Intermediate-pressure phases of cerium studied by an LDA + Gutzwiller method

The thermodynamic stable phase of cerium metal in the intermediate pressure regime (5.0--13.0 GPa) is studied in detail by the newly developed local-density approximation (LDA)+ Gutzwiller method, which can include the strong correlation effect among the 4\textit{f} electrons in cerium metal properly. Our numerical results show that the $\alpha"$ phase, which has the distorted body-centered-tetragonal structure, is the thermodynamic stable phase in the intermediate pressure regime and all the other phases including the $\alpha'$ phase ($\alpha$-U structure), $\alpha$ phase (fcc structure), and bct phases are either metastable or unstable. Our results are quite consistent with the most recent experimental data.Comment: 17 pages, 7 figure

Particle and spin transports of spin-orbit coupled Fermi gas through a Quantum Point Contact

The particle and spin transport through a quantum point contact between two Fermi gases with Raman-induced spin-orbit coupling are investigated. We show that the particle and spin conductances both demonstrate the structure of plateau due to the mesoscopic scale of the quantum point contact. Compared with the normal Fermi gases the particle conductance can be significantly enhanced by the spin-orbit coupling effect. Furthermore, the conversion of the particle and spin currents can take place in the spin-orbit coupled system, and we find that it is controlled by the parameter of two-photon detuning. When the parameter of two-photon detuning vanishes the particle and spin currents decouple.Comment: 7 pages, 4 figure

Graphene Based Waveguides

Graphene, which is well known as a one-atom thick carbon allotrope, has drawn lots of attention since its first announcement due to remarkable performance in mechanical, electrical, magnetic, thermal, and optical areas. In particular, unique properties of graphene such as low net absorption in broadband optical band, notably high nonlinear optical effects, and gate-variable optical conductivity make it an excellent candidate for high speed, high performance, and broadband electronic and photonics devices. Embedding graphene into optical devices longitudinally would enhance the light-graphene interaction, which shows great potential in photonic components. Since the carrier density of graphene could be tuned by external gate voltage, chemical doping, light excitation, graphene-based waveguide modulator could be designed to have high flexibility in controlling the absorption and modulation depth. Furthermore, graphene-based waveguides could take advantages in detection, sensing, polarizer, and so on

LDA+Gutzwiller Method for Correlated Electron Systems: Formalism and Its Applications

We introduce in detail our newly developed \textit{ab initio} LDA+Gutzwiller method, in which the Gutzwiller variational approach is naturally incorporated with the density functional theory (DFT) through the "Gutzwiller density functional theory (GDFT)" (which is a generalization of original Kohn-Sham formalism). This method can be used for ground state determination of electron systems ranging from weakly correlated metal to strongly correlated insulators with long-range ordering. We will show that its quality for ground state is as high as that by dynamic mean field theory (DMFT), and yet it is computationally much cheaper. In additions, the method is fully variational, the charge-density self-consistency can be naturally achieved, and the quantities, such as total energy, linear response, can be accurately obtained similar to LDA-type calculations. Applications on several typical systems are presented, and the characteristic aspects of this new method are clarified. The obtained results using LDA+Gutzwiller are in better agreement with existing experiments, suggesting significant improvements over LDA or LDA+U.Comment: 20 pages, 11 figure

Optical characterization of multi-scale morphologically complex heterogeneous media – Application to snow with soot impurities

A multi-scale methodology for the radiative transfer analysis of heterogeneous media composed of morphologically-complex components on two distinct scales is presented. The methodology incorporates the exact morphology at the various scales and utilizes volume-averaging approaches with the corresponding effective properties to couple the scales. At the continuum level, the volume-averaged coupled radiative transfer equations are solved utilizing i) effective radiative transport properties obtained by direct Monte Carlo simulations at the pore level, and ii) averaged bulk material properties obtained at particle level by Lorenz-Mie theory or discrete dipole approximation calculations. This model is applied to a soot-contaminated snow layer, and is experimentally validated with reflectance measurements of such layers. A quantitative and decoupled understanding of the morphological effect on the radiative transport is achieved, and a significant influence of the dual-scale morphology on the macroscopic optical behavior is observed. Our results show that with a small amount of soot particles, of the order of 1ppb in volume fraction, the reduction in reflectance of a snow layer with large ice grains can reach up to 77% (at a wavelength of 0.3 μm). Soot impurities modeled as compact agglomerates yield 2-3% lower reduction of the reflectance in a thick show layer compared to snow with soot impurities modeled as chain-like agglomerates. Soot impurities modeled as equivalent spherical particles underestimate the reflectance reduction by 2-8%. This study implies that the morphology of the heterogeneities in a media significantly affects the macroscopic optical behavior and, specifically for the soot-contaminated snow, indicates the non-negligible role of soot on the absorption behavior of snow layers. It can be equally used in technical applications for the assessment and optimization of optical performance in multi-scale media

