Sparse, hierarchical and shared-factors priors for representation learning

La représentation en caractéristiques est une préoccupation centrale des systèmes d’apprentissage automatique d’aujourd’hui. Une représentation adéquate peut faciliter une tâche d’apprentissage complexe. C’est le cas lorsque par exemple cette représentation est de faible dimensionnalité et est constituée de caractéristiques de haut niveau. Mais comment déterminer si une représentation est adéquate pour une tâche d’apprentissage ? Les récents travaux suggèrent qu’il est préférable de voir le choix de la représentation comme un problème d’apprentissage en soi. C’est ce que l’on nomme l’apprentissage de représentation. Cette thèse présente une série de contributions visant à améliorer la qualité des représentations apprises. La première contribution élabore une étude comparative des approches par dictionnaire parcimonieux sur le problème de la localisation de points de prises (pour la saisie robotisée) et fournit une analyse empirique de leurs avantages et leurs inconvénients. La deuxième contribution propose une architecture réseau de neurones à convolution (CNN) pour la détection de points de prise et la compare aux approches d’apprentissage par dictionnaire. Ensuite, la troisième contribution élabore une nouvelle fonction d’activation paramétrique et la valide expérimentalement. Finalement, la quatrième contribution détaille un nouveau mécanisme de partage souple de paramètres dans un cadre d’apprentissage multitâche.Feature representation is a central concern of today’s machine learning systems. A proper representation can facilitate a complex learning task. This is the case when for instance the representation has low dimensionality and consists of high-level characteristics. But how can we determine if a representation is adequate for a learning task? Recent work suggests that it is better to see the choice of representation as a learning problem in itself. This is called Representation Learning. This thesis presents a series of contributions aimed at improving the quality of the learned representations. The first contribution elaborates a comparative study of Sparse Dictionary Learning (SDL) approaches on the problem of grasp detection (for robotic grasping) and provides an empirical analysis of their advantages and disadvantages. The second contribution proposes a Convolutional Neural Network (CNN) architecture for grasp detection and compares it to SDL. Then, the third contribution elaborates a new parametric activation function and validates it experimentally. Finally, the fourth contribution details a new soft parameter sharing mechanism for multitasking learning

Sparse image approximation with application to flexible image coding

Natural images are often modeled through piecewise-smooth regions. Region edges, which correspond to the contours of the objects, become, in this model, the main information of the signal. Contours have the property of being smooth functions along the direction of the edge, and irregularities on the perpendicular direction. Modeling edges with the minimum possible number of terms is of key importance for numerous applications, such as image coding, segmentation or denoising. Standard separable basis fail to provide sparse enough representation of contours, due to the fact that this kind of basis do not see the regularity of edges. In order to be able to detect this regularity, a new method based on (possibly redundant) sets of basis functions able to capture the geometry of images is needed. This thesis presents, in a first stage, a study about the features that basis functions should have in order to provide sparse representations of a piecewise-smooth image. This study emphasizes the need for edge-adapted basis functions, capable to accurately capture local orientation and anisotropic scaling of image structures. The need of different anisotropy degrees and orientations in the basis function set leads to the use of redundant dictionaries. However, redundant dictionaries have the inconvenience of giving no unique sparse image decompositions, and from all the possible decompositions of a signal in a redundant dictionary, just the sparsest is needed. There are several algorithms that allow to find sparse decompositions over redundant dictionaries, but most of these algorithms do not always guarantee that the optimal approximation has been recovered. To cope with this problem, a mathematical study about the properties of sparse approximations is performed. From this, a test to check whether a given sparse approximation is the sparsest is provided. The second part of this thesis presents a novel image approximation scheme, based on the use of a redundant dictionary. This scheme allows to have a good approximation of an image with a number of terms much smaller than the dimension of the signal. This novel approximation scheme is based on a dictionary formed by a combination of anisotropically refined and rotated wavelet-like mother functions and Gaussians. An efficient Full Search Matching Pursuit algorithm to perform the image decomposition in such a dictionary is designed. Finally, a geometric image coding scheme based on the image approximated over the anisotropic and rotated dictionary of basis functions is designed. The coding performances of this dictionary are studied. Coefficient quantization appears to be of crucial importance in the design of a Matching Pursuit based coding scheme. Thus, a quantization scheme for the MP coefficients has been designed, based on the theoretical energy upper bound of the MP algorithm and the empirical observations of the coefficient distribution and evolution. Thanks to this quantization, our image coder provides low to medium bit-rate image approximations, while it allows for on the fly resolution switching and several other affine image transformations to be performed directly in the transformed domain

Delineating the Posthuman Subject in the Anthropocene. Margaret Atwood's Dystopian Novels: The Handmaid's Tale, MaddAddam and The Heart Goes Last.D.

