Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested

Proceedings. 19. Workshop Computational Intelligence, Dortmund, 2. - 4. Dezember 2009

Dieser Tagungsband enthält die Beiträge des 19. Workshops „Computational Intelligence“ des Fachausschusses 5.14 der VDI/VDE-Gesellschaft für Mess- und Automatisierungstechnik (GMA) und der Fachgruppe „Fuzzy-Systeme und Soft-Computing“ der Gesellschaft für Informatik (GI), der vom 2.-4. Dezember 2009 im Haus Bommerholz bei Dortmund stattfindet

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Incorporating complex cells into neural networks for pattern classification

Dans le domaine des neurosciences computationnelles, l'hypothèse a été émise que le système visuel, depuis la rétine et jusqu'au cortex visuel primaire au moins, ajuste continuellement un modèle probabiliste avec des variables latentes, à son flux de perceptions. Ni le modèle exact, ni la méthode exacte utilisée pour l'ajustement ne sont connus, mais les algorithmes existants qui permettent l'ajustement de tels modèles ont besoin de faire une estimation conditionnelle des variables latentes. Cela nous peut nous aider à comprendre pourquoi le système visuel pourrait ajuster un tel modèle; si le modèle est approprié, ces estimé conditionnels peuvent aussi former une excellente représentation, qui permettent d'analyser le contenu sémantique des images perçues. Le travail présenté ici utilise la performance en classification d'images (discrimination entre des types d'objets communs) comme base pour comparer des modèles du système visuel, et des algorithmes pour ajuster ces modèles (vus comme des densités de probabilité) à des images. Cette thèse (a) montre que des modèles basés sur les cellules complexes de l'aire visuelle V1 généralisent mieux à partir d'exemples d'entraînement étiquetés que les réseaux de neurones conventionnels, dont les unités cachées sont plus semblables aux cellules simples de V1; (b) présente une nouvelle interprétation des modèles du système visuels basés sur des cellules complexes, comme distributions de probabilités, ainsi que de nouveaux algorithmes pour les ajuster à des données; et (c) montre que ces modèles forment des représentations qui sont meilleures pour la classification d'images, après avoir été entraînés comme des modèles de probabilités. Deux innovations techniques additionnelles, qui ont rendu ce travail possible, sont également décrites : un algorithme de recherche aléatoire pour sélectionner des hyper-paramètres, et un compilateur pour des expressions mathématiques matricielles, qui peut optimiser ces expressions pour processeur central (CPU) et graphique (GPU).Computational neuroscientists have hypothesized that the visual system from the retina to at least primary visual cortex is continuously fitting a latent variable probability model to its stream of perceptions. It is not known exactly which probability model, nor exactly how the fitting takes place, but known algorithms for fitting such models require conditional estimates of the latent variables. This gives us a strong hint as to why the visual system might be fitting such a model; in the right kind of model those conditional estimates can also serve as excellent features for analyzing the semantic content of images perceived. The work presented here uses image classification performance (accurate discrimination between common classes of objects) as a basis for comparing visual system models, and algorithms for fitting those models as probability densities to images. This dissertation (a) finds that models based on visual area V1's complex cells generalize better from labeled training examples than conventional neural networks whose hidden units are more like V1's simple cells, (b) presents novel interpretations for complex-cell-based visual system models as probability distributions and novel algorithms for fitting them to data, and (c) demonstrates that these models form better features for image classification after they are first trained as probability models. Visual system models based on complex cells achieve some of the best results to date on the CIFAR-10 image classification benchmark, and samples from their probability distributions indicate that they have learnt to capture important aspects of natural images. Two auxiliary technical innovations that made this work possible are also described: a random search algorithm for selecting hyper-parameters, and an optimizing compiler for matrix-valued mathematical expressions which can target both CPU and GPU devices

