Localization in tame and wild coalgebras

We apply the theory of localization for tame and wild coalgebras in order to prove the following theorem: "Let Q be an acyclic quiver. Then any tame admissible subcoalgebra of KQ is the path coalgebra of a quiver with relations".Comment: 23 pages, to appear in Journal of Pure and Applied Algebr

Cross-Country Income Mobility Comparisons Under Panel Attrition: The Relevance of Weighting Schemes

This paper aims to present an assessment of the effects of panel attrition on income mobility comparisons for some EU-countries by using the European Community Household Panel (ECHP). There are different possibilities of correcting the attrition problem by means of alternative longitudinal weighting schemes. The sensitivity of mobility estimates to these attrition correction procedures is tested in the paper. Our results show that ECHP attrition is characterised by a certain degree of selectivity but only affecting some variables and countries. Different probability models corroborate the existence of a certain non-random attrition. The model chosen to construct the longitudinal weights to correct attrition offers up rather different results than those obtained when Eurostat’s longitudinal weights are used. Although attrition does not seem to have a great effect on aggregated mobility indicators, it does have a decisive effect on decomposition exercises. Finally, the tests conducted on income mobility indicators reveal a certain sensitivity to the weighting system used.Income mobility, attrition, European Community Household Panel.

Feature selection for microarray gene expression data using simulated annealing guided by the multivariate joint entropy

In this work a new way to calculate the multivariate joint entropy is presented. This measure is the basis for a fast information-theoretic based evaluation of gene relevance in a Microarray Gene Expression data context. Its low complexity is based on the reuse of previous computations to calculate current feature relevance. The mu-TAFS algorithm --named as such to differentiate it from previous TAFS algorithms-- implements a simulated annealing technique specially designed for feature subset selection. The algorithm is applied to the maximization of gene subset relevance in several public-domain microarray data sets. The experimental results show a notoriously high classification performance and low size subsets formed by biologically meaningful genes.Postprint (published version