341 research outputs found

### Palette-colouring: a belief-propagation approach

We consider a variation of the prototype combinatorial-optimisation problem
known as graph-colouring. Our optimisation goal is to colour the vertices of a
graph with a fixed number of colours, in a way to maximise the number of
different colours present in the set of nearest neighbours of each given
vertex. This problem, which we pictorially call "palette-colouring", has been
recently addressed as a basic example of problem arising in the context of
distributed data storage. Even though it has not been proved to be NP complete,
random search algorithms find the problem hard to solve. Heuristics based on a
naive belief propagation algorithm are observed to work quite well in certain
conditions. In this paper, we build upon the mentioned result, working out the
correct belief propagation algorithm, which needs to take into account the
many-body nature of the constraints present in this problem. This method
improves the naive belief propagation approach, at the cost of increased
computational effort. We also investigate the emergence of a satisfiable to
unsatisfiable "phase transition" as a function of the vertex mean degree, for
different ensembles of sparse random graphs in the large size ("thermodynamic")
limit.Comment: 22 pages, 7 figure

### On Cavity Approximations for Graphical Models

We reformulate the Cavity Approximation (CA), a class of algorithms recently
introduced for improving the Bethe approximation estimates of marginals in
graphical models. In our new formulation, which allows for the treatment of
multivalued variables, a further generalization to factor graphs with arbitrary
order of interaction factors is explicitly carried out, and a message passing
algorithm that implements the first order correction to the Bethe approximation
is described. Furthermore we investigate an implementation of the CA for
pairwise interactions. In all cases considered we could confirm that CA[k] with
increasing $k$ provides a sequence of approximations of markedly increasing
precision. Furthermore in some cases we could also confirm the general
expectation that the approximation of order $k$, whose computational complexity
is $O(N^{k+1})$ has an error that scales as $1/N^{k+1}$ with the size of the
system. We discuss the relation between this approach and some recent
developments in the field.Comment: Extension to factor graphs and comments on related work adde

### Dynamic rewiring in small world networks

We investigate equilibrium properties of small world networks, in which both
connectivity and spin variables are dynamic, using replicated transfer matrices
within the replica symmetric approximation. Population dynamics techniques
allow us to examine order parameters of our system at total equilibrium,
probing both spin- and graph-statistics. Of these, interestingly, the degree
distribution is found to acquire a Poisson-like form (both within and outside
the ordered phase). Comparison with Glauber simulations confirms our results
satisfactorily.Comment: 21 pages, 5 figure

### Fast Decoders for Topological Quantum Codes

We present a family of algorithms, combining real-space renormalization
methods and belief propagation, to estimate the free energy of a topologically
ordered system in the presence of defects. Such an algorithm is needed to
preserve the quantum information stored in the ground space of a topologically
ordered system and to decode topological error-correcting codes. For a system
of linear size L, our algorithm runs in time log L compared to L^6 needed for
the minimum-weight perfect matching algorithm previously used in this context
and achieves a higher depolarizing error threshold.Comment: 4 pages, 4 figure

### Statistical-mechanical iterative algorithms on complex networks

The Ising models have been applied for various problems on information
sciences, social sciences, and so on. In many cases, solving these problems
corresponds to minimizing the Bethe free energy. To minimize the Bethe free
energy, a statistical-mechanical iterative algorithm is often used. We study
the statistical-mechanical iterative algorithm on complex networks. To
investigate effects of heterogeneous structures on the iterative algorithm, we
introduce an iterative algorithm based on information of heterogeneity of
complex networks, in which higher-degree nodes are likely to be updated more
frequently than lower-degree ones. Numerical experiments clarified that the
usage of the information of heterogeneity affects the algorithm in BA networks,
but does not influence that in ER networks. It is revealed that information of
the whole system propagates rapidly through such high-degree nodes in the case
of Barab{\'a}si-Albert's scale-free networks.Comment: 7 pages, 6 figure

### Gaussian Belief with dynamic data and in dynamic network

In this paper we analyse Belief Propagation over a Gaussian model in a
dynamic environment. Recently, this has been proposed as a method to average
local measurement values by a distributed protocol ("Consensus Propagation",
Moallemi & Van Roy, 2006), where the average is available for read-out at every
single node. In the case that the underlying network is constant but the values
to be averaged fluctuate ("dynamic data"), convergence and accuracy are
determined by the spectral properties of an associated Ruelle-Perron-Frobenius
operator. For Gaussian models on Erdos-Renyi graphs, numerical computation
points to a spectral gap remaining in the large-size limit, implying
exceptionally good scalability. In a model where the underlying network also
fluctuates ("dynamic network"), averaging is more effective than in the dynamic
data case. Altogether, this implies very good performance of these methods in
very large systems, and opens a new field of statistical physics of large (and
dynamic) information systems.Comment: 5 pages, 7 figure

