Using a natural deconvolution for analysis of perturbed integer sampling in shift-invariant spaces

An important cornerstone of both wavelet and sampling theory is shift-invariant spaces, that is, spaces V spanned by a Riesz basis of integer-translates of a single function. Under some mild differentiability and decay assumptions on the Fourier transform of this function, we show that V also is generated by a function with Fourier transform for some g with . We explain why analysis of this particular generating function can be more likely to provide large jitter bounds ε such that any f∈V can be reconstructed from perturbed integer samples f(k+εk) whenever supk∈Z|εk|?ε. We use this natural deconvolution of to further develop analysis techniques from a previous paper. Then we demonstrate the resulting analysis method on the class of spaces for which g has compact support and bounded variation (including all spaces generated by Meyer wavelet scaling functions), on some particular choices of φ for which we know of no previously published bounds and finally, we use it to improve some previously known bounds for B-spline shift-invariant spaces.     

Coexistence and optimal control problems for a degenerate predator-prey model

In this paper we present a predator-prey mathematical model for two biological populations which dislike crowding. The model consists of a system of two degenerate parabolic equations with nonlocal terms and drifts. We provide conditions on the system ensuring the periodic coexistence, namely the existence of two non-trivial non-negative periodic solutions representing the densities of the two populations. We assume that the predator species is harvested if its density exceeds a given threshold. A minimization problem for a cost functional associated with this process and with some other significant parameters of the model is also considered.     

Quantile regression with an epsilon-insensitive loss in a reproducing kernel Hilbert space

This paper proposes a method to estimate the conditional quantile function using an epsilon-insensitive loss in a reproducing kernel Hilbert space. When choosing a smoothing parameter in nonparametric frameworks, it is necessary to evaluate the complexity of the model. In this regard, we provide a simple formula for computing an effective number of parameters when implementing an epsilon-insensitive loss. We also investigate the effects of the epsilon-insensitive loss.     

Weak consistency of the Support Vector Machine Quantile Regression approach when covariates are functions

This paper deals with a nonparametric estimation of conditional quantile regression when the explanatory variable X takes its values in a bounded subspace of a functional space X and the response Y takes its values in a compact of the space Y?R. The functional observations, X₁,…,X_n, are projected onto a finite dimensional subspace having a suitable orthonormal system. The X_i’s will be characterized by their coordinates in this basis. We perform the Support Vector Machine Quantile Regression approach in finite dimension with the selected coefficients. Then we establish weak consistency of this estimator. The various parameters needed for the construction of this estimator are automatically selected by data-splitting and by penalized empirical risk minimization.     

Equivariant heat invariants of the laplacian and nonmininmal operators on differential forms

In this paper,we compute the first two equivariant heat kernel coeffcients of the Bochner Laplacian on differential forms.The first two equivariant heat kernel coeffcients of the Bochner Laplacian with torsion are also given.We also study the equivariant heat kernel coeffcients of nonminimal operators on differential forms and get the equivariant Gilkey-Branson-Fulling formula.     

混合气体线性化Boltzmann算子的特征值与特征函数

考虑了混合气体线性化Boltzmann算子的特征值与特征函数问题.通过对算子中核函数的分析得出了更一般的混合气体线性化Boltzmann算子的特征值与特征函数.而且结论还适用于单一气体情况.     

一个新的带参数的Hilbert型积分不等式

本文研究Hilbert积分不等式的推广问题.利用引入参数和对数积分核函数,建立了一种新的Hilbert型积分不等式,证明了用Euler数和π来表示的常数因子是最佳的,推广了经典的Hilbert积分不等式.     

随机梯度下降法的一些性质(英文)

本文研究了一般核空间下的随机梯度下降法.通过迭代方法,给出了该算法的一些重要性质,这些性质对于研究收敛速度起到至关重要的作用.     

Optimal learning rates for least squares regularized regression with unbounded sampling

A standard assumption in theoretical study of learning algorithms for regression is uniform boundedness of output sample values. This excludes the common case with Gaussian noise. In this paper we investigate the learning algorithm for regression generated by the least squares regularization scheme in reproducing kernel Hilbert spaces without the assumption of uniform boundedness for sampling. By imposing some incremental conditions on moments of the output variable, we derive learning rates in terms of regularity of the regression function and capacity of the hypothesis space. The novelty of our analysis is a new covering number argument for bounding the sample error.     

Effects of screening on the rate of energy loss in degenerate surface layers of compound semiconductors at low lattice temperatures

The energy loss rate of an electron in degenerate surface layers of a compound semiconductor for inelastic interaction with deformation and piezoelectric acoustic phonons is calculated with due account of the screening of the perturbing potential under the condition of low lattice temperature when the approximations of the well known traditional theory is not valid. The numerical results obtained for GaAs and CdS exhibits interesting features, significantly different from what follows if one either makes the traditional approximation of negligible phonon energy or disregards the screening of the scattering potential.