Forecasting Global Temperature Variations by Neural Networks

Global temperature variations between 1861 and 1984 are forecast usingsregularization networks, multilayer perceptrons and linearsautoregression. The regularization network, optimized by stochasticsgradient descent associated with colored noise, gives the bestsforecasts. For all the models, prediction errors noticeably increasesafter 1965. These results are consistent with the hypothesis that thesclimate dynamics is characterized by low-dimensional chaos and thatsthe it may have changed at some point after 1965, which is alsosconsistent with the recent idea of climate change.s

Biologically Plausible Neural Circuits for Realization of Maximum Operations

Object recognition in the visual cortex is based on a hierarchical architecture, in which specialized brain regions along the ventral pathway extract object features of increasing levels of complexity, accompanied by greater invariance in stimulus size, position, and orientation. Recent theoretical studies postulate a non-linear pooling function, such as the maximum (MAX) operation could be fundamental in achieving such invariance. In this paper, we are concerned with neurally plausible mechanisms that may be involved in realizing the MAX operation. Four canonical circuits are proposed, each based on neural mechanisms that have been previously discussed in the context of cortical processing. Through simulations and mathematical analysis, we examine the relative performance and robustness of these mechanisms. We derive experimentally verifiable predictions for each circuit and discuss their respective physiological considerations.

Biologically Plausible Neural Model for the Recognition of Biological Motion and Actions

The visual recognition of complex movements and actions is crucial for communication and survival in many species. Remarkable sensitivity and robustness of biological motion perception have been demonstrated in psychophysical experiments. In recent years, neurons and cortical areas involved in action recognition have been identified in neurophysiological and imaging studies. However, the detailed neural mechanisms that underlie the recognition of such complex movement patterns remain largely unknown. This paper reviews the experimental results and summarizes them in terms of a biologically plausible neural model. The model is based on the key assumption that action recognition is based on learned prototypical patterns and exploits information from the ventral and the dorsal pathway. The model makes specific predictions that motivate new experiments.

Exact Solution of the Nonlinear Dynamics of Recurrent Neural Mechanisms for Direction Selectivity

Different theoretical models have tried to investigate the feasibility of recurrent neural mechanisms for achieving direction selectivity in the visual cortex. The mathematical analysis of such models has been restricted so far to the case of purely linear networks. We present an exact analytical solution of the nonlinear dynamics of a class of direction selective recurrent neural models with threshold nonlinearity. Our mathematical analysis shows that such networks have form-stable stimulus-locked traveling pulse solutions that are appropriate for modeling the responses of direction selective cortical neurons. Our analysis shows also that the stability of such solutions can break down giving raise to a different class of solutions ("lurching activity waves") that are characterized by a specific spatio-temporal periodicity. These solutions cannot arise in models for direction selectivity with purely linear spatio-temporal filtering.

Nonpoint source pollution, space, time and asymetric information - a deposit refund approach

The incorporation of space allows the establishment of a more precise relationship between a contaminating input, a contaminating byproduct and emissions that reach the final receptor. However, the presence of asymmetric information impedes the implementation of the first-best policy. As a solution to this problem a site specific deposit refund system for the contaminating input and the contaminating byproduct are proposed. Moreover, the utilization of a successive optimization technique first over space and second over time enables definition of the optimal intertemporal site specific deposit refund system

Room-temperature continuous-wave operation of GaInNAs/GaAs quantum dot laser with GaAsN barrier grown by solid source molecular beam epitaxy

We present the results of GaInNAs/GaAs quantum dot structures with GaAsN barrier layers grown by solid source molecular beam epitaxy. Extension of the emission wavelength of GaInNAs quantum dots by ~170nm was observed in samples with GaAsN barriers in place of GaAs. However, optimization of the GaAsN barrier layer thickness is necessary to avoid degradation in luminescence intensity and structural property of the GaInNAs dots. Lasers with GaInNAs quantum dots as active layer were fabricated and room-temperature continuous-wave lasing was observed for the first time. Lasing occurs via the ground state at ~1.2μm, with threshold current density of 2.1kA/cm[superscript 2] and maximum output power of 16mW. These results are significantly better than previously reported values for this quantum-dot system.

