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