Computational Methods for Identification of Vibrating Structures

System identification deals with the problem of building mathematical models of dynamical systems based on observed data from the system" [1]. In the context of civil engineering, the system refers to a large scale structure such as a building, bridge, or an offshore structure, and identification mostly involves the determination of modal parameters (the natural frequencies, damping ratios, and mode shapes). This paper presents some modal identification results obtained using a state-of-the-art time domain system identification method (data-driven stochastic subspace algorithms [2]) applied to the output-only data measured in a steel arch bridge. First, a three dimensional finite element model was developed for the numerical analysis of the structure using ANSYS. Modal analysis was carried out and modal parameters were extracted in the frequency range of interest, 0-10 Hz. The results obtained from the finite element modal analysis were used to determine the location of the sensors. After that, ambient vibration tests were conducted during April 23-24, 2009. The response of the structure was measured using eight accelerometers. Two stations of three sensors were formed (triaxial stations). These sensors were held stationary for reference during the test. The two remaining sensors were placed at the different measurement points along the bridge deck, in which only vertical and transversal measurements were conducted (biaxial stations). Point estimate and interval estimate have been carried out in the state space model using these ambient vibration measurements. In the case of parametric models (like state space), the dynamic behaviour of a system is described using mathematical models. Then, mathematical relationships can be established between modal parameters and estimated point parameters (thus, it is common to use experimental modal analysis as a synonym for system identification). Stable modal parameters are found using a stabilization diagram. Furthermore, this paper proposes a method for assessing the precision of estimates of the parameters of state-space models (confidence interval). This approach employs the nonparametric bootstrap procedure [3] and is applied to subspace parameter estimation algorithm. Using bootstrap results, a plot similar to a stabilization diagram is developed. These graphics differentiate system modes from spurious noise modes for a given order system. Additionally, using the modal assurance criterion, the experimental modes obtained have been compared with those evaluated from a finite element analysis. A quite good agreement between numerical and experimental results is observed.

The Landscape of Ontology Reuse in Linked Data

Abstract. The uptake of Linked Data (LD) has promoted the proliferation of datasets and their associated ontologies for describing different domains. Ac-cording to LD principles, developers should reuse as many available terms as possible to describe their data. Importing ontologies or referring to their terms’ URIs are the two main ways to reuse knowledge from available ontologies. In this paper, we have analyzed 18589 terms appearing within 196 ontologies in-cluded in the Linked Open Vocabularies (LOV) registry with the aim of under-standing the current state of ontology reuse in the LD context. In order to char-acterize the landscape of ontology reuse in this context, we have extracted sta-tistics about currently reused elements, calculated ratios for reuse, and drawn graphs about imports and references between ontologies. Keywords: ontology, vocabulary, reuse, linked data, ontology import

A Reuse-based Lightweight Method for Developing Linked Data Ontologies and Vocabularies

The uptake of Linked Data (LD) has promoted the proliferation of datasets and their associated ontologies for describing different domains. Par-ticular LD development characteristics such as agility and web-based architec-ture necessitate the revision, adaption, and lightening of existing methodologies for ontology development. This thesis proposes a lightweight method for ontol-ogy development in an LD context which will be based in data-driven agile de-velopments, existing resources to be reused, and the evaluation of the obtained products considering both classical ontological engineering principles and LD characteristics.

Generating descriptions that summarize geospatial and temporal data

Effective data summarization methods that use AI techniques can help humans understand large sets of data. In this paper, we describe a knowledge-based method for automatically generating summaries of geospatial and temporal data, i.e. data with geographical and temporal references. The method is useful for summarizing data streams, such as GPS traces and traffic information, that are becoming more prevalent with the increasing use of sensors in computing devices. The method presented here is an initial architecture for our ongoing research in this domain. In this paper we describe the data representations we have designed for our method, our implementations of components to perform data abstraction and natural language generation. We also discuss evaluation results that show the ability of our method to generate certain types of geospatial and temporal descriptions.

Análisis y modelización de la dinámica emergente durante el proceso de difusión de información en las redes sociales de internet

