Electronic structure of Zr-Ni-Sn systems: role of clustering and nanostructures in Half-Heusler and Heusler limits

Half-Heusler and Heusler compounds have been of great interest for several decades for thermoelectric, magnetic, half-metallic and many other interesting properties. Among these systems, Zr-Ni-Sn compounds are interesting thermoelectrics which can go from semiconducting half-Heusler (HH) limit, ZrNiSn, to metallic Heusler (FH) limit, ZrNi$_2$Sn. Recently Makogo et al. [J. Am. Chem. Soc. 133, 18843 (2011)] found that dramatic improvement in the thermoelectric power factor of HH can be achieved by putting excess Ni into the system. This was attributed to an energy filtering mechanism due to the formation of FH nanostructures in the HH matrix. Using density functional theory we have investigated clustering and nanostructure formation in HH$_{1-x}$FH$_x$ systems near the HH and FH ends and found that excess Ni atoms in HH tend to stay close to each other and form nanoclusters of FH. On the other hand, there is competing interaction between Ni-vacancies in FH which prevent them from forming HH nano clusters. Effects of nano inclusions on the electronic structure at both HH and FH ends will be discussed.Comment: Published in J. Phys.: Condens. Matte

Adaptive Optics Observations of the Galactic Center Young Stars

Adaptive Optics observations have dramatically improved the quality and versatility of high angular resolution measurements of the center of our Galaxy. In this paper, we quantify the quality of our Adaptive Optics observations and report on the astrometric precision for the young stellar population that appears to reside in a stellar disk structure in the central parsec. We show that with our improved astrometry and a 16 year baseline, including 10 years of speckle and 6 years of laser guide star AO imaging, we reliably detect accelerations in the plane of the sky as small as 70 microarcsec/yr/yr (~2.5 km/s/yr) and out to a projected radius from the supermassive black hole of 1.5" (~0.06 pc). With an increase in sensitivity to accelerations by a factor of ~6 over our previous efforts, we are able to directly probe the kinematic structure of the young stellar disk, which appears to have an inner radius of 0.8". We find that candidate disk members are on eccentric orbits, with a mean eccentricity of = 0.30 +/- 0.07. Such eccentricities cannot be explained by the relaxation of a circular disk with a normal initial mass function, which suggests the existence of a top-heavy IMF or formation in an initially eccentric disk.Comment: 7 pages, 4 figures, SPIE Astronomical Telescopes and Instrumentation 201

Two-dimensional pseudo-gravity model: particles motion in a non-potential singular force field

We analyze a simple macroscopic model describing the evolution of a cloud of particles confined in a magneto-optical trap. The behavior of the particles is mainly driven by self-consistent attractive forces. In contrast to the standard model of gravitational forces, the force field does not result from a potential; moreover, the nonlinear coupling is more singular than the coupling based on the Poisson equation. We establish the existence of solutions under a suitable smallness condition on the total mass or, equivalently, for a sufficiently large diffusion coefficient. When a symmetry assumption is fulfilled, the solutions satisfy strengthened estimates (exponential moments). We also investigate the convergence of the $N$-particles description towards the PDE system in the mean field regime

Deep Discrete Hashing with Self-supervised Pairwise Labels

Hashing methods have been widely used for applications of large-scale image retrieval and classification. Non-deep hashing methods using handcrafted features have been significantly outperformed by deep hashing methods due to their better feature representation and end-to-end learning framework. However, the most striking successes in deep hashing have mostly involved discriminative models, which require labels. In this paper, we propose a novel unsupervised deep hashing method, named Deep Discrete Hashing (DDH), for large-scale image retrieval and classification. In the proposed framework, we address two main problems: 1) how to directly learn discrete binary codes? 2) how to equip the binary representation with the ability of accurate image retrieval and classification in an unsupervised way? We resolve these problems by introducing an intermediate variable and a loss function steering the learning process, which is based on the neighborhood structure in the original space. Experimental results on standard datasets (CIFAR-10, NUS-WIDE, and Oxford-17) demonstrate that our DDH significantly outperforms existing hashing methods by large margin in terms of~mAP for image retrieval and object recognition. Code is available at \url{https://github.com/htconquer/ddh}

Numerical Simulations of Flows over a Forced Oscillating Cylinder

A numerical study of incompressible laminar flow past a circular cylinder forced to oscillate longitudinally, transversely and at an angle to the uniform freestream is performed using the dynamic mesh method. The simulations are conducted at a fixed Reynolds number of 80 with amplitude ratios varying between 0.14 to 0.50 and excitation frequency ratios of 0.05 to 3.0. Good agreement to previous experimental and numerical investigations is achieved in the prediction of the lock-on range, force amplifications and vortex shedding modes for longitudinal and transverse oscillations. For excitations at an angle of 60 degrees relative to the oncoming flow, previously identified modes of AI and, AII were successfully predicted. In addition, at higher amplitude ratios the entire synchronised von Karman wake street displayed a deviating effect from the centreline. Analysis of the wake response via phase plane diagrams and the transverse force coefficients revealed two lock-on regions. The extents of these lock-on regions, and the variation of the forces and near wake vortex shedding modes are presented and discussed herein