia de vitamina B12 como factor de riesgo de enfermedades neurodegenerativas (cognitivas y funcionales) en las personas mayores institucionalizadas.

Prevalence of vitamin B12 deficiency is very common in elderly people and can reach values as high as 40.5% of the population. It can be the result of the interaction among several factors. Vitamin B12 deficiencies have been associated with neurological, cognitive deterioration, haematological abnormalities and cardiovascular diseases that have an important influence on the health of the elderly and their quality of life. It is necessary to approach the problems arisen from the lack of data relative to them. The main objective of this thesis was to analyse the evolution of vitamin B12 status and related parameters, lipid and haematological profiles and their relationship to health risk factors, and to functional and cognitive status over one year and to determine the effect of an oral supplementation of 500 μg of cyanocobalamin for a short period of 28 days. An additional objective was to analyze the possible effects of medicine intakes on vitamin B status. Three studies were performed: a) a one year longitudinal follow-up with four measure points; b) an intervention study providing an oral liquid supplement of 500 μg of cyanocobalamin for a 28 days period; and c) analysis of the possible effect of medication intake on vitamin B status using the ATC classification of medicines. The participants for these studies were recruited from nursing homes for the elderly in the Region of Madrid. Sixty elders (mean age 84 _ 7y, 19 men and 41 women) were recruited for Study I and 64 elders (mean age 82 _ 7y, 24 men and 40 women) for Study II. For Study III, baseline data from the initially recruited participants of the first two studies were used. An informed consent was obtained from all participants or their mentors. The studies were approved by the Ethical Committee of the University of Granada. Blood samples were obtained at each examination date and were analyzed for serum cobalamin, holoTC, serum and RBC folate and total homocysteine according to laboratory standard procedures. The haematological parameters analyzed were haematocrit, haemoglobin and MCV. For the lipid profile TG, total cholesterol, LDL- and HDLcholesterol were analyzed. Anthropometric measures (BMI, skinfolds [triceps and subscapular], waist girth and waist to hip ratio), functional tests (hand grip, arm and leg strength tests, static balance) and MMSE were obtained or administered by trained personal. The vitamin B12 supplement of Study II was administered with breakfast and the medication intake was taken from the residents’ anamnesis. Data were analyzed by parametric and non-parametric statistics depending on the obtained data. Comparisons were done using the appropriate ANOVAs or non-parametric tests. Pearsons’ partial correlations with the variable “time” as control were used to define the association of the analyzed parameters. XIII The results showed that: A) Over one year, in relationship to vitamin B status, serum cobalamin decreased, serum folate and mean corpuscular volumen increased significantly and total homocysteine concentrations were stable. Regarding blood lipid profile, triglycerides increased and HDL-cholesterol decreased significantly. Regarding selected anthropometric measurements, waist circumference increased significantly. No significant changes were observed for the rest of parameters. B) Prevalence of hyperhomocysteinemia was high in the elderly studied, ranging from 60% to 90 % over the year depending on the cut-off used for the classification. LDL-cholesterol values were high, especially among women, and showed a tendency to increase over the year. Results of the balance test showed a deficiency and a tendency to decrease; this indicates that the population studied is at high risk for falls. Lower extremity muscular function was deficient and showed a tendency to decrease. A highly significant relationship was observed between the skinfold of the triceps and blood lipid profile. C) Low cobalamin concentrations correlated significantly with low MMSE scores in the elderly studied. No correlations were observed between vitamin B12 status and functional parameters. D) Regarding vitamin B12 status, holo-transcobalamin seems to be more sensitive for diagnosis; 5-10% of the elderly had a deficiency using serum cobalamin as a criterion, and 45-52% had a deficiency when using serum holotranscobalamin as a criterion. E) 500 μg of cyanocobalamin administered orally during 28 days significantly improved vitamin B12 status and significantly decreased total homocysteine concentrations in institutionalized elderly. No effect of the intervention was observed on functional and cognitive parameters. F) The relative change (%) of improvement of vitamin B12 status was higher when using serum holo-transcobalamin as a criterion than serum cobalamin. G) Antiaenemic drug intake normalized cobalamin, urologic drugs and corticosteroids serum folate, and psychoanaleptics holo-transcobalamin levels. Drugs treating pulmonary obstruction increased total homocysteine concentration significantly. H) The daily mean drug intake was 5.1. Fiftynine percent of the elderly took medication belonging to 5 or more different ATC groups. The most prevalent were psycholeptic (53%), antiacid (53%) and antithrombotic (47%) drugs.