A Statistical Perspective on Algorithmic Leveraging

One popular method for dealing with large-scale data sets is sampling. For example, by using the empirical statistical leverage scores as an importance sampling distribution, the method of algorithmic leveraging samples and rescales rows/columns of data matrices to reduce the data size before performing computations on the subproblem. This method has been successful in improving computational efficiency of algorithms for matrix problems such as least-squares approximation, least absolute deviations approximation, and low-rank matrix approximation. Existing work has focused on algorithmic issues such as worst-case running times and numerical issues associated with providing high-quality implementations, but none of it addresses statistical aspects of this method. In this paper, we provide a simple yet effective framework to evaluate the statistical properties of algorithmic leveraging in the context of estimating parameters in a linear regression model with a fixed number of predictors. We show that from the statistical perspective of bias and variance, neither leverage-based sampling nor uniform sampling dominates the other. This result is particularly striking, given the well-known result that, from the algorithmic perspective of worst-case analysis, leverage-based sampling provides uniformly superior worst-case algorithmic results, when compared with uniform sampling. Based on these theoretical results, we propose and analyze two new leveraging algorithms. A detailed empirical evaluation of existing leverage-based methods as well as these two new methods is carried out on both synthetic and real data sets. The empirical results indicate that our theory is a good predictor of practical performance of existing and new leverage-based algorithms and that the new algorithms achieve improved performance.Comment: 44 pages, 17 figure

FDserver: A web service for protein folding research

*Summary:* To facilitate the study of protein folding, we have developed a web service for protein folding rate and folding type prediction as well as for the calculation of a variety of topological parameters of protein structure, which is freely available to the community.
*Availability:* http://sdbi.sdut.edu.cn/FDserve

Photon-assisted Fano Resonance and Corresponding Shot-Noise in a Quantum Dot

We have studied the Fano resonance in photon-assisted transport in a quantum dot and calculated both the coherent current and spectral density of shot noise. It is predicted, for the first time, that the shape of Fano profile will also appear in satellite peaks. It is found that the variations of Fano profiles with the strengths of nonresonant transmissions are not synchronous in absorption and emission sidebands. The effect of interference on photon-assisted pumped current has been also investigated. We further predict the current and spectral density of shot noise as a function of the phase, which exhibits an intrinsic property of resonant and nonresonant channels in the structures.Comment: 4 pages, 5 figure

Investigating the topological structure of quenched lattice QCD with overlap fermions by using multi-probing approximation

The topological charge density and topological susceptibility are determined by multi-probing approximation using overlap fermions in quenched SU(3) gauge theory. Then we investigate the topological structure of the quenched QCD vacuum, and compare it with results from the all-scale topological density, the results are consistent. Random permuted topological charge density is used to check whether these structures represent underlying ordered properties. Pseudoscalar glueball mass is extracted from the two-point correlation function of the topological charge density. We study $3$ ensembles of different lattice spacing $a$ with the same lattice volume $16^{3}\times32$, the results are compatible with the results of all-scale topological charge density, and the topological structures revealed by multi-probing are much closer to all-scale topological charge density than that by eigenmode expansion.Comment: 12 pages,34 figure