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.     

韧性金属Kaiser效应方向独立性的实验研究

Kaiser效应是声发射技术实际应用的理论基础。本文以韧性金属材料Q345不同方向拉伸实验为例,探究力的方向对韧性金属Kaiser效应的影响。实验结果表明,韧性金属材料在某一方向加载时,表现出了Kaiser效应;当改变加载方向时,呈现出与第一次加载方向类似的声发射特性,说明声发射现象对其他加载方向上所承受的最大载荷不具有记忆的能力,而仅与该方向承载历史有关,即韧性金属Kaiser效应与力的方向性间具有独立性。同时,利用细观损伤理论对这一现象产生的机理进行了定性的解释。     

Sufficient Conditions for a Digraph to be Supereulerian

A (di)graph is supereulerian if it contains a spanning eulerian sub(di)graph. This property is a relaxation of hamiltonicity. Inspired by this analogy with hamiltonian cycles and by similar results in supereulerian graph theory, we analyze a number of sufficient Ore type conditions for a digraph to be supereulerian. Furthermore, we study the following conjecture due to Thomassé and the first author: if the arc‐connectivity of a digraph is not smaller than its independence number, then the digraph is supereulerian. As a support for this conjecture we prove it for digraphs that are semicomplete multipartite or quasitransitive and verify the analogous statement for undirected graphs.     

On percolation and ‐hardness

The edge‐percolation and vertex‐percolation random graph models start with an arbitrary graph G, and randomly delete edges or vertices of G with some fixed probability. We study the computational complexity of problems whose inputs are obtained by applying percolation to worst‐case instances. Specifically, we show that a number of classical ‐hard problems on graphs remain essentially as hard on percolated instances as they are in the worst‐case (assuming ). We also prove hardness results for other ‐hard problems such as Constraint Satisfaction Problems and Subset‐Sum, with suitable definitions of random deletions. Along the way, we establish that for any given graph G the independence number and the chromatic number are robust to percolation in the following sense. Given a graph G, let be the graph obtained by randomly deleting edges of G with some probability . We show that if is small, then remains small with probability at least 0.99. Similarly, we show that if is large, then remains large with probability at least 0.99. We believe these results are of independent interest.     

Consistency between independence theorems and generalized self-consistent method

Recently Zheng & Hwang established a series of independence theorems concerning with planar effective elastic properties. It is manifested that the estimation of the effective elastic properties of microcracked solids through the generalized self-consistent method (GSCM) contradicts with these independence theorems. In this paper it is shown that such contradiction is actually caused by the approximate algorithm adopted, while the exact solution of GSCM is consistent with these rigorously established independence theorems. Since only an approximate algorithm in GCSM is available in dealing with problems involving non-circular inclusions or holes, an intrinsic GSCM is proposed, which can be performed based on an approximate algorithm and the corresponding estimations are consistent with the independence theorems.     

Ensemble-SISPLS近红外光谱变量选择方法

近红外光谱具有高维小样本的特点，变量选择是提高定量分析模型稳健性和可解释性的一种有效方法。确定独立筛选（SIS）是一种基于边际相关性的超高维数据变量选择方法，广泛用于基因微阵列数据的变量选择。SIS具有将数据维度降低至样本大小规模的能力，其降维能力与LASSO相当，在相当宽泛的近似条件下，由于具有安全筛选性质，所有重要变量被保留的概率趋于1。基于确定独立筛选偏最小二乘（SIS-SPLS）的变量选择是一种迭代式的SIS变量选择方法，首先利用SIS方法完成光谱重要变量的初选；然后根据重要变量的边际相关性大小进行逐步前向选择：建立偏最小二乘回归模型，依据贝叶斯信息准则（BIC）确定最终的变量选择结果。SIS-SPLS以逐步前向选择的方式实现对重要变量的增量式筛选，随着潜变量个数的增加及因变量残差的逐步减小，SIS-SPLS方法选择的变量个数将趋于稳定。然而仅以边际相关性对变量重要性进行评价，当光谱变量个数远大于样本数时，该方法也存在选择的变量过多、变量选择结果不够稳健等问题。为进一步提高小样本情况下变量选择的稳健性，将集成学习引入SIS-SPLS方法之中，提出了一种集成SIS-SPLS变量选择方法（Ensemble-SISPLS）。该方法首先对校正集样本进行自助重采样，对采样得到的每一个校正子集分别使用SIS-SPLS方法进行变量筛选，通过投票机制并设置频次阈值对所有校正子集的变量选择结果进行集成，选择出现频次大于给定阈值的变量并建立偏最小二乘回归模型，计算5折交叉验证均方根误差。对频次阈值和潜变量个数两个关键参数使用网格搜索法进行优选，根据子模型的交叉验证均方根误差和变量个数对子模型性能进行综合评价，以最优子模型包含的变量作为最终的变量选择结果。分别在Corn数据集和当归数据集上进行变量选择实验，比较Ensemble-SISPLS，SIS-SPLS和UVE-PLS三种变量选择方法的性能。其中当归数据集共77个样本，样本采自甘肃岷县和渭源县，使用Nicolet-6700型近红外光谱仪扫描得到所有样本的近红外光谱并对当归中的阿魏酸含量进行预测。Ensemble-SISPLS方法在Corn数据集上选择的变量个数、RMSEP和决定系数分别为22，0.000 8和0.999 8；SIS-SPLS方法在Corn数据集上选择的变量个数、RMSEP和决定系数分别为97，0.007 3和0.998 8。Ensemble-SISPLS方法在当归数据集上选择的变量个数、RMSEP和决定系数分别为24，0.018 1和0.996 3；SIS-SPLS方法在当归数据集上选择的变量个数、RMSEP和决定系数分别为38，0.022 6和0.994 3。结果表明，该方法进一步提高了变量选择结果的稳健性和预测能力。Ensemble-SISPLS变量选择方法有效结合了SIS-SPLS较强的变量选择能力和集成学习良好的泛化能力，提高了变量选择的稳健性。此外，由于在子模型的预测能力和变量个数之间进行了折中，一定程度上减少了选择变量的个数，提高了模型的可解释性。     

