Assessment of the ensembles regional climate models in the representation of precipitation variability and extremes over Portugal

A new data set of daily gridded observations of precipitation, computed from over 400 stations in Portugal, is used to assess the performance of 12 regional climate models at 25 km resolution, from the ENSEMBLES set, all forced by ERA-40 boundary conditions, for the 1961-2000 period. Standard point error statistics, calculated from grid point and basin aggregated data, and precipitation related climate indices are used to analyze the performance of the different models in representing the main spatial and temporal features of the regional climate, and its extreme events. As a whole, the ENSEMBLES models are found to achieve a good representation of those features, with good spatial correlations with observations. There is a small but relevant negative bias in precipitation, especially in the driest months, leading to systematic errors in related climate indices. The underprediction of precipitation occurs in most percentiles, although this deficiency is partially corrected at the basin level. Interestingly, some of the conclusions concerning the performance of the models are different of what has been found for the contiguous territory of Spain; in particular, ENSEMBLES models appear too dry over Portugal and too wet over Spain. Finally, models behave quite differently in the simulation of some important aspects of local climate, from the mean climatology to high precipitation regimes in localized mountain ranges and in the subsequent drier regions.

Modelo de data mining para detecção de tumores em exames de rastreio

Dissertação para obtenção do grau de Mestre em Engenharia Informática

Intérêts du travail isolé du pouce dans le jeu de la harpe: rapport de stage

Résumé I (Pratiques Pédagogiques)- Ce compte-rendu du stage réalisé pour ma deuxième année de master rapporte le résultat de l’observation des cours donnés à trois élèves de niveaux différents par le professeur de harpe de l’Ecole de Musique « Nossa Senhora do Cabo » à Linda-a-Velha, commune de Oeiras, située près de Lisbonne. Grâce à l’activité du professeur, et par le suivi de l’évolution de ses élèves tout au long de l’année scolaire 2012-2013, tant sur le plan technique que sur le plan musical, j’ai pu participer à toutes les étapes de leur apprentissage et retrouver quelques principes pédagogiques fondamentaux. Ainsi, j’ai constaté la nécessité d’une organisation didactique solide dans la définition d’objectifs, la planification du travail, le choix des méthodes d’étude, mais souple par la régulation des rythmes d’apprentissage et des techniques d’acquisition. La métacognition est aussi une notion composante essentielle de la pratique du professeur, dont un des grands objectifs est de développer chez ses élèves la capacité de se prendre en charge seul. J’ai également apprécié l’importance de l’aspect relationnel intrinsèque à toute situation d’apprentissage, ainsi que celle de la connaissance des théories de la motivation, atout important permettant d’agir au niveau psychologique sur les élèves et d’obtenir à plus ou moins long terme des changements comportementaux influents sur la qualité de ces apprentissages. J’ai enfin essayé de dégager différents types d’approches pédagogiques possibles, parmi les stratégies observées chez le professeur, ainsi que d’après quelques éléments de réflexion personnelle.

Concepções dos professores sobre a disciplina de interpretação do curso profissional de artes do espetáculo

Dissertação apresentada à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Educação Artística na especialização de Teatro na Educação

A MAP approach to evidence accumulation clustering

The Evidence Accumulation Clustering (EAC) paradigm is a clustering ensemble method which derives a consensus partition from a collection of base clusterings obtained using different algorithms. It collects from the partitions in the ensemble a set of pairwise observations about the co-occurrence of objects in a same cluster and it uses these co-occurrence statistics to derive a similarity matrix, referred to as co-association matrix. The Probabilistic Evidence Accumulation for Clustering Ensembles (PEACE) algorithm is a principled approach for the extraction of a consensus clustering from the observations encoded in the co-association matrix based on a probabilistic model for the co-association matrix parameterized by the unknown assignments of objects to clusters. In this paper we extend the PEACE algorithm by deriving a consensus solution according to a MAP approach with Dirichlet priors defined for the unknown probabilistic cluster assignments. In particular, we study the positive regularization effect of Dirichlet priors on the final consensus solution with both synthetic and real benchmark data.