Durante la actividad diaria, la sociedad actual interactúa constantemente por medio de dispositivos electrónicos y servicios de telecomunicaciones, tales como el teléfono, correo electrónico, transacciones bancarias o redes sociales de Internet. Sin saberlo, masivamente dejamos rastros de nuestra actividad en las bases de datos de empresas proveedoras de servicios. Estas nuevas fuentes de datos tienen las dimensiones necesarias para que se puedan observar patrones de comportamiento humano a grandes escalas. Como resultado, ha surgido una reciente explosión sin precedentes de estudios de sistemas sociales, dirigidos por el análisis de datos y procesos computacionales. En esta tesis desarrollamos métodos computacionales y matemáticos para analizar sistemas sociales por medio del estudio combinado de datos derivados de la actividad humana y la teoría de redes complejas. Nuestro objetivo es caracterizar y entender los sistemas emergentes de interacciones sociales en los nuevos espacios tecnológicos, tales como la red social Twitter y la telefonía móvil. Analizamos los sistemas por medio de la construcción de redes complejas y series temporales, estudiando su estructura, funcionamiento y evolución en el tiempo. También, investigamos la naturaleza de los patrones observados por medio de los mecanismos que rigen las interacciones entre individuos, así como medimos el impacto de eventos críticos en el comportamiento del sistema. Para ello, hemos propuesto modelos que explican las estructuras globales y la dinámica emergente con que fluye la información en el sistema. Para los estudios de la red social Twitter, hemos basado nuestros análisis en conversaciones puntuales, tales como protestas políticas, grandes acontecimientos o procesos electorales. A partir de los mensajes de las conversaciones, identificamos a los usuarios que participan y construimos redes de interacciones entre los mismos. Específicamente, construimos una red para representar quién recibe los mensajes de quién y otra red para representar quién propaga los mensajes de quién. En general, hemos encontrado que estas estructuras tienen propiedades complejas, tales como crecimiento explosivo y distribuciones de grado libres de escala. En base a la topología de estas redes, hemos indentificado tres tipos de usuarios que determinan el flujo de información según su actividad e influencia. Para medir la influencia de los usuarios en las conversaciones, hemos introducido una nueva medida llamada eficiencia de usuario. La eficiencia se define como el número de retransmisiones obtenidas por mensaje enviado, y mide los efectos que tienen los esfuerzos individuales sobre la reacción colectiva. Hemos observado que la distribución de esta propiedad es ubicua en varias conversaciones de Twitter, sin importar sus dimensiones ni contextos. Con lo cual, sugerimos que existe universalidad en la relación entre esfuerzos individuales y reacciones colectivas en Twitter. Para explicar los factores que determinan la emergencia de la distribución de eficiencia, hemos desarrollado un modelo computacional que simula la propagación de mensajes en la red social de Twitter, basado en el mecanismo de cascadas independientes. Este modelo nos permite medir el efecto que tienen sobre la distribución de eficiencia, tanto la topología de la red social subyacente, como la forma en que los usuarios envían mensajes. Los resultados indican que la emergencia de un grupo selecto de usuarios altamente eficientes depende de la heterogeneidad de la red subyacente y no del comportamiento individual. Por otro lado, hemos desarrollado técnicas para inferir el grado de polarización política en redes sociales. Proponemos una metodología para estimar opiniones en redes sociales y medir el grado de polarización en las opiniones obtenidas. Hemos diseñado un modelo donde estudiamos el efecto que tiene la opinión de un pequeño grupo de usuarios influyentes, llamado élite, sobre las opiniones de la mayoría de usuarios. El modelo da como resultado una distribución de opiniones sobre la cual medimos el grado de polarización. Aplicamos nuestra metodología para medir la polarización en redes de difusión de mensajes, durante una conversación en Twitter de una sociedad políticamente polarizada. Los resultados obtenidos presentan una alta correspondencia con los datos offline. Con este estudio, hemos demostrado que la metodología propuesta es capaz de determinar diferentes grados de polarización dependiendo de la estructura de la red. Finalmente, hemos estudiado el comportamiento humano a partir de datos de telefonía móvil. Por una parte, hemos caracterizado el impacto que tienen desastres naturales, como innundaciones, sobre el comportamiento colectivo. Encontramos que los patrones de comunicación se alteran de forma abrupta en las áreas afectadas por la catástofre. Con lo cual, demostramos que se podría medir el impacto en la región casi en tiempo real y sin necesidad de desplegar esfuerzos en el terreno. Por otra parte, hemos estudiado los patrones de actividad y movilidad humana para caracterizar las interacciones entre regiones de un país en desarrollo. Encontramos que las redes de llamadas y trayectorias humanas tienen estructuras de comunidades asociadas a regiones y centros urbanos. En resumen, hemos mostrado que es posible entender procesos sociales complejos por medio del análisis de datos de actividad humana y la teoría de redes complejas. A lo largo de la tesis, hemos comprobado que fenómenos sociales como la influencia, polarización política o reacción a eventos críticos quedan reflejados en los patrones estructurales y dinámicos que presentan la redes construidas a partir de datos de conversaciones en redes sociales de Internet o telefonía móvil. ABSTRACT During daily routines, we are constantly interacting with electronic devices and telecommunication services. Unconsciously, we are massively leaving traces of our activity in the service providers’ databases. These new data sources have the dimensions required to enable the observation of human behavioral patterns at large scales. As a result, there has been an unprecedented explosion of data-driven social research. In this thesis, we develop computational and mathematical methods to analyze social systems by means of the combined study of human activity data and the theory of complex networks. Our goal is to characterize and understand the emergent systems from human interactions on the new technological spaces, such as the online social network Twitter and mobile phones. We analyze systems by means of the construction of complex networks and temporal series, studying their structure, functioning and temporal evolution. We also investigate on the nature of the observed patterns, by means of the mechanisms that rule the interactions among individuals, as well as on the impact of critical events on the system’s behavior. For this purpose, we have proposed models that explain the global structures and the emergent dynamics of information flow in the system. In the studies of the online social network Twitter, we have based our analysis on specific conversations, such as political protests, important announcements and electoral processes. From the messages related to the conversations, we identify the participant users and build networks of interactions with them. We specifically build one network to represent whoreceives- whose-messages and another to represent who-propagates-whose-messages. In general, we have found that these structures have complex properties, such as explosive growth and scale-free degree distributions. Based on the topological properties of these networks, we have identified three types of user behavior that determine the information flow dynamics due to their influence. In order to measure the users’ influence on the conversations, we have introduced a new measure called user efficiency. It is defined as the number of retransmissions obtained by message posted, and it measures the effects of the individual activity on the collective reacixtions. We have observed that the probability distribution of this property is ubiquitous across several Twitter conversation, regardlessly of their dimension or social context. Therefore, we suggest that there is a universal behavior in the relationship between individual efforts and collective reactions on Twitter. In order to explain the different factors that determine the user efficiency distribution, we have developed a computational model to simulate the diffusion of messages on Twitter, based on the mechanism of independent cascades. This model, allows us to measure the impact on the emergent efficiency distribution of the underlying network topology, as well as the way that users post messages. The results indicate that the emergence of an exclusive group of highly efficient users depends upon the heterogeneity of the underlying network instead of the individual behavior. Moreover, we have also developed techniques to infer the degree of polarization in social networks. We propose a methodology to estimate opinions in social networks and to measure the degree of polarization in the obtained opinions. We have designed a model to study the effects of the opinions of a small group of influential users, called elite, on the opinions of the majority of users. The model results in an opinions distribution to which we measure the degree of polarization. We apply our methodology to measure the polarization on graphs from the messages diffusion process, during a conversation on Twitter from a polarized society. The results are in very good agreement with offline and contextual data. With this study, we have shown that our methodology is capable of detecting several degrees of polarization depending on the structure of the networks. Finally, we have also inferred the human behavior from mobile phones’ data. On the one hand, we have characterized the impact of natural disasters, like flooding, on the collective behavior. We found that the communication patterns are abruptly altered in the areas affected by the catastrophe. Therefore, we demonstrate that we could measure the impact of the disaster on the region, almost in real-time and without needing to deploy further efforts. On the other hand, we have studied human activity and mobility patterns in order to characterize regional interactions on a developing country. We found that the calls and trajectories networks present community structure associated to regional and urban areas. In summary, we have shown that it is possible to understand complex social processes by means of analyzing human activity data and the theory of complex networks. Along the thesis, we have demonstrated that social phenomena, like influence, polarization and reaction to critical events, are reflected in the structural and dynamical patterns of the networks constructed from data regarding conversations on online social networks and mobile phones.

