Magnetic properties of undoped Cu2O fine powders with magnetic impurities and/or cation vacancies

Fine powders of micron- and submicron-sized particles of undoped Cu2O semiconductor, with three different sizes and morphologies have been synthesized by different chemical processes. These samples include nanospheres 200 nm in diameter, octahedra of size 1 micron, and polyhedra of size 800 nm. They exhibit a wide spectrum of magnetic properties. At low temperature, T = 5 K, the octahedron sample is diamagnetic. The nanosphere is paramagnetic. The other two polyhedron samples synthesized in different runs by the same process are found to show different magnetic properties. One of them exhibits weak ferromagnetism with T_C = 455 K and saturation magnetization, M_S = 0.19 emu/g at T = 5 K, while the other is paramagnetic. The total magnetic moment estimated from the detected impurity concentration of Fe, Co, and Ni, is too small to account for the observed magnetism by one to two orders of magnitude. Calculations by the density functional theory (DFT) reveal that cation vacancies in the Cu2O lattice are one of the possible causes of induced magnetic moments. The results further predict that the defect-induced magnetic moments favour a ferromagnetically coupled ground state if the local concentration of cation vacancies, n_C, exceeds 12.5%. This offers a possible scenario to explain the observed magnetic properties. The limitations of the investigations in the present work, in particular in the theoretical calculations, are discussed and possible areas for further study are suggested.Comment: 20 pages, 5 figures 2 tables, submitted to J Phys Condense Matte

Superpixel Convolutional Networks using Bilateral Inceptions

In this paper we propose a CNN architecture for semantic image segmentation. We introduce a new 'bilateral inception' module that can be inserted in existing CNN architectures and performs bilateral filtering, at multiple feature-scales, between superpixels in an image. The feature spaces for bilateral filtering and other parameters of the module are learned end-to-end using standard backpropagation techniques. The bilateral inception module addresses two issues that arise with general CNN segmentation architectures. First, this module propagates information between (super) pixels while respecting image edges, thus using the structured information of the problem for improved results. Second, the layer recovers a full resolution segmentation result from the lower resolution solution of a CNN. In the experiments, we modify several existing CNN architectures by inserting our inception module between the last CNN (1x1 convolution) layers. Empirical results on three different datasets show reliable improvements not only in comparison to the baseline networks, but also in comparison to several dense-pixel prediction techniques such as CRFs, while being competitive in time.Comment: European Conference on Computer Vision (ECCV), 201

Accelerating Atomic Orbital-based Electronic Structure Calculation via Pole Expansion and Selected Inversion

We describe how to apply the recently developed pole expansion and selected inversion (PEXSI) technique to Kohn-Sham density function theory (DFT) electronic structure calculations that are based on atomic orbital discretization. We give analytic expressions for evaluating the charge density, the total energy, the Helmholtz free energy and the atomic forces (including both the Hellman-Feynman force and the Pulay force) without using the eigenvalues and eigenvectors of the Kohn-Sham Hamiltonian. We also show how to update the chemical potential without using Kohn-Sham eigenvalues. The advantage of using PEXSI is that it has a much lower computational complexity than that associated with the matrix diagonalization procedure. We demonstrate the performance gain by comparing the timing of PEXSI with that of diagonalization on insulating and metallic nanotubes. For these quasi-1D systems, the complexity of PEXSI is linear with respect to the number of atoms. This linear scaling can be observed in our computational experiments when the number of atoms in a nanotube is larger than a few hundreds. Both the wall clock time and the memory requirement of PEXSI is modest. This makes it even possible to perform Kohn-Sham DFT calculations for 10,000-atom nanotubes with a sequential implementation of the selected inversion algorithm. We also perform an accurate geometry optimization calculation on a truncated (8,0) boron-nitride nanotube system containing 1024 atoms. Numerical results indicate that the use of PEXSI does not lead to loss of accuracy required in a practical DFT calculation

Ab initio studies on the mechanism for linear and nonlinear optical effects in YAl3(BO3)4

