831,102 research outputs found
A Recursive Algorithm for Computing Inferences in Imprecise Markov Chains
We present an algorithm that can efficiently compute a broad class of
inferences for discrete-time imprecise Markov chains, a generalised type of
Markov chains that allows one to take into account partially specified
probabilities and other types of model uncertainty. The class of inferences
that we consider contains, as special cases, tight lower and upper bounds on
expected hitting times, on hitting probabilities and on expectations of
functions that are a sum or product of simpler ones. Our algorithm exploits the
specific structure that is inherent in all these inferences: they admit a
general recursive decomposition. This allows us to achieve a computational
complexity that scales linearly in the number of time points on which the
inference depends, instead of the exponential scaling that is typical for a
naive approach
Towards a Universal Wordnet by Learning from Combined Evidenc
Lexical databases are invaluable sources of knowledge about words and their meanings, with numerous applications in areas like NLP, IR, and AI. We propose a methodology for the automatic construction of a large-scale multilingual lexical database where words of many languages are hierarchically organized in terms of their meanings and their semantic relations to other words. This resource is bootstrapped from WordNet, a well-known English-language resource. Our approach extends WordNet with around 1.5 million meaning links for 800,000 words in over 200 languages, drawing on evidence extracted from a variety of resources including existing (monolingual) wordnets, (mostly bilingual) translation dictionaries, and parallel corpora. Graph-based scoring functions and statistical learning techniques are used to iteratively integrate this information and build an output graph. Experiments show that this wordnet has a high level of precision and coverage, and that it can be useful in applied tasks such as cross-lingual text classification
On the 1-loop calculations of softly broken fermion-torsion theory in curved space using the Stuckelberg procedure
The soft breaking of gauge or other symmetries is the typical Quantum Field
Theory phenomenon. In many cases one can apply the Stuckelberg procedure, which
means introducing some additional field (or fields) and restore the gauge
symmetry. The original softly broken theory corresponds to a particular choice
of the gauge fixing condition. In this paper we use this scheme for performing
quantum calculations for fermion-torsion theory, softly broken by the torsion
mass in arbitrary curved spacetime.Comment: Talk given at the 7th Alexander Friedmann International Seminar on
Gravitation and Cosmology, Joao Pessoa, Brazil, 29 Jun - 5 Jul 2008. 4 pages
and one figur
Interacting spin-1 bosons in a two-dimensional optical lattice
We study, using quantum Monte Carlo (QMC) simulations, the ground state
properties of spin-1 bosons trapped in a square optical lattice. The phase
diagram is characterized by the mobility of the particles (Mott insulating or
superfluid phase) and by their magnetic properties. For ferromagnetic on-site
interactions, the whole phase diagram is ferromagnetic and the Mott
insulators-superfluid phase transitions are second order. For antiferromagnetic
on-site interactions, spin nematic order is found in the odd Mott lobes and in
the superfluid phase. Furthermore, the superfluid-insulator phase transition is
first or second order depending on whether the density in the Mott is even or
odd. Inside the even Mott lobes, we observe a singlet-to-nematic transition for
certain values of the interactions. This transition appears to be first order
An exact representation of the fermion dynamics in terms of Poisson processes and its connection with Monte Carlo algorithms
We present a simple derivation of a Feynman-Kac type formula to study
fermionic systems. In this approach the real time or the imaginary time
dynamics is expressed in terms of the evolution of a collection of Poisson
processes. A computer implementation of this formula leads to a family of
algorithms parametrized by the values of the jump rates of the Poisson
processes. From these an optimal algorithm can be chosen which coincides with
the Green Function Monte Carlo method in the limit when the latter becomes
exact.Comment: 4 pages, 1 PostScript figure, REVTe
The Luminosity Function of Omega Centauri
Deep HST-WFPC2 observations of the stellar population in the globular cluster
Omega Cen (NGC 5139) have been used to measure the luminosity function of main
sequence stars at the low-mass end. Two fields have been investigated, located
respectively ~4.6' and ~7' away from the cluster center, or near the half-light
radius of this cluster (~4.8'). The color-magnitude diagrams derived from these
data show the cluster main sequence extending to the detection limit at I ~ 24.
Information on both color and magnitude is used to build the luminosity
functions of main sequence stars in these fields and the two independent
determinations are found to agree very well with each other within the
observational uncertainty. Both functions show a peak in the stellar
distribution around M_I ~ 9 followed by a drop at fainter magnitudes well
before photometric incompleteness becomes significant, as is typical of other
globular clusters observed with the HST. This result is at variance with
previous claims that the luminosity function of Omega Cen stays flat at low
masses, but is in excellent agreement with recent near-IR NICMOS observations
of the same cluster.Comment: To appear in The Astronomical Journal. Contains 7 pages, 4 figures,
prepared with the AAS LaTeX macr
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