On nonlinearity in neural encoding models applied to the primary visual cortex

Within the regression framework, we show how different levels of nonlinearity influence the instantaneous firing rate prediction of single neurons. Nonlinearity can be achieved in several ways. In particular, we can enrich the predictor set with basis expansions of the input variables (enlarging the number of inputs) or train a simple but different model for each area of the data domain. Spline-based models are popular within the first category. Kernel smoothing methods fall into the second category. Whereas the first choice is useful for globally characterizing complex functions, the second is very handy for temporal data and is able to include inner-state subject variations. Also, interactions among stimuli are considered. We compare state-of-the-art firing rate prediction methods with some more sophisticated spline-based nonlinear methods: multivariate adaptive regression splines and sparse additive models. We also study the impact of kernel smoothing. Finally, we explore the combination of various local models in an incremental learning procedure. Our goal is to demonstrate that appropriate nonlinearity treatment can greatly improve the results. We test our hypothesis on both synthetic data and real neuronal recordings in cat primary visual cortex, giving a plausible explanation of the results from a biological perspective.

Forward Stagewise Naive Bayes

The naïve Bayes approach is a simple but often satisfactory method for supervised classification. In this paper, we focus on the naïve Bayes model and propose the application of regularization techniques to learn a naïve Bayes classifier. The main contribution of the paper is a stagewise version of the selective naïve Bayes, which can be considered a regularized version of the naïve Bayes model. We call it forward stagewise naïve Bayes. For comparison’s sake, we also introduce an explicitly regularized formulation of the naïve Bayes model, where conditional independence (absence of arcs) is promoted via an L 1/L 2-group penalty on the parameters that define the conditional probability distributions. Although already published in the literature, this idea has only been applied for continuous predictors. We extend this formulation to discrete predictors and propose a modification that yields an adaptive penalization. We show that, whereas the L 1/L 2 group penalty formulation only discards irrelevant predictors, the forward stagewise naïve Bayes can discard both irrelevant and redundant predictors, which are known to be harmful for the naïve Bayes classifier. Both approaches, however, usually improve the classical naïve Bayes model’s accuracy.

Learning an L1-regularized Gaussian Bayesian Network in the Equivalence Class Space

Learning the structure of a graphical model from data is a common task in a wide range of practical applications. In this paper, we focus on Gaussian Bayesian networks, i.e., on continuous data and directed acyclic graphs with a joint probability density of all variables given by a Gaussian. We propose to work in an equivalence class search space, specifically using the k-greedy equivalence search algorithm. This, combined with regularization techniques to guide the structure search, can learn sparse networks close to the one that generated the data. We provide results on some synthetic networks and on modeling the gene network of the two biological pathways regulating the biosynthesis of isoprenoids for the Arabidopsis thaliana plant

Lazy Lasso for local regression

Locally weighted regression is a technique that predicts the response for new data items from their neighbors in the training data set, where closer data items are assigned higher weights in the prediction. However, the original method may suffer from overfitting and fail to select the relevant variables. In this paper we propose combining a regularization approach with locally weighted regression to achieve sparse models. Specifically, the lasso is a shrinkage and selection method for linear regression. We present an algorithm that embeds lasso in an iterative procedure that alternatively computes weights and performs lasso-wise regression. The algorithm is tested on three synthetic scenarios and two real data sets. Results show that the proposed method outperforms linear and local models for several kinds of scenarios

Network convergence and QoS for future multimedia services in the VISION project

The emerging use of real-time 3D-based multimedia applications imposes strict quality of service (QoS) requirements on both access and core networks. These requirements and their impact to provide end-to-end 3D videoconferencing services have been studied within the Spanish-funded VISION project, where different scenarios were implemented showing an agile stereoscopic video call that might be offered to the general public in the near future. In view of the requirements, we designed an integrated access and core converged network architecture which provides the requested QoS to end-to-end IP sessions. Novel functional blocks are proposed to control core optical networks, the functionality of the standard ones is redefined, and the signaling improved to better meet the requirements of future multimedia services. An experimental test-bed to assess the feasibility of the solution was also deployed. In such test-bed, set-up and release of end-to-end sessions meeting specific QoS requirements are shown and the impact of QoS degradation in terms of the user perceived quality degradation is quantified. In addition, scalability results show that the proposed signaling architecture is able to cope with large number of requests introducing almost negligible delay.

