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