25,518 research outputs found
Supporting Complex Scientific Database Schemas in a Grid Middleware
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Density Functional Theory Characterization of the Multiferroicity in Spin Spiral Chain Cuprates
The ferroelectricity of the spiral magnets LiCu2O2 and LiCuVO4 was examined
by calculating the electric polarizations of their spin spiral states on the
basis of density functional theory with spin-orbit coupling. Our work
unambiguously reveals that spin-orbit coupling is responsible for the
ferroelectricity with the primary contribution from the spin-orbit coupling on
the Cu sites, but the asymmetric density distribution responsible for the
electric polarization occurs mainly around the O atoms. The electric
polarization is calculated to be much greater for the ab- than for the bc-plane
spin spiral. The observed spin-spiral plane is found to be consistent with the
observed direction of the electric polarization for LiCuVO4, but inconsistent
for LiCu2O2.Comment: Phys. Rev. Lett., in prin
Superfluid response in electron-doped cuprate superconductors
We propose a weakly coupled two-band model with pairing
symmetry to account for the anomalous temperature dependence of superfluid
density in electron-doped cuprate superconductors. This model gives a
unified explanation to the presence of a upward curvature in near
and a weak temperature dependence of in low temperatures. Our
work resolves a discrepancy in the interpretation of different experimental
measurements and suggests that the pairing in electron-doped cuprates has
predominately symmetry in the whole doping range.Comment: 4 pages, 3 figures, title changed and references adde
Relaxed Byzantine Vector Consensus
Exact Byzantine consensus problem requires that non-faulty processes reach
agreement on a decision (or output) that is in the convex hull of the inputs at
the non-faulty processes. It is well-known that exact consensus is impossible
in an asynchronous system in presence of faults, and in a synchronous system,
n>=3f+1 is tight on the number of processes to achieve exact Byzantine
consensus with scalar inputs, in presence of up to f Byzantine faulty
processes. Recent work has shown that when the inputs are d-dimensional vectors
of reals, n>=max(3f+1,(d+1)f+1) is tight to achieve exact Byzantine consensus
in synchronous systems, and n>= (d+2)f+1 for approximate Byzantine consensus in
asynchronous systems.
Due to the dependence of the lower bound on vector dimension d, the number of
processes necessary becomes large when the vector dimension is large. With the
hope of reducing the lower bound on n, we consider two relaxed versions of
Byzantine vector consensus: k-Relaxed Byzantine vector consensus and
(delta,p)-Relaxed Byzantine vector consensus. In k-relaxed consensus, the
validity condition requires that the output must be in the convex hull of
projection of the inputs onto any subset of k-dimensions of the vectors. For
(delta,p)-consensus the validity condition requires that the output must be
within distance delta of the convex hull of the inputs of the non-faulty
processes, where L_p norm is used as the distance metric. For
(delta,p)-consensus, we consider two versions: in one version, delta is a
constant, and in the second version, delta is a function of the inputs
themselves.
We show that for k-relaxed consensus and (delta,p)-consensus with constant
delta>=0, the bound on n is identical to the bound stated above for the
original vector consensus problem. On the other hand, when delta depends on the
inputs, we show that the bound on n is smaller when d>=3
Unsupervised learning of generative topic saliency for person re-identification
(c) 2014. The copyright of this document resides with its authors.
It may be distributed unchanged freely in print or electronic forms.© 2014. The copyright of this document resides with its authors. Existing approaches to person re-identification (re-id) are dominated by supervised learning based methods which focus on learning optimal similarity distance metrics. However, supervised learning based models require a large number of manually labelled pairs of person images across every pair of camera views. This thus limits their ability to scale to large camera networks. To overcome this problem, this paper proposes a novel unsupervised re-id modelling approach by exploring generative probabilistic topic modelling. Given abundant unlabelled data, our topic model learns to simultaneously both (1) discover localised person foreground appearance saliency (salient image patches) that are more informative for re-id matching, and (2) remove busy background clutters surrounding a person. Extensive experiments are carried out to demonstrate that the proposed model outperforms existing unsupervised learning re-id methods with significantly simplified model complexity. In the meantime, it still retains comparable re-id accuracy when compared to the state-of-the-art supervised re-id methods but without any need for pair-wise labelled training data
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