Intrinsic Spin and Orbital-Angular-Momentum Hall Effect

A generalized definition of intrinsic and extrinsic transport coefficients is introduced. We show that transport coefficients from the intrinsic origin are solely determined by local electronic structure, and thus the intrinsic spin Hall effect is not a transport phenomenon. The intrinsic spin Hall current is always accompanied by an equal but opposite intrinsic orbital-angular-momentum Hall current. We prove that the intrinsic spin Hall effect does not induce a spin accumulation at the edge of the sample or near the interface.Comment: References update

Secure Quantum Secret Sharing Based on Reusable GHZ States as Secure Carriers

We show a potential eavesdropper can eavesdrop whole secret information when the legitimate users use secure carrier to encode and decode classical information repeatedly in the protocol [proposed in Bagherinezhad S and Karimipour V 2003 Phys. Rev. A \textbf{67} 044302]. Then we present a revised quantum secret sharing protocol by using Greenberger-Horne-Zeilinger state as secure carrier. Our protocol can resist Eve's attack

Resonant Tunneling through double-bended Graphene Nanoribbons

We investigate theoretically resonant tunneling through double-bended graphene nanoribbon structures, i.e., armchair-edged graphene nanoribbons (AGNRs) in between two semi-infinite zigzag graphene nanoribbon (ZGNR) leads. Our numerical results demonstrate that the resonant tunneling can be tuned dramatically by the Fermi energy and the length and/or widths of the AGNR for both the metallic and semiconductor-like AGNRs. The structure can also be use to control the valley polarization of the tunneling currents and could be useful for potential application in valleytronics devices.Comment: 4 pages, 4 figure

Image tag completion by local learning

The problem of tag completion is to learn the missing tags of an image. In this paper, we propose to learn a tag scoring vector for each image by local linear learning. A local linear function is used in the neighborhood of each image to predict the tag scoring vectors of its neighboring images. We construct a unified objective function for the learning of both tag scoring vectors and local linear function parame- ters. In the objective, we impose the learned tag scoring vectors to be consistent with the known associations to the tags of each image, and also minimize the prediction error of each local linear function, while reducing the complexity of each local function. The objective function is optimized by an alternate optimization strategy and gradient descent methods in an iterative algorithm. We compare the proposed algorithm against different state-of-the-art tag completion methods, and the results show its advantages