Bias optimality for multichain continuous-time Markov decision processes

This paper deals with the bias optimality of multichain models for finite continuous-time Markov decision processes. Based on new performance difference formulas developed here, we prove the convergence of a so-called bias-optimal policy iteration algorithm, which can be used to obtain bias-optimal policies in a finite number of iterations.     

浅析基金评审的模糊属性

同行评议是科学基金项目评审的重要环节。高质量的同行评议是实现基金精准资助的前提和保障。本文以国家自然科学基金通讯评审为例,针对同行评议中出现的评审偏差,探讨了基金评审的模糊属性,提出了通过构建评审结果的模糊集合及一致性函数来解决该问题的可能性,为进一步优化评审结果、实现基金精准资助指出了新方向。     

汞溴红敏化TiO2纳米多孔膜电极的光电化学性质研究

用汞溴红(Mercurochrome)作敏化剂敏化TiO2纳米多孔膜电极,UV-Vis,光电流作用谱和SPS谱表明,该敏化剂能有效地敏化TiO2电极,并且染料聚集体能扩大染料的敏化区间,增加了太阳光的利用效率,对电极施加不同的偏压,测试电极产生的瞬态光电流,研究了偏压对光生电荷转移及复合速率的影响,从不同方向照射电极,对阳极光电流影响显著,结合电化学、光电化学原理对这些现象进行了讨论.     

A fully automated algorithm under modified FCM framework for improved brain MR image segmentation

Automated brain magnetic resonance image (MRI) segmentation is a complex problem especially if accompanied by quality depreciating factors such as intensity inhomogeneity and noise. This article presents a new algorithm for automated segmentation of both normal and diseased brain MRI. An entropy driven homomorphic filtering technique has been employed in this work to remove the bias field. The initial cluster centers are estimated using a proposed algorithm called histogram-based local peak merger using adaptive window. Subsequently, a modified fuzzy c-mean (MFCM) technique using the neighborhood pixel considerations is applied. Finally, a new technique called neighborhood-based membership ambiguity correction (NMAC) has been used for smoothing the boundaries between different tissue classes as well as to remove small pixel level noise, which appear as misclassified pixels even after the MFCM approach. NMAC leads to much sharper boundaries between tissues and, hence, has been found to be highly effective in prominently estimating the tissue and tumor areas in a brain MR scan. The algorithm has been validated against MFCM and FMRIB software library using MRI scans from BrainWeb. Superior results to those achieved with MFCM technique have been observed along with the collateral advantages of fully automatic segmentation, faster computation and faster convergence of the objective function.     

Stein–type shrinkage quantile estimation

The simultaneous asymptotic estimation theory of quantiles is considered for an arbitrary population. The Stein–type estimator and its positive version are considered. The relative merits of the proposed estimators are compared with those of the usual estimator using asymptotic quadratic distributional risk those of the usual estimator using asymptotic quadratic distributional risk under local alternatives. It is shown that both proposed estimators are asymptotically superior to the classical estimator. Further, it is demonstrated that the Stein-type estimator is dominated by its positive part     

Reducing asymptotic bias of weak instrumental estimation using independently repeated cross-sectional information

In this paper, we consider the instrumental variable estimation (the two-stage least squares estimator and the limited information maximum likelihood estimator) using weak instruments in a repeated measurements or a panel data model. We show that independently repeated cross-sectional data can reduce the asymptotic bias of the instrumental variable estimation when instruments are weakly correlated with endogenous variables. When the number of repeated measurements tends to infinity, we can achieve consistent instrumental variable estimation with weak instruments.     

Effect of insignificant bias and its uncertainty on the coverage probability of uncertainty intervals Part 1. Evaluation for a given value of the true bias

This paper investigates the coverage probability of the uncertainty intervals determined in compliance with the GUM and EURACHEM Guide, which are defined by expanded uncertainty U about the results uncorrected with the insignificant biases and corrected with the significant biases. This coverage probability can significantly fall below the chosen level of confidence in some cases as Maroto et al. discovered by using the Monte Carlo method. Their numerical results obtained provided that only the β errors have occurred in the test significance and findings that the coverage reduction depends on the mutual proportions of the magnitudes of the systematic error, overall uncertainty and bias uncertainty are confirmed in this paper by using probability calculus and numerical integration. This problem is also studied when all possible experimental biases, both significant and insignificant, are considered. From this point of view, the reduction of the coverage probability turns out to be less severe than from the previous one. The coverage probability is also investigated for some uncertainty intervals computed in different ways than the above mentioned documents recommend. The intervals defined by U about the results corrected with both significant and insignificant bias give always the same coverage probability equalling the chosen level of confidence. The intervals with some uncertainties modified or enlarged with the insignificant biases remove or moderate the coverage reduction.     

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采用PIC/MCC模型,通过数值模拟的方法研究了束线离子和靶台复合加速离子注入过程中靶台偏压大小对注入过程离子动力学行为的影响,重点考察了不同偏压作用下离子的注入能量、注入剂量、注入角度以及注入范围的变化.结果表明,靶台上施加脉冲偏压后,在束流离子的作用下空间电荷分布发生变化,束流正下方的电势线会发生凹曲,凹曲的电场同时又作用于空间中的带电粒子,影响粒子的运动;靶台偏压越高,零电势线距离靶台越远,靶台电场对离子的作用范围越大.离子的注入剂量、注入能量随着靶台偏压的增大而增大,而偏压对离子注入角度的影响并不大,大部分离子都以垂直入射的方式注入到靶台表面.另外,离子注入到靶台上的面积会因束流离子在靶台电场中飞行偏转而增大,并且偏压越大注入面积增大越明显.     

Multivariate density estimation using dimension reducing information and tail flattening transformations

We propose a nonparametric multiplicative bias corrected transformation estimator designed for heavy tailed data. The multiplicative correction is based on prior knowledge and has a dimension reducing effect at the same time as the original dimension of the estimation problem is retained. Adding a tail flattening transformation improves the estimation significantly-particularly in the tail-and provides significant graphical advantages by allowing the density estimation to be visualized in a simple way. The combined method is demonstrated on a fire insurance data set and in a data-driven simulation study.