5,285 research outputs found
The IBMAP approach for Markov networks structure learning
In this work we consider the problem of learning the structure of Markov
networks from data. We present an approach for tackling this problem called
IBMAP, together with an efficient instantiation of the approach: the IBMAP-HC
algorithm, designed for avoiding important limitations of existing
independence-based algorithms. These algorithms proceed by performing
statistical independence tests on data, trusting completely the outcome of each
test. In practice tests may be incorrect, resulting in potential cascading
errors and the consequent reduction in the quality of the structures learned.
IBMAP contemplates this uncertainty in the outcome of the tests through a
probabilistic maximum-a-posteriori approach. The approach is instantiated in
the IBMAP-HC algorithm, a structure selection strategy that performs a
polynomial heuristic local search in the space of possible structures. We
present an extensive empirical evaluation on synthetic and real data, showing
that our algorithm outperforms significantly the current independence-based
algorithms, in terms of data efficiency and quality of learned structures, with
equivalent computational complexities. We also show the performance of IBMAP-HC
in a real-world application of knowledge discovery: EDAs, which are
evolutionary algorithms that use structure learning on each generation for
modeling the distribution of populations. The experiments show that when
IBMAP-HC is used to learn the structure, EDAs improve the convergence to the
optimum
Normalized ghost imaging
We present an experimental comparison between different iterative ghost imaging algorithms. Our experimental setup utilizes a spatial light modulator for generating known random light fields to illuminate a partially-transmissive object. We adapt the weighting factor used in the traditional ghost imaging algorithm to account for changes in the efficiency of the generated light field. We show that our normalized weighting algorithm can match the performance of differential ghost imaging
Close-packed floating clusters: granular hydrodynamics beyond the freezing point?
Monodisperse granular flows often develop regions with hexagonal close
packing of particles. We investigate this effect in a system of inelastic hard
spheres driven from below by a "thermal" plate. Molecular dynamics simulations
show, in a wide range of parameters, a close-packed cluster supported by a
low-density region. Surprisingly, the steady-state density profile, including
the close-packed cluster part, is well described by a variant of Navier-Stokes
granular hydrodynamics (NSGH). We suggest a simple explanation for the success
of NSGH beyond the freezing point.Comment: 4 pages, 5 figures. To appear in Phys. Rev. Let
Minimal surfaces and particles in 3-manifolds
We use minimal (or CMC) surfaces to describe 3-dimensional hyperbolic,
anti-de Sitter, de Sitter or Minkowski manifolds. We consider whether these
manifolds admit ``nice'' foliations and explicit metrics, and whether the space
of these metrics has a simple description in terms of Teichm\"uller theory. In
the hyperbolic settings both questions have positive answers for a certain
subset of the quasi-Fuchsian manifolds: those containing a closed surface with
principal curvatures at most 1. We show that this subset is parameterized by an
open domain of the cotangent bundle of Teichm\"uller space. These results are
extended to ``quasi-Fuchsian'' manifolds with conical singularities along
infinite lines, known in the physics literature as ``massive, spin-less
particles''.
Things work better for globally hyperbolic anti-de Sitter manifolds: the
parameterization by the cotangent of Teichm\"uller space works for all
manifolds. There is another description of this moduli space as the product two
copies of Teichm\"uller space due to Mess. Using the maximal surface
description, we propose a new parameterization by two copies of Teichm\"uller
space, alternative to that of Mess, and extend all the results to manifolds
with conical singularities along time-like lines. Similar results are obtained
for de Sitter or Minkowski manifolds.
Finally, for all four settings, we show that the symplectic form on the
moduli space of 3-manifolds that comes from parameterization by the cotangent
bundle of Teichm\"uller space is the same as the 3-dimensional gravity one.Comment: 53 pages, no figure. v2: typos corrected and refs adde
Jets Produced in Ï^-, Ï^+, and Proton Interactions at 200 GeV on Hydrogen and Aluminum Targets
This paper presents results from an experiment on the production of jets (groups of particles) with high p_â„ produced in 200-GeV/c interactions. Results are presented on the comparison of jet cross sections on aluminum and hydrogen targets. The jet fragmentation distributions are also examined. Both the cross section and the jet structure are found to depend strongly on the beam and target types
Measurement of Forward Jets Produced in High-Transverse-Momentum Hadron-Proton Collisions
A measurement of charged-particle production is reported for the forward region in events triggered by high-transverse-momentum (pâ„) jets and single particles. The momentum distributions of forward-going particles are observed to scale in a simple pâ„-dependent longitudinal variable. Forward-going (beam) jets are observed to be tilted away from the original direction by an amount which agrees with muon-pair data when interpreted in a parton (quantum-chromodynamics) model
Experimental Tests of Quantum Chromodynamics in High-p_â„ Jet Production in 200-GeV/c Hadron-Proton Collisions
Data on inclusive jet production in the transverse-momentum (p_â„) range 0-8 GeV/c for 200-GeV/c p, Ï^-, Ï^+, K^-, K^+, and p incident on a hydrogen target are presented. The jet cross section is fully corrected for losses and biases, and compared with the predictions of a model based on quantum chromodynamics. Both the absolute cross section and the inclusive charged-particle distributions inside and outside the jet are in qualitative agreement with the model
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