11,807 research outputs found
Simpler is better: a novel genetic algorithm to induce compact multi-label chain classifiers
Multi-label classification (MLC) is the task of assigning multiple class labels to an object based on the features that describe the object. One of the most effective MLC methods is known as Classifier Chains (CC). This approach consists in training q binary classifiers linked in a chain, y1 â y2 â ... â yq, with each responsible for classifying a specific label in {l1, l2, ..., lq}. The chaining mechanism allows each individual classifier to incorporate the predictions of the previous ones as additional information at classification time. Thus, possible correlations among labels can be automatically exploited. Nevertheless, CC suffers from two important drawbacks: (i) the label ordering is decided at random, although it usually has a strong effect on predictive accuracy; (ii) all labels are inserted into the chain, although some of them might carry irrelevant information to discriminate the others. In this paper we tackle both problems at once, by proposing a novel genetic algorithm capable of searching for a single optimized label ordering, while at the same time taking into consideration the utilization of partial chains. Experiments on benchmark datasets demonstrate that our approach is able to produce models that are both simpler and more accurate
No-horizon theorem for spacetimes with spacelike G1 isometry groups
We consider four-dimensional spacetimes which obey the
Einstein equations , and admit a global spacelike
isometry group. By means of dimensional reduction and local
analyis on the reduced (2+1) spacetime, we obtain a sufficient condition on
which guarantees that cannot contain apparent
horizons. Given any (3+1) spacetime with spacelike translational isometry, the
no-horizon condition can be readily tested without the need for dimensional
reduction. This provides thus a useful and encompassing apparent horizon test
for -symmetric spacetimes. We argue that this adds further evidence
towards the validity of the hoop conjecture, and signals possible violations of
strong cosmic censorship.Comment: 8 pages, LaTeX, uses IOP package; published in Class. Quantum Gra
Viabilidade técnico-econÎmico de uma unidade de produção de leite nas mesorregiÔes metropolitas de Belém e Nordeste Paraense.
Scaling limits of a tagged particle in the exclusion process with variable diffusion coefficient
We prove a law of large numbers and a central limit theorem for a tagged
particle in a symmetric simple exclusion process in the one-dimensional lattice
with variable diffusion coefficient. The scaling limits are obtained from a
similar result for the current through -1/2 for a zero-range process with bond
disorder. For the CLT, we prove convergence to a fractional Brownian motion of
Hurst exponent 1/4.Comment: 9 page
Distinguishing noise from chaos: objective versus subjective criteria using Horizontal Visibility Graph
A recently proposed methodology called the Horizontal Visibility Graph (HVG)
[Luque {\it et al.}, Phys. Rev. E., 80, 046103 (2009)] that constitutes a
geometrical simplification of the well known Visibility Graph algorithm [Lacasa
{\it et al.\/}, Proc. Natl. Sci. U.S.A. 105, 4972 (2008)], has been used to
study the distinction between deterministic and stochastic components in time
series [L. Lacasa and R. Toral, Phys. Rev. E., 82, 036120 (2010)].
Specifically, the authors propose that the node degree distribution of these
processes follows an exponential functional of the form , in which is the node degree and is a
positive parameter able to distinguish between deterministic (chaotic) and
stochastic (uncorrelated and correlated) dynamics. In this work, we investigate
the characteristics of the node degree distributions constructed by using HVG,
for time series corresponding to chaotic maps and different stochastic
processes. We thoroughly study the methodology proposed by Lacasa and Toral
finding several cases for which their hypothesis is not valid. We propose a
methodology that uses the HVG together with Information Theory quantifiers. An
extensive and careful analysis of the node degree distributions obtained by
applying HVG allow us to conclude that the Fisher-Shannon information plane is
a remarkable tool able to graphically represent the different nature,
deterministic or stochastic, of the systems under study.Comment: Submitted to PLOS On
Cosmic homogeneity: a spectroscopic and model-independent measurement
Cosmology relies on the Cosmological Principle, i.e., the hypothesis that the
Universe is homogeneous and isotropic on large scales. This implies in
particular that the counts of galaxies should approach a homogeneous scaling
with volume at sufficiently large scales. Testing homogeneity is crucial to
obtain a correct interpretation of the physical assumptions underlying the
current cosmic acceleration and structure formation of the Universe. In this
Letter, we use the Baryon Oscillation Spectroscopic Survey to make the first
spectroscopic and model-independent measurements of the angular homogeneity
scale . Applying four statistical estimators, we show that the
angular distribution of galaxies in the range 0.46 < z < 0.62 is consistent
with homogeneity at large scales, and that varies with
redshift, indicating a smoother Universe in the past. These results are in
agreement with the foundations of the standard cosmological paradigm.Comment: 5 pages, 2 figures, Version accepted by MNRA
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