Bayesian nonparametric models for data exploration

Mención Internacional en el título de doctorMaking sense out of data is one of the biggest challenges of our time. With the emergence of technologies such as the Internet, sensor networks or deep genome sequencing, a true data explosion has been unleashed that affects all fields of science and our everyday life. Recent breakthroughs, such as self-driven cars or champion-level Go player programs, have demonstrated the potential benefits from exploiting data, mostly in well-defined supervised tasks. However, we have barely started to actually explore and truly understand data. In fact, data holds valuable information for answering most important questions for humanity: How does aging impact our physical capabilities? What are the underlying mechanisms of cancer? Which factors make countries wealthier than others? Most of these questions cannot be stated as well-defined supervised problems, and might benefit enormously from multidisciplinary research efforts involving easy-to-interpret models and rigorous data exploratory analyses. Efficient data exploration might lead to life-changing scientific discoveries, which can later be turned into a more impactful exploitation phase, to put forward more informed policy recommendations, decision-making systems, medical protocols or improved models for highly accurate predictions. This thesis proposes tailored Bayesian nonparametric (BNP) models to solve specific data exploratory tasks across different scientific areas including sport sciences, cancer research, and economics. We resort to BNP approaches to facilitate the discovery of unexpected hidden patterns within data. BNP models place a prior distribution over an infinite-dimensional parameter space, which makes them particularly useful in probabilistic models where the number of hidden parameters is unknown a priori. Under this prior distribution, the posterior distribution of the hidden parameters given the data will assign high probability mass to those configurations that best explain the observations. Hence, inference over the hidden variables can be performed using standard Bayesian inference techniques, therefore avoiding expensive model selection steps. This thesis is application-focused and highly multidisciplinary. More precisely, we propose an automatic grading system for sportive competitions to compare athletic performance regardless of age, gender and environmental aspects; we develop BNP models to perform genetic association and biomarker discovery in cancer research, either using genetic information and Electronic Health Records or clinical trial data; finally, we present a flexible infinite latent factor model of international trade data to understand the underlying economic structure of countries and their evolution over time.Uno de los principales desafíos de nuestro tiempo es encontrar sentido dentro de los datos. Con la aparición de tecnologías como Internet, redes de sensores, o métodos de secuenciación profunda del genoma, una verdadera explosión digital se ha visto desencadenada, afectando todos los campos científicos, así como nuestra vida diaria. Logros recientes como pueden ser los coches auto-dirigidos o programas que ganan a los seres humanos al milenario juego del Go, han demostrado con creces los posibles beneficios que podemos obtener de la explotación de datos, mayoritariamente en tareas supervisadas bien definidas. No obstante, apenas hemos empezado con la exploración de datos y su verdadero entendimiento. En verdad, los datos encierran información muy valiosa para responder a muchas de las preguntas más importantes para la humanidad: ¿Cómo afecta el envejecimiento a nuestras aptitudes físicas? ¿Cuáles son los mecanismos subyacentes del cáncer? ¿Qué factores explican la riqueza de ciertos países frente a otros? Si bien la mayoría de estas preguntas no pueden formularse como problemas supervisados bien definidos, éstas pueden ser abordadas mediante esfuerzos de investigación multidisciplinar que involucren modelos fáciles de interpretar y análisis exploratorios rigurosos. Explorar los datos de manera eficiente abre potencialmente la puerta a un sinnúmero de descubrimientos científicos en diversas áreas con impacto real en nuestras vidas, descubrimientos que a su vez pueden llevarnos a una mejor explotación de los datos, resultando en recomendaciones políticas adecuadas, sistemas precisos de toma de decisión, protocolos médicos optimizados o modelos con mejores capacidades predictivas. Esta tesis propone modelos Bayesianos no-paramétricos (BNP) adecuados para la resolución específica de tareas explorativas de los datos en diversos ámbitos científicos incluyendo ciencias del deporte, investigación contra el cáncer, o economía. Recurrimos a un planteamiento BNP para facilitar el descubrimiento de patrones ocultos inesperados subyacentes en los datos. Los modelos BNP definen una distribución a priori sobre un espacio de parámetros de dimensión infinita, lo cual los hace especialmente atractivos para enfoques probabilísticos donde el número de parámetros latentes es en principio desconocido. Bajo dicha distribución a priori, la distribución a posteriori de los parámetros ocultos dados los datos asignará mayor probabilidad a aquellas configuraciones que mejor explican las observaciones. De esta manera, la inferencia sobre el espacio de variables ocultas puede realizarse mediante técnicas estándar de inferencia Bayesiana, evitando el proceso de selección de modelos. Esta tesis se centra en el ámbito de las aplicaciones, y es de naturaleza multidisciplinar. En concreto, proponemos un sistema de gradación automática para comparar el rendimiento deportivo de atletas independientemente de su edad o género, así como de otros factores del entorno. Desarrollamos modelos BNP para descubrir asociaciones genéticas y biomarcadores dentro de la investigación contra el cáncer, ya sea contrastando información genética con la historia clínica electrónica de los pacientes, o utilizando datos de ensayos clínicos; finalmente, presentamos un modelo flexible de factores latentes infinito para datos de comercio internacional, con el objetivo de entender la estructura económica de los distintos países y su correspondiente evolución a lo largo del tiempo.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Joaquín Míguez Arenas.- Secretario: Daniel Hernández Lobato.- Vocal: Cédric Archambea

