With a series of local-density approximation plus Hubbard U calculations, we have demonstrated that for lanthanum cobaltite LaCoO3 , the electric field gradient at the cobalt nucleus can be used as a fingerprint to identify the spin state of the cobalt ion. Therefore, in principle, the spin state of the cobalt ion can be unambiguously determined from nuclear magnetic resonance spectra. Our calc...

We present a new approach to the evaluation of the on-site repulsion energy U for use in the LDA+U method of Anisimov and collaborators. Our objectives are to make the method more firmly based, to concentrate primarily on ground state properties rather than spectra, and to test the method in cases where only modest changes in orbital occupations are expected, as well as for highly correlated ma...

The electronic structure of ErGa3 and its isostructural compounds with Tm, Yb, and Lu are investigated with a highly accurate band structure scheme in LDA and GGA and warped muffin-tin approximation. In contrast to other investigations, the 4f electrons of the constituent Re are also treated as part of the valence bands. The position of the corresponding 4f bands relative to the Fermi energy EF...

We find, using a local density approximation +Hubbard U method, that oxygen vacancies tend to cluster in a linear way in SrTiO(3), a prototypical perovskite oxide, accompanied by strong electron localization at the 3d state of the nearby Ti transition metal ion. The vacancy clustering and the associated electron localization lead to a profound impact on materials properties, e.g., the reduction...

U radu je predstavljen pristup za modelovanje tema i klasifikaciju tekstualnih dokumenata. Konkretno, vršena 1) primena LDA (Latent Dirichlet Allocation) nad tekstom zarad dobijanja tema, pri čemu evaluacija rađena kvalitativno, kroz semantiku pronađenih tema; 2) klasifikacija dokumenta primenom reprezentacije teksta dobijene kombinacijom tf-idf obeležja izvučenih pomoću LSA Semantic Analysis);...

Linear discriminant analysis (LDA) is a classical approach for dimensionality reduction. It aims to maximize between-class scatter and minimize within-class scatter, thus maximize the class discriminant. However, for undersampled problems where the data dimensionality is larger than the sample size, all scatter matrices are singular and the classical LDA encounters computational difficulty. Rec...