Hyers-Ulam-Rassias Stability of Functional Differential Systems with Point and Distributed Delays

This paper investigates stability and asymptotic properties of the error with respect to its nominal version of a nonlinear time-varying perturbed functional differential system subject to point, finite-distributed, and Volterra-type distributed delays associated with linear dynamics together with a class of nonlinear delayed dynamics. The boundedness of the error and its asymptotic convergence to zero are investigated with the results being obtained based on the Hyers-Ulam-Rassias analysis.

Programa de apoyo psicológico a inmigrantes: estudio de casos

419 p.

Orígenes indoeuropeos (y no indoeuropeos) de la vocal [y] eslava

[ES] La vocal 'y' de las lenguas eslavas procede esencialmente de i.e. *ū. Pero en el dominio de la flexión tanto nominal como verbal se puede encontrar asimismo una vocal 'y' de origen intraeslavo y derivada de diversas secuencias en final de palabra. Al estudio de estas evoluciones así como al análisis de los distintos estratos de préstamos léxicos que incrementan la presencia de la vocal y en el léxico eslavo se dedica buena parte de este trabajo. En él se rastrean, además, los datos comparativos e internos que permiten detectar otro origen para la vocal eslava 'y' los diptongos i.e. *ou y *au.

Towards Application of One-Class Classification Methods to Medical Data

In the problem of one-class classification (OCC) one of the classes, the target class, has to be distinguished from all other possible objects, considered as nontargets. In many biomedical problems this situation arises, for example, in diagnosis, image based tumor recognition or analysis of electrocardiogram data. In this paper an approach to OCC based on a typicality test is experimentally compared with reference state-of-the-art OCC techniques-Gaussian, mixture of Gaussians, naive Parzen, Parzen, and support vector data description-using biomedical data sets. We evaluate the ability of the procedures using twelve experimental data sets with not necessarily continuous data. As there are few benchmark data sets for one-class classification, all data sets considered in the evaluation have multiple classes. Each class in turn is considered as the target class and the units in the other classes are considered as new units to be classified. The results of the comparison show the good performance of the typicality approach, which is available for high dimensional data; it is worth mentioning that it can be used for any kind of data (continuous, discrete, or nominal), whereas state-of-the-art approaches application is not straightforward when nominal variables are present.