Morales-Casique et al.(Adv.Water Res.,29(2006),pp.1238-1255)developed exact first and second nonlocal moment equations for advective-dispersive transport in finite,randomly heterogeneous geologic media.The velocity an...Morales-Casique et al.(Adv.Water Res.,29(2006),pp.1238-1255)developed exact first and second nonlocal moment equations for advective-dispersive transport in finite,randomly heterogeneous geologic media.The velocity and concentration in these equations are generally nonstationary due to trends in heterogeneity,conditioning on site data and the influence of forcing terms.Morales-Casique et al.(Adv.Water Res.,29(2006),pp.1399-1418)solved the Laplace transformed versions of these equations recursively to second order in the standard deviationσY of(natural)log hydraulic conductivity,and iteratively to higher-order,by finite elements followed by numerical inversion of the Laplace transform.They did the same for a space-localized version of the mean transport equation.Here we recount briefly their theory and algorithms;compare the numerical performance of the Laplace-transform finite element scheme with that of a high-accuracy ULTIMATE-QUICKEST algorithm coupled with an alternating split operator approach;and review some computational results due to Morales-Casique et al.(Adv.Water Res.,29(2006),pp.1399-1418)to shed light on the accuracy and computational efficiency of their recursive and iterative solutions in comparison to conditional Monte Carlo simulations in two spatial dimensions.展开更多
As stochastic gradient and Skorohod integral operators, is an adjoint pair of unbounded operators on Guichardet Spaces. In this paper, we define an adjoint pair of operator , where with being the conditional expectati...As stochastic gradient and Skorohod integral operators, is an adjoint pair of unbounded operators on Guichardet Spaces. In this paper, we define an adjoint pair of operator , where with being the conditional expectation operator. We show that (resp.) is essentially a kind of localization of the stochastic gradient operators (resp. Skorohod integral operators δ). We examine that and satisfy a local CAR (canonical ani-communication relation) and forms a mutually orthogonal operator sequence although each is not a projection operator. We find that is s-adapted operator if and only if is s-adapted operator. Finally we show application exponential vector formulation of QS calculus.展开更多
Precise fluorescence imaging of single l-DNA molecules for base pair distance analysis requires a superresolution technique, as these distances are on the order of diffraction limit. Individual l-DNA molecules interca...Precise fluorescence imaging of single l-DNA molecules for base pair distance analysis requires a superresolution technique, as these distances are on the order of diffraction limit. Individual l-DNA molecules intercalated with the fluorescent dye YOYO-1 were investigated at subdiffraction spatial resolution by direct stochastic optical reconstruction microscopy（d STORM）. Various dye-to-DNA base pair ratios were imaged by photoswitching YOYO-1 between the fluorescent state and the dark state using two laser sources. The acquired images were reconstructed into a super-resolution image by applying Gaussian fitting to the centroid of the point spread function. By measuring the distances between localized fluorophores, the base pair distances in single DNA molecules for dye-to-DNA base pair ratios of 1：50,1：100, and 1：500 were calculated to be 17.1 0.8 nm, 34.3 2.2 nm, and 170.3 8.1 nm[17_TD$IF], respectively,which were in agreement with theoretical values. These results demonstrate that intercalating dye in a single DNA molecule can be photoswitched without the use of an activator fluorophore, and that super-localization precision at a spatial resolution of 17 nm was experimentally achieved.展开更多
运用测试集对程序错误语句定位的算法被统称为TBFL(Testing Based Fault Localization)方法。目前通用算法一般都没有利用测试员、程序员关于测试用例和程序的先验知识,致使这些“资源”被浪费。随机TBFL方法是一类新型TBFL方法,其精神...运用测试集对程序错误语句定位的算法被统称为TBFL(Testing Based Fault Localization)方法。目前通用算法一般都没有利用测试员、程序员关于测试用例和程序的先验知识,致使这些“资源”被浪费。随机TBFL方法是一类新型TBFL方法,其精神就是在随机理论的框架下,把这些先验知识(抽象为先验分布)和实际测试活动结合起来,从而更好地定位程序错误语句。事实上,随机TBFL算法可以看成这类算法的一般“模式”,人们可以从这个一般框架里开发出不同的算法。文中方法就是将随机TBFL算法加以简化得到的,主要是从各个测试用例的具体测试活动着手,对程序变量X的先验概率加以校正,如果测试集里有n个用例,便可以得到程序变量X的n个校正值,将n个校正值效应迭加,并且标准化,即得到程序变量X的后验概率,用它作为寻找错误语句的向导。由于提出的简化算法是借助一个校正因子矩阵而得到的,因此所提算法被称为基于校正因子的随机TBFL方法。文中还提出了3个有关不同TBFL算法的比较标准,并依据它们在一些具体实例上的表现证实所提算法的有效性。展开更多
基金This work was supported in part by NSF/ITR Grant EAR-0110289through a scholarship granted to the lead author by CONACYT of Mexico.
