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.

AMELIE: Authoring Multimedia-Enhanced Learning Interactive Environment for e-Health Contents

This paper presents the AMELIE Authoring Tool for e-health applications. AMELIE provides the means for creating video-based contents with a focus on e-learning and telerehabilitation processes. The main core of AMELIE lies in the efficient exploitation of raw multimedia resources, which may be already available at clinical centers or recorded ad hoc for learning purposes by health professionals. Three real use cases scenarios involving different target users are presented: (1) cognitive skills? training of surgeons in minimally invasive surgery (medical professionals), (2) training of informal carers for elderly home assistance and (3) cognitive rehabilitation of patients with acquired brain injury. Preliminary validation in the field of surgery hints at the potential of AMELIE; and its versatility in different medical applications is patent from the use cases described. Regardless, new validation studies are planned in the three main application areas identified in this work.

Development and implementation of a virtual laboratory for training process improvement in the mechanics of continuous media

The project arises from the need to develop improved teaching methodologies in field of the mechanics of continuous media. The objective is to offer the student a learning process to acquire the necessary theoretical knowledge, cognitive skills and the responsibility and autonomy to professional development in this area. Traditionally the teaching of the concepts of these subjects was performed through lectures and laboratory practice. During these lessons the students attitude was usually passive, and therefore their effectiveness was poor. The proposed methodology has already been successfully employed in universities like University Bochum, Germany, University the South Australia and aims to improve the effectiveness of knowledge acquisition through use by the student of a virtual laboratory. This laboratory allows to adapt the curricula and learning techniques to the European Higher Education and improve current learning processes in the University School of Public Works Engineers -EUITOP- of the Technical University of Madrid -UPM-, due there are not laboratories in this specialization. The virtual space is created using a software platform built on OpenSim, manages 3D virtual worlds, and, language LSL -Linden Scripting Language-, which imprints specific powers to objects. The student or user can access this virtual world through their avatar -your character in the virtual world- and can perform practices within the space created for the purpose, at any time, just with computer with internet access and viewfinder. The virtual laboratory has three partitions. The virtual meeting rooms, where the avatar can interact with peers, solve problems and exchange existing documentation in the virtual library. The interactive game room, where the avatar is has to resolve a number of issues in time. And the video room where students can watch instructional videos and receive group lessons. Each audiovisual interactive element is accompanied by explanations framing it within the area of knowledge and enables students to begin to acquire a vocabulary and practice of the profession for which they are being formed. Plane elasticity concepts are introduced from the tension and compression testing of test pieces of steel and concrete. The behavior of reticulated and articulated structures is reinforced by some interactive games and concepts of tension, compression, local and global buckling will by tests to break articulated structures. Pure bending concepts, simple and composite torsion will be studied by observing a flexible specimen. Earthquake resistant design of buildings will be checked by a laboratory test video.

Managing team learning in a Spanish commercial bank

Purpose – The purpose of this paper is to analyze how team management affects team-learning activities. Design/methodology/approach – The authors empirically study 68 teams as they operate in the natural business context of a major Spanish bank. Quantitative research utilizing multiple regression analyses is used to test hypotheses. Findings – The leadership behaviour (consideration, initiation of structure) displayed by the team leader plays a key role in facilitating team learning. Team leader behaviour characterised by consideration and in particular by initiation of structure are both positively related to team-learning activities. Cross-training of team members also contributes to team-learning behaviour. Research limitations/implications – A specific setting may limit the generalizability of findings. Further research may accordingly investigate to what extent these results can be generalized to other settings or other aspects of team learning. Practical implications – The leadership style adopted by the team leader, as well as cross-training of members, affect team-learning activities. These results link leadership theory to collective learning in teams and organizations, and suggest ways leaders can contribute to improved learning. Originality/value – The study provides new insight into how management of teams facilitates team-learning activities. While consideration is somewhat related to team learning, initiation of structure as well as cross-training appear as key variables.

