Dispersive approach to hadronic light-by-light scattering and the muon ɡ − 2

The largest uncertainties in the Standard Model calculation of the anomalous magnetic moment of the muon (ɡ − 2)μ come from hadronic contributions. In particular, it can be expected that in a few years the subleading hadronic light-by-light (HLbL) contribution will dominate the theory uncertainty. We present a dispersive description of the HLbL tensor. This new, model-independent approach opens up an avenue towards a data-driven determination of the HLbL contribution to the (ɡ − 2)μ.

Dispersion relation for hadronic light-by-light scattering: theoretical foundations

In this paper we make a further step towards a dispersive description of the hadronic light-by-light (HLbL) tensor, which should ultimately lead to a data-driven evaluation of its contribution to (g − 2) μ . We first provide a Lorentz decomposition of the HLbL tensor performed according to the general recipe by Bardeen, Tung, and Tarrach, generalizing and extending our previous approach, which was constructed in terms of a basis of helicity amplitudes. Such a tensor decomposition has several advantages: the role of gauge invariance and crossing symmetry becomes fully transparent; the scalar coefficient functions are free of kinematic singularities and zeros, and thus fulfill a Mandelstam double-dispersive representation; and the explicit relation for the HLbL contribution to (g − 2) μ in terms of the coefficient functions simplifies substantially. We demonstrate explicitly that the dispersive approach defines both the pion-pole and the pion-loop contribution unambiguously and in a model-independent way. The pion loop, dispersively defined as pion-box topology, is proven to coincide exactly with the one-loop scalar QED amplitude, multiplied by the appropriate pion vector form factors.

Hadronic light-by-light scattering and the muon g−2

The largest uncertainties in the Standard Model calculation of the anomalous magnetic moment of the muon (g − 2)μ come from hadronic contributions. In particular, it can be expected that in a few years the subleading hadronic light-by-light (HLbL) contribution will dominate the theory uncertainty. We present a dispersive description of the HLbL tensor, which is based on unitarity, analyticity, crossing symmetry, and gauge invariance. Such a model-independent Approach opens up an avenue towards a data-driven determination of the HLbL contribution to the (g − 2)μ.

The Comal County Needs Assessment Youth Survey

A crucial link in preserving and protecting the future of our communities resides in maintaining the health and well being of our youth. While every member of the community owns an opinion regarding where to best utilize monies for prevention and intervention, the data to support such opinion is often scarce. In an effort to generate data-driven indices for community planning and action, the United Way of Comal County, Texas partnered with the University Of Texas - Houston Health Science Center, School Of Public Health to accomplish a county-specific needs assessment. A community-based participatory research emphasis utilizing the Mobilization for Action through Planning and Partnership (MAPP) format developed by the National Association of City and County Health Officials (NACCHO) was implemented to engage community members in identifying and addressing community priorities. The single greatest area of consensus and concern identified by community members was the health and well being of the youth population. Thus, a youth survey, targeting these specific areas of community concern, was designed, coordinated and administered to all 9-11th grade students in the county. 20% of the 3,698 completed surveys (72% response rate) were randomly selected for analysis. These 740 surveys were coded and scanned into an electronic survey database. Statistical analysis provided youth-reported data on the status of the multiple issues affecting the health and well being of the community's youth. These data will be reported back to the community stakeholders, as part of the larger Comal County Needs Assessment, for the purposes of community planning and action. Survey data will provide community planners with an awareness of the high risk behaviors and habit patterns amongst their youth. This knowledge will permit more effective targeting of the means for encouraging healthy behaviors and preventing the spread of disease. Further, the community-oriented, population-based nature of this effort will provide answers to questions raised by the community and will provide an effective launching pad for the development and implementation of targeted, preventive health strategies. ^

The Open Door Mission: Measuring and predicting outcomes of one community-based substance abuse treatment program

The objectives of this study were to identify and measure the average outcomes of the Open Door Mission's nine-month community-based substance abuse treatment program, identify predictors of successful outcomes, and make recommendations to the Open Door Mission for improving its treatment program.^ The Mission's program is exclusive to adult men who have limited financial resources: most of which were homeless or dependent on parents or other family members for basic living needs. Many, but not all, of these men are either chemically dependent or have a history of substance abuse.^ This study tracked a cohort of the Mission's graduates throughout this one-year study and identified various indicators of success at short-term intervals, which may be predictive of longer-term outcomes. We tracked various levels of 12-step program involvement, as well as other social and spiritual activities, such as church affiliation and recovery support.^ Twenty-four of the 66 subjects, or 36% met the Mission's requirements for success. Specific to this success criteria; Fifty-four, or 82% reported affiliation with a home church; Twenty-six, or 39% reported full-time employment; Sixty-one, or 92% did not report or were not identified as having any post-treatment arrests or incarceration, and; Forty, or 61% reported continuous abstinence from both drugs and alcohol.^ Five research-based hypotheses were developed and tested. The primary analysis tool was the web-based non-parametric dependency modeling tool, B-Course, which revealed some strong associations with certain variables, and helped the researchers generate and test several data-driven hypotheses. Full-time employment is the greatest predictor of abstinence: 95% of those who reported full time employment also reported continuous post-treatment abstinence, while 50% of those working part-time were abstinent and 29% of those with no employment were abstinent. Working with a 12-step sponsor, attending aftercare, and service with others were identified as predictors of abstinence.^ This study demonstrates that associations with abstinence and the ODM success criteria are not simply based on one social or behavioral factor. Rather, these relationships are interdependent, and show that abstinence is achieved and maintained through a combination of several 12-step recovery activities. This study used a simple assessment methodology, which demonstrated strong associations across variables and outcomes, which have practical applicability to the Open Door Mission for improving its treatment program. By leveraging the predictive capability of the various success determination methodologies discussed and developed throughout this study, we can identify accurate outcomes with both validity and reliability. This assessment instrument can also be used as an intervention that, if operationalized to the Mission’s clients during the primary treatment program, may measurably improve the effectiveness and outcomes of the Open Door Mission.^