文摘Morales-Casique et al.(Adv.Water Res.,29(2006),pp.1238-1255)developed exact first and second nonlocal moment equations for advective-dispersive transport in finite,randomly heterogeneous geologic media.The velocity and concentration in these equations are generally nonstationary due to trends in heterogeneity,conditioning on site data and the influence of forcing terms.Morales-Casique et al.(Adv.Water Res.,29(2006),pp.1399-1418)solved the Laplace transformed versions of these equations recursively to second order in the standard deviationσY of(natural)log hydraulic conductivity,and iteratively to higher-order,by finite elements followed by numerical inversion of the Laplace transform.They did the same for a space-localized version of the mean transport equation.Here we recount briefly their theory and algorithms;compare the numerical performance of the Laplace-transform finite element scheme with that of a high-accuracy ULTIMATE-QUICKEST algorithm coupled with an alternating split operator approach;and review some computational results due to Morales-Casique et al.(Adv.Water Res.,29(2006),pp.1399-1418)to shed light on the accuracy and computational efficiency of their recursive and iterative solutions in comparison to conditional Monte Carlo simulations in two spatial dimensions.
文摘As stochastic gradient and Skorohod integral operators, is an adjoint pair of unbounded operators on Guichardet Spaces. In this paper, we define an adjoint pair of operator , where with being the conditional expectation operator. We show that (resp.) is essentially a kind of localization of the stochastic gradient operators (resp. Skorohod integral operators δ). We examine that and satisfy a local CAR (canonical ani-communication relation) and forms a mutually orthogonal operator sequence although each is not a projection operator. We find that is s-adapted operator if and only if is s-adapted operator. Finally we show application exponential vector formulation of QS calculus.
基金supported by a grant from Kyung Hee University in 2015(No.KHU-20150618)
文摘Precise fluorescence imaging of single l-DNA molecules for base pair distance analysis requires a superresolution technique, as these distances are on the order of diffraction limit. Individual l-DNA molecules intercalated with the fluorescent dye YOYO-1 were investigated at subdiffraction spatial resolution by direct stochastic optical reconstruction microscopy（d STORM）. Various dye-to-DNA base pair ratios were imaged by photoswitching YOYO-1 between the fluorescent state and the dark state using two laser sources. The acquired images were reconstructed into a super-resolution image by applying Gaussian fitting to the centroid of the point spread function. By measuring the distances between localized fluorophores, the base pair distances in single DNA molecules for dye-to-DNA base pair ratios of 1：50,1：100, and 1：500 were calculated to be 17.1 0.8 nm, 34.3 2.2 nm, and 170.3 8.1 nm[17_TD$IF], respectively,which were in agreement with theoretical values. These results demonstrate that intercalating dye in a single DNA molecule can be photoswitched without the use of an activator fluorophore, and that super-localization precision at a spatial resolution of 17 nm was experimentally achieved.
文摘运用测试集对程序错误语句定位的算法被统称为TBFL(Testing Based Fault Localization)方法。目前通用算法一般都没有利用测试员、程序员关于测试用例和程序的先验知识,致使这些“资源”被浪费。随机TBFL方法是一类新型TBFL方法,其精神就是在随机理论的框架下,把这些先验知识(抽象为先验分布)和实际测试活动结合起来,从而更好地定位程序错误语句。事实上,随机TBFL算法可以看成这类算法的一般“模式”,人们可以从这个一般框架里开发出不同的算法。文中方法就是将随机TBFL算法加以简化得到的,主要是从各个测试用例的具体测试活动着手,对程序变量X的先验概率加以校正,如果测试集里有n个用例,便可以得到程序变量X的n个校正值,将n个校正值效应迭加,并且标准化,即得到程序变量X的后验概率,用它作为寻找错误语句的向导。由于提出的简化算法是借助一个校正因子矩阵而得到的,因此所提算法被称为基于校正因子的随机TBFL方法。文中还提出了3个有关不同TBFL算法的比较标准,并依据它们在一些具体实例上的表现证实所提算法的有效性。