Real time power plant simulation platform for training on Electrical Protections and Automatic Voltage Regulators

Electrical Protection systems and Automatic Voltage Regulators (AVR) are essential components of actual power plants. Its installation and setting is performed during the commissioning, and it needs extensive experience since any failure in this process or in the setting, may entails some risk not only for the generator of the power plant, but also for the reliability of the power grid. In this paper, a real time power plant simulation platform is presented as a tool for improving the training and learning process on electrical protections and automatic voltage regulators. The activities of the commissioning procedure which can be practiced are described, and the applicability of this tool for improving the comprehension of this important part of the power plants is discussed. A commercial AVR and a multifunction protective relay have been tested with satisfactory results.

Sistema de e-learning en el entorno AFRICA BUILD Portal

Diversos estudios consideran que las claves para el desarrollo técnico, social y económico de los países en desarrollo, y en especial en África, son la educación, la investigación y la innovación. No obstante, en el ámbito sanitario en particular, la formación e investigación en África sigue siendo escasa por diversos motivos. Por un lado, los investigadores africanos encuentran dificultades añadidas para obtener financiación frente a investigadores en países de desarrollo. Y, por otro lado, los investigadores no disponen de las herramientas y tecnologías adecuadas para acceder a información relevante para su trabajo. En este contexto, el proyecto AFRICA BUILD tiene como principal objetivo mejorar y fomentar la investigación y educación sanitaria en África a través de las nuevas tecnologías. Uno de los resultados principales de este proyecto es el AFRICA BUILD Portal (ABP), que pretende ser un punto de encuentro para los investigadores africanos, donde podrán encontrar un amplio abanico de herramientas que les permita no sólo mejorar su educación, sino compartir conocimientos con otros investigadores. Este portal, que está actualmente en desarrollo, pretende fomentar la comunicación entre instituciones africanas y les proveerá herramientas biomédicas avanzadas. En este Trabajo de Fin de Grado se han marcado como principal objetivo la mejora del sistema de e-learning que existía en el ABP. El nuevo sistema será: (i) más potente, óptimo y rico en funcionalidades, (ii) tendrá una importante base social permitiendo a los usuarios aportar información y compartir conocimiento, y (iii) será distribuido y escalable, capaz de integrar distintos gestores de contenidos didácticos como fuentes de información. Los resultados obtenidos son positivos y el nuevo sistema de aprendizaje ha mejorado la usabilidad, funcionalidad y apariencia del anterior. Para concluir, la herramienta posee aún un gran margen de mejora, pero gracias al trabajo realizado el sistema de e-learning es ahora más social, distribuido, escalable e innovador. ---ABSTRACT---Several studies find that the keys to the technical, social and economic development of developing countries, especially in Africa, are education, research and innovation. However, in the health sector in particular, training and research in Africa remains low for various reasons. On the one hand, African researchers experience additional difficulties for funding against researchers in developing countries. And on the other hand, researchers do not have the right tools and technologies to access information relevant to their work. In this context, the AFRICA BUILD project's main objective is to improve and enhance research and health education in Africa through new technologies. One of the main results of this project is the AFRICA BUILD Portal (ABP), which aims to be a meeting point for African researchers, where they can find a wide range of tools that allows them to not only improve their education but to share knowledge with other researchers. This portal, which is currently under development, aims to promote communication between African institutions and will provide advanced biomedical tools. The main objective of this Final Year Project is the improvement of the former elearning system that already existed in the ABP. The new system will be: (i) more powerful, richer in features, (ii) will have an important social base allowing users to contribute information and knowledge sharing; and (iii) will be distributed and scalable, capable of integrating different learning management systems as sources of information. The final results obtained are fairly positive and the new e-learning system has improved its usability, functionality and appearance of the former. To conclude, the tool still has much room for improvement, but thanks to the new features of the improved elearning system has become more social, distributed, scalable and innovative.