Un Modelo para evaluar el futuro de Proyectos Open Source

Presentación de CENATI, Centro Nacional de Referencia de Aplicación de las TIC basadas en fuentes abiertas

Introducción a los conceptos y a la utilización del SIG Open-Source GRASS

In this presentation, we would like to focus on GRASS GIS (version 5.x and latest 6.x), one of the oldest GIS developped by the Open-Source Community. Our goal is trying to make it accessible to any GIS user by explaining its main concepts

Un Servidor de mapas open source para la gestión sanitaria: atlas electrónico de salud de Catalunya

La salud catalana está basada en una extensa red de cientos de centros especializados en el territorio y en unas zonificaciones sanitarias para ordenar su gestión. El proyecto ha creado un servidor de mapas basado en Minnessota Map Server para visualizar la dimensión geográfica sanitaria, sus actuaciones y recursos: el Atlas Electrónico de Salud

Transition between metadata models: approach based on the use open source software

This article shows the work developed for adapting metadata conform to the official Colombian metadata standard NTC 4611 to the international standard ISO 19115. CatMDedit, an open source metadata editor, is used in this task. CatMDedit is able of import variants of CSDGM such as NTC 4611 and export to the stable version of ISO 19139 (the XML implementation model of ISO 19115)

CatalogConnector Open Source. Un cliente CSW para conectar catálogos de metadatos

Este artículo refleja los problemas de interoperabilidad que existen entre las diferentes implementaciones de catálogos de metadatos que contemplan la especificación CSW [1] (Catalog Service for the Web) de OGC (Open GeoSpatial Consortium ) . Esta situación ha llevado al desarrollo de una aplicación cliente para poder lanzar peticiones simultáneas a diferentes catálogos de metadatos, con la intención de poder visualizar los resultados de forma unitaria. En el artículo se detalla tanto la arquitectura como todo proceso de desarrollo de la aplicación

Sistema multimodal para la planificación de viajes en transportes públicos de código abierto y basado en estándares 'de facto' = A multimodal journey planner based on open source software and 'de facto' standards

Este artículo muestra cómo con bajo coste y riesgo se puede desarrollar un sistema de planificación de viaje multimodal, basado en un enfoque de código abierto y estándares ‘de facto’. Se ha desarrollado completamente una solución de código abierto para un sistema de información de transporte público puerta a puerta basado en estándares ‘de facto’. El cálculo de rutas se realiza mediante Graphserver, mientras que la cartografía se basa en OpenStreetMap. También se ha demostrado cómo exportar una base de datos real de horarios de transporte público como la del operador ETM (Empresa de Transporte Metropolitano de València) a la especificación de Google Transit, para permitir el cálculo de rutas, tanto desde nuestro prototipo como desde Google Transit

Efficient learning of reactive robot behaviors with a Neural-Q_learning approach

The purpose of this paper is to propose a Neural-Q_learning approach designed for online learning of simple and reactive robot behaviors. In this approach, the Q_function is generalized by a multi-layer neural network allowing the use of continuous states and actions. The algorithm uses a database of the most recent learning samples to accelerate and guarantee the convergence. Each Neural-Q_learning function represents an independent, reactive and adaptive behavior which maps sensorial states to robot control actions. A group of these behaviors constitutes a reactive control scheme designed to fulfill simple missions. The paper centers on the description of the Neural-Q_learning based behaviors showing their performance with an underwater robot in a target following task. Real experiments demonstrate the convergence and stability of the learning system, pointing out its suitability for online robot learning. Advantages and limitations are discussed

Semi-online neural-Q_leaming for real-time robot learning

Reinforcement learning (RL) is a very suitable technique for robot learning, as it can learn in unknown environments and in real-time computation. The main difficulties in adapting classic RL algorithms to robotic systems are the generalization problem and the correct observation of the Markovian state. This paper attempts to solve the generalization problem by proposing the semi-online neural-Q_learning algorithm (SONQL). The algorithm uses the classic Q_learning technique with two modifications. First, a neural network (NN) approximates the Q_function allowing the use of continuous states and actions. Second, a database of the most representative learning samples accelerates and stabilizes the convergence. The term semi-online is referred to the fact that the algorithm uses the current but also past learning samples. However, the algorithm is able to learn in real-time while the robot is interacting with the environment. The paper shows simulated results with the "mountain-car" benchmark and, also, real results with an underwater robot in a target following behavior

Two methods for voltage sag source location

This paper presents and compares two approaches to estimate the origin (upstream or downstream) of voltage sag registered in distribution substations. The first approach is based on the application of a single rule dealing with features extracted from the impedances during the fault whereas the second method exploit the variability of waveforms from an statistical point of view. Both approaches have been tested with voltage sags registered in distribution substations and advantages, drawbacks and comparative results are presented