Comparison of linear and non-linear 2D+T registration methods for DE-MRI cardiac perfusion studies

A series of motion compensation algorithms is run on the challenge data including methods that optimize only a linear transformation, or a non-linear transformation, or both – first a linear and then a non-linear transformation. Methods that optimize a linear transformation run an initial segmentation of the area of interest around the left myocardium by means of an independent component analysis (ICA) (ICA-*). Methods that optimize non-linear transformations may run directly on the full images, or after linear registration. Non-linear motion compensation approaches applied include one method that only registers pairs of images in temporal succession (SERIAL), one method that registers all image to one common reference (AllToOne), one method that was designed to exploit quasi-periodicity in free breathing acquired image data and was adapted to also be usable to image data acquired with initial breath-hold (QUASI-P), a method that uses ICA to identify the motion and eliminate it (ICA-SP), and a method that relies on the estimation of a pseudo ground truth (PG) to guide the motion compensation.

LDP4j: a framework for the development of interoperable read-write linked data applications

Enterprises are increasingly using a wide range of heterogeneous information systems for executing and governing their business activities. Even if the adoption of service orientation has improved loose coupling and reusability, applications are still isolated data silos whose integration requires complex transformations and mediations. However, by leveraging Linked Data principles those data silos can now be seamlessly integrated, and this opens the door to new data-driven approaches for Enterprise Application Integration (EAI). In this paper we present LDP4j, an open souce Java-based framework for the development of interoperable read-write Linked Data applications, based on the W3C Linked Data Platform (LDP) specification.

Understanding long-term hunting statistics: the case of Spain (1972-2007)

Hunting is assuming a growing role in the current European forestry and agroforestry landscape. However, consistent statistical sources that provide quantitative information for policy-making, planning and management of game resources are often lacking. In addition, in many instances statistical information can be used without sufficient evaluation or criticism. Recently, the European Commission has declared the importance of high quality hunting statistics and the need to set up a common scheme in Europe for their collection, interpretation and proper use. This work aims to contribute to this current debate on hunting statistics in Europe by exploring data from the last 35 years of Spanish hunting statistics. The analysis focuses on the three major pillars underpinning hunting activity: hunters, hunting grounds and game animals. First, the study aims to provide a better understanding of official hunting statistics for use by researchers, game managers and other potential users. Second, the study highlights the major strengths and weaknesses of the statistical information that was collected. The results of the analysis indicate that official hunting statistics can be incomplete, dispersed and not always homogeneous over a long period of time. This is an issue of which one should be aware when using official hunting data for scientific or technical work. To improve statistical deficiencies associated with hunting data in Spain, our main suggestion is the adoption of a common protocol on data collection to which different regions agree. This protocol should be in accordance with future European hunting statistics and based on robust and well-informed data collection methods. Also it should expand the range of biological, ecological and economic concepts currently included to take account of the profound transformations experienced by the hunting sector in recent years. As much as possible, any future changes in the selection of hunting statistics should allow for comparisons between new variables with the previous ones.

Automatic Adaptation of Airport Surface Surveillance to Sensor Quality

This paper describes a novel method to enhance current airport surveillance systems used in Advanced Surveillance Monitoring Guidance and Control Systems (A-SMGCS). The proposed method allows for the automatic calibration of measurement models and enhanced detection of nonideal situations, increasing surveillance products integrity. It is based on the definition of a set of observables from the surveillance processing chain and a rule based expert system aimed to change the data processing methods

Análisis de los factores determinantes de la repetición del proceso de compra en el comercio electrónico entre empresas y consumidores finales. Aplicación al mercado español