Electric-field induced droplet vertical vibration and horizontal motion: Experiments and simulations

In this work, Electrowetting on Dielectric (EWOD) and electrostatic induction (ESI) are employed to manipulate droplet on the PDMS-ITO substrate. Firstly, we report large vertical vibrations of the droplet, induced by EWOD, within a voltage range of 40 to 260 V. The droplet's transition from a vibrating state to a static equilibrium state are investigated in detail. It is indicated that the contact angle changes synchronously with voltage during the vibration. The electric signal in the circuit is measured to analyze the vibration state that varies with time. By studying the influence of driving voltage on the contact angle and the amplitude in the vibration, it is shown that the saturation voltage of both contact angle and amplitude is about 120 V. The intrinsic connection between contact angle saturation and amplitude saturation is clarified by studying the surface energy of the droplet. A theoretical model is constructed to numerically simulate the vibration morphology and amplitude of the droplet. Secondly, we realize the horizontal motion of droplets by ESI at the voltage less than 1000 V. The charge and electric force on the droplet are numerically calculated. The frictional resistance coefficients of the droplet are determined by the deceleration of the droplet. Under consideration of frictional resistance of the substrate and viscous resistance of the liquid, the motion of the droplet is calculated at 400 V and 1000 V, respectively. This work introduces a new method for manipulating various forms of droplet motion using the single apparatus

Unsupervised Annotation of Phenotypic Abnormalities via Semantic Latent Representations on Electronic Health Records

The extraction of phenotype information which is naturally contained in electronic health records (EHRs) has been found to be useful in various clinical informatics applications such as disease diagnosis. However, due to imprecise descriptions, lack of gold standards and the demand for efficiency, annotating phenotypic abnormalities on millions of EHR narratives is still challenging. In this work, we propose a novel unsupervised deep learning framework to annotate the phenotypic abnormalities from EHRs via semantic latent representations. The proposed framework takes the advantage of Human Phenotype Ontology (HPO), which is a knowledge base of phenotypic abnormalities, to standardize the annotation results. Experiments have been conducted on 52,722 EHRs from MIMIC-III dataset. Quantitative and qualitative analysis have shown the proposed framework achieves state-of-the-art annotation performance and computational efficiency compared with other methods.Comment: Accepted by BIBM 2019 (Regular

Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification

Different aspects of a clinical sample can be revealed by multiple types of omics data. Integrated analysis of multi-omics data provides a comprehensive view of patients, which has the potential to facilitate more accurate clinical decision making. However, omics data are normally high dimensional with large number of molecular features and relatively small number of available samples with clinical labels. The "dimensionality curse" makes it challenging to train a machine learning model using high dimensional omics data like DNA methylation and gene expression profiles. Here we propose an end-to-end deep learning model called OmiVAE to extract low dimensional features and classify samples from multi-omics data. OmiVAE combines the basic structure of variational autoencoders with a classification network to achieve task-oriented feature extraction and multi-class classification. The training procedure of OmiVAE is comprised of an unsupervised phase without the classifier and a supervised phase with the classifier. During the unsupervised phase, a hierarchical cluster structure of samples can be automatically formed without the need for labels. And in the supervised phase, OmiVAE achieved an average classification accuracy of 97.49% after 10-fold cross-validation among 33 tumour types and normal samples, which shows better performance than other existing methods. The OmiVAE model learned from multi-omics data outperformed that using only one type of omics data, which indicates that the complementary information from different omics datatypes provides useful insights for biomedical tasks like cancer classification.Comment: 7 pages, 4 figure