A PCA-based super-resolution algorithm for short image sequences

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. C. Miravet, and F. B. Rodríguez, "A PCA-based super-resolution algorithm for short image sequences", 17th IEEE International Conference on Image Processing (ICIP), Hong Kong, China, 2010, pp. 2025 - 2028In this paper, we present a novel, learning-based, two-step super-resolution (SR) algorithm well suited to solve the specially demanding problem of obtaining SR estimates from short image sequences. The first step, devoted to increase the sampling rate of the incoming images, is performed by fitting linear combinations of functions generated from principal components (PC) to reproduce locally the sparse projected image data, and using these models to estimate image values at nodes of the high-resolution grid. PCs were obtained from local image patches sampled at sub-pixel level, which were generated in turn from a database of high-resolution images by application of a physically realistic observation model. Continuity between local image models is enforced by minimizing an adequate functional in the space of model coefficients. The second step, dealing with restoration, is performed by a linear filter with coefficients learned to restore residual interpolation artifacts in addition to low-resolution blurring, providing an effective coupling between both steps of the method. Results on a demanding five-image scanned sequence of graphics and text are presented, showing the excellent performance of the proposed method compared to several state-of-the-art two-step and Bayesian Maximum a Posteriori SR algorithms.This work was supported by the Spanish Ministry of Education and Science under TIN 2007-65989 and CAM S-SEM-0255- 2006, and by COINCIDENTE project DN8644, RESTAURA

Reducing the loss of information through annealing text distortion

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Granados, A. ;Cebrian, M. ; Camacho, D. ; de Borja Rodriguez, F. "Reducing the Loss of Information through Annealing Text Distortion". IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 7 pp. 1090 - 1102, July 2011Compression distances have been widely used in knowledge discovery and data mining. They are parameter-free, widely applicable, and very effective in several domains. However, little has been done to interpret their results or to explain their behavior. In this paper, we take a step toward understanding compression distances by performing an experimental evaluation of the impact of several kinds of information distortion on compression-based text clustering. We show how progressively removing words in such a way that the complexity of a document is slowly reduced helps the compression-based text clustering and improves its accuracy. In fact, we show how the nondistorted text clustering can be improved by means of annealing text distortion. The experimental results shown in this paper are consistent using different data sets, and different compression algorithms belonging to the most important compression families: Lempel-Ziv, Statistical and Block-Sorting.This work was supported by the Spanish Ministry of Education and Science under TIN2010-19872 and TIN2010-19607 projects

Procesos de estabilización, sincronización y aprendizaje en redes neuronales estocásticas

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid. Escuela Técnica Superior de Informática, Departamento de Ingeniería Informática. Fecha de lectura: 21-12-199

Learning block memories with metric networks

An attractor neural network on the small-world topology is studied. A learning pattern is presented to the network, then a stimulus carrying local information is applied to the neurons and the retrieval of block-like structure is investigated. A synaptic noise decreases the memory capability. The change of stability from local to global attractors is shown to depend on the long-range character of the network connectivity.This work was supported by TIN 2004-04363-CO03-03, TIN 2007-65989 and CAM S-SEM-0255-2006

Regulation of specialists and generalists by neural variability improves pattern recognition performance

This is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neurocomputing, VOL 151, Part 1, (2015), DOI 10.1016/j.neucom.2014.09.073To analyze the impact of neural threshold variability in the mushroom body (MB) for pattern recognition, we used a computational model based on the olfactory system of insects. This model is a single-hidden-layer neural network (SLN) where the input layer represents the antennal lobe (AL). The remaining layers are in the MBs that are formed by the Kenyon cell (KC) layer and the output neurons that are responsible for odor learning. The binary code obtained for each odorant in the output layer by unsupervised learning was used to measure the classification error. This classification error allows us to identify the neural variability paradigm that achieves a better odor classification. The neural variability is provided by the neural threshold of activation. We compare two hypotheses: a unique threshold for all the neurons in the MB layer, which leads to no variability (homogeneity), and different thresholds for each MB layer (heterogeneity). The results show that, when there is threshold variability, odor classification performance improves. Neural variability induces populations of neurons that are specialists and generalists. Specialist neurons respond to fewer stimulus than the generalists. The proper combination of these two neuron types leads to performance improvement in the bioinspired classifier.This work was supported by the Spanish Government project TIN2010-19607 and predoctoral research grant BES-2011- 049274. R.H. acknowledges partial support by NIDCDR01DC011422- 01