University Networking in Developing Countries based on C4D-Learning

In recent years, the establishment of cooperation networks between universities is one of the most important trends in higher education all over the world. Well recognized local and international university networks have been implemented in most educational institutions. It is common to find associations of various prestigious universities collaborating in a high-­‐technology research project including a very specialized teaching as well. This is the most common cooperation networks among higher education institutions in developed countries. An increasingly common type of networking between developed and developing universities is related to cooperation for development. This is the case of many universities in Africa that are needed for external help in order to improve its capabilities. Numerous memorandums of understanding regarding first world institutions that collaborate with universities in developing countries describe contributions of eventual visiting professors, teaching material and courses. But probably there exist another type of more important, but less explored association, such as networking among developing universities. The new goal, in this case, is not only the excellence but also the mutual development.

The educational learning lifecycle and the cloud computing

Currently, student dropout rates are a matter of concern among universities. Many research studies, aimed at discovering the causes, have been carried out. However, few solutions, that could serve all students and related problems, have been proposed so far. One such problem is caused by the lack of the "knowledge chain educational links" that occurs when students move onto higher studies without mastering their basic studies. Most regulated studies imparted at universities are designed so that some basic subjects serve as support for other, more complicated, subjects, thus forming a complicated knowledge network. When a link in this chain fails, student frustration occurs as it prevents him from fully understanding the following educational links. In this proposal we try to mitigate these disasters that stem, for the most part, the student?s frustration beyond his college stay. On one hand, we make a dissertation on the student?s learning process, which we divide into a series of phases that amount to what we call the "learning lifecycle." Also, we analyze at what stage the action by the stakeholders involved in this scenario: teachers and students; is most important. On the other hand, we consider that Information and Communication Technologies ICT, such as Cloud Computing, can help develop new ways, allowing for the teaching of higher education, while easing and facilitating the student?s learning process. But, methods and processes need to be defined as to direct the use of such technologies; in the teaching process in general, and within higher education in particular; in order to achieve optimum results. Our methodology integrates, as another actor, the ICT into the "Learning Lifecycle". We stimulate students to stop being merely spectators of their own education, and encourage them to take an active part in their training process. To do this, we offer a set of useful tools to determine not only academic failure causes, (for self assessment), but also to remedy these failures (with corrective actions); "discovered the causes it is easier to determine solutions?. We believe this study will be useful for both students and teachers. Students learn from their own experience and improve their learning process, while obtaining all of the "knowledge chain educational links? required in their studies. We stand by the motto "Studying to learn instead of studying to pass". Teachers will also be benefited by detecting where and how to strengthen their teaching proposals. All of this will also result in decreasing dropout rates.

Fast 2D to 3D conversion using a clustering-based hierarchical search in a machine learning framework

Automatic 2D-to-3D conversion is an important application for filling the gap between the increasing number of 3D displays and the still scant 3D content. However, existing approaches have an excessive computational cost that complicates its practical application. In this paper, a fast automatic 2D-to-3D conversion technique is proposed, which uses a machine learning framework to infer the 3D structure of a query color image from a training database with color and depth images. Assuming that photometrically similar images have analogous 3D structures, a depth map is estimated by searching the most similar color images in the database, and fusing the corresponding depth maps. Large databases are desirable to achieve better results, but the computational cost also increases. A clustering-based hierarchical search using compact SURF descriptors to characterize images is proposed to drastically reduce search times. A significant computational time improvement has been obtained regarding other state-of-the-art approaches, maintaining the quality results.

An interactive web-based learning assistant for structural analysis of continuous beams

