Hyperspherical Close-Coupling Calculation of D-wave Positronium Formation and Excitation Cross Sections in Positron-Hydrogen Scattering

Hyperspherical close-coupling method is used to calculate the elastic, positronium formation and excitation cross sections for positron collisions with atomic hydrogen at energies below the H(n=4) threshold for the J=2 partial wave. The resonances below each inelastic threshold are also analyzed. The adiabatic hyperspherical potential curves are used to identify the nature of these resonances.Comment: 12 pages(in a TeX file) +8 Postscript figure

Improved Compressive Sensing Of Natural Scenes Using Localized Random Sampling

Compressive sensing (CS) theory demonstrates that by using uniformly-random sampling, rather than uniformly-spaced sampling, higher quality image reconstructions are often achievable. Considering that the structure of sampling protocols has such a profound impact on the quality of image reconstructions, we formulate a new sampling scheme motivated by physiological receptive field structure, localized random sampling, which yields significantly improved CS image reconstructions. For each set of localized image measurements, our sampling method first randomly selects an image pixel and then measures its nearby pixels with probability depending on their distance from the initially selected pixel. We compare the uniformly-random and localized random sampling methods over a large space of sampling parameters, and show that, for the optimal parameter choices, higher quality image reconstructions can be consistently obtained by using localized random sampling. In addition, we argue that the localized random CS optimal parameter choice is stable with respect to diverse natural images, and scales with the number of samples used for reconstruction. We expect that the localized random sampling protocol helps to explain the evolutionarily advantageous nature of receptive field structure in visual systems and suggests several future research areas in CS theory and its application to brain imaging

The balanced Voronoi formulas for GL(n)

In this paper we show how the GL(N) Voronoi summation formula of [MiSc2] can be rewritten to incorporate hyper-Kloosterman sums of various dimensions on both sides. This generalizes a formula for GL(4) with ordinary Kloosterman sums on both sides that was considered by Xiaoqing Li and the first-named author, and later by the second-named author in [Zho]