Dimensional Regularization and Dimensional Reduction in the Light Cone

We calculate all the 2 to 2 scattering process in Yang-Mills theory in the Light Cone gauge, with the dimensional regulator as the UV regulator. The IR is regulated with a cutoff in $q^+$. It supplements our earlier work, where a Lorentz non-covariant regulator was used and the final results bear some problems in gauge fixing. Supersymmetry relations among various amplitudes are checked using the light cone superfields.Comment: current version accepted by PR

On fast radial propagation of parametrically excited geodesic acoustic mode

The spatial and temporal evolution of parametrically excited geodesic acoustic mode (GAM) initial pulse is investigated both analytically and numerically. Our results show that the nonlinearly excited GAM propagates at a group velocity which is, typically, much larger than that due to finite ion Larmor radius as predicted by the linear theory. The nonlinear dispersion relation of GAM driven by a finite amplitude drift wave pump is also derived, showing a nonlinear frequency increment of GAM. Further implications of these findings for interpreting experimental observations are also discussed

Dynamical properties of a trapped dipolar Fermi gas at finite temperature

We investigate the dynamical properties of a trapped finite-temperature normal Fermi gas with dipole-dipole interaction. For the free expansion dynamics, we show that the expanded gas always becomes stretched along the direction of the dipole moment. In addition, we present the temperature and interaction dependences of the asymptotical aspect ratio. We further study the collapse dynamics of the system by suddenly increasing the dipolar interaction strength. We show that, in contrast to the anisotropic collapse of a dipolar Bose-Einstein condensate, a dipolar Fermi gas always collapses isotropically when the system becomes globally unstable. We also explore the interaction and temperature dependences for the frequencies of the low-lying collective excitations.Comment: 11 pages, 7 figure

Effects of energetic particles on zonal flow generation by toroidal Alfven eigenmode

Generation of zonal ow (ZF) by energetic particle (EP) driven toroidal Alfven eigenmode (TAE) is investigated using nonlinear gyrokinetic theory. It is found that, nonlinear resonant EP contri- bution dominates over the usual Reynolds and Maxwell stresses due to thermal plasma nonlinear response. ZF can be forced driven in the linear growth stage of TAE, with the growth rate being twice the TAE growth rate. The ZF generation mechanism is shown to be related to polarization induced by resonant EP nonlinearity. The generated ZF has both the usual meso-scale and micro- scale radial structures. Possible consequences of this forced driven ZF on the nonlinear dynamics of TAE are also discussed.Comment: To be submitted to Physics of Plasma

A composite objective measure on subjective evaluation of speech enhancement algorithms

© 2018 Elsevier Ltd Speech enhancement algorithms is to improve speech quality, naturalness and intelligibility by eliminating the background noise and improving signal to noise ratio. There are several objective measures predicting the quality of noisy speech enhanced by noise suppression algorithms, and different objective measures capture different characteristics of the degraded signal. In this paper, the multiple linear regression analysis is used to obtain a composite measure which has high correlation with subjective tests, and the performance of several speech enhancement algorithms under car noise conditions is compared. The uncertainty of the results of the proposed measures on different speech enhancement algorithms is analyzed, and the reliability of the results is discussed

A Deep Relevance Matching Model for Ad-hoc Retrieval

In recent years, deep neural networks have led to exciting breakthroughs in speech recognition, computer vision, and natural language processing (NLP) tasks. However, there have been few positive results of deep models on ad-hoc retrieval tasks. This is partially due to the fact that many important characteristics of the ad-hoc retrieval task have not been well addressed in deep models yet. Typically, the ad-hoc retrieval task is formalized as a matching problem between two pieces of text in existing work using deep models, and treated equivalent to many NLP tasks such as paraphrase identification, question answering and automatic conversation. However, we argue that the ad-hoc retrieval task is mainly about relevance matching while most NLP matching tasks concern semantic matching, and there are some fundamental differences between these two matching tasks. Successful relevance matching requires proper handling of the exact matching signals, query term importance, and diverse matching requirements. In this paper, we propose a novel deep relevance matching model (DRMM) for ad-hoc retrieval. Specifically, our model employs a joint deep architecture at the query term level for relevance matching. By using matching histogram mapping, a feed forward matching network, and a term gating network, we can effectively deal with the three relevance matching factors mentioned above. Experimental results on two representative benchmark collections show that our model can significantly outperform some well-known retrieval models as well as state-of-the-art deep matching models.Comment: CIKM 2016, long pape