There are significant levels of concern about the relevance and the difficulty of learning some issues on Strength of Materials and Structural Analysis. Most students of Continuum Mechanics and Structural Analysis in Civil Engineering usually point out some key learning aspects as especially difficult for acquiring specific skills. These key concepts entail comprehension difficulties but ease access and applicability to structural analysis in more advanced subjects. Likewise, some elusive but basic structural concepts, such as flexibility, stiffness or influence lines, are paramount for developing further skills required for advanced structural design: tall buildings, arch-type structures as well as bridges. As new curricular itineraries are currently being implemented, it appears appropriate to devise a repository of interactive web-based applications for training in those basic concepts. That will hopefully train the student to understand the complexity of such concepts, to develop intuitive knowledge on actual structural response and to improve their preparation for exams. In this work, a web-based learning assistant system for influence lines on continuous beams is presented. It consists of a collection of interactive user-friendly applications accessible via Web. It is performed in both Spanish and English languages. Rather than a “black box” system, the procedure involves open interaction with the student, who can simulate and virtually envisage the structural response. Thus, the student is enabled to set the geometric, topologic and mechanic layout of a continuous beam and to change or shift the loading and the support conditions. Simultaneously, the changes in the beam response prompt on the screen, so that the effects of the several issues involved in structural analysis become apparent. The system is performed through a set of web pages which encompasses interactive exercises and problems, written in JavaScript under JQuery and DyGraphs frameworks, given that their efficiency and graphic capabilities are renowned. Students can freely boost their self-study on this subject in order to face their exams more confidently. Besides, this collection is expected to be added to the "Virtual Lab of Continuum Mechanics" of the UPM, launched in 2013 (http://serviciosgate.upm.es/laboratoriosvirtuales/laboratorios/medios-continuos-en-construcci%C3%B3n)

Diseño del curso para operador "Coeficientes inherentes de reactividad" a plataforma e-Learning

Las centrales nucleares necesitan de personal altamente especializado y formado. Es por ello por lo que el sector de la formación especializada en centrales nucleares necesita incorporar los últimos avances en métodos formativos. Existe una gran cantidad de cursos de formación presenciales y es necesario transformar dichos cursos para utilizarlos con las nuevas tecnologías de la información. Para ello se necesitan equipos multidisciplinares, en los que se incluyen ingenieros, que deben identificar los objetivos formativos, competencias, contenidos y el control de calidad del propio curso. En este proyecto se utilizan técnicas de ingeniería del conocimiento como eje metodológico para transformar un curso de formación presencial en formación on-line a través de tecnologías de la información. En la actualidad, las nuevas tecnologías de la información y comunicación están en constante evolución. De esta forma se han sumergido en el mundo transformando la visión que teníamos de éste para dar lugar a nuevas oportunidades. Es por ello que este proyecto busca la unión entre el e-learning y el mundo empresarial. El objetivo es el diseño, en plataforma e-learning, de un curso técnico que instruya a operadores de sala de control de una central nuclear. El trabajo realizado en este proyecto ha sido, además de transformar un curso presencial en on-line, en obtener una metodología para que otros cursos se puedan transformar. Para conseguir este cometido, debemos preocuparnos tanto por el contenido de los cursos como por su gestión. Por este motivo, el proyecto comienza con definiciones básicas de terminología propia de e-learning. Continúa con la generación de una metodología que aplique la gestión de conocimiento para transformar cualquier curso presencial a esta plataforma. Definida la metodología, se aplicará para el diseño del curso específico de Coeficientes Inherentes de Reactividad. Finaliza con un estudio económico que dé viabilidad al proyecto y con la creación de un modelo económico que estime el precio para cualquier curso futuro. Abstract Nuclear power plants need highly specialized and trained personnel. Thus, nuclear power plant Specialized Training Sector requires the incorporation of the latest advances in training methods. A large array of face-to-face training courses exist and it has become necessary to transform said courses in order to apply them with the new information systems available. For this, multidisciplinary equipment is needed where the engineering workforce must identify educational objectives, competences and abilities, contents and quality control of the different courses. In this project, knowledge engineering techniques are employed as the methodological axis in order to transform a face-to-face training course into on-line training through the use of new information technologies. Nowadays, new information and communication technologies are in constant evolution. They have introduced themselves into our world, transforming our previous vision of them, leading to new opportunities. For this reason, the present Project seeks to unite the use of e-learning and the Business and Corporate world. The main objective is the design, in an e-learning platform, of a technical course that will train nuclear power plant control-room operators. The work carried out in this Project has been, in addition to the transformation of a face-to-face course into an online one, the obtainment of a methodology to employ in the future transformation of other courses. In order to achieve this mission, our interest must focus on the content as well as on the management of the various courses. Hence, the Project starts with basic definitions of e-learning terminology. Next, a methodology that applies knowledge management for the transformation of any face-to-face course into e-learning has been generated. Once this methodology is defined, it has been applied for the design process of the Inherent Coefficients of Reactivity course. Finally, an economic study has been developed in order to determine the viability of the Project and an economic model has been created to estimate the price of any given course

