Why are we fat? Discussions on the socioeconomic dimensions and responses to obesity

This paper draws together contributions to a scientific table discussion on obesity at the European Science Open Forum 2008 which took place in Barcelona, Spain. Socioeconomic dimensions of global obesity, including those factors promoting it, those surrounding the social perceptions of obesity and those related to integral public health solutions, are discussed. It argues that although scientific accounts of obesity point to large-scale changes in dietary and physical environments, media representations of obesity, which context public policy, pre-eminently follow individualistic models of explanation. While the debate at the forum brought together a diversity of views, all the contributors agreed that this was a global issue requiring an equally global response. Furthermore, an integrated ecological model of obesity proposes that to be effective, policy will need to address not only human health but also planetary health, and that therefore, public health and environmental policies coincide.

Características antropométricas, condición física y velocidad de lanzamiento en balonmano de élite y amateur

166 p.

Optimal Investment in Energy Efficiency under Uncertainty

41 p.

Optimal Abandonment of Coal-Fired Stations in the EU

36 p.

On the optimal usage of labelled examples in semi-supervised multi-class classification problems

In recent years, the performance of semi-supervised learning has been theoretically investigated. However, most of this theoretical development has focussed on binary classification problems. In this paper, we take it a step further by extending the work of Castelli and Cover [1] [2] to the multi-class paradigm. Particularly, we consider the key problem in semi-supervised learning of classifying an unseen instance x into one of K different classes, using a training dataset sampled from a mixture density distribution and composed of l labelled records and u unlabelled examples. Even under the assumption of identifiability of the mixture and having infinite unlabelled examples, labelled records are needed to determine the K decision regions. Therefore, in this paper, we first investigate the minimum number of labelled examples needed to accomplish that task. Then, we propose an optimal multi-class learning algorithm which is a generalisation of the optimal procedure proposed in the literature for binary problems. Finally, we make use of this generalisation to study the probability of error when the binary class constraint is relaxed.

Optimal Taxation and Indeterminacy in the Uzawa-Lucas Model with Sector-specific Externalities

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