User-friendly parallel computations with econometric examples

This paper shows how a high level matrix programming language may be used to perform Monte Carlo simulation, bootstrapping, estimation by maximum likelihood and GMM, and kernel regression in parallel on symmetric multiprocessor computers or clusters of workstations. The implementation of parallelization is done in a way such that an investigator may use the programs without any knowledge of parallel programming. A bootable CD that allows rapid creation of a cluster for parallel computing is introduced. Examples show that parallelization can lead to important reductions in computational time. Detailed discussion of how the Monte Carlo problem was parallelized is included as an example for learning to write parallel programs for Octave.

ParallelKnoppix - Rapid deployment of a linux cluster for MPI parallel processing using non-dedicated computers

This note describes ParallelKnoppix, a bootable CD that allows creation of a Linux cluster in very little time. An experienced user can create a cluster ready to execute MPI programs in less than 10 minutes. The computers used may be heterogeneous machines, of the IA-32 architecture. When the cluster is shut down, all machines except one are in their original state, and the last can be returned to its original state by deleting a directory. The system thus provides a means of using non-dedicated computers to create a cluster. An example session is documented.

Can genetic algorithms explain experimental anomalies? An application to common property resources

It is common to find in experimental data persistent oscillations in the aggregate outcomes and high levels of heterogeneity in individual behavior. Furthermore, it is not unusual to find significant deviations from aggregate Nash equilibrium predictions. In this paper, we employ an evolutionary model with boundedly rational agents to explain these findings. We use data from common property resource experiments (Casari and Plott, 2003). Instead of positing individual-specific utility functions, we model decision makers as selfish and identical. Agent interaction is simulated using an individual learning genetic algorithm, where agents have constraints in their working memory, a limited ability to maximize, and experiment with new strategies. We show that the model replicates most of the patterns that can be found in common property resource experiments.

Two algorithms to find a power integral basis

"Vegeu el resum a l'inici del fitxer adjunt."

Modelo de pérdida de paquetes para redes híbridas

El objetivo de este proyecto es la predicción de la pérdida de paquete. Para ello necesitaremos el modelado del canal. De esta manera, podremos determinar cuando una transmisión llega con éxito o no. En primer lugar, se han estudiado los algoritmos de adaptación de la tasa. Estos algoritmos mejoran el rendimiento de la comunicación. Por este motivo, el programa de simulación se basa en algunos de estos algoritmos. En paralelo, se han capturado medidas del canal terrestre para realizar el modelado. Finalmente, con un programa mucho más completo se ha simulado el comportamiento de una transmisión con el modelado del canal físico, y se han asumido algunas consideraciones, como las colisiones. Por lo tanto, se ha obtenido un resultado más realista, con el cual se ha analizado teóricamente las posibilidades de un enlace entre el canal terrestre y el canal satélite, para crear una red híbrida.

Getting in touch: segregated somatosensory what and where pathways in humans revealed by electrical neuroimaging.

Whether the somatosensory system, like its visual and auditory counterparts, is comprised of parallel functional pathways for processing identity and spatial attributes (so-called what and where pathways, respectively) has hitherto been studied in humans using neuropsychological and hemodynamic methods. Here, electrical neuroimaging of somatosensory evoked potentials (SEPs) identified the spatio-temporal mechanisms subserving vibrotactile processing during two types of blocks of trials. What blocks varied stimuli in their frequency (22.5 Hz vs. 110 Hz) independently of their location (left vs. right hand). Where blocks varied the same stimuli in their location independently of their frequency. In this way, there was a 2x2 within-subjects factorial design, counterbalancing the hand stimulated (left/right) and trial type (what/where). Responses to physically identical somatosensory stimuli differed within 200 ms post-stimulus onset, which is within the same timeframe we previously identified for audition (De Santis, L., Clarke, S., Murray, M.M., 2007. Automatic and intrinsic auditory "what" and "where" processing in humans revealed by electrical neuroimaging. Cereb Cortex 17, 9-17.). Initially (100-147 ms), responses to each hand were stronger to the what than where condition in a statistically indistinguishable network within the hemisphere contralateral to the stimulated hand, arguing against hemispheric specialization as the principal basis for somatosensory what and where pathways. Later (149-189 ms) responses differed topographically, indicative of the engagement of distinct configurations of brain networks. A common topography described responses to the where condition irrespective of the hand stimulated. By contrast, different topographies accounted for the what condition and also as a function of the hand stimulated. Parallel, functionally specialized pathways are observed across sensory systems and may be indicative of a computationally advantageous organization for processing spatial and identity information.