Information Geometry

This Special Issue of the journal Entropy, titled “Information Geometry I”, contains a collection of 17 papers concerning the foundations and applications of information geometry. Based on a geometrical interpretation of probability, information geometry has become a rich mathematical field employing the methods of differential geometry. It has numerous applications to data science, physics, and neuroscience. Presenting original research, yet written in an accessible, tutorial style, this collection of papers will be useful for scientists who are new to the field, while providing an excellent reference for the more experienced researcher. Several papers are written by authorities in the field, and topics cover the foundations of information geometry, as well as applications to statistics, Bayesian inference, machine learning, complex systems, physics, and neuroscience

Active Perception for Autonomous Systems : In a Deep Space Navigation Scenario

Autonomous systems typically pursue certain goals for an extended amount of time in a self-sustainable fashion. To this end, they are equipped with a set of sensors and actuators to perceive certain aspects of the world and thereupon manipulate it in accordance with some given goals. This kind of interaction can be thought of as a closed loop in which a perceive-reason-act process takes place. The bi-directional interface between an autonomous system and the outer world is then given by a sequence of imperfect observations of the world and corresponding controls which are as well imperfectly actuated. To be able to reason in such a setting, it is customary for an autonomous system to maintain a probabilistic state estimate. The quality of the estimate -- or its uncertainty -- is, in turn, dependent on the information acquired within the perceive-reason-act loop described above. Hence, this thesis strives to investigate the question of how to actively steer such a process in order to maximize the quality of the state estimate. The question will be approached by introducing different probabilistic state estimation schemes jointly working on a manifold-based encapsuled state representation. On top of the resultant state estimate different active perception approaches are introduced, which determine optimal actions with respect to uncertainty minimization. The informational value of the particular actions is given by the expected impact of measurements on the uncertainty. The latter can be obtained by different direct and indirect measures, which will be introduced and discussed. The active perception schemes for autonomous systems will be investigated with a focus on two specific deep space navigation scenarios deduced from a potential mining mission to the main asteroid belt. In the first scenario, active perception strategies are proposed, which foster the correctional value of the sensor information acquired within a heliocentric navigation approach. Here, the expected impact of measurements is directly estimated, thus omitting counterfactual updates of the state based on hypothetical actions. Numerical evaluations of this scenario show that active perception is beneficial, i.e., the quality of the state estimate is increased. In addition, it is shown that the more uncertain a state estimate is, the more the value of active perception increases. In the second scenario, active autonomous deep space navigation in the vicinity of asteroids is investigated. A trajectory and a map are jointly estimated by a Graph SLAM algorithm based on measurements of a 3D Flash-LiDAR. The active perception strategy seeks to trade-off the exploration of the asteroid against the localization performance. To this end, trajectories are generated as well as evaluated in a novel twofold approach specifically tailored to the scenario. Finally, the position uncertainty can be extracted from the graph structure and subsequently be used to dynamically control the trade-off between localization and exploration. In a numerical evaluation, it is shown that the localization performance of the Graph SLAM approach to navigation in the vicinity of asteroids is generally high. Furthermore, the active perception strategy is able to trade-off between localization performance and the degree of exploration of the asteroid. Finally, when the latter process is dynamically controlled, based on the current localization uncertainty, a joint improvement of localization as well as exploration performance can be achieved. In addition, this thesis comprises an excursion into active sensorimotor object recognition. A sensorimotor feature is derived from biological principles of the human perceptual system. This feature is then employed in different probabilistic classification schemes. Furthermore, it enables the implementation of an active perception strategy, which can be thought of as a feature selection process in a classification scheme. It is shown that those strategies might be driven by top-down factors, i.e., based on previously learned information, or by bottom-up factors, i.e., based on saliency detected in the currently considered data. Evaluations are conducted based on real data acquired by a camera mounted on a robotic arm as well as on datasets. It is shown that the integrated representation of perception and action fosters classification performance and that the application of an active perception strategy accelerates the classification process