### Sums over geometries and improvements on the mean field approximation

The saddle points of a Lagrangian due to Efetov are analyzed. This Lagrangian
was originally proposed as a tool for calculating systematic corrections to the
Bethe approximation, a mean-field approximation which is important in
statistical mechanics, glasses, coding theory, and combinatorial optimization.
Detailed analysis shows that the trivial saddle point generates a sum over
geometries reminiscent of dynamically triangulated quantum gravity, which
suggests new possibilities to design sums over geometries for the specific
purpose of obtaining improved mean field approximations to $D$-dimensional
theories. In the case of the Efetov theory, the dominant geometries are locally
tree-like, and the sum over geometries diverges in a way that is similar to
quantum gravity's divergence when all topologies are included. Expertise from
the field of dynamically triangulated quantum gravity about sums over
geometries may be able to remedy these defects and fulfill the Efetov theory's
original promise. The other saddle points of the Efetov Lagrangian are also
analyzed; the Hessian at these points is nonnormal and pseudo-Hermitian, which
is unusual for bosonic theories. The standard formula for Gaussian integrals is
generalized to nonnormal kernels.Comment: Accepted for publication in Physical Review D, probably in November
2007. At the reviewer's request, material was added which made the article
more assertive, confident, and clear. No changes in substanc

### Exact solution of the Bose-Hubbard model on the Bethe lattice

The exact solution of a quantum Bethe lattice model in the thermodynamic
limit amounts to solve a functional self-consistent equation. In this paper we
obtain this equation for the Bose-Hubbard model on the Bethe lattice, under two
equivalent forms. The first one, based on a coherent state path integral, leads
in the large connectivity limit to the mean field treatment of Fisher et al.
[Phys. Rev. B {\bf 40}, 546 (1989)] at the leading order, and to the bosonic
Dynamical Mean Field Theory as a first correction, as recently derived by
Byczuk and Vollhardt [Phys. Rev. B {\bf 77}, 235106 (2008)]. We obtain an
alternative form of the equation using the occupation number representation,
which can be easily solved with an arbitrary numerical precision, for any
finite connectivity. We thus compute the transition line between the superfluid
and Mott insulator phases of the model, along with thermodynamic observables
and the space and imaginary time dependence of correlation functions. The
finite connectivity of the Bethe lattice induces a richer physical content with
respect to its infinitely connected counterpart: a notion of distance between
sites of the lattice is preserved, and the bosons are still weakly mobile in
the Mott insulator phase. The Bethe lattice construction can be viewed as an
approximation to the finite dimensional version of the model. We show indeed a
quantitatively reasonable agreement between our predictions and the results of
Quantum Monte Carlo simulations in two and three dimensions.Comment: 27 pages, 16 figures, minor correction

### Probabilistic Bag-Of-Hyperlinks Model for Entity Linking

Many fundamental problems in natural language processing rely on determining
what entities appear in a given text. Commonly referenced as entity linking,
this step is a fundamental component of many NLP tasks such as text
understanding, automatic summarization, semantic search or machine translation.
Name ambiguity, word polysemy, context dependencies and a heavy-tailed
distribution of entities contribute to the complexity of this problem.
We here propose a probabilistic approach that makes use of an effective
graphical model to perform collective entity disambiguation. Input mentions
(i.e.,~linkable token spans) are disambiguated jointly across an entire
document by combining a document-level prior of entity co-occurrences with
local information captured from mentions and their surrounding context. The
model is based on simple sufficient statistics extracted from data, thus
relying on few parameters to be learned.
Our method does not require extensive feature engineering, nor an expensive
training procedure. We use loopy belief propagation to perform approximate
inference. The low complexity of our model makes this step sufficiently fast
for real-time usage. We demonstrate the accuracy of our approach on a wide
range of benchmark datasets, showing that it matches, and in many cases
outperforms, existing state-of-the-art methods

### Belief propagation algorithm for computing correlation functions in finite-temperature quantum many-body systems on loopy graphs

Belief propagation -- a powerful heuristic method to solve inference problems
involving a large number of random variables -- was recently generalized to
quantum theory. Like its classical counterpart, this algorithm is exact on
trees when the appropriate independence conditions are met and is expected to
provide reliable approximations when operated on loopy graphs. In this paper,
we benchmark the performances of loopy quantum belief propagation (QBP) in the
context of finite-tempereture quantum many-body physics. Our results indicate
that QBP provides reliable estimates of the high-temperature correlation
function when the typical loop size in the graph is large. As such, it is
suitable e.g. for the study of quantum spin glasses on Bethe lattices and the
decoding of sparse quantum error correction codes.Comment: 5 pages, 4 figure

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