Las Tecnologías de la Información y la Comunicación en general e Internet en particular han supuesto una revolución en nuestra forma de comunicarnos, relacionarnos, producir, comprar y vender acortando tiempo y distancias entre proveedores y consumidores. A la paulatina penetración del ordenador, los teléfonos inteligentes y la banda ancha fija y/o móvil ha seguido un mayor uso de estas tecnologías entre ciudadanos y empresas. El comercio electrónico empresa–consumidor (B2C) alcanzó en 2010 en España un volumen de 9.114 millones de euros, con un incremento del 17,4% respecto al dato registrado en 2009. Este crecimiento se ha producido por distintos hechos: un incremento en el porcentaje de internautas hasta el 65,1% en 2010 de los cuales han adquirido productos o servicios a través de la Red un 43,1% –1,6 puntos porcentuales más respecto a 2010–. Por otra parte, el gasto medio por comprador ha ascendido a 831€ en 2010, lo que supone un incremento del 10,9% respecto al año anterior. Si segmentamos a los compradores según por su experiencia anterior de compra podemos encontrar dos categorías: el comprador novel –que adquirió por primera vez productos o servicios en 2010– y el comprador constante –aquel que había adquirido productos o servicios en 2010 y al menos una vez en años anteriores–. El 85,8% de los compradores se pueden considerar como compradores constantes: habían comprado en la Red en 2010, pero también lo habían hecho anteriormente. El comprador novel tiene un perfil sociodemográfico de persona joven de entre 15–24 años, con estudios secundarios, de clase social media y media–baja, estudiante no universitario, residente en poblaciones pequeñas y sigue utilizando fórmulas de pago como el contra–reembolso (23,9%). Su gasto medio anual ascendió en 2010 a 449€. El comprador constante, o comprador que ya había comprado en Internet anteriormente, tiene un perfil demográfico distinto: estudios superiores, clase alta, trabajador y residente en grandes ciudades, con un comportamiento maduro en la compra electrónica dada su mayor experiencia –utiliza con mayor intensidad canales exclusivos en Internet que no disponen de tienda presencial–. Su gasto medio duplica al observado en compradores noveles (con una media de 930€ anuales). Por tanto, los compradores constantes suponen una mayoría de los compradores con un gasto medio que dobla al comprador que ha adoptado el medio recientemente. Por consiguiente es de interés estudiar los factores que predicen que un internauta vuelva a adquirir un producto o servicio en la Red. La respuesta a esta pregunta no se ha revelado sencilla. En España, la mayoría de productos y servicios aún se adquieren de manera presencial, con una baja incidencia de las ventas a distancia como la teletienda, la venta por catálogo o la venta a través de Internet. Para dar respuesta a las preguntas planteadas se ha investigado desde distintos puntos de vista: se comenzará con un estudio descriptivo desde el punto de vista de la demanda que trata de caracterizar la situación del comercio electrónico B2C en España, poniendo el foco en las diferencias entre los compradores constantes y los nuevos compradores. Posteriormente, la investigación de modelos de adopción y continuidad en el uso de las tecnologías y de los factores que inciden en dicha continuidad –con especial interés en el comercio electrónico B2C–, permiten afrontar el problema desde la perspectiva de las ecuaciones estructurales pudiendo también extraer conclusiones de tipo práctico. Este trabajo sigue una estructura clásica de investigación científica: en el capítulo 1 se introduce el tema de investigación, continuando con una descripción del estado de situación del comercio electrónico B2C en España utilizando fuentes oficiales (capítulo 2). Posteriormente se desarrolla el marco teórico y el estado del arte de modelos de adopción y de utilización de las tecnologías (capítulo 3) y de los factores principales que inciden en la adopción y continuidad en el uso de las tecnologías (capítulo 4). El capítulo 5 desarrolla las hipótesis de la investigación y plantea los modelos teóricos. Las técnicas estadísticas a utilizar se describen en el capítulo 6, donde también se analizan los resultados empíricos sobre los modelos desarrollados en el capítulo 5. El capítulo 7 expone las principales conclusiones de la investigación, sus limitaciones y propone nuevas líneas de investigación. La primera parte corresponde al capítulo 1, que introduce la investigación justificándola desde un punto de vista teórico y práctico. También se realiza una breve introducción a la teoría del comportamiento del consumidor desde una perspectiva clásica. Se presentan los principales modelos de adopción y se introducen los modelos de continuidad de utilización que se estudiarán más detalladamente en el capítulo 3. En este capítulo se desarrollan los objetivos principales y los objetivos secundarios, se propone el mapa mental de la investigación y se planifican en un cronograma los principales hitos del trabajo. La segunda parte corresponde a los capítulos dos, tres y cuatro. En el capítulo 2 se describe el comercio electrónico B2C en España utilizando fuentes secundarias. Se aborda un diagnóstico del sector de comercio electrónico y su estado de madurez en España. Posteriormente, se analizan las diferencias entre los compradores constantes, principal interés de este trabajo, frente a los compradores noveles, destacando las diferencias de perfiles y usos. Para los dos segmentos se estudian aspectos como el lugar de acceso a la compra, la frecuencia de compra, los medios de pago utilizados o las actitudes hacia la compra. El capítulo 3 comienza desarrollando los principales conceptos sobre la teoría del comportamiento del consumidor, para continuar estudiando los principales modelos de adopción de tecnología existentes, analizando con especial atención su aplicación en comercio electrónico. Posteriormente se analizan los modelos de continuidad en el uso de tecnologías (Teoría de la Confirmación de Expectativas; Teoría de la Justicia), con especial atención de nuevo a su aplicación en el comercio electrónico. Una vez estudiados los principales modelos de adopción y continuidad en el uso de tecnologías, el capítulo 4 analiza los principales factores que se utilizan en los modelos: calidad, valor, factores basados en la confirmación de expectativas –satisfacción, utilidad percibida– y factores específicos en situaciones especiales –por ejemplo, tras una queja– como pueden ser la justicia, las emociones o la confianza. La tercera parte –que corresponde al capítulo 5– desarrolla el diseño de la investigación y la selección muestral de los modelos. En la primera parte del capítulo se enuncian las hipótesis –que van desde lo general a lo particular, utilizando los factores específicos analizados en el capítulo 4– para su posterior estudio y validación en el capítulo 6 utilizando las técnicas estadísticas apropiadas. A partir de las hipótesis, y de los modelos y factores estudiados en los capítulos 3 y 4, se definen y vertebran dos modelos teóricos originales que den respuesta a los retos de investigación planteados en el capítulo 1. En la segunda parte del capítulo se diseña el trabajo empírico de investigación definiendo los siguientes aspectos: alcance geográfico–temporal, tipología de la investigación, carácter y ambiente de la investigación, fuentes primarias y secundarias utilizadas, técnicas de recolección de datos, instrumentos de medida utilizados y características de la muestra utilizada. Los resultados del trabajo de investigación constituyen la cuarta parte de la investigación y se desarrollan en el capítulo 6, que comienza analizando las técnicas estadísticas basadas en Modelos de Ecuaciones Estructurales. Se plantean dos alternativas, modelos confirmatorios correspondientes a Métodos Basados en Covarianzas (MBC) y modelos predictivos. De forma razonada se eligen las técnicas predictivas dada la naturaleza exploratoria de la investigación planteada. La segunda parte del capítulo 6 desarrolla el análisis de los resultados de los modelos de medida y modelos estructurales construidos con indicadores formativos y reflectivos y definidos en el capítulo 4. Para ello se validan, sucesivamente, los modelos de medida y los modelos estructurales teniendo en cuenta los valores umbrales de los parámetros estadísticos necesarios para la validación. La quinta parte corresponde al capítulo 7, que desarrolla las conclusiones basándose en los resultados del capítulo 6, analizando los resultados desde el punto de vista de las aportaciones teóricas y prácticas, obteniendo conclusiones para la gestión de las empresas. A continuación, se describen las limitaciones de la investigación y se proponen nuevas líneas de estudio sobre distintos temas que han ido surgiendo a lo largo del trabajo. Finalmente, la bibliografía recoge todas las referencias utilizadas a lo largo de este trabajo. Palabras clave: comprador constante, modelos de continuidad de uso, continuidad en el uso de tecnologías, comercio electrónico, B2C, adopción de tecnologías, modelos de adopción tecnológica, TAM, TPB, IDT, UTAUT, ECT, intención de continuidad, satisfacción, confianza percibida, justicia, emociones, confirmación