Theory of ferromagnetism in planar heterostructures of (Mn,III)-V semiconductors

A density-functional theory of ferromagnetism in heterostructures of compound semiconductors doped with magnetic impurities is presented. The variable functions in the density-functional theory are the charge and spin densities of the itinerant carriers and the charge and localized spins of the impurities. The theory is applied to study the Curie temperature of planar heterostructures of III-V semiconductors doped with manganese atoms. The mean-field, virtual-crystal and effective-mass approximations are adopted to calculate the electronic structure, including the spin-orbit interaction, and the magnetic susceptibilities, leading to the Curie temperature. By means of these results, we attempt to understand the observed dependence of the Curie temperature of planar δ-doped ferromagnetic structures on variation of their properties. We predict a large increase of the Curie temperature by additional confinement of the holes in a δ-doped layer of Mn by a quantum well.

Algorithm for the detection of outliers based on the theory of rough sets

Outliers are objects that show abnormal behavior with respect to their context or that have unexpected values in some of their parameters. In decision-making processes, information quality is of the utmost importance. In specific applications, an outlying data element may represent an important deviation in a production process or a damaged sensor. Therefore, the ability to detect these elements could make the difference between making a correct and an incorrect decision. This task is complicated by the large sizes of typical databases. Due to their importance in search processes in large volumes of data, researchers pay special attention to the development of efficient outlier detection techniques. This article presents a computationally efficient algorithm for the detection of outliers in large volumes of information. This proposal is based on an extension of the mathematical framework upon which the basic theory of detection of outliers, founded on Rough Set Theory, has been constructed. From this starting point, current problems are analyzed; a detection method is proposed, along with a computational algorithm that allows the performance of outlier detection tasks with an almost-linear complexity. To illustrate its viability, the results of the application of the outlier-detection algorithm to the concrete example of a large database are presented.