2,797 research outputs found
Testing the homogeneity of the Universe using gamma-ray bursts
In this paper, we study the homogeneity of the GRB distribution using a
subsample of the Greiner GRB catalogue, which contains 314 objects with
redshift (244 of them discovered by the Swift GRB Mission). We try to
reconcile the dilemma between the new observations and the current theory of
structure formation and growth. To test the results against the possible biases
in redshift determination and the incompleteness of the Greiner sample, we also
apply our analysis to the 244 GRBs discovered by Swift and the subsample
presented by the Swift Gamma-Ray Burst Host Galaxy Legacy Survey (SHOALS). The
real space two-point correlation function (2PCF) of GRBs, is
calculated using a Landy-Szalay estimator. We perform a standard least-
fit to the measured 2PCFs of GRBs. We use the best-fit 2PCF to deduce a
recently defined homogeneity scale. The homogeneity scale, , is defined as
the comoving radius of the sphere inside which the number of GRBs is
proportional to within , or equivalently above which the correlation
dimension of the sample is within of . For the Swift
subsample of 244 GRBs, the correlation length and slope are Mpc and (at confidence level).
The corresponding scale for a homogeneous distribution of GRBs is Mpc. The results help to alleviate the tension between the new
discovery of the excess clustering of GRBs and the cosmological principle of
large-scale homogeneity. It implies that very massive structures in the
relatively local Universe do not necessarily violate the cosmological principle
and could conceivably be present.Comment: 7 pages, 5 figures, accepted by Astronomy & Astrophysics. The data
used in this work (e.g. Tables 1 and 2) are publicly available online in
electronic form at the CDS via anonymous ftp to cdsarc.u-strasbg.fr
(130.79.128.5) or via http://cdsweb.u-strasbg.fr/cgi-bin/qcat?J/A+A
Efficient Optimization of Performance Measures by Classifier Adaptation
In practical applications, machine learning algorithms are often needed to
learn classifiers that optimize domain specific performance measures.
Previously, the research has focused on learning the needed classifier in
isolation, yet learning nonlinear classifier for nonlinear and nonsmooth
performance measures is still hard. In this paper, rather than learning the
needed classifier by optimizing specific performance measure directly, we
circumvent this problem by proposing a novel two-step approach called as CAPO,
namely to first train nonlinear auxiliary classifiers with existing learning
methods, and then to adapt auxiliary classifiers for specific performance
measures. In the first step, auxiliary classifiers can be obtained efficiently
by taking off-the-shelf learning algorithms. For the second step, we show that
the classifier adaptation problem can be reduced to a quadratic program
problem, which is similar to linear SVMperf and can be efficiently solved. By
exploiting nonlinear auxiliary classifiers, CAPO can generate nonlinear
classifier which optimizes a large variety of performance measures including
all the performance measure based on the contingency table and AUC, whilst
keeping high computational efficiency. Empirical studies show that CAPO is
effective and of high computational efficiency, and even it is more efficient
than linear SVMperf.Comment: 30 pages, 5 figures, to appear in IEEE Transactions on Pattern
Analysis and Machine Intelligence, 201
Finslerian MOND versus the Strong Gravitational Lensing of the Early-type Galaxies
The gravitational lensing of Bullet Clusters and early-type galaxies pose
serious challenges on the validity of MOND. Recently, Finslerian MOND, a
generalization of MOND in the framework of Finsler gravity, has been proposed
to explain the mass discrepancy problem of Bullet Cluster 1E 0657\ 558. In this
paper, we check the validity of the Finslerian MOND in describing the strong
gravitational lensing of early-type galaxies. The investigation on ten strong
lenses of the CASTLES samples shows that there is no strong evidence for the
existence of dark matter.Comment: 11 pages, 2 figures, 2 table
Evolving Optical Networks for Latency-Sensitive Smart-Grid Communications via Optical Time Slice Switching (OTSS) Technologies
In this paper, we proposed a novel OTSS-assisted optical network architecture
for smart-grid communication networks, which has unique requirements for
low-latency connections. Illustrative results show that, OTSS can provide
extremely better performance in latency and blocking probability than
conventional flexi-grid optical networks.Comment: IEEE Photonics Society 1st Place Best Poster Award, on
CLEO-PR/OECC/PGC 201
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