INTERACTOR: Natural Interaction Platform

Interactor is an Interaction Platform based on Natural Interaction (human-like) techniques developped by the Laboratorio de Bases de Datos Avanzadas (Labda) at Universidad Carlos III de Madrid. It enables to implement with little effort a corpus-based Task Oriented Interaction Domain for bipartite dialogues. Thus it assumes an application and provides access to it through Natural Interaction.Program: Mejorando el Acceso y la Visibilidad de la Información Multilingüe en Red para la Comunidad de Madrid Acronym: MAVIR Ref: TIC-0267 Coordinator: M. Felisa Verdejo (UNED

The ozone flame

The Ozone flame structure is analyzed from the point of view of asymptotic methods, using the fact that the ratios of the characteristic times associated with the different reactions, which change with the flame temperature and therefore with the initial Ozone concentration, take values very large or very small compared with unity. A two-zone structure is found for the flame, with simplified forms of the reaction mechanism for each of the zones

See5 Algorithm versus Discriminant Analysis. An Application to the Prediction of Insolvency in Spanish Non-life Insurance Companies

Prediction of insurance companies insolvency has arised as an important problem in the field of financial research, due to the necessity of protecting the general public whilst minimizing the costs associated to this problem. Most methods applied in the past to tackle this question are traditional statistical techniques which use financial ratios as explicative variables. However, these variables do not usually satisfy statistical assumptions, what complicates the application of the mentioned methods.In this paper, a comparative study of the performance of a well-known parametric statistical technique (Linear Discriminant Analysis) and a non-parametric machine learning technique (See5) is carried out. We have applied the two methods to the problem of the prediction of insolvency of Spanish non-life insurance companies upon the basis of a set of financial ratios. Results indicate a higher performance of the machine learning technique, what shows that this method can be a useful tool to evaluate insolvency of insurance firms.Insolvency, Insurance Companies, Discriminant Analysis, See5.

Variational description of Gibbs-non-Gibbs dynamical transitions for spin-flip systems with a Kac-type interaction

We continue our study of Gibbs-non-Gibbs dynamical transitions. In the present paper we consider a system of Ising spins on a large discrete torus with a Kac-type interaction subject to an independent spin-flip dynamics (infinite-temperature Glauber dynamics). We show that, in accordance with the program outlined in \cite{vEFedHoRe10}, in the thermodynamic limit Gibbs-non-Gibbs dynamical transitions are \emph{equivalent} to bifurcations in the set of global minima of the large-deviation rate function for the trajectories of the empirical density \emph{conditional} on their endpoint. More precisely, the time-evolved measure is non-Gibbs if and only if this set is not a singleton for \emph{some} value of the endpoint. A partial description of the possible scenarios of bifurcation is given, leading to a characterization of passages from Gibbs to non-Gibbs and vice versa, with sharp transition times. Our analysis provides a conceptual step-up from our earlier work on Gibbs-non-Gibbs dynamical transitions for the Curie-Weiss model, where the mean-field interaction allowed us to focus on trajectories of the empirical magnetization rather than the empirical density.Comment: Key words and phrases: Curie-Weiss model, Kac model, spin-flip dynamics, Gibbs versus non-Gibbs, dynamical transition, large deviation principles, action integral, bifurcation of rate functio