Learning 3D structure from 2D images using LBP features

An automatic machine learning strategy for computing the 3D structure of monocular images from a single image query using Local Binary Patterns is presented. The 3D structure is inferred through a training set composed by a repository of color and depth images, assuming that images with similar structure present similar depth maps. Local Binary Patterns are used to characterize the structure of the color images. The depth maps of those color images with a similar structure to the query image are adaptively combined and filtered to estimate the final depth map. Using public databases, promising results have been obtained outperforming other state-of-the-art algorithms and with a computational cost similar to the most efficient 2D-to-3D algorithms.

Glutamate infused posttraining into the hippocampus or caudate-putamen differentially strengthens place and response learning

A cross-maze task that can be acquired through either place or response learning was used to examine the hypothesis that posttraining neurochemical manipulation of the hippocampus or caudate-putamen can bias an animal toward the use of a specific memory system. Male Long-Evans rats received four trials per day for 7 days, a probe trial on day 8, further training on days 9–15, and an additional probe trial on day 16. Training occurred in a cross-maze task in which rats started from a consistent start-box (south), and obtained food from a consistent goal-arm (west). On days 4–6 of training, rats received posttraining intrahippocampal (1 μg/0.5 μl) or intracaudate (2 μg/0.5 μl) injections of either glutamate or saline (0.5 μl). On days 8 and 16, a probe trial was given in which rats were placed in a novel start-box (north). Rats selecting the west goal-arm were designated “place” learners, and those selecting the east goal-arm were designated “response” learners. Saline-treated rats predominantly displayed place learning on day 8 and response learning on day 16, indicating a shift in control of learned behavior with extended training. Rats receiving intrahippocampal injections of glutamate predominantly displayed place learning on days 8 and 16, indicating that manipulation of the hippocampus produced a blockade of the shift to response learning. Rats receiving intracaudate injections of glutamate displayed response learning on days 8 and 16, indicating an accelerated shift to response learning. The findings suggest that posttraining intracerebral glutamate infusions can (i) modulate the distinct memory processes mediated by the hippocampus and caudate-putamen and (ii) bias the brain toward the use of a specific memory system to control learned behavior and thereby influence the timing of the switch from the use of cognitive memory to habit learning to guide behavior.

Low-status monkeys “play dumb” when learning in mixed social groups

Many primates, including humans, live in complex hierarchical societies where social context and status affect daily life. Nevertheless, primate learning studies typically test single animals in limited laboratory settings where the important effects of social interactions and relationships cannot be studied. To investigate the impact of sociality on associative learning, we compared the individual performances of group-tested rhesus monkeys (Macaca mulatta) across various social contexts. We used a traditional discrimination paradigm that measures an animal’s ability to form associations between cues and the obtaining of food in choice situations; but we adapted the task for group testing. After training a 55-member colony to separate on command into two subgroups, composed of either high- or low-status families, we exposed animals to two color discrimination problems, one with all monkeys present (combined condition), the other in their “dominant” and “subordinate” cohorts (split condition). Next, we manipulated learning history by testing animals on the same problems, but with the social contexts reversed. Monkeys from dominant families excelled in all conditions, but subordinates performed well in the split condition only, regardless of learning history. Subordinate animals had learned the associations, but expressed their knowledge only when segregated from higher-ranking animals. Because aggressive behavior was rare, performance deficits probably reflected voluntary inhibition. This experimental evidence of rank-related, social modulation of performance calls for greater consideration of social factors when assessing learning and may also have relevance for the evaluation of human scholastic achievement.

Material matters for learning in virtual networks : A case study of a professional learning programme hosted in a Google+ online community

Date of Acceptance: 13/07/2015