Blocks adjustment—reduction of bias and variance of detrended fluctuation analysis using Monte Carlo simulation

The length of minimal and maximal blocks equally distant on log-log scale versus fluctuation function considerably influences bias and variance of DFA. Through a number of extensive Monte Carlo simulations and different fractional Brownian motion/fractional Gaussian noise generators, we found the pair of minimal and maximal blocks that minimizes the sum of mean-squared error of estimated Hurst exponents for the series of length . Sensitivity of DFA to sort-range correlations was examined using ARFIMA(p,d,q) generator. Due to the bias of the estimator for anti-persistent processes, we narrowed down the range of Hurst exponent to      

Multi-scale simulation reveals that an amino acid substitution increases photosensitizing reaction inputs in Rhodopsins

Evaluating the availability of molecular oxygen (O₂) and energy of excited states in the retinal binding site of rhodopsin is a crucial challenging first step to understand photosensitizing reactions in wild-type (WT) and mutant rhodopsins by absorbing visible light. In the present work, energies of the ground and excited states related to 11-cis-retinal and the O₂ accessibility to the β-ionone ring are evaluated inside WT and human M207R mutant rhodopsins. Putative O₂ pathways within rhodopsins are identified by using molecular dynamics simulations, Voronoi-diagram analysis, and implicit ligand sampling while retinal energetic properties are investigated through density functional theory, and quantum mechanical/molecular mechanical methods. Here, the predictions reveal that an amino acid substitution can lead to enough energy and O₂ accessibility in the core hosting retinal of mutant rhodopsins to favor the photosensitized singlet oxygen generation, which can be useful in understanding retinal degeneration mechanisms and in designing blue-lighting-absorbing proteic photosensitizers.     

The empirical behavior of sampling methods for stochastic programming

We investigate the quality of solutions obtained from sample-average approximations to two-stage stochastic linear programs with recourse. We use a recently developed software tool executing on a computational grid to solve many large instances of these problems, allowing us to obtain high-quality solutions and to verify optimality and near-optimality of the computed solutions in various ways. Research supported by the Mathematical, Information, and Computational Sciences Division subprogram of the Office of Advanced Scientific Computing Research, U.S. Department of Energy, under Contract W-31-109-Eng-38, and by the National Science Foundation under Grant 9726385. Research supported by the Mathematical, Information, and Computational Sciences Division subprogram of the Office of Advanced Scientific Computing Research, U.S. Department of Energy, under Contract W-31-109-Eng-38, and by the National Science Foundation under Grant DMS-0073770. Research supported by the Mathematical, Information, and Computational Sciences Division subprogram of the Office of Advanced Scientific Computing Research, U.S. Department of Energy, under Contract W-31-109-Eng-38, and by the National Science Foundation under Grants 9726385 and 0082065.     

Shannon sampling II: Connections to learning theory

We continue our study [S. Smale, D.X. Zhou, Shannon sampling and function reconstruction from point values, Bull. Amer. Math. Soc. 41 (2004) 279–305] of Shannon sampling and function reconstruction. In this paper, the error analysis is improved. Then we show how our approach can be applied to learning theory: a functional analysis framework is presented; dimension independent probability estimates are given not only for the error in the L² spaces, but also for the error in the reproducing kernel Hilbert space where the learning algorithm is performed. Covering number arguments are replaced by estimates of integral operators.     

Vector sampling expansions in shift invariant subspaces

Multi-input multi-output (MIMO) sampling scheme which is motivated by applications in multi-channel deconvolution and multi-source separation has been investigated in many aspects. Common for most of results on MIMO systems is that the input signals are supposed to be band-limited. In this paper, we study vector sampling expansions on general finitely generated shift-invariant subspaces. Necessary and sufficient conditions for a vector sampling theorem to hold are given. We also give several examples to illustrate the main result.     

非平均加权采样的重建

主要讨论了在加权的多生成平移不变空间Vv^p(Ф）中由非均匀采样{∫R^df(x)gxj(x)dx:j∈J}来重建信号的问题．同时也讨论了投影逼近迭代算法．     

随机效应模型中方差分量的经验Bayes检验问题

给出了双向分类随机效应模型中方差分量的Bayes检验的判决函数,利用核估计的方法,构造了相应的经验Bayes(EB)检验的判决函数．在适当的条件下证明了EB判决函数是渐近最优的且有收敛速度．给出了模型的特例和推广．最后,举出一个满足定理条件的例子．     

Automated rejection sampling from product of distributions

Summary  Sampling from probability density functions (pdfs) has become more and more important in many areas of applied science, and has therefore been the subject of great attention. Many sampling procedures proposed allow for approximate or asymptotic sampling. On the other hand, very few methods allow for exact sampling. Direct sampling of standard pdfs is feasible, but sampling of much more complicated pdfs is often required. Rejection sampling allows to exactly sample from univariate pdfs, but has the huge drawback of needing a case-by-case calculation of a comparison function that often reveals as a tremendous chore, whose results dramatically affect the efficiency of the sampling procedure. In this paper, we restrict ourselves to a pdf that is proportional to a product of standard distributions. From there, we show that an automated selection of both the comparison function and the upper bound is possible. Moreover, this choice is performed in order to optimize the sampling efficiency among a range of potential solutions. Finally, the method is illustrated on a few examples.     

Approximation by Nonlinear Multivariate Sampling Kantorovich Type Operators and Applications to Image Processing

In this article, we study a nonlinear version of the sampling Kantorovich type operators in a multivariate setting and we show applications to image processing. By means of the above operators, we are able to reconstruct continuous and uniformly continuous signals/images (functions). Moreover, we study the modular convergence of these operators in the setting of Orlicz spaces L ^?(? ⁿ ) that allows us to deal the case of not necessarily continuous signals/images. The convergence theorems in L ^p (? ⁿ )-spaces, L ^αlog ^β L(? ⁿ )-spaces and exponential spaces follow as particular cases. Several graphical representations, for the various examples and image processing applications are included.     

Evolutionary variational inequalities with a volterra term

The mathematical model of the injection molding process is characterized by evolutionary inequalities with an elliptic principal part and a Volterra term. Existence and uniqueness of the solution of these problems are derived by means of an argument based on the Banach fixed point theorem. Regularity results are obtained by using the penalty and Rothe method. The results can be considered as a starting point for the numerical treatment and for the application to injection molding.