24,593 research outputs found
Improved bilinears in unquenched lattice QCD
We summarize the extent to which one can use Ward identities to
non-perturbatively improve flavor singlet and non-singlet bilinears with three
flavors of non-degenerate dynamical Wilson-like fermions.Comment: Lattice2003(improve) (3 pages, no figures
Contribution from unresolved discrete sources to the Extragalactic Gamma-Ray Background (EGRB)
The origin of the extragalactic gamma-ray background (EGRB) is still an open
question, even after nearly forty years of its discovery. The emission could
originate from either truly diffuse processes or from unresolved point sources.
Although the majority of the 271 point sources detected by EGRET (Energetic
Gamma Ray Experiment Telescope) are unidentified, of the identified sources,
blazars are the dominant candidates. Therefore, unresolved blazars may be
considered the main contributor to the EGRB, and many studies have been carried
out to understand their distribution, evolution and contribution to the EGRB.
Considering that gamma-ray emission comes mostly from jets of blazars and that
the jet emission decreases rapidly with increasing jet to line-of-sight angle,
it is not surprising that EGRET was not able to detect many large inclination
angle active galactic nuclei (AGNs). Though Fermi could only detect a few large
inclination angle AGNs in the first three months' survey, it is expected to
detect many such sources in the near future. Since non-blazar AGNs are expected
to have higher density as compared to blazars, these could also contribute
significantly to the EGRB. In this paper we discuss contributions from
unresolved discrete sources including normal galaxies, starburst galaxies,
blazars and off-axis AGNs to the EGRB.Comment: 11 pages, 4 figures, accepted for publication in RA
A Differentiation Theory for It\^o's Calculus
A peculiar feature of It\^o's calculus is that it is an integral calculus
that gives no explicit derivative with a systematic differentiation theory
counterpart, as in elementary calculus. So, can we define a pathwise stochastic
derivative of semimartingales with respect to Brownian motion that leads to a
differentiation theory counterpart to It\^o's integral calculus? From It\^o's
definition of his integral, such a derivative must be based on the quadratic
covariation process. We give such a derivative in this note and we show that it
leads to a fundamental theorem of stochastic calculus, a generalized stochastic
chain rule that includes the case of convex functions acting on continuous
semimartingales, and the stochastic mean value and Rolle's theorems. In
addition, it interacts with basic algebraic operations on semimartingales
similarly to the way the deterministic derivative does on deterministic
functions, making it natural for computations. Such a differentiation theory
leads to many interesting applications some of which we address in an upcoming
article.Comment: 10 pages, 9/9 papers from my 2000-2006 collection. I proved these
results and more earlier in 2004. I generalize this theory in upcoming
articles. I also apply this theory in upcoming article
Uncertainty And Evolutionary Optimization: A Novel Approach
Evolutionary algorithms (EA) have been widely accepted as efficient solvers
for complex real world optimization problems, including engineering
optimization. However, real world optimization problems often involve uncertain
environment including noisy and/or dynamic environments, which pose major
challenges to EA-based optimization. The presence of noise interferes with the
evaluation and the selection process of EA, and thus adversely affects its
performance. In addition, as presence of noise poses challenges to the
evaluation of the fitness function, it may need to be estimated instead of
being evaluated. Several existing approaches attempt to address this problem,
such as introduction of diversity (hyper mutation, random immigrants, special
operators) or incorporation of memory of the past (diploidy, case based
memory). However, these approaches fail to adequately address the problem. In
this paper we propose a Distributed Population Switching Evolutionary Algorithm
(DPSEA) method that addresses optimization of functions with noisy fitness
using a distributed population switching architecture, to simulate a
distributed self-adaptive memory of the solution space. Local regression is
used in the pseudo-populations to estimate the fitness. Successful applications
to benchmark test problems ascertain the proposed method's superior performance
in terms of both robustness and accuracy.Comment: In Proceedings of the The 9th IEEE Conference on Industrial
Electronics and Applications (ICIEA 2014), IEEE Press, pp. 988-983, 201
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