5,577 research outputs found
Properties of the energy landscape of network models for covalent glasses
We investigate the energy landscape of two dimensional network models for
covalent glasses by means of the lid algorithm. For three different particle
densities and for a range of network sizes, we exhaustively analyse many
configuration space regions enclosing deep-lying energy minima. We extract the
local densities of states and of minima, and the number of states and minima
accessible below a certain energy barrier, the 'lid'. These quantities show on
average a close to exponential growth as a function of their respective
arguments. We calculate the configurational entropy for these pockets of states
and find that the excess specific heat exhibits a peak at a critical
temperature associated with the exponential growth in the local density of
states, a feature of the specific heat also observed in real glasses at the
glass transition.Comment: RevTeX, 19 pages, 7 figure
Capacity estimation of two-dimensional channels using Sequential Monte Carlo
We derive a new Sequential-Monte-Carlo-based algorithm to estimate the
capacity of two-dimensional channel models. The focus is on computing the
noiseless capacity of the 2-D one-infinity run-length limited constrained
channel, but the underlying idea is generally applicable. The proposed
algorithm is profiled against a state-of-the-art method, yielding more than an
order of magnitude improvement in estimation accuracy for a given computation
time
Nested Sequential Monte Carlo Methods
We propose nested sequential Monte Carlo (NSMC), a methodology to sample from
sequences of probability distributions, even where the random variables are
high-dimensional. NSMC generalises the SMC framework by requiring only
approximate, properly weighted, samples from the SMC proposal distribution,
while still resulting in a correct SMC algorithm. Furthermore, NSMC can in
itself be used to produce such properly weighted samples. Consequently, one
NSMC sampler can be used to construct an efficient high-dimensional proposal
distribution for another NSMC sampler, and this nesting of the algorithm can be
done to an arbitrary degree. This allows us to consider complex and
high-dimensional models using SMC. We show results that motivate the efficacy
of our approach on several filtering problems with dimensions in the order of
100 to 1 000.Comment: Extended version of paper published in Proceedings of the 32nd
International Conference on Machine Learning (ICML), Lille, France, 201
Sequential Monte Carlo for Graphical Models
We propose a new framework for how to use sequential Monte Carlo (SMC)
algorithms for inference in probabilistic graphical models (PGM). Via a
sequential decomposition of the PGM we find a sequence of auxiliary
distributions defined on a monotonically increasing sequence of probability
spaces. By targeting these auxiliary distributions using SMC we are able to
approximate the full joint distribution defined by the PGM. One of the key
merits of the SMC sampler is that it provides an unbiased estimate of the
partition function of the model. We also show how it can be used within a
particle Markov chain Monte Carlo framework in order to construct
high-dimensional block-sampling algorithms for general PGMs
Strongly enhanced shot noise in chains of quantum dots
We study charge transport through a chain of quantum dots. The dots are fully
coherent among each other and weakly coupled to metallic electrodes via the
dots at the interface, thus modelling a molecular wire. If the non-local
Coulomb interactions dominate over the inter-dot hopping we find strongly
enhanced shot noise above the sequential tunneling threshold. The current is
not enhanced in the region of enhanced noise, thus rendering the noise
super-Poissonian. In contrast to earlier work this is achieved even in a fully
symmetric system. The origin of this novel behavior lies in a competition of
"slow" and "fast" transport channels that are formed due to the differing
non-local wave functions and total spin of the states participating in
transport. This strong enhancement may allow direct experimental detection of
shot noise in a chain of lateral quantum dots.Comment: 4 pages, 2 figures, submitted to PR
Charge Transport in Voltage-Biased Superconducting Single-Electron Transistors
Charge is transported through superconducting SSS single-electron transistors
at finite bias voltages by a combination of coherent Cooper-pair tunneling and
quasiparticle tunneling. At low transport voltages the effect of an ``odd''
quasiparticle in the island leads to a -periodic dependence of the current
on the gate charge. We evaluate the characteristic in the framework of a
model which accounts for these effects as well as for the influence of the
electromagnetic environment. The good agreement between our model calculation
and experimental results demonstrates the importance of coherent Cooper-pair
tunneling and parity effects.Comment: RevTeX, 12 pages, 4 figure
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