2,709 research outputs found
Superconducting film with randomly magnetized dots: A realization of the 2D XY model with random phase shifts
We consider a thin superconducting film with randomly magnetized dots on top
of it. The dots produce a disordered pinning potential for vortices in the
film. We show that for dots with permanent and random magnetization normal or
parallel to the film surface, our system is an experimental realization of the
two-dimensional XY model with random phase shifts. The low-temperature
superconducting phase, that exists without magnetic dots, survives in the
presence of magnetic dots for sufficiently small disorder.Comment: 5 pages, 1 figur
Hierarchical Models for Independence Structures of Networks
We introduce a new family of network models, called hierarchical network
models, that allow us to represent in an explicit manner the stochastic
dependence among the dyads (random ties) of the network. In particular, each
member of this family can be associated with a graphical model defining
conditional independence clauses among the dyads of the network, called the
dependency graph. Every network model with dyadic independence assumption can
be generalized to construct members of this new family. Using this new
framework, we generalize the Erd\"os-R\'enyi and beta-models to create
hierarchical Erd\"os-R\'enyi and beta-models. We describe various methods for
parameter estimation as well as simulation studies for models with sparse
dependency graphs.Comment: 19 pages, 7 figure
Large Area Crop Inventory Experiment (LACIE). Intensive test site assessment report
There are no author-identified significant results in this report
Dynamical replica analysis of disordered Ising spin systems on finitely connected random graphs
We study the dynamics of macroscopic observables such as the magnetization
and the energy per degree of freedom in Ising spin models on random graphs of
finite connectivity, with random bonds and/or heterogeneous degree
distributions. To do so we generalize existing implementations of dynamical
replica theory and cavity field techniques to systems with strongly disordered
and locally tree-like interactions. We illustrate our results via application
to the dynamics of e.g. spin-glasses on random graphs and of the
overlap in finite connectivity Sourlas codes. All results are tested against
Monte Carlo simulations.Comment: 4 pages, 14 .eps file
Quantifying Self-Organization with Optimal Predictors
Despite broad interest in self-organizing systems, there are few
quantitative, experimentally-applicable criteria for self-organization. The
existing criteria all give counter-intuitive results for important cases. In
this Letter, we propose a new criterion, namely an internally-generated
increase in the statistical complexity, the amount of information required for
optimal prediction of the system's dynamics. We precisely define this
complexity for spatially-extended dynamical systems, using the probabilistic
ideas of mutual information and minimal sufficient statistics. This leads to a
general method for predicting such systems, and a simple algorithm for
estimating statistical complexity. The results of applying this algorithm to a
class of models of excitable media (cyclic cellular automata) strongly support
our proposal.Comment: Four pages, two color figure
Peer-to-Peer Secure Multi-Party Numerical Computation Facing Malicious Adversaries
We propose an efficient framework for enabling secure multi-party numerical
computations in a Peer-to-Peer network. This problem arises in a range of
applications such as collaborative filtering, distributed computation of trust
and reputation, monitoring and other tasks, where the computing nodes is
expected to preserve the privacy of their inputs while performing a joint
computation of a certain function. Although there is a rich literature in the
field of distributed systems security concerning secure multi-party
computation, in practice it is hard to deploy those methods in very large scale
Peer-to-Peer networks. In this work, we try to bridge the gap between
theoretical algorithms in the security domain, and a practical Peer-to-Peer
deployment.