de expectativas, calidad, valor, PLS. ABSTRACT Information and Communication Technologies in general, but more specifically those related to the Internet in particular, have changed the way in which we communicate, relate to one another, produce, and buy and sell products, reducing the time and shortening the distance between suppliers and consumers. The steady breakthrough of computers, Smartphones and landline and/or wireless broadband has been greatly reflected in its large scale use by both individuals and businesses. Business–to–consumer (B2C) e–commerce reached a volume of 9,114 million Euros in Spain in 2010, representing a 17.4% increase with respect to the figure in 2009. This growth is due in part to two different facts: an increase in the percentage of web users to 65.1% en 2010, 43.1% of whom have acquired products or services through the Internet– which constitutes 1.6 percentage points higher than 2010. On the other hand, the average spending by individual buyers rose to 831€ en 2010, constituting a 10.9% increase with respect to the previous year. If we select buyers according to whether or not they have previously made some type of purchase, we can divide them into two categories: the novice buyer–who first made online purchases in 2010– and the experienced buyer: who also made purchases in 2010, but had done so previously as well. The socio–demographic profile of the novice buyer is that of a young person between 15–24 years of age, with secondary studies, middle to lower–middle class, and a non–university educated student who resides in smaller towns and continues to use payment methods such as cash on delivery (23.9%). In 2010, their average purchase grew to 449€. The more experienced buyer, or someone who has previously made purchases online, has a different demographic profile: highly educated, upper class, resident and worker in larger cities, who exercises a mature behavior when making online purchases due to their experience– this type of buyer frequently uses exclusive channels on the Internet that don’t have an actual store. His or her average purchase doubles that of the novice buyer (with an average purchase of 930€ annually.) That said, the experienced buyers constitute the majority of buyers with an average purchase that doubles that of novice buyers. It is therefore of interest to study the factors that help to predict whether or not a web user will buy another product or use another service on the Internet. The answer to this question has proven not to be so simple. In Spain, the majority of goods and services are still bought in person, with a low amount of purchases being made through means such as the Home Shopping Network, through catalogues or Internet sales. To answer the questions that have been posed here, an investigation has been conducted which takes into consideration various viewpoints: it will begin with a descriptive study from the perspective of the supply and demand that characterizes the B2C e–commerce situation in Spain, focusing on the differences between experienced buyers and novice buyers. Subsequently, there will be an investigation concerning the technology acceptance and continuity of use of models as well as the factors that have an effect on their continuity of use –with a special focus on B2C electronic commerce–, which allows for a theoretic approach to the problem from the perspective of the structural equations being able to reach practical conclusions. This investigation follows the classic structure for a scientific investigation: the subject of the investigation is introduced (Chapter 1), then the state of the B2C e–commerce in Spain is described citing official sources of information (Chapter 2), the theoretical framework and state of the art of technology acceptance and continuity models are developed further (Chapter 3) and the main factors that affect their acceptance and continuity (Chapter 4). Chapter 5 explains the hypothesis behind the investigation and poses the theoretical models that will be confirmed or rejected partially or completely. In Chapter 6, the technical statistics that will be used are described briefly as well as an analysis of the empirical results of the models put forth in Chapter 5. Chapter 7 explains the main conclusions of the investigation, its limitations and proposes new projects. First part of the project, chapter 1, introduces the investigation, justifying it from a theoretical and practical point of view. It is also a brief introduction to the theory of consumer behavior from a standard perspective. Technology acceptance models are presented and then continuity and repurchase models are introduced, which are studied more in depth in Chapter 3. In this chapter, both the main and the secondary objectives are developed through a mind map and a timetable which highlights the milestones of the project. The second part of the project corresponds to Chapters Two, Three and Four. Chapter 2 describes the B2C e–commerce in Spain from the perspective of its demand, citing secondary official sources. A diagnosis concerning the e–commerce sector and the status of its maturity in Spain is taken on, as well as the barriers and alternative methods of e–commerce. Subsequently, the differences between experienced buyers, which are of particular interest to this project, and novice buyers are analyzed, highlighting the differences between their profiles and their main transactions. In order to study both groups, aspects such as the place of purchase, frequency with which online purchases are made, payment methods used and the attitudes of the purchasers concerning making online purchases are taken into consideration. Chapter 3 begins by developing the main concepts concerning consumer behavior theory in order to continue the study of the main existing acceptance models (among others, TPB, TAM, IDT, UTAUT and other models derived from them) – paying special attention to their application in e–commerce–. Subsequently, the models of technology reuse are analyzed (CDT, ECT; Theory of Justice), focusing again specifically on their application in e–commerce. Once the main technology acceptance and reuse models have been studied, Chapter 4 analyzes the main factors that are used in these models: quality, value, factors based on the contradiction of expectations/failure to meet expectations– satisfaction, perceived usefulness– and specific factors pertaining to special situations– for example, after receiving a complaint justice, emotions or confidence. The third part– which appears in Chapter 5– develops the plan for the investigation and the sample selection for the models that have been designed. In the first section of the Chapter, the hypothesis is presented– beginning with general ideas and then becoming more specific, using the detailed factors that were analyzed in Chapter 4– for its later study and validation in Chapter 6– as well as the corresponding statistical factors. Based on the hypothesis and the models and factors that were studied in Chapters 3 and 4, two original theoretical models are defined and organized in order to answer the questions posed in Chapter 1. In the second part of the Chapter, the empirical investigation is designed, defining the following aspects: geographic–temporal scope, type of investigation, nature and setting of the investigation, primary and secondary sources used, data gathering methods, instruments according to the extent of their use and characteristics of the sample used. The results of the project constitute the fourth part of the investigation and are developed in Chapter 6, which begins analyzing the statistical techniques that are based on the Models of Structural Equations. Two alternatives are put forth: confirmatory models which correspond to Methods Based on Covariance (MBC) and predictive models– Methods Based on Components–. In a well–reasoned manner, the predictive techniques are chosen given the explorative nature of the investigation. The second part of Chapter 6 explains the results of the analysis of the measurement models and structural models built by the formative and reflective indicators defined in Chapter 4. In order to do so, the measurement models and the structural models are validated one by one, while keeping in mind the threshold values of the necessary statistic parameters for their validation. The fifth part corresponds to Chapter 7 which explains the conclusions of the study, basing them on the results found in Chapter 6 and analyzing them from the perspective of the theoretical and practical contributions, and consequently obtaining conclusions for business management. The limitations of the investigation are then described and new research lines about various topics that came up during the project are proposed. Lastly, all of the references that were used during the project are listed in a final bibliography. Key Words: constant buyer, repurchase models, continuity of use of technology, e–commerce, B2C, technology acceptance, technology acceptance models, TAM, TPB, IDT, UTAUT, ECT, intention of repurchase, satisfaction, perceived trust/confidence, justice, feelings, the contradiction of expectations, quality, value, PLS.

