Computing congruences of modular forms and Galois representations modulo prime powers

This article starts a computational study of congruences of modular forms and modular Galoisrepresentations modulo prime powers. Algorithms are described that compute the maximum integermodulo which two monic coprime integral polynomials have a root in common in a sensethat is defined. These techniques are applied to the study of congruences of modular forms andmodular Galois representations modulo prime powers. Finally, some computational results withimplications on the (non-)liftability of modular forms modulo prime powers and possible generalisationsof level raising are presented.

Validation procedures in radiological diagnostic models. Neural network and logistic regression

The objective of this paper is to compare the performance of twopredictive radiological models, logistic regression (LR) and neural network (NN), with five different resampling methods. One hundred and sixty-seven patients with proven calvarial lesions as the only known disease were enrolled. Clinical and CT data were used for LR and NN models. Both models were developed with cross validation, leave-one-out and three different bootstrap algorithms. The final results of each model were compared with error rate and the area under receiver operating characteristic curves (Az). The neural network obtained statistically higher Az than LR with cross validation. The remaining resampling validation methods did not reveal statistically significant differences between LR and NN rules. The neural network classifier performs better than the one based on logistic regression. This advantage is well detected by three-fold cross-validation, but remains unnoticed when leave-one-out or bootstrap algorithms are used.

Parallelizing remote sensing image geometric correction

Remote sensing spatial, spectral, and temporal resolutions of images, acquired over a reasonably sized image extent, result in imagery that can be processed to represent land cover over large areas with an amount of spatial detail that is very attractive for monitoring, management, and scienti c activities. With Moore's Law alive and well, more and more parallelism is introduced into all computing platforms, at all levels of integration and programming to achieve higher performance and energy e ciency. Being the geometric calibration process one of the most time consuming processes when using remote sensing images, the aim of this work is to accelerate this process by taking advantage of new computing architectures and technologies, specially focusing in exploiting computation over shared memory multi-threading hardware. A parallel implementation of the most time consuming process in the remote sensing geometric correction has been implemented using OpenMP directives. This work compares the performance of the original serial binary versus the parallelized implementation, using several multi-threaded modern CPU architectures, discussing about the approach to nd the optimum hardware for a cost-e ective execution.

Estudi de cloud cumputing i desenvolupament d'un gestor de currículums en eyeOS

El Cloud Computing o computació en el núvol és un conjunt de tecnologies que busca tenir tots els nostres arxius i informació a Internet sense dependre de tenir la capacitat suficient per emmagatzemar informació, tan sols d'oferir-nos el servei que volem en el moment que necessitem. En aquest projecte es detalla l'estudi de les diferents solucions cloud a nivell personal necessàries pel nostre dia a dia amb l'objectiu d'obtenir una eina que ens permeti posteriorment desenvolupar una petita aplicació per a gestionar els nostres currículums vitae.

Implantació de SAP a l'empresa BluTronics Technologies, SA

En aquest treball es presenten les característiques dels sistemes ERP i quines són les necessitats que cobreixen, així com els principals ERP que hi ha al mercat, d'entre els quals se'n seleccionarà un per a la implantació en la nostra empresa, explicant els processos i passos necessaris per a aquesta implantació.

Identification of optimal structural connectivity using functional connectivity and neural modeling.

The complex network dynamics that arise from the interaction of the brain's structural and functional architectures give rise to mental function. Theoretical models demonstrate that the structure-function relation is maximal when the global network dynamics operate at a critical point of state transition. In the present work, we used a dynamic mean-field neural model to fit empirical structural connectivity (SC) and functional connectivity (FC) data acquired in humans and macaques and developed a new iterative-fitting algorithm to optimize the SC matrix based on the FC matrix. A dramatic improvement of the fitting of the matrices was obtained with the addition of a small number of anatomical links, particularly cross-hemispheric connections, and reweighting of existing connections. We suggest that the notion of a critical working point, where the structure-function interplay is maximal, may provide a new way to link behavior and cognition, and a new perspective to understand recovery of function in clinical conditions.

