Dominant Spin-Flip Effects for the Hadronic Produced J/ψJ/\psi Polarization at TEVATRON

Dominant spin-flip effects for the direct and prompt $J/\psi$ polarizations at TEVATRON run II with collision energy 1.96 TeV and rapidity cut $|y^{J/\psi}|<0.6$, have been systematically studied, especially, the spin-flip effect for the transition of $(c\bar{c})_8[^3S_1]$ into $J/\psi$ has been carefully discussed. It is found that the spin-flip effect shall always dilute the $J/\psi$ polarization, and with a suitable choice of the parameters $a_{0,1}$ and $c_{0,1,2}$, the $J/\psi$ polarization puzzle can be solved to a certain degree. At large transverse momentum $p_t$, $\alpha$ for the prompt $J/\psi$ is reduced by $\sim50$ for $f_0 = v^2$ and by $\sim80$ for $f_0=1$. We also study the indirect $J/\psi$ polarization from the $b$-decays, which however is slightly affected by the same spin-flip effect and then shall provide a better platform to determine the color-octet matrix elements.Comment: 19 pages, 5 figures. References added. Revised version to be published in Phys.Rev.

Zener Tunneling in Semiconducting Nanotube and Graphene Nanoribbon p-n Junctions

A theory is developed for interband tunneling in semiconducting carbon nanotube and graphene nanoribbon p-n junction diodes. Characteristic length and energy scales that dictate the tunneling probabilities and currents are evaluated. By comparing the Zener tunneling processes in these structures to traditional group IV and III-V semiconductors, it is proved that for identical bandgaps, carbon based 1D structures have higher tunneling probabilities. The high tunneling current magnitudes for 1D carbon structures suggest the distinct feasibility of high-performance tunneling-based field-effect transistors.Comment: 4 Pages, 2 Figure

Max-margin Metric Learning for Speaker Recognition

Probabilistic linear discriminant analysis (PLDA) is a popular normalization approach for the i-vector model, and has delivered state-of-the-art performance in speaker recognition. A potential problem of the PLDA model, however, is that it essentially assumes Gaussian distributions over speaker vectors, which is not always true in practice. Additionally, the objective function is not directly related to the goal of the task, e.g., discriminating true speakers and imposters. In this paper, we propose a max-margin metric learning approach to solve the problems. It learns a linear transform with a criterion that the margin between target and imposter trials are maximized. Experiments conducted on the SRE08 core test show that compared to PLDA, the new approach can obtain comparable or even better performance, though the scoring is simply a cosine computation