Esta disertación, titulada ¿Delineating the Posthuman Subject in the Anthropocene. Margaret Atwood¿s Dystopian Novels: The Handmaid¿s Tale, MaddAddam, and The Heart Goes Last¿, surge de la idea de que el proyecto literario de Atwood a lo largo de toda su carrera ha demostrado un interés generalizado en las implicaciones éticas de los problemas sociales, de género y medioambientales. Además, el fuerte compromiso de Atwood con las nuevas tecnologías y medios de comunicación requirió un paradigma que pudiera tener en cuenta la influencia que todos estos elementos tienen en la construcción del sujeto posthumano en sus interacciones con la ciencia y la tecnología, y el Antropoceno como un campo cultural de investigación. Las distopías de Atwood son retratos sombríos de futuros potenciales que intentan advertir a sus lectores sobre temas relevantes como la contaminación, la ingeniería genética o el ataque a los derechos humanos. Es, pues, el propósito de esta disertación discutir y rastrear la evolución de la ideología de la autora en las tres novelas del corpus siempre en conexión con el momento histórico en el que se escribieron. Por tanto, la investigación se centra en tres características principales presentes en las novelas. En primer lugar, el análisis plantea la cuestión de qué significa ser humano en sociedades que privan a sus ciudadanos de cualquier derecho. Luego, discute la representación del sujeto posthumano y la implicación de vivir en la era del Antropoceno, como una herramienta literaria/teórica que desencadena el nacimiento de un nuevo género, la ficción climática. Finalmente, la disertación se centra en consideraciones genéricas para ilustrar la estructura de las novelas como ficciones distópicas y la evolución del género para adaptarse al siglo XXI. Esto conduce finalmente a la valoración de otras cuestiones éticas que están presentes en las novelas de Atwood: el valor social y ético de la ficción, así como el significado y el poder del acto de escribir. El marco teórico proporcionado por las teorías posthumanas, y las consideraciones genéricas de las novelas como distopías y ficción climática pueden llamar positivamente la atención sobre el papel de la literatura y los escritores y la contribución decisiva de Atwood al análisis y denuncia de los problemas existentes en el mundo (occidental).<br /

Risk-Based Optimal Scheduling for the Predictive Maintenance of Railway Infrastructure

In this thesis a risk-based decision support system to schedule the predictive maintenance activities, is proposed. The model deals with the maintenance planning of a railway infrastructure in which the due-dates are defined via failure risk analysis.The novelty of the approach consists of the risk concept introduction in railway maintenance scheduling, according to ISO 55000 guidelines, thus implying that the maintenance priorities are based on asset criticality, determined taking into account the relevant failure probability, related to asset degradation conditions, and the consequent damages

Machines That Learn: Aesthetics of Adaptive Behaviors in Agent-based Art

Since the post-war era, artists have been exploring the use of embodied, artificial agents. This artistic activity runs parallel to research in computer science, in domains such as Cybernetics, Artificial Intelligence and Artificial Life. This thesis offers an account of a particular facet of this broader work — namely, a study of the artistic practice of agent-based, adaptive computational artistic installations that make use of Machine Learning methods. Machine Learning is a sub-field of the computer science area of Artificial Intelligence that employs mathematical models to classify and make predictions based on data or experience rather than on logical rules. These artworks that integrate Machine Learning into their structures raise a number of important questions: (1) What new forms of aesthetic experience do Machine Learning methods enable or make possible when utilized outside of their intended context, and are instead carried over into artistic works? (2) What characterizes the practice of using adaptive computational methods in agent-based artworks? And finally, (3) what kind of worldview are these works fostering? To address these questions, I examine the history of Machine Learning in both art and science, illustrating how artists and engineers alike have made use of these methods historically. I also analyze the defining scientific characteristics of Machine Learning through a practitioner’s lens, concretely articulating how properties of Machine Learning interplay in media artworks that behave and evolve in real time. I later develop a framework for understanding machine behaviors based on the morphological aspects of the temporal unfolding of agent behaviors as a tool for comprehending both adaptive and non-adaptive behaviors in works of art. Finally, I expose how adaptive technologies suggest a new worldview for art that accounts for the performative engagement of agents adapting to one another, which implies a certain way of losing control in the face of the indeterminacy and the unintelligibility of alien agencies and their behaviors