[[abstract]]First-principles studies of the linear and nonlinear optical properties for YAl3(BO3)4 (YAB) are presented. Based upon the electronic band structure, the optical refractive indices, birefringence, and second harmonic generation (SHG) coefficients of YAB are calculated, which are in good agreement with experimental values. In addition, the SHG-weighted electron density analysis and the real-space atom-cutting method are adopted to elucidate the origin of the linear and nonlinear optical effects in YAB. The results show that the anionic (BO3) groups have dominant contributions to the birefringence. The contribution of the Al cations to the optical effects is negligibly small. However, the Y cations bond to the neighbor O anions and form the deformed (YO6) octahedra, which results in the large SHG effects in YAB.[[journaltype]]國外[[incitationindex]]SCI[[ispeerreviewed]]Y[[booktype]]紙

Deep Regionlets for Object Detection

In this paper, we propose a novel object detection framework named "Deep Regionlets" by establishing a bridge between deep neural networks and conventional detection schema for accurate generic object detection. Motivated by the abilities of regionlets for modeling object deformation and multiple aspect ratios, we incorporate regionlets into an end-to-end trainable deep learning framework. The deep regionlets framework consists of a region selection network and a deep regionlet learning module. Specifically, given a detection bounding box proposal, the region selection network provides guidance on where to select regions to learn the features from. The regionlet learning module focuses on local feature selection and transformation to alleviate local variations. To this end, we first realize non-rectangular region selection within the detection framework to accommodate variations in object appearance. Moreover, we design a "gating network" within the regionlet leaning module to enable soft regionlet selection and pooling. The Deep Regionlets framework is trained end-to-end without additional efforts. We perform ablation studies and conduct extensive experiments on the PASCAL VOC and Microsoft COCO datasets. The proposed framework outperforms state-of-the-art algorithms, such as RetinaNet and Mask R-CNN, even without additional segmentation labels.Comment: Accepted to ECCV 201

Turning motivation into action: A strategic orientation model for green supply chain management

This study examines the key motivations for a firm to adopt green supply chain management (GSCM) strategic orientation, and the mechanisms that subsequently influence GSCM practices. Three components of GSCM orientation were examined, i.e. strategic emphasis, management support, and resource commitment. Data were collected from a sample of 296 manufacturing firms in China. The results indicate that the most important motivation is environmental concern, followed by customer requirements, cost saving and competitive pressure, while legal requirements were not a significant factor. The results confirm that strategic orientation plays mediating role between motivations and the actual practices. Within the three components of strategic orientation, resource commitment and strategic emphasis have stronger direct impact on practices, whereas the effect of management support on GSCM practices is indirect through resource commitment. This study contributes to the literature by clarifying the key role of strategic orientation in turning GSCM motivations into actions

Toward a Relational Account of Neighborhood Governance: Territory-Based Networks and Residential Outcomes in Urban China

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Geometric, electronic properties and the thermodynamics of pure and Al--doped Li clusters

The first--principles density functional molecular dynamics simulations have been carried out to investigate the geometric, the electronic, and the finite temperature properties of pure Li clusters (Li$_{10}$, Li$_{12}$) and Al--doped Li clusters (Li$_{10}$Al, Li$_{10}$Al$_2$). We find that addition of two Al impurities in Li$_{10}$ results in a substantial structural change, while the addition of one Al impurity causes a rearrangement of atoms. Introduction of Al--impurities in Li$_{10}$ establishes a polar bond between Li and nearby Al atom(s), leading to a multicentered bonding, which weakens the Li--Li metallic bonds in the system. These weakened Li--Li bonds lead to a premelting feature to occur at lower temperatures in Al--doped clusters. In Li$_{10}$Al$_2$, Al atoms also form a weak covalent bond, resulting into their dimer like behavior. This causes Al atoms not to `melt' till 800 K, in contrast to the Li atoms which show a complete diffusive behavior above 400 K. Thus, although one Al impurity in Li$_{10}$ cluster does not change its melting characteristics significantly, two impurities results in `surface melting' of Li atoms whose motions are confined around Al dimer.Comment: 9 pages, 7 figure