A Model-to-model analysis of the repeated Prisoners' Dilemma: genetic algorithms vs. evolutionary dynamics

We study the properties of the well known Replicator Dynamics when applied to a finitely repeated version of the Prisoners' Dilemma game. We characterize the behavior of such dynamics under strongly simplifying assumptions (i.e. only 3 strategies are available) and show that the basin of attraction of defection shrinks as the number of repetitions increases. After discussing the difficulties involved in trying to relax the 'strongly simplifying assumptions' above, we approach the same model by means of simulations based on genetic algorithms. The resulting simulations describe a behavior of the system very close to the one predicted by the replicator dynamics without imposing any of the assumptions of the analytical model. Our main conclusion is that analytical and computational models are good complements for research in social sciences. Indeed, while on the one hand computational models are extremely useful to extend the scope of the analysis to complex scenar

Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident.

Fast numerical algorithms for the computation of invariant tori in Hamiltonian systems

In this paper, we develop numerical algorithms that use small requirements of storage and operations for the computation of invariant tori in Hamiltonian systems (exact symplectic maps and Hamiltonian vector fields). The algorithms are based on the parameterization method and follow closely the proof of the KAM theorem given in [LGJV05] and [FLS07]. They essentially consist in solving a functional equation satisfied by the invariant tori by using a Newton method. Using some geometric identities, it is possible to perform a Newton step using little storage and few operations. In this paper we focus on the numerical issues of the algorithms (speed, storage and stability) and we refer to the mentioned papers for the rigorous results. We show how to compute efficiently both maximal invariant tori and whiskered tori, together with the associated invariant stable and unstable manifolds of whiskered tori. Moreover, we present fast algorithms for the iteration of the quasi-periodic cocycles and the computation of the invariant bundles, which is a preliminary step for the computation of invariant whiskered tori. Since quasi-periodic cocycles appear in other contexts, this section may be of independent interest. The numerical methods presented here allow to compute in a unified way primary and secondary invariant KAM tori. Secondary tori are invariant tori which can be contracted to a periodic orbit. We present some preliminary results that ensure that the methods are indeed implementable and fast. We postpone to a future paper optimized implementations and results on the breakdown of invariant tori.

Alineamiento múltiple de secuencias con T-Coffee: una aproximación paralela

Las aplicaciones de alineamiento múltiple de secuencias son prototipos de aplicaciones que requieren elevada potencia de cómputo y memoria. Se destacan por la relevancia científica que tienen los resultados que brindan a investigaciones científicas en el campo de la biomedicina, genética y farmacología. Las aplicaciones de alineamiento múltiple tienen la limitante de que no son capaces de procesar miles de secuencias, por lo que se hace necesario crear un modelo para resolver la problemática. Analizando el volumen de datos que se manipulan en el área de las ciencias biológica y la complejidad de los algoritmos de alineamiento de secuencias, la única vía de solución del problema es a través de la utilización de entornos de cómputo paralelos y la computación de altas prestaciones. La investigación realizada por nosotros tiene como objetivo la creación de un modelo paralelo que le permita a los algoritmos de alineamiento múltiple aumentar el número de secuencias a procesar, tratando de mantener la calidad en los resultados para garantizar la precisión científica. El modelo que proponemos emplea como base la clusterización de las secuencias de entrada utilizando criterios biológicos que permiten mantener la calidad de los resultados. Además, el modelo se enfoca en la disminución del tiempo de cómputo y consumo de memoria. Para presentar y validar el modelo utilizamos T-Coffee, como plataforma de desarrollo e investigación. El modelo propuesto pudiera ser aplicado a cualquier otro algoritmo de alineamiento múltiple de secuencias.

Parallel spinors on pseudo-riemannian spin(q) manifolds

We study simply-connected irreducible non-locally symmetric pseudo-Riemannian Spin(q) manifolds admitting parallel quaternionic spinors.

A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.