Functional and effective connectivity in MEG. Application to the study of epilepsy

Nuestro cerebro contiene cerca de 1014 sinapsis neuronales. Esta enorme cantidad de conexiones proporciona un entorno ideal donde distintos grupos de neuronas se sincronizan transitoriamente para provocar la aparición de funciones cognitivas, como la percepción, el aprendizaje o el pensamiento. Comprender la organización de esta compleja red cerebral en base a datos neurofisiológicos, representa uno de los desafíos más importantes y emocionantes en el campo de la neurociencia. Se han propuesto recientemente varias medidas para evaluar cómo se comunican las diferentes partes del cerebro a diversas escalas (células individuales, columnas corticales, o áreas cerebrales). Podemos clasificarlos, según su simetría, en dos grupos: por una parte, la medidas simétricas, como la correlación, la coherencia o la sincronización de fase, que evalúan la conectividad funcional (FC); mientras que las medidas asimétricas, como la causalidad de Granger o transferencia de entropía, son capaces de detectar la dirección de la interacción, lo que denominamos conectividad efectiva (EC). En la neurociencia moderna ha aumentado el interés por el estudio de las redes funcionales cerebrales, en gran medida debido a la aparición de estos nuevos algoritmos que permiten analizar la interdependencia entre señales temporales, además de la emergente teoría de redes complejas y la introducción de técnicas novedosas, como la magnetoencefalografía (MEG), para registrar datos neurofisiológicos con gran resolución. Sin embargo, nos hallamos ante un campo novedoso que presenta aun varias cuestiones metodológicas sin resolver, algunas de las cuales trataran de abordarse en esta tesis. En primer lugar, el creciente número de aproximaciones para determinar la existencia de FC/EC entre dos o más señales temporales, junto con la complejidad matemática de las herramientas de análisis, hacen deseable organizarlas todas en un paquete software intuitivo y fácil de usar. Aquí presento HERMES (http://hermes.ctb.upm.es), una toolbox en MatlabR, diseñada precisamente con este fin. Creo que esta herramienta será de gran ayuda para todos aquellos investigadores que trabajen en el campo emergente del análisis de conectividad cerebral y supondrá un gran valor para la comunidad científica. La segunda cuestión practica que se aborda es el estudio de la sensibilidad a las fuentes cerebrales profundas a través de dos tipos de sensores MEG: gradiómetros planares y magnetómetros, esta aproximación además se combina con un enfoque metodológico, utilizando dos índices de sincronización de fase: phase locking value (PLV) y phase lag index (PLI), este ultimo menos sensible a efecto la conducción volumen. Por lo tanto, se compara su comportamiento al estudiar las redes cerebrales, obteniendo que magnetómetros y PLV presentan, respectivamente, redes más densamente conectadas que gradiómetros planares y PLI, por los valores artificiales que crea el problema de la conducción de volumen. Sin embargo, cuando se trata de caracterizar redes epilépticas, el PLV ofrece mejores resultados, debido a la gran dispersión de las redes obtenidas con PLI. El análisis de redes complejas ha proporcionado nuevos conceptos que mejoran caracterización de la interacción de sistemas dinámicos. Se considera que una red está compuesta por nodos, que simbolizan sistemas, cuyas interacciones se representan por enlaces, y su comportamiento y topología puede caracterizarse por un elevado número de medidas. Existe evidencia teórica y empírica de que muchas de ellas están fuertemente correlacionadas entre sí. Por lo tanto, se ha conseguido seleccionar un pequeño grupo que caracteriza eficazmente estas redes, y condensa la información redundante. Para el análisis de redes funcionales, la selección de un umbral adecuado para decidir si un determinado valor de conectividad de la matriz de FC es significativo y debe ser incluido para un análisis posterior, se convierte en un paso crucial. En esta tesis, se han obtenido resultados más precisos al utilizar un test de subrogadas, basado en los datos, para evaluar individualmente cada uno de los enlaces, que al establecer a priori un umbral fijo para la densidad de conexiones. Finalmente, todas estas cuestiones se han aplicado al estudio de la epilepsia, caso práctico en el que se analizan las redes funcionales MEG, en estado de reposo, de dos grupos de pacientes epilépticos (generalizada idiopática y focal frontal) en comparación con sujetos control sanos. La epilepsia es uno de los trastornos neurológicos más comunes, con más de 55 millones de afectados en el mundo. Esta enfermedad se caracteriza por la predisposición a generar ataques epilépticos de actividad neuronal anormal y excesiva o bien síncrona, y por tanto, es el escenario perfecto para este tipo de análisis al tiempo que presenta un gran interés tanto desde el punto de vista clínico como de investigación. Los resultados manifiestan alteraciones especificas en la conectividad y un cambio en la topología de las redes en cerebros epilépticos, desplazando la importancia del ‘foco’ a la ‘red’, enfoque que va adquiriendo relevancia en las investigaciones recientes sobre epilepsia. ABSTRACT There are about 1014 neuronal synapses in the human brain. This huge number of connections provides the substrate for neuronal ensembles to become transiently synchronized, producing the emergence of cognitive functions such as perception, learning or thinking. Understanding the complex brain network organization on the basis of neuroimaging data represents one of the most important and exciting challenges for systems neuroscience. Several measures have been recently proposed to evaluate at various scales (single cells, cortical columns, or brain areas) how the different parts of the brain communicate. We can classify them, according to their symmetry, into two groups: symmetric measures, such as correlation, coherence or phase synchronization indexes, evaluate functional connectivity (FC); and on the other hand, the asymmetric ones, such as Granger causality or transfer entropy, are able to detect effective connectivity (EC) revealing the direction of the interaction. In modern neurosciences, the interest in functional brain networks has increased strongly with the onset of new algorithms to study interdependence between time series, the advent of modern complex network theory and the introduction of powerful techniques to record neurophysiological data, such as magnetoencephalography (MEG). However, when analyzing neurophysiological data with this approach several questions arise. In this thesis, I intend to tackle some of the practical open problems in the field. First of all, the increase in the number of time series analysis algorithms to study brain FC/EC, along with their mathematical complexity, creates the necessity of arranging them into a single, unified toolbox that allow neuroscientists, neurophysiologists and researchers from related fields to easily access and make use of them. I developed such a toolbox for this aim, it is named HERMES (http://hermes.ctb.upm.es), and encompasses several of the most common indexes for the assessment of FC and EC running for MatlabR environment. I believe that this toolbox will be very helpful to all the researchers working in the emerging field of brain connectivity analysis and will entail a great value for the scientific community. The second important practical issue tackled in this thesis is the evaluation of the sensitivity to deep brain sources of two different MEG sensors: planar gradiometers and magnetometers, in combination with the related methodological approach, using two phase synchronization indexes: phase locking value (PLV) y phase lag index (PLI), the latter one being less sensitive to volume conduction effect. Thus, I compared their performance when studying brain networks, obtaining that magnetometer sensors and PLV presented higher artificial values as compared with planar gradiometers and PLI respectively. However, when it came to characterize epileptic networks it was the PLV which gives better results, as PLI FC networks where very sparse. Complex network analysis has provided new concepts which improved characterization of interacting dynamical systems. With this background, networks could be considered composed of nodes, symbolizing systems, whose interactions with each other are represented by edges. A growing number of network measures is been applied in network analysis. However, there is theoretical and empirical evidence that many of these indexes are strongly correlated with each other. Therefore, in this thesis I reduced them to a small set, which could more efficiently characterize networks. Within this framework, selecting an appropriate threshold to decide whether a certain connectivity value of the FC matrix is significant and should be included in the network analysis becomes a crucial step, in this thesis, I used the surrogate data tests to make an individual data-driven evaluation of each of the edges significance and confirmed more accurate results than when just setting to a fixed value the density of connections. All these methodologies were applied to the study of epilepsy, analysing resting state MEG functional networks, in two groups of epileptic patients (generalized and focal epilepsy) that were compared to matching control subjects. Epilepsy is one of the most common neurological disorders, with more than 55 million people affected worldwide, characterized by its predisposition to generate epileptic seizures of abnormal excessive or synchronous neuronal activity, and thus, this scenario and analysis, present a great interest from both the clinical and the research perspective. Results revealed specific disruptions in connectivity and network topology and evidenced that networks’ topology is changed in epileptic brains, supporting the shift from ‘focus’ to ‘networks’ which is gaining importance in modern epilepsy research.