Another characterization of homogeneous Poisson processes

For a general renewal process N (allowing delay, defect and multiple simultaneous arrivals) the independence of the first renewal epochs of the marked processes got from N by Bernoulli 0/1 thinning is characterized. This independence is well-known to hold true in the case of homogeneous Poisson processes; by way of corollary one obtains the interesting observation that, when coupled with some minimal extra conditions, it in fact already identifies them. The proof is analytic in character.     

The trigonometric triangle

A procedure is presented by which the integration of a function such as cos ⁿ (x) or sin ⁿ (x) with n = 1, 2, 3, 4 … is facilitated.     

Convergence issues in using high‐resolution schemes and lower–upper symmetric Gauss–Seidel method for steady shock‐induced combustion problems

This paper reports numerical convergence study for simulations of steady shock‐induced combustion problems with high‐resolution shock‐capturing schemes. Five typical schemes are used: the Roe flux‐based monotone upstream‐centered scheme for conservation laws (MUSCL) and weighted essentially non‐oscillatory (WENO) schemes, the Lax–Friedrichs splitting‐based non‐oscillatory no‐free parameter dissipative (NND) and WENO schemes, and the Harten–Yee upwind total variation diminishing (TVD) scheme. These schemes are implemented with the finite volume discretization on structured quadrilateral meshes in dimension‐by‐dimension way and the lower–upper symmetric Gauss–Seidel (LU–SGS) relaxation method for solving the axisymmetric multispecies reactive Navier–Stokes equations. Comparison of iterative convergence between different schemes has been made using supersonic combustion flows around a spherical projectile with Mach numbers M = 3.55 and 6.46 and a ram accelerator with M = 6.7. These test cases were regarded as steady combustion problems in literature. Calculations on gradually refined meshes show that the second‐order NND, MUSCL, and TVD schemes can converge well to steady states from coarse through fine meshes for M = 3.55 case in which shock and combustion fronts are separate, whereas the (nominally) fifth‐order WENO schemes can only converge to some residual level. More interestingly, the numerical results show that all the schemes do not converge to steady‐state solutions for M = 6.46 in the spherical projectile and M = 6.7 in the ram accelerator cases on fine meshes although they all converge on coarser meshes or on fine meshes without chemical reactions. The result is based on the particular preconditioner of LU–SGS scheme. Possible reasons for the nonconvergence in reactive flow simulation are discussed.Copyright © 2012 John Wiley & Sons, Ltd.     

Analysis of the multiple roots of the Lundberg fundamental equation in the PH (n) risk model

In the study of the Sparre Andersen risk model with phase‐type (n) inter‐claim times (PH (n) risk model), the distinct roots of the Lundberg fundamental equation in the right half of the complex plane and the linear independence of the eigenvectors related to the Lundberg matrix L_δ(s) play important roles. In this paper, we study the case where the Lundberg fundamental equation has multiple roots or the corresponding eigenvectors are linearly dependent in the PH (n) risk model. We show that the multiple roots of the Lundberg fundamental equation det[L_δ(s)] = 0 can be approximated by the distinct roots of the generalized Lundberg equation introduced in this paper and that the linearly dependent eigenvectors can be approximated by the corresponding linearly independent ones as well. Using this result we derive the expressions for the Gerber–Shiu penalty function. Two special cases of the generalized Erlang(n) risk model and a Coxian(3) risk model are discussed in detail, which illustrate the applicability of main results. Finally, we consider the PH(2) risk model and conclude that the roots of the Lundberg fundamental equation in the right half of the complex plane are distinct and that the corresponding eigenvectors are linearly independent. Copyright © 2011 John Wiley & Sons, Ltd.