Fredson Silva de Souza Aguiar

Many applications have shown the use and success of classical compartment models in estimating the states and evolution of disease spreads. Similarly, the study of Optimal Control Theory has shown positive results in those scenarios but, sometimes, lack computational and theoretical tractability, specially in the cases of long-term problems or uncertain scenarios. In that context, Model Predictive Control (MPC) has become apparent, as it provides a more tractable version of a (Stochastic) Optimal Control Problem by approximating the solution through a simplified version of the control problem over a shorter horizon. Nevertheless, the current state or uncertainty can affect the local optimal solution, what provides niche to the introduction of learning methods in the process. We show how this kind of approach could be a reliable option for epidemic disease mitigation. Future works include the discussion of a more complete set of compartments, for vaccine effectiveness estimation, and robust control under uncertain vaccination availability.

The maintenance of wordnets and lexical knwoledge bases typically relies on time-consuming manual effort. In order to minimise this issue, we propose the exploitation of models of distributional semantics, namely word embeddings learned from corpora, in the automatic identification of relation instances missing in a wordnet. Analogy-solving methods are first used for learning a set of relations from analogy tests focused on each relation. Despite their low accuracy, we noted that a portion of the top-given answers are good suggestions of relation instances that could be included in the wordnet. This procedure is applied to the enrichment of OpenWordNet-PT, a public Portuguese wordnet. Relations are learned from data acquired from this resource, and illustrative examples are provided. Results are promising for accelerating the identification of missing relation instances, as we estimate that about 17% of the potential suggestions are good, a proportion that almost doubles if some are automatically invalidated.