Abstract

Abstract

Abstract
This paper seeks to analyse how behavioural factors influence the financial decisions of young Mozambican investors. The standard theory of finance assumes investors make rational financial decisions, seeking to minimise risk and maximise their expected utility. However, several studies have been conducted criticizing the assumption that investors are rational, opening the way to behavioural finance theory. According to the behavioural finance approach, financial decisions made by individuals are not based on rational thinking and their risk taking behaviour depends on their beliefs or feelings. Our analysis reveals that young Mozambicans are risk averse towards certain gains and risk lovers when faced with certain losses; they are excessively optimistic about the future; they use the information available as an anchor for their estimates; and they are so overconfident that they believe estimates in uncertain situations to be more accurate than they really are.

Abstract
It is well known that many social networks follow the homophily principle, dictating that individuals tend to connect with similar peers. Past studies focused on non-topological properties, such as the age, gender, beliefs or educations. In this paper we focus precisely on the topology itself, exploring the possible existence of pairwise role dependency, that is, purely structural homophily. We show that while pairwise dependency is necessary for some structural roles, it may be misleading for others. We also present SR-Diffuse, a novel method for identifying the structural roles of nodes within a network. It is an iterative algorithm following an optimization model able to learn simultaneously from topological features and structural homophily, combining both aspects. For assessing our method, we applied it in a classification problem in information cascades, comparing its performance against several baseline methods. The experimental results with Flickr and Digg data show that SR-Diffuse can improve the quality of the discovered roles and can better represent the profile of the individuals, leading to a better prediction of social classes within information cascades.

Abstract
Obstructive Sleep Apnea (OSA) is a disease that affects approximately 4% of men and 2% of women worldwide but is still underestimated and underdiagnosed. The standard method for assessing this index, and therefore defining the OSA diagnosis, is polysomnography (PSG). Previous work developed relevant Bayesian network models but those were based only on variables univariatedly associated with the outcome, yielding a bias on the possible knowledge representation of the models. The aim of this work was to develop and validate new Bayesian network decision support models that could be used during sleep consult to assess the need for PSG. Bayesian models were developed using a) expert opinion, b) hill-climbing, c) naive Bayes and d) TAN structures. Resulting models validity was assessed with in-sample AUC and stratified cross-validation, also comparing with previously published model. Overall, models achieved good discriminative power (AUC>70%) and validity (measures consistently above 70%). Main conclusions are a) the need to integrate a wider range of variables in the final models and b) the support of using Bayesian networks in the diagnosis of obstructive sleep apnea.

Abstract
This paper addresses the problem of determining the cutting patterns of metal sheets, which arises in a manufacturer of metal cages, in order to minimize the waste, the number of cuts performed, the number of metal sheets used or a weighted combination of the three. A two stage approach, to solve a 2D guillotine cutting stock problem with single and multiple stock sizes, is presented and compared with the company approach and state-of-the-art algorithms. The results show great improvement compared to the company approach and a very good performance compared to state-of-the-art algorithms.