Wall-crossing formulae and strong piecewise polynomiality for mixed Grothendieck dessins d'enfant, monotone, and double simple Hurwitz numbers

We derive explicit formulae for the generating series of mixed Grothendieck dessins d'enfant/monotone/simple Hurwitz numbers, via the semi-infinite wedge formalism. This reveals the strong piecewise polynomiality in the sense of Goulden–Jackson–Vakil, generalising a result of Johnson, and provides a new explicit proof of the piecewise polynomiality of the mixed case. Moreover, we derive wall-crossing formulae for the mixed case. These statements specialise to any of the three types of Hurwitz numbers, and to the mixed case of any pair

Flavor Changing Higgs Decays in Supersymmetry with Minimal Flavor Violation

We study the flavor changing neutral current decays of the MSSM Higgs bosons into strange and bottom quarks. We focus on a scenario of minimum flavor violation here, namely only that induced by the CKM matrix. Taking into account constraint from $b\to s \gamma$, $\delta\rho$ as well as experimental constraints on the MSSM spectrum, we show that the branching ratio of $(\Phi\to b\bar{s})$ and $(\Phi \to \bar{b}s)$ combined, for $\Phi$ being either one of the CP even Higgs states, can reach the order $10^{-4}$-$10^{-3}$ for large $\tan\beta$, large $\mu$, and large $A_t$. The result illustrates the significance of minimal flavor violation scenario which can induce competitive branching fraction for flavor changing Higgs decays. This can be compared with the previous studies where similar branching fraction has been reported, but with additional sources of flavor violations in squark mass matrices. We also discuss some basic features of the flavor violating decays in the generic case.Comment: 16 pages on Revtex, with 5 figures from 10 eps files incorporated; discussion on issues related more precise calculations elaborated; proof-edited version to appear in Phys. Lett.

Chebychev trajectory optimization program /CHEBYTOP 2/ Final report

Computer program for electric propelled spacecraft interplanetary, flyby, and rendezvous trajectory optimization based on Chebyshev approximation and polynomial representation

A comparative study of nonparametric methods for pattern recognition

The applied research discussed in this report determines and compares the correct classification percentage of the nonparametric sign test, Wilcoxon's signed rank test, and K-class classifier with the performance of the Bayes classifier. The performance is determined for data which have Gaussian, Laplacian and Rayleigh probability density functions. The correct classification percentage is shown graphically for differences in modes and/or means of the probability density functions for four, eight and sixteen samples. The K-class classifier performed very well with respect to the other classifiers used. Since the K-class classifier is a nonparametric technique, it usually performed better than the Bayes classifier which assumes the data to be Gaussian even though it may not be. The K-class classifier has the advantage over the Bayes in that it works well with non-Gaussian data without having to determine the probability density function of the data. It should be noted that the data in this experiment was always unimodal