Improving water network management by efficient division into supply clusters

El agua es un recurso escaso que, como tal, debe ser gestionado de manera eficiente. Así, uno de los propósitos de dicha gestión debiera ser la reducción de pérdidas de agua y la mejora del funcionamiento del abastecimiento. Para ello, es necesario crear un marco de trabajo basado en un conocimiento profundo de la redes de distribución. En los casos reales, llegar a este conocimiento es una tarea compleja debido a que estos sistemas pueden estar formados por miles de nodos de consumo, interconectados entre sí también por miles de tuberías y sus correspondientes elementos de alimentación. La mayoría de las veces, esas redes no son el producto de un solo proceso de diseño, sino la consecuencia de años de historia que han dado respuesta a demandas de agua continuamente crecientes con el tiempo. La división de la red en lo que denominaremos clusters de abastecimiento, permite la obtención del conocimiento hidráulico adecuado para planificar y operar las tareas de gestión oportunas, que garanticen el abastecimiento al consumidor final. Esta partición divide las redes de distribución en pequeñas sub-redes, que son virtualmente independientes y están alimentadas por un número prefijado de fuentes. Esta tesis propone un marco de trabajo adecuado en el establecimiento de vías eficientes tanto para dividir la red de abastecimiento en sectores, como para desarrollar nuevas actividades de gestión, aprovechando esta estructura dividida. La propuesta de desarrollo de cada una de estas tareas será mediante el uso de métodos kernel y sistemas multi-agente. El spectral clustering y el aprendizaje semi-supervisado se mostrarán como métodos con buen comportamiento en el paradigma de encontrar una red sectorizada que necesite usar el número mínimo de válvulas de corte. No obstante, sus algoritmos se vuelven lentos (a veces infactibles) dividiendo una red de abastecimiento grande.Herrera Fernández, AM. (2011). Improving water network management by efficient division into supply clusters [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/11233Palanci

Social Network Dynamics

This thesis focuses on the analysis of structural and topological network problems. In particular, in this work the privileged subjects of investigation will be both static and dynamic social networks. Nowadays, the constantly growing availability of Big Data describing human behaviors (i.e., the ones provided by online social networks, telco companies, insurances, airline companies. . . ) offers the chance to evaluate and validate, on large scale realities, the performances of algorithmic approaches and the soundness of sociological theories. In this scenario, exploiting data-driven methodologies enables for a more careful modeling and thorough understanding of observed phenomena. In the last decade, graph theory has lived a second youth: the scientific community has extensively adopted, and sharpened, its tools to shape the so called Network Science. Within this highly active field of research, it is recently emerged the need to extend classic network analytical methodologies in order to cope with a very important, previously underestimated, semantic information: time. Such awareness has been the linchpin for recent works that have started to redefine form scratch well known network problems in order to better understand the evolving nature of human interactions. Indeed, social networks are highly dynamic realities: nodes and edges appear and disappear as time goes by describing the natural lives of social ties: for this reason. it is mandatory to assess the impact that time-aware approaches have on the solution of network problems. Moving from the analysis of the strength of social ties, passing through node ranking and link prediction till reaching community discovery, this thesis aims to discuss data-driven methodologies specifically tailored to approach social network issues in semantic enriched scenarios. To this end, both static and dynamic analytical processes will be introduced and tested on real world data

Mixing greedy and evolutive approaches to improve pursuit strategies

The prey-predator pursuit problem is a generic multi-agent testbed referenced many times in literature. Algorithms and conclusions obtained in this domain can be extended and applied to many particular problems. In first place, greedy algorithms seem to do the job. But when concurrence problems arise, agent communication and coordination is needed to get a reasonable solution. It is quite popular to face these issues directly with non-supervised learning algorithms to train prey and predators. However, results got by most of these approaches still leave a great margin of improvement which should be exploited. In this paper we propose to start from a greedy strategy and extend and improve it by adding communication and machine learning. In this proposal, predator agents get a previous movement decision by using a greedy approach. Then, they focus on learning how to coordinate their own pre-decisions with the ones taken by other surrounding agents. Finally, they get a final decission trying to optimize their chase of the prey without colliding between them. For the learning step, a neuroevolution approach is used. The final results show improvements and leave room for open discussion

Urban food strategies in Central and Eastern Europe: what's specific and what's at stake?

Integrating a larger set of instruments into Rural Development Programmes implied an increasing focus on monitoring and evaluation. Against the highly diversified experience with regard to implementation of policy instruments the Common Monitoring and Evaluation Framework has been set up by the EU Commission as a strategic and streamlined method of evaluating programmes’ impacts. Its indicator-based approach mainly reflects the concept of a linear, measure-based intervention logic that falls short of the true nature of RDP operation and impact capacity on rural changes. Besides the different phases of the policy process, i.e. policy design, delivery and evaluation, the regional context with its specific set of challenges and opportunities seems critical to the understanding and improvement of programme performance. In particular the role of local actors can hardly be grasped by quantitative indicators alone, but has to be addressed by assessing processes of social innovation. This shift in the evaluation focus underpins the need to take account of regional implementation specificities and processes of social innovation as decisive elements for programme performance.

Chercheur-e-s parents en cours d’insertion dans la carrière scientifique : quelles inégalités ?

peer reviewedDans cette communication, nous proposons tout d’abord quelques clés de lecture sociologique du monde universitaire afin de saisir l’environnement dans lequel œuvrent et se meuvent aujourd'hui les chercheurs. Nous exposons ensuite des résultats d'enquête sur l'expérience et les rapports à la carrière scientifique, soulignant le rôle qu'y jouent le genre et l'interférence travail/famille