Camera localization using trajectories and maps

We propose a new Bayesian framework for automatically determining the position (location and orientation) of an uncalibrated camera using the observations of moving objects and a schematic map of the passable areas of the environment. Our approach takes advantage of static and dynamic information on the scene structures through prior probability distributions for object dynamics. The proposed approach restricts plausible positions where the sensor can be located while taking into account the inherent ambiguity of the given setting. The proposed framework samples from the posterior probability distribution for the camera position via data driven MCMC, guided by an initial geometric analysis that restricts the search space. A Kullback-Leibler divergence analysis is then used that yields the final camera position estimate, while explicitly isolating ambiguous settings. The proposed approach is evaluated in synthetic and real environments, showing its satisfactory performance in both ambiguous and unambiguous settings.

Linear latent force models using Gaussian Processes

Purely data-driven approaches for machine learning present difficulties when data are scarce relative to the complexity of the model or when the model is forced to extrapolate. On the other hand, purely mechanistic approaches need to identify and specify all the interactions in the problem at hand (which may not be feasible) and still leave the issue of how to parameterize the system. In this paper, we present a hybrid approach using Gaussian processes and differential equations to combine data-driven modeling with a physical model of the system. We show how different, physically inspired, kernel functions can be developed through sensible, simple, mechanistic assumptions about the underlying system. The versatility of our approach is illustrated with three case studies from motion capture, computational biology, and geostatistics.

