The Development of an Immersive Virtual Reality Environment for Manufacturing and Improving Organizational Learning

This paper examines the applicability of an immersive virtual reality (VR) system to the process of organizational learning in a manufacturing context. The work focuses on the extent to which realism has to be represented in a simulated product build scenario in order to give the user an effective learning experience for an assembly task. Current technologies allow the visualization and manipulation of objects in VR systems but physical behaviors such as contact between objects and the effects of gravity are not commonly represented in off the shelf simulation solutions and the computational power required to facilitate these functions remains a challenge. This work demonstrates how physical behaviors can be coded and represented through the development of more effective mechanisms for the computer aided design (CAD) and VR interface.

Learning from time series:Supervised Aggregative Feature Extraction

Many modeling problems require to estimate a scalar output from one or more time series. Such problems are usually tackled by extracting a fixed number of features from the time series (like their statistical moments), with a consequent loss in information that leads to suboptimal predictive models. Moreover, feature extraction techniques usually make assumptions that are not met by real world settings (e.g. uniformly sampled time series of constant length), and fail to deliver a thorough methodology to deal with noisy data. In this paper a methodology based on functional learning is proposed to overcome the aforementioned problems; the proposed Supervised Aggregative Feature Extraction (SAFE) approach allows to derive continuous, smooth estimates of time series data (yielding aggregate local information), while simultaneously estimating a continuous shape function yielding optimal predictions. The SAFE paradigm enjoys several properties like closed form solution, incorporation of first and second order derivative information into the regressor matrix, interpretability of the generated functional predictor and the possibility to exploit Reproducing Kernel Hilbert Spaces setting to yield nonlinear predictive models. Simulation studies are provided to highlight the strengths of the new methodology w.r.t. standard unsupervised feature selection approaches. © 2012 IEEE.

Toward Characterization Of The Type IIP Supernova Progenitor Population: A Statistical Sample Of Light Curves From Pan-STARRS1

In recent years, wide-field sky surveys providing deep multi-band imaging have presented a new path for indirectly characterizing the progenitor populations of core-collapse supernovae (SN): systematic light curve studies. We assemble a set of 76 grizy-band Type IIP SN light curves from Pan-STARRS1, obtained over a constant survey program of 4 years and classified using both spectroscopy and machine learning-based photometric techniques. We develop and apply a new Bayesian model for the full multi-band evolution of each light curve in the sample. We find no evidence of a sub-population of fast-declining explosions (historically referred to as "Type IIL" SNe). However, we identify a highly significant relation between the plateau phase decay rate and peak luminosity among our SNe IIP. These results argue in favor of a single parameter, likely determined by initial stellar mass, predominantly controlling the explosions of red supergiants. This relation could also be applied for supernova cosmology, offering a standardizable candle good to an intrinsic scatter of 0.2 mag. We compare each light curve to physical models from hydrodynamic simulations to estimate progenitor initial masses and other properties of the Pan-STARRS1 Type IIP SN sample. We show that correction of systematic discrepancies between modeled and observed SN IIP light curve properties and an expanded grid of progenitor properties, are needed to enable robust progenitor inferences from multi-band light curve samples of this kind. This work will serve as a pathfinder for photometric studies of core-collapse SNe to be conducted through future wide field transient searches.

Machine learning for transient discovery in Pan-STARRS1 difference imaging

Efficient identification and follow-up of astronomical transients is hindered by the need for humans to manually select promising candidates from data streams that contain many false positives. These artefacts arise in the difference images that are produced by most major ground-based time-domain surveys with large format CCD cameras. This dependence on humans to reject bogus detections is unsustainable for next generation all-sky surveys and significant effort is now being invested to solve the problem computationally. In this paper, we explore a simple machine learning approach to real-bogus classification by constructing a training set from the image data of similar to 32 000 real astrophysical transients and bogus detections from the Pan-STARRS1 Medium Deep Survey. We derive our feature representation from the pixel intensity values of a 20 x 20 pixel stamp around the centre of the candidates. This differs from previous work in that it works directly on the pixels rather than catalogued domain knowledge for feature design or selection. Three machine learning algorithms are trained (artificial neural networks, support vector machines and random forests) and their performances are tested on a held-out subset of 25 per cent of the training data. We find the best results from the random forest classifier and demonstrate that by accepting a false positive rate of 1 per cent, the classifier initially suggests a missed detection rate of around 10 per cent. However, we also find that a combination of bright star variability, nuclear transients and uncertainty in human labelling means that our best estimate of the missed detection rate is approximately 6 per cent.

