28,677 research outputs found
Pattern-recalling processes in quantum Hopfield networks far from saturation
As a mathematical model of associative memories, the Hopfield model was now
well-established and a lot of studies to reveal the pattern-recalling process
have been done from various different approaches. As well-known, a single
neuron is itself an uncertain, noisy unit with a finite unnegligible error in
the input-output relation. To model the situation artificially, a kind of 'heat
bath' that surrounds neurons is introduced. The heat bath, which is a source of
noise, is specified by the 'temperature'. Several studies concerning the
pattern-recalling processes of the Hopfield model governed by the
Glauber-dynamics at finite temperature were already reported. However, we might
extend the 'thermal noise' to the quantum-mechanical variant. In this paper, in
terms of the stochastic process of quantum-mechanical Markov chain Monte Carlo
method (the quantum MCMC), we analytically derive macroscopically deterministic
equations of order parameters such as 'overlap' in a quantum-mechanical variant
of the Hopfield neural networks (let us call "quantum Hopfield model" or
"quantum Hopfield networks"). For the case in which non-extensive number of
patterns are embedded via asymmetric Hebbian connections, namely,
for the number of neuron ('far from saturation'), we evaluate
the recalling processes for one of the built-in patterns under the influence of
quantum-mechanical noise.Comment: 10 pages, 3 figures, using jpconf.cls, Proc. of Statphys-Kolkata VI
Loopy belief propagation and probabilistic image processing
Estimation of hyperparameters by maximization of the marginal likelihood in probabilistic image processing is investigated by using the cluster variation method. The algorithms are substantially equivalent to generalized loopy belief propagation
Giant Intrinsic Spin and Orbital Hall Effects in Sr2MO4 (M=Ru,Rh,Mo)
We investigate the intrinsic spin Hall conductivity (SHC) and the d-orbital
Hall conductivity (OHC) in metallic d-electron systems, by focusing on the
t_{2g}-orbital tight-binding model for Sr2MO4 (M=Ru,Rh,Mo). The conductivities
obtained are one or two orders of magnitude larger than predicted values for
p-type semiconductors with 5% hole doping. The origin of these giant Hall
effects is the ``effective Aharonov-Bohm phase'' that is induced by the
d-atomic angular momentum in connection with the spin-orbit interaction and the
inter-orbital hopping integrals. The huge SHC and OHC generated by this
mechanism are expected to be ubiquitous in multiorbital transition metal
complexes, which pens the possibility of realizing spintronics as well as
orbitronics devices.Comment: 5 pages, accepted for publication in PR
Tunnel magnetoresistance and interfacial electronic state
We study the relation between tunnel magnetoresistance (TMR) and interfacial
electronic states modified by magnetic impurities introduced at the interface
of the ferromagnetic tunnel junctions, by making use of the periodic Anderson
model and the linear response theory. It is indicated that the TMR ratio is
strongly reduced depending on the position of the -levels of impurities,
based on reduction in the spin-dependent -electron tunneling in the majority
spin state. The results are compared with experimental results for Cr-dusted
ferromagnetic tunnel junctions, and also with results for metallic multilayers
for which similar reduction in giant magnetoresistance has been reported.Comment: 5 pages, 4 figures, 2 column revtex4 format, ICMFS 2002 (Kyoto
Exact Computation of Influence Spread by Binary Decision Diagrams
Evaluating influence spread in social networks is a fundamental procedure to
estimate the word-of-mouth effect in viral marketing. There are enormous
studies about this topic; however, under the standard stochastic cascade
models, the exact computation of influence spread is known to be #P-hard. Thus,
the existing studies have used Monte-Carlo simulation-based approximations to
avoid exact computation.
We propose the first algorithm to compute influence spread exactly under the
independent cascade model. The algorithm first constructs binary decision
diagrams (BDDs) for all possible realizations of influence spread, then
computes influence spread by dynamic programming on the constructed BDDs. To
construct the BDDs efficiently, we designed a new frontier-based search-type
procedure. The constructed BDDs can also be used to solve other
influence-spread related problems, such as random sampling without rejection,
conditional influence spread evaluation, dynamic probability update, and
gradient computation for probability optimization problems.
We conducted computational experiments to evaluate the proposed algorithm.
The algorithm successfully computed influence spread on real-world networks
with a hundred edges in a reasonable time, which is quite impossible by the
naive algorithm. We also conducted an experiment to evaluate the accuracy of
the Monte-Carlo simulation-based approximation by comparing exact influence
spread obtained by the proposed algorithm.Comment: WWW'1
Electronic structure of CaSrVO: a tale of two energy-scales
We investigate the electronic structure of CaSrVO using
photoemission spectroscopy. Core level spectra establish an electronic phase
separation at the surface, leading to distinctly different surface electronic
structure compared to the bulk. Analysis of the photoemission spectra of this
system allowed us to separate the surface and bulk contributions. These results
help us to understand properties related to two vastly differing energy-scales,
namely the low energy-scale of thermal excitations (~) and the
high-energy scale related to Coulomb and other electronic interactions.Comment: 4 pages and 3 figures. Europhysics Letters (appearing
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