Aplicación de la teoría de redes complejas a procesos dinámicos en la sociedad

Podemos definir la sociedad como un sistema complejo que emerge de la cooperación y coordinación de billones de individuos y centenares de países. En este sentido no vivimos en una isla sino que estamos integrados en redes sociales que influyen en nuestro comportamiento. En esta tesis doctoral, presentamos un modelo analítico y una serie de estudios empíricos en los que analizamos distintos procesos sociales dinámicos desde una perspectiva de la teoría de redes complejas. En primer lugar, introducimos un modelo para explorar el impacto que las redes sociales en las que vivimos inmersos tienen en la actividad económica que transcurre sobre ellas, y mas concretamente en hasta qué punto la estructura de estas redes puede limitar la meritocracia de una sociedad. Como concepto contrario a meritocracia, en esta tesis, introducimos el término topocracia. Definimos un sistema como topocrático cuando la influencia o el poder y los ingresos de los individuos vienen principalmente determinados por la posición que ocupan en la red. Nuestro modelo es perfectamente meritocrático para redes completamente conectadas (todos los nodos están enlazados con el resto de nodos). Sin embargo nuestro modelo predice una transición hacia la topocracia a medida que disminuye la densidad de la red, siendo las redes poco densascomo las de la sociedad- topocráticas. En este modelo, los individuos por un lado producen y venden contenidos, pero por otro lado también distribuyen los contenidos producidos por otros individuos mediando entre comprador y vendedor. La producción y distribución de contenidos definen dos medios por los que los individuos reciben ingresos. El primero de ellos es meritocrático, ya que los individuos ingresan de acuerdo a lo que producen. Por el contrario el segundo es topocrático, ya que los individuos son compensados de acuerdo al número de cadenas mas cortas de la red que pasan a través de ellos. En esta tesis resolvemos el modelo computacional y analíticamente. Los resultados indican que un sistema es meritocrático solamente si la conectividad media de los individuos es mayor que una raíz del número de individuos que hay en el sistema. Por tanto, a la luz de nuestros resultados la estructura de la red social puede representar una limitación para la meritocracia de una sociedad. En la segunda parte de esta tesis se presentan una serie de estudios empíricos en los que se analizan datos extraídos de la red social Twitter para caracterizar y modelar el comportamiento humano. En particular, nos centramos en analizar conversaciones políticas, como las que tienen lugar durante campañas electorales. Nuestros resultados indican que la atención colectiva está distribuida de una forma muy heterogénea, con una minoría de cuentas extremadamente influyente. Además, la capacidad de los individuos para diseminar información en Twitter está limitada por la estructura y la posición que ocupan en la red de seguidores. Por tanto, de acuerdo a nuestras observaciones las redes sociales de Internet no posibilitan que la mayoría sea escuchada por la mayoría. De hecho, nuestros resultados implican que Twitter es topocrático, ya que únicamente una minoría de cuentas ubicadas en posiciones privilegiadas en la red de seguidores consiguen que sus mensajes se expandan por toda la red social. En conversaciones políticas, esta minoría de cuentas influyentes se compone principalmente de políticos y medios de comunicación. Los políticos son los mas mencionados ya que la gente les dirige y se refiere a ellos en sus tweets. Mientras que los medios de comunicación son las fuentes desde las que la gente propaga información. En un mundo en el que los datos personales quedan registrados y son cada día mas abundantes y precisos, los resultados del modelo presentado en esta tesis pueden ser usados para fomentar medidas que promuevan la meritocracia. Además, los resultados de los estudios empíricos sobre Twitter que se presentan en la segunda parte de esta tesis son de vital importancia para entender la nueva "sociedad digital" que emerge. En concreto hemos presentado resultados relevantes que caracterizan el comportamiento humano en Internet y que pueden ser usados para crear futuros modelos. Abstract Society can be defined as a complex system that emerges from the cooperation and coordination of billions of individuals and hundreds of countries. Thus, we do not live in social vacuum and the social networks in which we are embedded inevitably shapes our behavior. Here, we present an analytical model and several empirical studies in which we analyze dynamical social systems through a network science perspective. First, we introduce a model to explore how the structure of the social networks underlying society can limit the meritocracy of the economies. Conversely to meritocracy, in this work we introduce the term topocracy. We say that a system is topocratic if the compensation and power available to an individual is determined primarily by her position in a network. Our model is perfectly meritocratic for fully connected networks but becomes topocratic for sparse networks-like the ones in society. In the model, individuals produce and sell content, but also distribute the content produced by others when they belong to the shortest path connecting a buyer and a seller. The production and distribution of content defines two channels of compensation: a meritocratic channel, where individuals are compensated for the content they produce, and a topocratic channel, where individual compensation is based on the number of shortest paths that go through them in the network. We solve the model analytically and show that the distribution of payoffs is meritocratic only if the average degree of the nodes is larger than a root of the total number of nodes. Hence, in the light of our model, the sparsity and structure of networks represents a fundamental constraint to the meritocracy of societies. Next, we present several empirical studies that use data gathered from Twitter to analyze online human behavioral patterns. In particular, we focus on political conversations such as electoral campaigns. We found that the collective attention is highly heterogeneously distributed, as there is a minority of extremely influential accounts. In fact, the ability of individuals to propagate messages or ideas through the platform is constrained by the structure of the follower network underlying the social media and the position they occupy on it. Hence, although people have argued that social media can allow more voices to be heard, our results suggest that Twitter is highly topocratic, as only the minority of well positioned users are widely heard. This minority of influential accounts belong mostly to politicians and traditional media. Politicians tend to be the most mentioned, while media are the sources of information from which people propagate messages. We also propose a methodology to study and measure the emergence of political polarization from social interactions. To this end, we first propose a model to estimate opinions in which a minority of influential individuals propagate their opinions through a social network. The result of the model is an opinion probability density function. Next, we propose an index to quantify the extent to which the resulting distribution is polarized. Finally, we illustrate our methodology by applying it to Twitter data. In a world where personal data is increasingly available, the results of the analytical model introduced in this work can be used to enhance meritocracy and promote policies that help to build more meritocratic societies. Moreover, the results obtained in the latter part, where we have analyzed Twitter, are key to understand the new data-driven society that is emerging. In particular, we have presented relevant information that can be used to benchmark future models for online communication systems or can be used as empirical rules characterizing our online behavior.

A computational model of the anterior intraparietal area

The monkey anterior intraparietal area (AIP) encodes visual information about three-dimensional object shape that is used to shape the hand for grasping. In robotics a similar role has been played by modules that fit point cloud data to the superquadric family of shapes and its various extensions. We developed a model of shape tuning in AIP based on cosine tuning to superquadric parameters. However, the model did not fit the data well, and we also found that it was difficult to accurately reproduce these parameters using neural networks with the appropriate inputs (modelled on the caudal intraparietal area, CIP). The latter difficulty was related to the fact that there are large discontinuities in the superquadric parameters between very similar shapes. To address these limitations we adopted an alternative shape parameterization based on an Isomap nonlinear dimension reduction. The Isomap was built using gradients and curvatures of object surface depth. This alternative parameterization was low-dimensional (like superquadrics), but data-driven (similar to an alternative clustering approach that is also sometimes used in robotics) and lacked large discontinuities. Isomaps with 16 or more dimensions reproduced the AIP data fairly well. Moreover, we found that the Isomap parameters could be approximated from CIP-like input much more accurately than the superquadric parameters. We conclude that Isomaps, or perhaps alternative dimension reductions of CIP signals, provide a promising model of AIP tuning. We have now started to integrate our model with a robot hand, to explore the efficacy of Isomap shape reductions in grasp planning. Future work will consider dynamics of spike responses and integration with related visual and motor area models.