Socio-economic benefits of using space technologies to monitor and respond to earthquakes

Earthquakes represent a major hazard for populations around the world, causing frequent loss of life,human suffering and enormous damage to homes, other buildings and infrastructure. The Technology Resources forEarthquake Monitoring and Response (TREMOR) Team of 36 space professionals analysed this problem over thecourse of the International Space University Summer Session Program and published their recommendations in the formof a report. The TREMOR Team proposes a series of space- and ground-based systems to provide improved capabilityto manage earthquakes. The first proposed system is a prototype earthquake early-warning system that improves theexisting knowledge of earthquake precursors and addresses the potential of these phenomena. Thus, the system willat first enable the definitive assessment of whether reliable earthquake early warning is possible through precursormonitoring. Should the answer be affirmative, the system itself would then form the basis of an operational earlywarningsystem. To achieve these goals, the authors propose a multi-variable approach in which the system will combine,integrate and process precursor data from space- and ground-based seismic monitoring systems (already existing andnew proposed systems) and data from a variety of related sources (e.g. historical databases, space weather data, faultmaps). The second proposed system, the prototype earthquake simulation and response system, coordinates the maincomponents of the response phase to reduce the time delays of response operations, increase the level of precisionin the data collected, facilitate communication amongst teams, enhance rescue and aid capabilities and so forth. It isbased in part on an earthquake simulator that will provide pre-event (if early warning is proven feasible) and post-eventdamage assessment and detailed data of the affected areas to corresponding disaster management actors by means of ageographic information system (GIS) interface. This is coupled with proposed mobile satellite communication hubs toprovide links between response teams. Business- and policy-based implementation strategies for these proposals, suchas the establishment of a non-governmental organisation to develop and operate the systems, are included.

The role of matrix metalloproteinase mmp-9 in peripheral neural system regeneration

Multiple lines of evidence show that matrix metalloproteinases (MMPs) are involved in the peripheral neural system degenerative and regenerative processes. MMP-9 was suggested in particular to play a role in the peripheral nerve after injury or during Wallerian degeneration. Interestingly, our previous analysis of Lpin1 mutant mice (which present morphological signs of active demyelination and acute inflammatory cell migration, similar to processes present in the PNS undergoing Wallerian degeneration) revealed an accumulation of MMP-9 in the endoneurium of affected animals. We therefore generated a mouse line lacking both the Lpin1 and the MMP-9 genes in order to determine if MMP-9 plays a role in either inhibition or potentiation of the demyelinating phenotype present in Lpin1 knockout mice. The inactivation of MMP-9 alone did not lead to defects in PNS structure or function. Interestingly we observed that the double mutant animals showed reduced nerve conduction velocity, lower myelin protein mRNA expressions, and had more histological abnormalities as compared to the Lpin1 single mutants. In addition, based on immunohistochemical analysis and macrophage markers mRNA expression, we found a lower macrophage content in the sciatic nerve of the double mutant animals. Together our data indicate that MMP-9 plays a role in macrophage recruitment during postinjury PNS regeneration processes and suggest that slower macrophage infiltration delays regenerative processes in PNS.

Incremental neural networks for function approximation

A new strategy for incremental building of multilayer feedforward neural networks is proposed in the context of approximation of functions from R-p to R-q using noisy data. A stopping criterion based on the properties of the noise is also proposed. Experimental results for both artificial and real data are performed and two alternatives of the proposed construction strategy are compared.

Desenvolvimento e análise de uma rede neural artificial para estimativa da erosividade da chuva para o Estado de São Paulo

O conhecimento do valor da erosividade da chuva (R) de determinada localidade é fundamental para a estimativa das perdas de solo feitas a partir da Equação Universal de Perdas de Solo, sendo, portanto, de grande importância no planejamento conservacionista. A fim de obter estimativas do valor de R para localidades onde este é desconhecido, desenvolveu-se uma rede neural artificial (RNA) e analisou-se a acurácia desta com o método de interpolação "Inverso de uma Potência da Distância" (ID). Comparando a RNA desenvolvida com o método de interpolação ID, verificou-se que a primeira apresentou menor erro relativo médio na estimativa de R e melhor índice de confiança, classificado como "Ótimo", podendo, portanto, ser utilizada no planejamento de uso, manejo e conservação do solo no Estado de São Paulo.