Evaluation of two alternative carbon capture and storage technologies

4 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

Documento de trabajo

Visibility recovery on images acquired in attenuating media. Application to underwater, fog, and mammographic imaging

136 p.

Natural Variation In Recovery From A Short Period Of Anoxia Due To A cGMP-Dependent Protein Kinase in Drosophila melanogaster

[EN] Protein Kinase G (PKG) or cGMP-dependent protein kinases (PKG) have been shown to play an important role in resistance to abiotic stressors such as high temperatures or oxygen deprivation in Drosophila melanogaster. In Drosophila, the foraging gene encodes a PKG; natural variants for this gene exist, which differ in the level of expression of PKG: rovers (forR allele) which express high PKG levels, and sitters (forS allele) which express lower PKG levels. This project explores the differences in recovery from short periods of anoxia between natural variants (focusing on forS2, flies with a sitter gene in a rover background), as well as mutants with insertions in the foraging gene and RNAi recombinants that show a reduced PKG expression. The parameters measured were time to recovery and level of activity after anoxia. The results showed lower activity after anoxia in sitters than in rovers, reflecting a worse recovery from the anoxic coma in flies with lower PKG levels.