2,466 research outputs found
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 matrices, where is the number of latent factors
(e.g. the number of topics), rather than in the -dimensional observed space
(typically ).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
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