Finite mixtures of matrix-variate Poisson-log normal distributions for three-way count data

Three-way data structures, characterized by three entities, the units, the variables and the occasions, are frequent in biological studies. In RNA sequencing, three-way data structures are obtained when high-throughput transcriptome sequencing data are collected for n genes across p conditions at r occasions. Matrix-variate distributions offer a natural way to model three-way data and mixtures of matrix-variate distributions can be used to cluster three-way data. Clustering of gene expression data is carried out as means to discovering gene co-expression networks. In this work, a mixture of matrix-variate Poisson-log normal distributions is proposed for clustering read counts from RNA sequencing. By considering the matrix-variate structure, full information on the conditions and occasions of the RNA sequencing dataset is simultaneously considered, and the number of covariance parameters to be estimated is reduced. A Markov chain Monte Carlo expectation-maximization algorithm is used for parameter estimation and information criteria are used for model selection. The models are applied to both real and simulated data, giving favourable clustering results

Structural origins of electronic conduction in amorphous copper-doped alumina

We perform an {\it ab initio} modeling of amorphous copper-doped alumina (a-Al$_2$O$_3$:Cu), a prospective memory material based on resistance switching, and study the structural origin of electronic conduction in this material. We generate molecular dynamics based models of a-Al$_2$O$_3$:Cu at various Cu-concentrations and study the structural, electronic and vibrational properties as a function of Cu-concentration. Cu atoms show a strong tendency to cluster in the alumina host, and metallize the system by filling the band gap uniformly for higher Cu-concentrations. We also study thermal fluctuations of the HOMO-LUMO energy splitting and observe the time evolution of the size of the band gap, which can be expected to have an important impact on the conductivity. We perform a numerical computation of conduction pathways, and show its explicit dependence on Cu connectivity in the host. We present an analysis of ion dynamics and structural aspects of localization of classical normal modes in our models

Density functional study of FeS, FeSe and FeTe: Electronic structure, magnetism, phonons and superconductivity

We report density functional calculations of the electronic structure, Fermi surface, phonon spectrum, magnetism and electron-phonon coupling for the superconducting phase FeSe, as well as the related compounds FeS and FeTe. We find that the Fermi surface structure of these compounds is very similar to that of the Fe-As based superconductors, with cylindrical electron sections at the zone corner, cylindrical hole surface sections, and depending on the compound, other small hole sections at the zone center. As in the Fe-As based materials, these surfaces are separated by a 2D nesting vector at ($\pi$,$\pi$). The density of states, nesting and Fermi surface size increase going from FeSe to FeTe. Both FeSe and FeTe show spin density wave ground states, while FeS is close to an instability. In a scenario where superconductivity is mediated by spin fluctuations at the SDW nesting vector, the strongest superconductor in this series would be doped FeTe.Comment: Added note regarding recent experimental observations of superconductivity under pressure. Some additional discussio