Learning efficient image representations: Connections between statistics and neuroscience

This thesis summarizes different works developed in the framework of analyzing the relation between image processing, statistics and neuroscience. These relations are analyzed from the efficient coding hypothesis point of view (H. Barlow [1961] and Attneave [1954]). This hypothesis suggests that the human visual system has been adapted during the ages in order to process the visual information in an efficient way, i.e. taking advantage of the statistical regularities of the visual world. Under this classical idea different works in different directions are developed. One direction is analyzing the statistical properties of a revisited, extended and fitted classical model of the human visual system. No statistical information is used in the model. Results show that this model obtains a representation with good statistical properties, which is a new evidence in favor of the efficient coding hypothesis. From the statistical point of view, different methods are proposed and optimized using natural images. The models obtained using these statistical methods show similar behavior to the human visual system, both in the spatial and color dimensions, which are also new evidences of the efficient coding hypothesis. Applications in image processing are an important part of the Thesis. Statistical and neuroscience based methods are employed to develop a wide set of image processing algorithms. Results of these methods in denoising, classification, synthesis and quality assessment are comparable to some of the most successful current methods

Estrategia de enseñanza y aprendizaje de programación basada en la idea de ’hackathon’

[EN] The acquisition of programming and data analysis skills in higher education is increa-singly necessary in all areas of Science and Engineering. In this paper we present a methodology for the motivation of programming learning, mainly focused on the deve-lopment of machine learning algorithms. This methodology is based on the hackathon idea and will have different levels. On the one hand the basic level where a competition is proposed in an improvised way during the development of the class. A second level where a programmed hackathon is proposed but within the classroom environment and using learning management systems such as Moodle. The last level consists of parti-cipation in an external hackathon and the delivery of a report. These levels have been adapted and tested in several undergraduate and master’s degree courses at the Uni-versity of Valencia. We include detailed information on how the methodology has been adapted to the teaching needs of the subject and we conducted anonymized surveys to students to know their degree of satisfaction. These surveys reveal a positive assessment of the experience by the students and include constructive comments for improvement in future editions.[ES] Se presenta una metodología para la motivación del aprendizaje de programación, principalmente enfocada al desarrollo de algoritmos de machine learning. Esta metodología está basada en la idea de hackathon o datathon y tendrá distintos niveles. Por un lado el nivel básico donde se plantea una competición de forma improvisada durante el desarrollo de la clase. Este nivel se puede utilizar para motivar, la evaluación tiene que restringirse a la mera participación. Un segundo nivel donde se plantea un hackathon programado pero dentro del entorno de la clase y utilizando sistemas de gestión de aprendizaje tipo Moodle. Este nivel se puede utilizar para evaluación tanto durante la clase como para un examen. El último nivel consiste en la participación en un hackathon externo y la entrega de un informe. Esta metodología puede servir para la evaluación de una tarea de clase y fomentar el trabajo en equipo. Además plantea un problema real en un entorno semi-profesional.Proyecto de innovación educativa “Explotación de las herramientas online de la Universitat de València para la evaluación a distancia de asignaturas en el área de ciencia” curso (2020-21) UV-SFPIE PID-1354708Piles, M.; Laparra Pérez-Muelas, V.; Peréz-Suay, A.; Mateo-García, G.; Girbés-Juan, V.; Moreno-Llácer, M.; Muñoz-Marí, J. (2021). Estrategia de enseñanza y aprendizaje de programación basada en la idea de ’hackathon’. En IN-RED 2021: VII Congreso de Innovación Edicativa y Docencia en Red. Editorial Universitat Politècnica de València. 1552-1564. https://doi.org/10.4995/INRED2021.2021.13785OCS1552156

Flipped evaluation: herramientas online para la evaluación participativa

[EN] The evaluation of a subject is a fundamental part of the teaching-learning process and one of the main concerns of our students. This is a complex task that requires a lot of effort from the teacher. This is a growing effort in line with the increased weight of con-tinuous evaluation in the current educational system. In this work, different methodo-logies focused on maximizing the student’s performance are presented, thus minimizing the extra effort for the teacher in the evaluation process. We provide several examples of activities throught Moodle platform such as the workshop, glossary, databases, ques-tionnaires, etc. Some of them allow self-assessment once configured, whereas others promote the participation of students in the correction and/or evaluation.[ES] La evaluación de una asignatura es una parte fundamental del proceso de enseñanza-aprendizaje y una de la que más preocupa a nuestros estudiantes. Se trata de una tarea compleja y que requiere un gran esfuerzo por parte del profesor. Un mayor esfuerzo que va parejo al incremento de la evaluación continua, una tendencia en el sistema educativo actual. En este trabajo se presentan diferentes metodologías que maximizan el rendimiento del alumno, tratando a su vez de minimizar el esfuerzo extra por parte del profesor en los procesos de corrección y evaluación. Se proporcionan diversos ejemplos de su uso mediante actividades disponibles en la plataforma Moodle como: taller, glosario, bases de datos, cuestionarios aleatorios, etc. Algunas de estas herramientas permiten la autoevaluación una vez configuradas, en otros casos se presentan metodologías que implican la participación del alumnado en la corrección y/o evaluación.Proyecto de innovación educativa “Explotación de las herramientas online de la Universitat de València para la evaluación a distancia de asignaturas en el área de ciencia” del curso 2020-21 (UV-SFPIE PID-1354708)Amorós López, J.; Ruescas Orient, A.; Esperante Pereira, D.; Girbés-Juan, V.; Fernandez-Moran, R.; Moreno Llácer, M.; Peréz-Suay, A.... (2021). Flipped evaluation: herramientas online para la evaluación participativa. En IN-RED 2021: VII Congreso de Innovación Edicativa y Docencia en Red. Editorial Universitat Politècnica de València. 675-689. https://doi.org/10.4995/INRED2021.2021.13461OCS67568

Physics-aware gaussian processes for earth observation

Earth observation from satellite sensory data pose challenging problems, where machine learning is currently a key player. In recent years, Gaussian Process (GP) regression and other kernel methods have excelled in biophysical parameter estimation tasks from space. GP regression is based on solid Bayesian statistics, and generally yield efficient and accurate parameter estimates. However, GPs are typically used for inverse modeling based on concurrent observations and in situ measurements only. Very often a forward model encoding the well-understood physical relations is available though. In this work, we review three GP models that respect and learn the physics of the underlying processes in the context of inverse modeling. First, we will introduce a Joint GP (JGP) model that combines in situ measurements and simulated data in a single GP model. Second, we present a latent force model (LFM) for GP modeling that encodes ordinary differential equations to blend data-driven modeling and physical models of the system. The LFM performs multi-output regression, adapts to the signal characteristics, is able to cope with missing data in the time series, and provides explicit latent functions that allow system analysis and evaluation. Finally, we present an Automatic Gaussian Process Emulator (AGAPE) that approximates the forward physical model via interpolation, reducing the number of necessary nodes. Empirical evidence of the performance of these models will be presented through illustrative examples of vegetation monitoring and atmospheric modeling