Dynamics of Vibrated Granular Monolayers

We study statistical properties of vibrated granular monolayers using molecular dynamics simulations. We show that at high excitation strengths, the system is in a gas state, particle motion is isotropic, and the velocity distributions are Gaussian. As the vibration strength is lowered the system's dimensionality is reduced from three to two. Below a critical excitation strength, a gas-cluster phase occurs, and the velocity distribution becomes bimodal. In this phase, the system consists of clusters of immobile particles arranged in close-packed hexagonal arrays, and gas particles whose energy equals the first excited state of an isolated particle on a vibrated plate.Comment: 4 pages, 6 figs, revte

Photon-assisted electron transmission resonance through a quantum well with spin-orbit coupling

Using the effective-mass approximation and Floquet theory, we study the electron transmission over a quantum well in semiconductor heterostructures with Dresselhaus spin-orbit coupling and an applied oscillation field. It is demonstrated by the numerical evaluations that Dresselhaus spin-orbit coupling eliminates the spin degeneracy and leads to the splitting of asymmetric Fano-type resonance peaks in the conductivity. In turn, the splitting of Fano-type resonance induces the spin- polarization-dependent electron-current. The location and line shape of Fano-type resonance can be controlled by adjusting the oscillation frequency and the amplitude of external field as well. These interesting features may be a very useful basis for devising tunable spin filters.Comment: 10pages,4figure

Galilean invariance of lattice Boltzmann models

It is well-known that the original lattice Boltzmann (LB) equation deviates from the Navier-Stokes equations due to an unphysical velocity dependent viscosity. This unphysical dependency violates the Galilean invariance and limits the validation domain of the LB method to near incompressible flows. As previously shown, recovery of correct transport phenomena in kinetic equations depends on the higher hydrodynamic moments. In this Letter, we give specific criteria for recovery of various transport coefficients. The Galilean invariance of a general class of LB models is demonstrated via numerical experiments

A convex formulation for semi-supervised multi-label feature selection

Copyright © 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Explosive growth of multimedia data has brought challenge of how to efficiently browse, retrieve and organize these data. Under this circumstance, different approaches have been proposed to facilitate multimedia analysis. Several semi-supervised feature selection algorithms have been proposed to exploit both labeled and unlabeled data. However, they are implemented based on graphs, such that they cannot handle large-scale datasets. How to conduct semi-supervised feature selection on large-scale datasets has become a challenging research problem. Moreover, existing multi-label feature selection algorithms rely on eigen-decomposition with heavy computational burden, which further prevent current feature selection algorithms from being applied for big data. In this paper, we propose a novel convex semi-supervised multi-label feature selection algorithm, which can be applied to large-scale datasets. We evaluate performance of the proposed algorithm over five benchmark datasets and compare the results with state- of-the-art supervised and semi-supervised feature selection algorithms as well as baseline using all features. The experimental results demonstrate that our proposed algorithm consistently achieve superiors performances

Evolution of superconductivity by oxygen annealing in FeTe0.8S0.2

Oxygen annealing dramatically improved the superconducting properties of solid-state-reacted FeTe0.8S0.2, which showed only a broad onset of superconducting transition just after the synthesis. The zero resistivity appeared and reached 8.5 K by the oxygen annealing at 200\degree C. The superconducting volume fraction was also enhanced from 0 to almost 100%. The lattice constants were compressed by the oxygen annealing, indicating that the evolution of bulk superconductivity in FeTe0.8S0.2 was correlated to the shrinkage of lattice.Comment: 13 pages, 6 figure

Improved Spectral Clustering via Embedded Label Propagation

Spectral clustering is a key research topic in the field of machine learning and data mining. Most of the existing spectral clustering algorithms are built upon Gaussian Laplacian matrices, which are sensitive to parameters. We propose a novel parameter free, distance consistent Locally Linear Embedding. The proposed distance consistent LLE promises that edges between closer data points have greater weight.Furthermore, we propose a novel improved spectral clustering via embedded label propagation. Our algorithm is built upon two advancements of the state of the art:1) label propagation,which propagates a node\'s labels to neighboring nodes according to their proximity; and 2) manifold learning, which has been widely used in its capacity to leverage the manifold structure of data points. First we perform standard spectral clustering on original data and assign each cluster to k nearest data points. Next, we propagate labels through dense, unlabeled data regions. Extensive experiments with various datasets validate the superiority of the proposed algorithm compared to current state of the art spectral algorithms