3,213 research outputs found
Adaptive Cluster Expansion for Inferring Boltzmann Machines with Noisy Data
We introduce a procedure to infer the interactions among a set of binary
variables, based on their sampled frequencies and pairwise correlations. The
algorithm builds the clusters of variables contributing most to the entropy of
the inferred Ising model, and rejects the small contributions due to the
sampling noise. Our procedure successfully recovers benchmark Ising models even
at criticality and in the low temperature phase, and is applied to
neurobiological data.Comment: Accepted for publication in Physical Review Letters (2011
Measuring thermodynamic length
Thermodynamic length is a metric distance between equilibrium thermodynamic
states. Among other interesting properties, this metric asymptotically bounds
the dissipation induced by a finite time transformation of a thermodynamic
system. It is also connected to the Jensen-Shannon divergence, Fisher
information and Rao's entropy differential metric. Therefore, thermodynamic
length is of central interest in understanding matter out-of-equilibrium. In
this paper, we will consider how to define thermodynamic length for a small
system described by equilibrium statistical mechanics and how to measure
thermodynamic length within a computer simulation. Surprisingly, Bennett's
classic acceptance ratio method for measuring free energy differences also
measures thermodynamic length.Comment: 4 pages; Typos correcte
Quantum Correlations in Large-Dimensional States of High Symmetry
In this article, we investigate how quantum correlations behave for the
so-called Werner and pseudo-pure families of states. The latter refers to
states formed by mixing any pure state with the totally mixed state. We derive
closed expressions for the Quantum Discord (QD) and the Relative Entropy of
Quantumness (REQ) for these families of states. For Werner states, the
classical correlations are seen to vanish in high dimensions while the amount
of quantum correlations remain bounded and become independent of whether or not
the the state is entangled. For pseudo-pure states, nearly the opposite effect
is observed with both the quantum and classical correlations growing without
bound as the dimension increases and only as the system becomes more entangled.
Finally, we verify that pseudo-pure states satisfy the conjecture of
[\textit{Phys. Rev. A} \textbf{84}, 052110 (2011)] which says that the
Geometric Measure of Discord (GD) always upper bounds the squared Negativity of
the state
Satellite remote sensing for ice sheet research
Potential research applications of satellite data over the terrestrial ice sheets of Greenland and Antarctica are assessed and actions required to ensure acquisition of relevant data and appropriate processing to a form suitable for research purposes are recommended. Relevant data include high-resolution visible and SAR imagery, infrared, passive-microwave and scatterometer measurements, and surface topography information from laser and radar altimeters
Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks
Learning deeper convolutional neural networks becomes a tendency in recent
years. However, many empirical evidences suggest that performance improvement
cannot be gained by simply stacking more layers. In this paper, we consider the
issue from an information theoretical perspective, and propose a novel method
Relay Backpropagation, that encourages the propagation of effective information
through the network in training stage. By virtue of the method, we achieved the
first place in ILSVRC 2015 Scene Classification Challenge. Extensive
experiments on two challenging large scale datasets demonstrate the
effectiveness of our method is not restricted to a specific dataset or network
architecture. Our models will be available to the research community later.Comment: Technical report for our submissions to the ILSVRC 2015 Scene
Classification Challenge, where we won the first plac
Entropy Rate of Diffusion Processes on Complex Networks
The concept of entropy rate for a dynamical process on a graph is introduced.
We study diffusion processes where the node degrees are used as a local
information by the random walkers. We describe analitically and numerically how
the degree heterogeneity and correlations affect the diffusion entropy rate. In
addition, the entropy rate is used to characterize complex networks from the
real world. Our results point out how to design optimal diffusion processes
that maximize the entropy for a given network structure, providing a new
theoretical tool with applications to social, technological and communication
networks.Comment: 4 pages (APS format), 3 figures, 1 tabl
Quantum superadditivity in linear optics networks: sending bits via multiple access Gaussian channels
We study classical capacity regions of quantum Gaussian multiple access
channels (MAC). In classical variants of such channels, whilst some capacity
superadditivity-type effects such as the so called {\it water filling effect}
may be achieved, a fundamental classical additivity law can still be
identified, {\it viz.} adding resources to one sender is never advantageous to
other senders in sending their respective information to the receiver. Here, we
show that quantum resources allows violation of this law, by providing two
illustrative schemes of experimentally feasible Gaussian MACs.Comment: 4 pages, 2 figure
Parameter estimation in pair hidden Markov models
This paper deals with parameter estimation in pair hidden Markov models
(pair-HMMs). We first provide a rigorous formalism for these models and discuss
possible definitions of likelihoods. The model being biologically motivated,
some restrictions with respect to the full parameter space naturally occur.
Existence of two different Information divergence rates is established and
divergence property (namely positivity at values different from the true one)
is shown under additional assumptions. This yields consistency for the
parameter in parametrization schemes for which the divergence property holds.
Simulations illustrate different cases which are not covered by our results.Comment: corrected typo
Dissipation: The phase-space perspective
We show, through a refinement of the work theorem, that the average
dissipation, upon perturbing a Hamiltonian system arbitrarily far out of
equilibrium in a transition between two canonical equilibrium states, is
exactly given by , where and are the
phase space density of the system measured at the same intermediate but
otherwise arbitrary point in time, for the forward and backward process.
is the relative entropy of versus
. This result also implies general inequalities, which are
significantly more accurate than the second law and include, as a special case,
the celebrated Landauer principle on the dissipation involved in irreversible
computations.Comment: 4 pages, 3 figures (4 figure files), accepted for PR
Sky maps without anisotropies in the cosmic microwave background are a better fit to WMAP's uncalibrated time ordered data than the official sky maps
The purpose of this reanalysis of the WMAP uncalibrated time ordered data
(TOD) was two fold. The first was to reassess the reliability of the detection
of the anisotropies in the official WMAP sky maps of the cosmic microwave
background (CMB). The second was to assess the performance of a proposed
criterion in avoiding systematic error in detecting a signal of interest. The
criterion was implemented by testing the null hypothesis that the uncalibrated
TOD was consistent with no anisotropies when WMAP's hourly calibration
parameters were allowed to vary. It was shown independently for all 20 WMAP
channels that sky maps with no anisotropies were a better fit to the TOD than
those from the official analysis. The recently launched Planck satellite should
help sort out this perplexing result.Comment: 11 pages with 1 figure and 2 tables. Extensively rewritten to explain
the research bette
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