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.

Classification of coral reef types in Torres Strait (GIS files in zip-archive 319 kB)

Coral reefs represent major accumulations of calcium carbonate (CaCO3). The particularly labyrinthine network of reefs in Torres Strait, north of the Great Barrier Reef (GBR), has been examined in order to estimate their gross CaCO3 productivity. The approach involved a two-step procedure, first characterising and classifying the morphology of reefs based on a classification scheme widely employed on the GBR and then estimating gross CaCO3 productivity rates across the region using a regional census-based approach. This was undertaken by independently verifying published rates of coral reef community gross production for use in Torres Strait, based on site-specific ecological and morphological data. A total of 606 reef platforms were mapped and classified using classification trees. Despite the complexity of the maze of reefs in Torres Strait, there are broad morphological similarities with reefs in the GBR. The spatial distribution and dimensions of reef types across both regions are underpinned by similar geological processes, sea-level history in the Holocene and exposure to the same wind/wave energetic regime, resulting in comparable geomorphic zonation. However, the presence of strong tidal currents flowing through Torres Strait and the relatively shallow and narrow dimensions of the shelf exert a control on local morphology and spatial distribution of the reef platforms. A total amount of 8.7 million tonnes of CaCO3 per year, at an average rate of 3.7 kg CaCO3 m-2 yr-1 (G), were estimated for the studied area. Extrapolated production rates based on detailed and regional census-based approaches for geomorphic zones across Torres Strait were comparable to those reported elsewhere, particularly values for the GBR based on alkalinity-reduction methods. However, differences in mapping methodologies and the impact of reduced calcification due to global trends in coral reef ecological decline and changing oceanic physical conditions warrant further research. The novel method proposed in this study to characterise the geomorphology of reef types based on classification trees provides an objective and repeatable data-driven approach that combined with regional census-based approaches has the potential to be adapted and transferred to different coral reef regions, depicting a more accurate picture of interactions between reef ecology and geomorphology.

Probabilistic models for the prediction of a ship performance in dynamic ice

We introduce two probabilistic, data-driven models that predict a ship's speed and the situations where a ship is probable to get stuck in ice based on the joint effect of ice features such as the thickness and concentration of level ice, ice ridges, rafted ice, moreover ice compression is considered. To develop the models to datasets were utilized. First, the data from the Automatic Identification System about the performance of a selected ship was used. Second, a numerical ice model HELMI, developed in the Finnish Meteorological Institute, provided information about the ice field. The relations between the ice conditions and ship movements were established using Bayesian learning algorithms. The case study presented in this paper considers a single and unassisted trip of an ice-strengthened bulk carrier between two Finnish ports in the presence of challenging ice conditions, which varied in time and space. The obtained results show good prediction power of the models. This means, on average 80% for predicting the ship's speed within specified bins, and above 90% for predicting cases where a ship may get stuck in ice. We expect this new approach to facilitate the safe and effective route selection problem for ice-covered waters where the ship performance is reflected in the objective function.

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.

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.

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.

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.

Towards Mapping Experience Design for the Internet of Things.

We are witnessing a fundamental transformation in how Internet of Things (IoT) is having an impact on the experience users have with data-driven devices, smart appliances, and connected products. The experience of any place is commonly defined as the result of a series of user engagements with a surrounding place in order to carry out daily activities (Golledge, 2002). Knowing about users? experiences becomes vital to the process of designing a map. In the near future, a user will be able to interact directly with any IoT device placed in his surrounding place and very little is known on what kinds of interactions and experiences a map might offer (Roth, 2015). The main challenge is to develop an experience design process to devise maps capable of supporting different user experience dimensions such as cognitive, sensory-physical, affective, and social (Tussyadiah and Zach, 2012). For example, in a smart city of the future, the IoT devices allowing a multimodal interaction with a map could help tourists in the assimilation of their knowledge about points of interest (cognitive experience), their association of sounds and smells to these places (sensory-physical experience), their emotional connection to them (affective experience) and their relationships with other nearby tourists (social experience). This paper aims to describe a conceptual framework for developing a Mapping Experience Design (MXD) process for building maps for smart connected places of the future. Our MXD process is focussed on the cognitive dimension of an experience in which a person perceives a place as a "living entity" that uses and feeds through his experiences. We want to help people to undergo a meaningful experience of a place through mapping what is being communicated during their interactions with the IoT devices situated in this place. Our purpose is to understand how maps can support a person?s experience in making better decisions in real-time.