We consider two security models. The first is the semi-honest model where
peers correctly follow the protocol, but try to reveal private information. We
provide three possible schemes for secure multi-party numerical computation for
this model and identify a single light-weight scheme which outperforms the
others. Using extensive simulation results over real Internet topologies, we
demonstrate that our scheme is scalable to very large networks, with up to
millions of nodes. The second model we consider is the malicious peers model,
where peers can behave arbitrarily, deliberately trying to affect the results
of the computation as well as compromising the privacy of other peers. For this
model we provide a fourth scheme to defend the execution of the computation
against the malicious peers. The proposed scheme has a higher complexity
relative to the semi-honest model. Overall, we provide the Peer-to-Peer network
designer a set of tools to choose from, based on the desired level of security.Comment: Submitted to Peer-to-Peer Networking and Applications Journal (PPNA)
200
Picturing classical and quantum Bayesian inference
We introduce a graphical framework for Bayesian inference that is
sufficiently general to accommodate not just the standard case but also recent
proposals for a theory of quantum Bayesian inference wherein one considers
density operators rather than probability distributions as representative of
degrees of belief. The diagrammatic framework is stated in the graphical
language of symmetric monoidal categories and of compact structures and
Frobenius structures therein, in which Bayesian inversion boils down to
transposition with respect to an appropriate compact structure. We characterize
classical Bayesian inference in terms of a graphical property and demonstrate
that our approach eliminates some purely conventional elements that appear in
common representations thereof, such as whether degrees of belief are
represented by probabilities or entropic quantities. We also introduce a
quantum-like calculus wherein the Frobenius structure is noncommutative and
show that it can accommodate Leifer's calculus of `conditional density
operators'. The notion of conditional independence is also generalized to our
graphical setting and we make some preliminary connections to the theory of
Bayesian networks. Finally, we demonstrate how to construct a graphical
Bayesian calculus within any dagger compact category.Comment: 38 pages, lots of picture
Scaling Analysis of Affinity Propagation
We analyze and exploit some scaling properties of the Affinity Propagation
(AP) clustering algorithm proposed by Frey and Dueck (2007). First we observe
that a divide and conquer strategy, used on a large data set hierarchically
reduces the complexity to , for a
data-set of size and a depth of the hierarchical strategy. For a
data-set embedded in a -dimensional space, we show that this is obtained
without notably damaging the precision except in dimension . In fact, for
larger than 2 the relative loss in precision scales like
. Finally, under some conditions we observe that there is a
value of the penalty coefficient, a free parameter used to fix the number
of clusters, which separates a fragmentation phase (for ) from a
coalescent one (for ) of the underlying hidden cluster structure. At
this precise point holds a self-similarity property which can be exploited by
the hierarchical strategy to actually locate its position. From this
observation, a strategy based on \AP can be defined to find out how many
clusters are present in a given dataset.Comment: 28 pages, 14 figures, Inria research repor
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Assimilation of thermal emission spectrometer atmospheric data during the Mars Global Surveyor aerobraking period
The Thermal Emission Spectrometer aboard the Mars Global
Surveyor spacecraft has produced an extensive atmospheric data set, beginning during aerobraking and continuing throughout the extended scientific mapping phase. Temperature profiles for the atmosphere below about 40 km, surface temperatures and total dust and water ice opacities, can be retrieved from infrared spectra in nadir viewing mode. This paper describes assimilation of nadir retrievals from the spacecraft aerobraking period, Ls=190-260, northern hemisphere autumn to winter, into a Mars general circulation model. The assimilation scheme is able to combine information from temperature and dust optical depth retrievals, making use of a model forecast containing information from the assimilation of earlier observations, to obtain a global, time-dependent analysis. Given sufficient temperature retrievals, the assimilation procedure indicates errors in the a priori dust distribution assumptions even when lacking dust observations; in this case there are relatively cold regions above the poles compared to a model which assumes a horizontally-uniform dust distribution. One major reason for using assimilation techniques is in order to investigate the transient wave behavior on Mars. Whilst the data from the 2-hour spacecraft mapping orbit phase is much more suitable for assimilation, even the longer (45--24 hour) period aerobraking orbit data contain useful information about the three-dimensional synoptic-scale martian circulation which the assimilation procedure can reconstruct in a consistent way. Assimilations from the period of the Noachis regional dust storm demonstrate that the combined assimilation of temperature and dust retrievals has a beneficial impact on the atmospheric analysis
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