Origin and role of neural signatures in bursting neurons

Copyright 2007 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics.A traditional view in neuroscience is that information arriving through one channel, i.e. a synapse, is encoded through a single code in the signal, e.g., the rate or the precise timing of the incoming events. However, not all the neural readers have to be interested in the same aspect of a common input signal, especially in multifunctional networks that can take advantage of several simultaneous codes. Multiple codes can be used to discriminate or contextualize certain inputs, even in single neurons. Dynamical mechanisms can add to the existing hard-wired connectivity for this task. Recent experiments have revealed the existence of neural signatures in the activity of bursting cells of invertebrate central pattern generators. These signatures consist of cell-specific spike timings in the bursting activity of the neurons. The signatures coexist with the information encoded in the frequency and/or phase relationships of the slow waves. The functional role of these neural fingerprints is still unclear. Based on experiments and using conductance-based models, we discuss the origin and the role of neural signatures as a part of a multicoding strategy for single cells in different types of neural circuits.This work was supported by Fundacion BBVA, MEC BFU2006-07902/BFI and MEC TIN2004-04363-C03-03

Effect of individual spiking activity on rhythm generation of central pattern generators

This is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neurocomputing 58-60 (2004):10.1016/j.neucom.2004.01.091Central Pattern Generators (CPGs) are highly specialized neural networks often with redundant elements that allow the system to act properly in case of error. CPGs are multifunctional circuits, i.e. the same CPG can produce many di®erent rhythms in response to modulatory or sensory inputs. All these rhythms have to be optimal for motor control and coordination. In this paper, we use a model of the well-known pyloric CPG of crustacean to analyze the importance of redundant connections and individual spiking activity in the generation of the CPG rhythm. In particular, we study the e®ect of di®erent spike distributions of a neuron on the collective behavior of the CPG.This work was supported by the Spanish MCyT (BFI-2000- 0157 and TIC 2002-572-C02-02

Dynamical invariants for CPG control in autonomous robots

This is an electronic version of the paper presented at the 7th International Conference on Informatics in Control, Automation and Robotics, held in Madeira on 2010Several studies have shown the usefulness of central pattern generator circuits to control autonomous rhythmic motion in robots. The traditional approach is building CPGs from nonlinear oscillators, adjusting a connectivity matrix and its weights to achieve the desired function. Compared to existing living CPGs, this approach seems still somewhat limited in resources. Living CPGs have a large number of available mechanisms to accomplish their task. The main function of a CPG is ensuring that some constraints regarding rhythmic activity are always kept, surmounting any disturbances from the external environment. We call this constraints the “dynamical invariant” of a CPG. Understanding the underlying biological mechanisms would take the design of robotic CPGs a step further. It would allow us to begin the design with a set of invariants to be preserved. The presence of these invariants will guarantee that, in response to unexpected conditions, an effective motor program will emerge that will perform the expected function, without the need of anticipating every possible scenario. In this paper we discuss how some bio-inspired elements contribute to building up these invariants.Work supported by MICINN BFU2009-08473, CAM S-SEM-0255-2006 and TIN 2007-65989. Fernando Herrero-Carrón with an FPU-UAM gran

Cervantes y la picaresca

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Filosofía y Letras, Departamento de Filología Española. Fecha de lectura: 15-06-201

La ruta hacia «El Monte de las Ánimas». Propuesta de una poética del cuento romántico

RESUMEN: «El Monte de las Ánimas» uno de los cuentos de Bécquer, ofrece al ser analizado una perfecta estructura técnica narrativa. Bécquer se apoya en los descubrimientos que una serie de escritores románticos, predecesores suyos, hicieron en el arte del relato breve. Enrique Gil y Carrasco, Mariano Roca de Togores, Serafín Estébanez de Calderón y Pedro de Madrazo fueron los nombres fundamentales que dieron lugar a a una poética del cuento que Gustavo Adolfo Bécquer supo aprovechar de forma magistral. Esta poética, el cuento dramatizado, nunca llega a tomar una forma teórica definida pero puede rastrearse por el análisis de las obras de los antecesores de Bécquer.ABSTRACT: An analysis of one of Bécquer's stories, «El Monte de las Animas», reveals a perfect technical narrative structure. Bécquer relies on the discoveries in the art of short stories made by a series of Romantic writers who came before him. Enrique Gil y Carrasco, Mariano Roca de Togores, Serafín Estébanez Calderón and Pedro de Madrazo were the fundamental figures who led the way to a poetics of the short story that Gustavo Adolfo Bécquer mastered. This technique, the dramatized story, never developed a defined theoretical form but it can be traced by way of an analysis of the works of Bécquer's predecessors