197,682 research outputs found
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
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