On the numerical solution of ill‐conditioned linear systems by regularization and iteration

We propose to reduce the (spectral) condition number of a given linear system by adding a suitable diagonal matrix to the system matrix, in particular by shifting its spectrum. Iterative procedures are then adopted to recover the solution of the original system. The case of real symmetric positive definite matrices is considered in particular, and several numerical examples are given. This approach has some close relations with Riley's method and with Tikhonov regularization. Moreover, we identify approximately the aforementioned procedure with a true action of preconditioning.     

Linear independence of a finite set of time-frequency shifts

This paper introduces an open conjecture in time-frequency analysis on the linear independence of a finite set of time-frequency shifts of a given L² function. Firstly, background and motivation for the conjecture are provided. Secondly, the main progress of this linear independence in the past twenty five years is reviewed. Finally, the partial results of the high dimensional case and other cases for the conjecture are briefly presented.     

An improved evaluation of the neutron background in the PandaX-Ⅱ experiment

In dark matter direct detection experiments,neutron is a serious source of background,which can mimic the dark matter-nucleus scattering signals.In this paper,we present an improved evaluation of the neutron background in the PandaX-II dark matter experiment by a novel approach.Instead of fully relying on the Monte Carlo simulation,the overall neutron background is determined from the neutron-induced high energy signals in the data.In addition,the probability of producing a dark-matter-like background per neutron is evaluated with a complete Monte Carlo generator,where the correlated emission of neutron(s)andγ(s)in the(α,n)reactions and spontaneous fissions is taken into consideration.With this method,the neutron backgrounds in the Run 9(26-ton-day)and Run 10(28-ton-day)data sets of PandaX-II are estimated to be(0.66±0.24)and(0.47±0.25)events,respectively.     

基于不确定理论的铁路货运需求预测

合理预测铁路货运需求是铁路管理部门建设、运营等决策基础。为应对铁路货运需求的复杂变化,基于Pearson相关性分析方法筛选出铁路货运需求的七个具有关键影响的因素,并结合不确定理论建立不确定多元线性回归模型,相应的铁路货运预测结果由传统单一值变成可能的需求区间范围,更加符合处于不确定环境下的铁路货运需求实际情况。选取国家统计局2004~2016年相关数据进实证研究,并与回归模型以及BP模型的预测结果对比分析,实验表明不确定多元线性回归的预测结果更加精确。