Data Sharpening for Nonparametric Inference Subject to Constraints

Data sharpening involves perturbing the data to improve the performance of a statistical method. The versions of it that have been proposed in the past have been for bias reduction in curve estimation, and the amount of perturbation of each datum has been determined by an explicit formula. This article suggests a distance-based form of data sharpening, in which the sum of the distances that data are moved is minimized subject to a constraint imposed on an estimator. The constraint could be one that leads to bias reduction, or to variance or variability reduction, or to a curve estimator being monotone or unimodal. In contrast to earlier versions of the method, in the form presented in this article the amount and extent of sharpening is determined implicitly by a formula that is typically given as the solution of a Lagrange-multiplier equation. Sometimes the solution can be found by Newton–Raphson iteration, although when qualitative constraints are imposed it usually requires quadratic programming or a related method.     

Stress impedance effects in flexible amorphous FeCoSiB magnetoelastic films

Amorphous FeCoSiB films were deposited on the flexible polyimide substrates (Kapton type (VN)) by DC magnetron sputtering. Stress impedance (SI) effects of the flexible amorphous FeCoSiB magnetoelastic films were investigated in details. The results show that a large stress impedance effect can be observed in the flexible amorphous FeCoSiB magnetoelastic films. And the results also show a bias magnetic field plays an important role in the stress impedance of FeCoSiB films. Applied a bias magnetic field during depositing can induce obvious in-plane anisotropy in the FeCoSiB films, and a larger SI effect can be obtained with a stronger anisotropy in FeCoSiB films. Argon pressure has a significant effect on the SI effect of the FeCoSiB films. The SI of the FeCoSiB films reaches a maximum of 7.6% at argon pressure of 1.5 Pa, which can be explained by the change of residual stress in FeCoSiB films.     

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.     

低偏置电流下半导体激光器1/f噪声的相关性

测量了高功率976nm InGaAs量子阱半导体激光器在低于1/30阈值电流下的低频电噪声,提出了以1/f噪声时域信号小波系数相关性与电流的关系来分析噪声来源的方法.结合1/f噪声源理论模型及小波变换系数的特性,完成了不同偏置电流下纯1/f噪声、加白噪声后的1/f噪声两种情况下的对比实验.实验结果表明:所测的低频噪声表现为明显的1/f噪声,对于纯1/f噪声,噪声幅度和小波系数相关性在判断噪声来源时具有相同的结果;对于加白噪声后的1/f噪声,噪声幅度变化很大且不能正确表征1/f噪声来源,而部分尺度下的小波系数相关性仍能作为判断噪声来源的可靠参量.     

Suppression of bias stress-induced degradation of pentacene-TFT using MoOx interlayer

The bias stress effect in pentacene thin-film transistors (TFTs) with and without MoO_x interlayer was characterized. The device without MoO_x interlayer showed a large threshold voltage shift of 5.1 V after stressing with a constant gate-source voltage of −40 V for 10000 s, while at the same condition, the device with MoO_x interlayer showed a low threshold voltage shift of 1.9 V. The results can be attributed to the stable interface between MoO_x/pentacene and small contact resistance change for the device with MoO_x/Cu electrode. Pentacene-TFTs with MoO_x interlayer showed a high field-effect mobility of 0.61 cm²/V s and excellent bias stability, which could be a significant step toward the commercialization of OTFT technology.     

Mean displacement moments and survival times in the trapping model with strong fields

The mean displacement moments for the strong field trapping model are calculated. Their asymptotic behaviour is Xⁿ(t) (V_efft)ⁿ, where V_eff is an effective drift velocity. This is in sharp contrast with the weak field case, for which Xⁿ(t) t^n/3. The mean survival (first passage) time and the upstream and downstream trapping probabilities are also obtained.     

基于LabVIEW的实时控制框架在J-TEXT上的应用

为了支持 J-TEXT 装置上等离子体偏滤器位形实验的研究,基于 LabVIEW 编程语言开发了一套实 时控制框架。在 J-TEXT 装置中,利用该实时控制框架以及选取合适的 NI 采集卡、控制器组建了偏滤器单零位形 的辅助电源-偏置电源的控制系统,并对该电源进行了测试。测试结果表明该电源能够在 1ms 的控制周期内输出 2kA 的电流,满足 J-TEXT 偏滤器单零位形放电的实验要求。该框架的开发极大地提高了组建实时控制系统的效 率,增强了控制程序的可读性以及可维护性,为 J-TEXT 装置提供了一套完善的、简便的,易于拓展及移植的实时控制框架。     

Image-guided regularization level set evolution for MR image segmentation and bias field correction

Magnetic resonance (MR) image segmentation is a crucial step in surgical and treatment planning. In this paper, we propose a level-set-based segmentation method for MR images with intensity inhomogeneous problem. To tackle the initialization sensitivity problem, we propose a new image-guided regularization to restrict the level set function. The maximum a posteriori inference is adopted to unify segmentation and bias field correction within a single framework. Under this framework, both the contour prior and the bias field prior are fully used. As a result, the image intensity inhomogeneity can be well solved. Extensive experiments are provided to evaluate the proposed method, showing significant improvements in both segmentation and bias field correction accuracies as compared with other state-of-the-art approaches.     

Bias correction for estimated distortion risk measure using the bootstrap

The bias of the empirical estimate of a given risk measure has recently been of interest in the risk management literature. In particular, Kim and Hardy (2007) showed that the bias can be corrected for the Conditional Tail Expectation (CTE, a.k.a. Tail-VaR or Expected Shortfall) using the bootstrap. This article extends their result to the distortion risk measure (DRM) class where the CTE is a special case. In particular, through the exact bootstrap, it is analytically proved that the bias of the empirical estimate of DRM with concave distortion function is negative and can be corrected on the bootstrap, using the fact that the bootstrapped loss is majorized by the original loss vector. Since the class of DRM is a subset of the L-estimator class, the result provides a sufficient condition for the bootstrap bias correction for L-estimators. Numerical examples are presented to show the effectiveness of the bootstrap bias correction. Later a practical guideline to choose the estimate with a lower mean squared error is also proposed based on the analytic form of the double bootstrapped estimate, which can be useful in estimating risk measures where the bias is non-cumulative across loss portfolio.     

用修正重标极差法对上证指数长期记忆性的研究

本文以上证指数周收益率为研究对象,分别采用重标极差分析法和修正重标极差分析法,通过计算V统计量的值对其进行长期记忆性的检验。由于不能排除V统计量的值存在超出上侧分位点的可能性,本文进行了双侧检验,并分析了R/S分析法产生偏差的原因。得出上证指数周收益率时间序列并未表现出显著的长期记忆性的结论。