Lateral diffusive spin transport in layered structures

A one dimensional theory of lateral spin-polarized transport is derived from the two dimensional flow in the vertical cross section of a stack of ferromagnetic and paramagnetic layers. This takes into account the influence of the lead on the lateral current underneath, in contrast to the conventional 1D modeling by the collinear configuration of lead/channel/lead. Our theory is convenient and appropriate for the current in plane configuration of an all-metallic spintronics structure as well as for the planar structure of a semiconductor with ferromagnetic contacts. For both systems we predict the optimal contact width for maximal magnetoresistance and propose an electrical measurement of the spin diffusion length for a wide range of materials.Comment: 4 pages, 3 figure

Spin-dependent properties of a two-dimensional electron gas with ferromagnetic gates

A theoretical prediction of the spin-dependent electron self-energy and in-plane transport of a two-dimensional electron gas in proximity with a ferromagnetic gate is presented. The application of the predicted spin-dependent properties is illustrated by the proposal of a device configuration with two neighboring ferromagnetic gates which produces a magnetoresistance effect on the channel current generated by nonmagnetic source and drain contacts. Specific results are shown for a silicon inversion layer with iron gates. The gate leakage current is found to be beneficial to the spin effects.Comment: 3 pages, 2 figures, Replaced with revised versio

A Spectral Algorithm for Latent Dirichlet Allocation

The problem of topic modeling can be seen as a generalization of the clustering problem, in that it posits that observations are generated due to multiple latent factors (e.g., the words in each document are generated as a mixture of several active topics, as opposed to just one). This increased representational power comes at the cost of a more challenging unsupervised learning problem of estimating the topic probability vectors (the distributions over words for each topic), when only the words are observed and the corresponding topics are hidden. We provide a simple and efficient learning procedure that is guaranteed to recover the parameters for a wide class of mixture models, including the popular latent Dirichlet allocation (LDA) model. For LDA, the procedure correctly recovers both the topic probability vectors and the prior over the topics, using only trigram statistics (i.e., third order moments, which may be estimated with documents containing just three words). The method, termed Excess Correlation Analysis (ECA), is based on a spectral decomposition of low order moments (third and fourth order) via two singular value decompositions (SVDs). Moreover, the algorithm is scalable since the SVD operations are carried out on $k\times k$ matrices, where $k$ is the number of latent factors (e.g. the number of topics), rather than in the $d$-dimensional observed space (typically $d \gg k$).Comment: Changed title to match conference version, which appears in Advances in Neural Information Processing Systems 25, 201

Quasiparticle Band Structure and Density Functional Theory: Single-Particle Excitations and Band Gaps in Lattice Models

We compare the quasiparticle band structure for a model insulator obtained from the fluctuation exchange approximation (FEA) with the eigenvalues of the corresponding density functional theory (DFT) and local density approximation (LDA). The discontinuity in the exchange-correlation potential for this model is small and the FEA and DFT band structures are in good agreement. In contrast to conventional wisdom, the LDA for this model overestimates the size of the band gap. We argue that this is a consequence of an FEA self-energy that is strongly frequency dependent, but essentially local.Comment: 8 pages, and 5 figure