Statistical Tests for Joint Analysis of Performance Measures

Recently there has been an increasing interest in the development of new methods using Pareto optimality to deal with multi-objective criteria (for example, accuracy and architectural complexity). Once one has learned a model based on their devised method, the problem is then how to compare it with the state of art. In machine learning, algorithms are typically evaluated by comparing their performance on different data sets by means of statistical tests. Unfortunately, the standard tests used for this purpose are not able to jointly consider performance measures. The aim of this paper is to resolve this issue by developing statistical procedures that are able to account for multiple competing measures at the same time. In particular, we develop two tests: a frequentist procedure based on the generalized likelihood-ratio test and a Bayesian procedure based on a multinomial-Dirichlet conjugate model. We further extend them by discovering conditional independences among measures to reduce the number of parameter of such models, as usually the number of studied cases is very reduced in such comparisons. Real data from a comparison among general purpose classifiers is used to show a practical application of our tests.

A aprendizagem musical como elemento de aperfeiçoamento de competências matemáticas

Tomando como ponto de partida a relação entre música e matemática, nesta investigação temos como principal objetivo estudar a influência da aprendizagem musical no desempenho matemático. Pretendeu-se ainda observar o efeito de um conjunto de preditores no referido desempenho, mais concretamente do nível socioeconómico, da inteligência e de variáveis cognitivo-motivacionais (motivação, expectativas de autoeficácia e atribuições causais). Numa primeira parte, delineámos as linhas teóricas desta investigação. Começámos por relatar a relação entre música e matemática no âmbito da musicologia histórica, da teoria e análise musicais, da acústica e das tendências na composição musical, evidenciando os mecanismos de ligação entre elementos e conceitos musicais e tópicos e temas matemáticos. Relatámos os benefícios da exposição musical ao nível do desenvolvimento cognitivo e intelectual, destacando o aumento do raciocínio espacial, do desempenho matemático e da inteligência com a aprendizagem musical. De seguida, descrevemos o impacto das aulas de música no aumento do desempenho académico a várias disciplinas, nomeadamente a Matemática, enfatizando a associação da duração da aprendizagem musical com o aumento das capacidades matemáticas; para além do efeito da aprendizagem musical, procurámos ainda explicação de um desempenho académico melhorado com base em variáveis potenciadoras da performance, tais como o nível socioeconómico e a inteligência. Nesta linha de abordagem, explorámos os efeitos de variáveis influentes do desempenho académico fora do contexto musical, reportando-nos ao nível socioeconómico, à inteligência e às dimensões cognitivo-motivacionais (motivação, expectativas de autoeficácia e atribuições causais), destacando o poder preditivo da inteligência, seguido do nível socioeconómico e da motivação. Por fim, referimo-nos à interação entre música e encéfalo por meio das temáticas da plasticidade neural estrutural e funcional, do efeito da aprendizagem e performance musicais, da cognição musical e domínios não musicais, bem como dos fatores genéticos; sublinhamos a possibilidade de ligações entre a cognição musical e os domínios espacial e matemático. Numa segunda parte, apresentamos a investigação que desenvolvemos em contexto escolar com 112 alunos do 7º ano de escolaridade provenientes de 12 escolas do Ensino Básico. Nove são do Ensino Especializado de Música e três são do Ensino Regular. No total, as escolas enquadram-se nas zonas urbanas de Braga, Coimbra e Lisboa. O estudo possui carácter longitudinal e abrange três anos letivos, do 7º ao 9º anos de escolaridade. Após explanação dos objetivos, das hipóteses de investigação, da caracterização da amostra, da descrição dos instrumentos de avaliação e respetiva validação empírica, relatamos os resultados que encontrámos. Estes permitiram, por um lado, validar a hipótese de que os alunos submetidos ao ensino formal de música apresentam um desempenho matemático superior comparativamente aos alunos que não frequentaram este tipo de ensino (H1) e, por outro, sustentar que o número de anos de aprendizagem musical contribui para o aumento do desempenho matemático (H3). Sublinha-se, ainda, que os alunos de instrumento de teclado revelaram desempenho matemático mais elevado em relação aos seus pares que estudaram outros instrumentos. Já no que se refere ao poder preditivo do tipo de ensino (Ensino Especializado de Música vs. Ensino Regular), apurámos que a formação em música prevê melhores desempenhos a matemática; destaca-se que as variáveis em estudo, tais como o nível socioeconómico, a motivação, as expectativas de autoeficácia e a inteligência adicionam capacidade explicativa do desempenho matemático, sendo que a presença da aprendizagem musical perdeu aptidão preditiva apenas na presença da inteligência. Contudo, após controlo estatístico da inteligência, foi possível concluir que a aprendizagem musical mantém o poder preditivo no desempenho matemático (H2). Os resultados permitiram identificar em que tópicos e temas matemáticos relacionados com os elementos e conceitos musicais os alunos com aprendizagem musical apresentam melhores desempenhos, evidenciando-se os tópicos no âmbito da Geometria (H4). Observámos, também, que é possível prever o desempenho matemático a partir do raciocínio espacial dos alunos (H5). Finalmente, referimos as limitações, refletimos sobre as implicações que estes resultados poderão trazer no âmbito do ensino da música em Portugal e apontamos pistas conducentes ao desenvolvimento de investigações futuras.

