322 research outputs found
Graphene Electrodynamics in the presence of the Extrinsic Spin Hall Effect
We extend the electrodynamics of two dimensional electron gases to account
for the extrinsic spin Hall effect (SHE). The theory is applied to doped
graphene decorated with a random distribution of absorbates that induce
spin-orbit coupling (SOC) by proximity. The formalism extends previous
semiclassical treatments of the SHE to the non-local dynamical regime. Within a
particle-number conserving approximation, we compute the conductivity,
dielectric function, and spin Hall angle in the small frequency and wave vector
limit. The spin Hall angle is found to decrease with frequency and wave number,
but it remains comparable to its zero-frequency value around the frequency
corresponding to the Drude peak. The plasmon dispersion and linewidth are also
obtained. The extrinsic SHE affects the plasmon dispersion in the long
wavelength limit, but not at large values of the wave number. This result
suggests an explanation for the rather similar plasmonic response measured in
exfoliated graphene, which does not exhibit the SHE, and graphene grown by
chemical vapor deposition, for which a large SHE has been recently reported.
Our theory also lays the foundation for future experimental searches of SOC
effects in the electrodynamic response of two-dimensional electron gases with
SOC disorder.Comment: 12 pages, 4 figure
An Improved Differential Evolution Algorithm Based on Adaptive Parameter
The differential evolution (DE) algorithm is a heuristic global optimization technique based on population which is easy to understand, simple to implement, reliable, and fast. The evolutionary parameters directly influence the performance of differential evolution algorithm. The adjustment of control parameters is a global behavior and has no general research theory to control the parameters in the evolution process at present. In this paper, we propose an adaptive parameter adjustment method which can dynamically adjust control parameters according to the evolution stage. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and faster convergence speed
GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization
Bioinformatics tools have been developed to interpret gene expression data at
the gene set level, and these gene set based analyses improve the biologists'
capability to discover functional relevance of their experiment design. While
elucidating gene set individually, inter gene sets association is rarely taken
into consideration. Deep learning, an emerging machine learning technique in
computational biology, can be used to generate an unbiased combination of gene
set, and to determine the biological relevance and analysis consistency of
these combining gene sets by leveraging large genomic data sets. In this study,
we proposed a gene superset autoencoder (GSAE), a multi-layer autoencoder model
with the incorporation of a priori defined gene sets that retain the crucial
biological features in the latent layer. We introduced the concept of the gene
superset, an unbiased combination of gene sets with weights trained by the
autoencoder, where each node in the latent layer is a superset. Trained with
genomic data from TCGA and evaluated with their accompanying clinical
parameters, we showed gene supersets' ability of discriminating tumor subtypes
and their prognostic capability. We further demonstrated the biological
relevance of the top component gene sets in the significant supersets. Using
autoencoder model and gene superset at its latent layer, we demonstrated that
gene supersets retain sufficient biological information with respect to tumor
subtypes and clinical prognostic significance. Superset also provides high
reproducibility on survival analysis and accurate prediction for cancer
subtypes.Comment: Presented in the International Conference on Intelligent Biology and
Medicine (ICIBM 2018) at Los Angeles, CA, USA and published in BMC Systems
Biology 2018, 12(Suppl 8):14
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