Hyperbolic metric,curvature of geodesics and hyperbolic discs in hyperbolic plane domains

The purpose of this paper is to investigate the relations among some geometric quantities defined for every hyperbolic plane domain of any connectivity, each of which measures, in some sense, how much the domain deviates either from a disc, convex domain, or simply connected domain on one hand, or a punctured domain on the other hand. Supported by the Landau Center for Mathematical Research in Analysis.     

Decomposable score function estimators for sensitivity analysis and optimization of queueing networks

This paper surveys some recent results and presents some new results on the so-called decomposable and truncated score functions (DSF and TSF) estimators for performance evaluation, sensitivity analysis and optimization of open non-Markovian (non-product) queueing networks. The idea behind the TSF estimators is based on truncation of the score function process, while the idea behind the DSF estimators is to decompose the queueing network into smaller units, calledmodules, such that each module contains several connected queues, and then approximate the unknown quantities by treating these modules as if they were completely independent. In other words, in the DSF estimators we use frequently occurrentlocal regenerative cycles at eachindividual module instead oftrue but seldom occurrentglobal ones of theentire system. Although the local cycles at each module interact with their neighbors, our numerical studies show that typically the contribution from the neighbors is quite small and thus DSF estimators approximate the unknown quantities rather well, in the sense that their bias is reasonably small and the variance is much smaller than that of the standard score function estimators. Both DSF and TSF estimators were implemented in a simulation package, called thequeueing network stabilizer and optimizer (QNSO). This package is suitable for performance evaluation, sensitivity analysis and optimization of general open non-Markovian queueing networks with respect to the parameter vector of an exponential family of distributions.Research supported by the L. Edelstein Research Fund of the Technion — Israel Institute of Technology and École Polytechnique Fédérale de Lausanne, Switzerland.     

A Linear Goal Programming Model for Planning the Exports of Emerging Countries

A linear goal programming model for planning the exports of emerging countries is formulated in this paper. The model includes some economic factors which may affect the exports, e.g. industrial structure, labour force, value added in exports, capital efficiency, imports of raw material used for manufacturing goods designated for exports, investment plan, etc. An illustrative example is presented. A sensitivity analysis was performed in order to examine the effect of various changes made in the model on the results. It is felt that the model presented in this study would be useful to economic planners in emerging countries.     

Optimizing the resolution of nanohole arrays in metal films for refractive-index sensing

We optimized the resolution of nanohole arrays in metal films for refractive-index sensing by increasing the sensitivity with modifications to the hole-array parameters and by increasing the signal to noise ratio of the sensor system. The nanohole-array parameters (including film thickness, periodicity and diameter) were first optimized by finite-difference time-domain simulations, and then the arrays were fabricated and tested, showing good agreement between the two cases (theory and experiment) in terms of optimal parameters. To improve the sensitivity and to reduce the noise, the laser source wavelength was optimized (including the efficiency of the camera for detection) and the intensity was increased. A?bulk resolution of 6×10^?7 refractive-index units was demonstrated. Due to the collinear microscope geometry and potential for multiplexing of nanohole arrays, these results are encouraging for future biosensing applications.     

Application of the cross-entropy method to clustering and vector quantization

We apply the cross-entropy (CE) method to problems in clustering and vector quantization. The CE algorithm for clustering involves the following iterative steps: (a) generate random clusters according to a specified parametric probability distribution, (b) update the parameters of this distribution according to the Kullback–Leibler cross-entropy. Through various numerical experiments, we demonstrate the high accuracy of the CE algorithm and show that it can generate near-optimal clusters for fairly large data sets. We compare the CE method with well-known clustering and vector quantization methods such as K-means, fuzzy K-means and linear vector quantization, and apply each method to benchmark and image analysis data.     

On nonlinear translation-varying operators

In this work nonlinear translation-varying operators are analyzed and represented by means of a generalized impulse response. This is the response of the transpose operator to the family of shifted impulse functionals. Continuous operators from a topological vector space into the space of functions on Rⁿ, as well as $\text{A}$-bounded operators, are investigated.