Uniform-Penalty Inversion of Multiexponential Decay Data: II. Data Spacing, T2 Data, Systematic Data Errors, and Diagnostics

The basic method of UPEN (uniform penalty inversion of multiexponential decay data) is given in an earlier publication (Borgia et al., J. Magn. Reson. 132, 65–77 (1998)), which also discusses the effects of noise, constraints, and smoothing on the resolution or apparent resolution of features of a computed distribution of relaxation times. UPEN applies negative feedback to a regularization penalty, allowing stronger smoothing for a broad feature than for a sharp line. This avoids unnecessarily broadening the sharp line and/or breaking the wide peak or tail into several peaks that the relaxation data do not demand to be separate. The experimental and artificial data presented earlier were T₁ data, and all had fixed data spacings, uniform in log-time. However, for T₂ data, usually spaced uniformly in linear time, or for data spaced in any manner, we have found that the data spacing does not enter explicitly into the computation. The present work shows the extension of UPEN to T₂ data, including the averaging of data in windows and the use of the corresponding weighting factors in the computation. Measures are implemented to control portions of computed distributions extending beyond the data range. The input smoothing parameters in UPEN are normally fixed, rather than data dependent. A major problem arises, especially at high signal-to-noise ratios, when UPEN is applied to data sets with systematic errors due to instrumental nonidealities or adjustment problems. For instance, a relaxation curve for a wide line can be narrowed by an artificial downward bending of the relaxation curve. Diagnostic parameters are generated to help identify data problems, and the diagnostics are applied in several examples, with particular attention to the meaningful resolution of two closely spaced peaks in a distribution of relaxation times. Where feasible, processing with UPEN in nearly real time should help identify data problems while further instrument adjustments can still be made. The need for the nonnegative constraint is greatly reduced in UPEN, and preliminary processing without this constraint helps identify data sets for which application of the nonnegative constraint is too expensive in terms of error of fit for the data set to represent sums of decaying positive exponentials plus random noise.     

求解病态问题的一种改进的Tikhonov正则化--(1)正则化方法的建立

对于带有右端扰动数据的第一类紧算子方程的病态问题 ,本文应用正则化子建立了一类新的正则化求解方法 ,称之为改进的Tikonov正则化 ;通过适当选取正则参数 ,证明了正则解具有最优的渐近收敛阶 .与通常的Tikhonov正则化相比 ,这种改进的正则化可使正则解取到足够高的最优渐近阶     

Regularizability of some classes of mappings that are inverses of integral operators

We study the regularizability of mappings that are inverses of integral operators acting fromC(0,1) toL ₂(0,1) and possessing noninjective continuation toL ₂(0,1). We construct classes of such operators with regularizable as well as nonregularizable inverses for which the continuation of the operators toL ₂(0,1) has an infinite-dimensional kernel. Translated fromMatematicheskie Zametki, Vol. 65, No. 2, pp. 222–229, February, 1999.     

Intrinsic Regularization Approach to Chiral Anomalies

With the help of the intrinsic regularization method, we present a new method which enables us to calculate the chiral anomalies in a much natural and self-consistent way. By checking Ward iden tities related to various.diagrams involving anomaly, we analyze anomalies in the σ model, in Abelian gauge theory, and in non-Abelian gauge theory as well. Our calculations prove to naively preserve all vector Ward identities and accordingly reproduce the famous ABJ-anomaly with no need of introducing any counterterms.     

一类确定表面热流的热传导反问题的正则化方法

考虑确定表面热流ux(0,t)的如下半无限长杆热传导方程反问题uxx=ut,0≤x,0相似文献   

基本解方法求解一个三维线弹性力学反问题

将用于求解椭圆型偏微分方程边值问题的基本解方法应用于求解一个三维线弹性反问题,即Navier方程组的Cauchy问题.基本解方法离散方程所得的线性方程组是高度病态的,常见的求解方法如最小二乘法等无法得到合理的解.文中应用Tikhonov正则化和截断奇异值分解这两种正则化方法求解线性方程组,所需正则化参数则根据L-曲线确定,克服了问题的病态性.数值算例表明,本文方法能有效地求解三维线弹性力学反问题,而且这两种正则化方法所得到的结果精度相当.     

双调和方程柯西问题的修正的Tikhonov正则化方法

本文研究了双调和方程柯西问题,这类是不适定的,即问题的解(如果存在)不连续依赖于测量数据.首先在精确解的先验假设下给出问题的条件稳定性结果.接着利用修正的Tikhonov正则化方法求解此不适定问题.在先验和后验正则化参数选取规则下,给出正则解和精确解之间的误差估计式.最后给出几个数值例子验证此正则化方法求解此类反问题的有效性.     

Continuous Viewpoint Planning in Conjunction with Dynamic Exploration for Active Object Recognition

Active object recognition (AOR) aims at collecting additional information to improve recognition performance by purposefully adjusting the viewpoint of an agent. How to determine the next best viewpoint of the agent, i.e., viewpoint planning (VP), is a research focus. Most existing VP methods perform viewpoint exploration in the discrete viewpoint space, which have to sample viewpoint space and may bring in significant quantization error. To address this challenge, a continuous VP approach for AOR based on reinforcement learning is proposed. Specifically, we use two separate neural networks to model the VP policy as a parameterized Gaussian distribution and resort the proximal policy optimization framework to learn the policy. Furthermore, an adaptive entropy regularization based dynamic exploration scheme is presented to automatically adjust the viewpoint exploration ability in the learning process. To the end, experimental results on the public dataset GERMS well demonstrate the superiority of our proposed VP method.     

Least Wasserstein distance between disjoint shapes with perimeter regularization

We prove the existence of global minimizers to the double minimization problem where $P(E)$ denotes the perimeter of the set E, $W_p$ is the p-Wasserstein distance between Borel probability measures, and $\lambda >0$ is arbitrary. The result holds in all space dimensions, for all $p\in [1,\infty )$, and for all positive λ. This answers a question of Buttazzo, Carlier, and Laborde.