Contribution To Signalling Of 3d Video Streams In Communication Systems Using The Session Initiation Protocol

Las tecnologías de vídeo en 3D han estado al alza en los últimos años, con abundantes avances en investigación unidos a una adopción generalizada por parte de la industria del cine, y una importancia creciente en la electrónica de consumo. Relacionado con esto, está el concepto de vídeo multivista, que abarca el vídeo 3D, y puede definirse como un flujo de vídeo compuesto de dos o más vistas. El vídeo multivista permite prestaciones avanzadas de vídeo, como el vídeo estereoscópico, el “free viewpoint video”, contacto visual mejorado mediante vistas virtuales, o entornos virtuales compartidos. El propósito de esta tesis es salvar un obstáculo considerable de cara al uso de vídeo multivista en sistemas de comunicación: la falta de soporte para esta tecnología por parte de los protocolos de señalización existentes, que hace imposible configurar una sesión con vídeo multivista mediante mecanismos estándar. Así pues, nuestro principal objetivo es la extensión del Protocolo de Inicio de Sesión (SIP) para soportar la negociación de sesiones multimedia con flujos de vídeo multivista. Nuestro trabajo se puede resumir en tres contribuciones principales. En primer lugar, hemos definido una extensión de señalización para configurar sesiones SIP con vídeo 3D. Esta extensión modifica el Protocolo de Descripción de Sesión (SDP) para introducir un nuevo atributo de nivel de medios, y un nuevo tipo de dependencia de descodificación, que contribuyen a describir los formatos de vídeo 3D que pueden emplearse en una sesión, así como la relación entre los flujos de vídeo que componen un flujo de vídeo 3D. La segunda contribución consiste en una extensión a SIP para manejar la señalización de videoconferencias con flujos de vídeo multivista. Se definen dos nuevos paquetes de eventos SIP para describir las capacidades y topología de los terminales de conferencia, por un lado, y la configuración espacial y mapeo de flujos de una conferencia, por el otro. También se describe un mecanismo para integrar el intercambio de esta información en el proceso de inicio de una conferencia SIP. Como tercera y última contribución, introducimos el concepto de espacio virtual de una conferencia, o un sistema de coordenadas que incluye todos los objetos relevantes de la conferencia (como dispositivos de captura, pantallas, y usuarios). Explicamos cómo el espacio virtual se relaciona con prestaciones de conferencia como el contacto visual, la escala de vídeo y la fidelidad espacial, y proporcionamos reglas para determinar las prestaciones de una conferencia a partir del análisis de su espacio virtual, y para generar espacios virtuales durante la configuración de conferencias.

Las tobas cuaternarias del río Añamaza (provincia de Soria, Cordillera Ibérica): aproximación cronológica

High-gradient, stepped fluvial tufa systems with dammed areas existed in the River Añamaza valley (NW Iberian Ranges, Spain) during Quaternary times. Single deposits range from a few meters to about 70 m thick, in which prograding-aggrading wedges separated by erosional surfaces exist. Several episodes of tufa formation have been distinguished by means of U-series, Amino-acid racemization and radiocarbon techniques. These correlate to MIS 8, 7, 5 and 1. The presence of MIS 9 is uncertain, as chronological data may also correspond to older stages. Most tufas in this area formed in MIS 5. Distinct tufa episodes can also be distinguished in the Holocene. These are the first chronological data presented for one of the northernmost Quaternary tufa systems in the Iberian Ranges.