Community response in disasters: an ecological learning framework

Natural disasters are frequently exacerbated by anthropogenic mechanisms and have social and political consequences for communities. The role of community learning in disasters is seen to be increasingly important. However, the ways in which such learning unfolds in a disaster can differ substantially from case to case. This article uses a comparative case study methodology to examine catastrophes and major disasters from five countries (Japan, New Zealand, UK, US and Germany) to consider how community learning and adaptation occurs. An ecological model of learning is considered, where community learning is of small loop (adaptive, incremental, experimental) type or large loop (paradigm changing) type. Using this model we consider that there are three types of community learning that occur in disasters (navigation, organisation, reframing). The type of community learning that actually develops in a disaster depends upon a range of social factors such as stress and trauma, civic innovation and coercion.

Adoção de uma plataforma de e-learning no ensino superior : o caso da Faculdade de Farmácia da Universidade de Lisboa

Trabalho de projeto de mestrado, Tecnologias e Metodologias em E-learning, Universidade de Lisboa, Instituto de Educação, Faculdade de Ciências, 2013

Desenvolvimento de um ambiente virtual de aprendizagem em regime de b-learning num curso de educação e formação de operadores de informática

Trabalho de projeto de mestrado, Educação (Área de Especialização em Educação e Tecnologias Digitais), Universidade de Lisboa, Instituto de Educação, 2014

Evolution of the rbcS gene family in Solanaceae: concerted evolution and gain and loss of introns, with a description of new statistical guidelines for determining the number of unique gene copies.

Thesis (Ph.D.)--University of Washington, 2014

Deciphering the mechanisms underlying the loss of BDNF neuroprotection in an Alzheimer’s disease model

Tese de mestrado, Neurociências, Faculdade de Medicina, Universidade de Lisboa, 2016

Application of Machine Learning Techniques to Acute Myeloid Leukemia

Thesis (Master's)--University of Washington, 2016-03

Gene Network Inference using Machine Learning and Graph Algorithms on Big Biomedical Data

Thesis (Master's)--University of Washington, 2016-03

Learning business strategies for competitive electronic marketplaces

This paper presents a Multi-Agent Market simulator designed for developing new agent market strategies based on a complete understanding of buyer and seller behaviors, preference models and pricing algorithms, considering user risk preferences and game theory for scenario analysis. This tool studies negotiations based on different market mechanisms and, time and behavior dependent strategies. The results of the negotiations between agents are analyzed by data mining algorithms in order to extract rules that give agents feedback to improve their strategies. The system also includes agents that are capable of improving their performance with their own experience, by adapting to the market conditions, and capable of considering other agent reactions.