Correlation effects in the electronic structure of the Ni-based superconducting KNi2S2

The LDA plus Gutzwiller variational method is used to investigate the groundstate physical properties of the newly discovered superconducting KNi2S2. Five Ni-3d Wannier-orbital basis are constructed by the density-functional theory, to combine with local Coulomb interaction to describe the normal state electronic structure of Ni-based superconductor. The band structure and the mass enhanced are studied based on a multiorbital Hubbard model by using Gutzwiller approximation method. Our results indicate that the correlation effects lead to the mass enhancement of KNi2S2. Different from the band structure calculated from the LDA results, there are three energy bands across the Fermi level along the X-Z line due to the existence of the correlation effects, which induces a very complicated Fermi surface along the X-Z line. We have also investigated the variation of the quasi-particle weight factor with the hole or electron doping and found that the mass enhancement character has been maintained with the doping.Comment: 12 pages, 6 figure

Game Model for “Shortage of Logistics” in Online Shopping in Service Engineering

AbstractThis paper analyzes the imbalance between e-commerce and logistics service by using factor sub-game perfect Nash equilibrium as an analytical tool from the view of system and links up the bargaining process between sellers and express enterprise involved in service engineering during online shopping with discount factor. The change of interests between sellers and express enterprise is systematically analyzed from the perspective of discount factor on the equilibrium solution through the application of model towards service engineering during holidays online shopping. Finally it is concluded that discount factor is a key factor influencing the express fee between sells and express enterprise in logistic system, and some recommendations are put forward accordingly

Lateral Stiffness and Damping Coefficient of Soils for Seismic Analysis of Buried Pipelines

The stiffness and damping coefficient of soil are critical parameters in the Winkler\u27s model for the seismic analysis of buried pipelines. This paper presents an analytical study in calculating the lateral dynamic stiffness (elastic and inelastic) and damping coefficient of soils for the seismic analysis of buried pipelines. The effects of the depth of the buried pipeline and the variation of Poisson\u27s ratio of soils are cons1dered. In the analysts, first, by using the elastic half-space theory, the elastic stiffness and geometrical damping coefficient of soils are obtained. From the numerical results, the elastic stiffness and geometrical damping coefficient by using best fitted formulas are presented. Secondly, empirical inelastic characteristics of soils are considered. The empirical data includes relationships among dynamic shear modulus, material damping ratio, and dynamic shear strain amplitude. The total damping coefficient of the system can be obtained by adding the material damping to the geometrical damping coefficient

An Interpretable Deep Architecture for Similarity Learning Built Upon Hierarchical Concepts

In general, development of adequately complex mathematical models, such as deep neural networks, can be an effective way to improve the accuracy of learning models. However, this is achieved at the cost of reduced post-hoc model interpretability, because what is learned by the model can become less intelligible and tractable to humans as the model complexity increases. In this paper, we target a similarity learning task in the context of image retrieval, with a focus on the model interpretability issue. An effective similarity neural network (SNN) is proposed not only to seek robust retrieval performance but also to achieve satisfactory post-hoc interpretability. The network is designed by linking the neuron architecture with the organization of a concept tree and by formulating neuron operations to pass similarity information between concepts. Various ways of understanding and visualizing what is learned by the SNN neurons are proposed. We also exhaustively evaluate the proposed approach using a number of relevant datasets against a number of state-of-the-art approaches to demonstrate the effectiveness of the proposed network. Our results show that the proposed approach can offer superior performance when compared against state-of-the-art approaches. Neuron visualization results are demonstrated to support the understanding of the trained neurons

An automated framework of inner segment/outer segment defect detection for retinal SD-OCT images

The integrity of inner segment/outer segment (IS/OS) has high correlation with lower visual acuity in patients suffering from blunt trauma. An automated 3D IS/OS defect detection method based on the SD-OCT images was proposed. First, 11 surfaces were automatically segmented using the multiscale 3D graph-search approach. Second, the sub-volumes between surface 7 and 8 containing IS/OS region around the fovea (diameter of mm) were extracted and flattened based on the segmented retinal pigment epithelium layer. Third, 5 kinds of texture based features were extracted for each voxel. A KNN classifier was trained and each voxel was classified as disrupted or nondisrupted and the responding defect volume was calculated. The proposed method was trained and tested on 9 eyes from 9 trauma subjects using the leave-one-out cross validation method. The preliminary results demonstrated the feasibility and efficiency of the proposed method