Free Energy Level Correction by Monte Carlo Resampling with Weighted Histogram Analysis Method

Free energy calculations may provide vital information for studying various chemical and biological processes. Quantum mechanical methods are required to accurately describe interaction energies, but their computations are often too demanding for conformational sampling. As a remedy, level correction schemes that allow calculating high level free energies based on conformations from lower level simulations have been developed. Here, we present a variation of a Monte Carlo (MC) resampling approach in relation to the weighted histogram analysis method (WHAM). We show that our scheme can generate free energy surfaces that can practically converge to the exact one with sufficient sampling, and that it treats cases with insufficient sampling in a more stable manner than the conventional WHAM-based level correction scheme. It can also provide a guide for checking the uncertainty of the level-corrected surface and a well-defined criterion for deciding the extent of smoothing on the free energy surface for its visual improvement. We demonstrate these aspects by obtaining the free energy maps associated with the alanine dipeptide and proton transfer network of the KillerRed protein in explicit water, and exemplify that the MC resampled WHAM scheme can be a practical tool for producing free energy surfaces of realistic systems.     

光学焦距测量误差分布的实验研究

为解决光学检测中的误差分布问题 ,提出了用实验方法验证其分布 ,而不是简单地依据经验估计其分布为均匀分布、三角分布或正态分布。通过人眼目视测量与CCD光电测试 ,分析比较大量的采样数据 ,通常人为操作因素及仪器机械精度是目视光学检测中的一个主要误差源 ,进而得到目视测焦距的误差分布 ,并进行拟合性检验     

Histogram equalization of vibration fringes in holography

Common reconstructed fringes in holographic interferometry of vibration show rapidly diminishing intensity with extended amplitude limiting the measurement range. Using computer-simulated fringe patterns, linear histogram equalization is applied to redistribute the intensity for significant enhancement in the higher amplitude range. The cases of time-average and real-time single-frequency sinusoidal vibration are described.     

Sampling free energy surfaces as slices by combining umbrella sampling and metadynamics

Metadynamics (MTD) is a very powerful technique to sample high‐dimensional free energy landscapes, and due to its self‐guiding property, the method has been successful in studying complex reactions and conformational changes. MTD sampling is based on filling the free energy basins by biasing potentials and thus for cases with flat, broad, and unbound free energy wells, the computational time to sample them becomes very large. To alleviate this problem, we combine the standard Umbrella Sampling (US) technique with MTD to sample orthogonal collective variables (CVs) in a simultaneous way. Within this scheme, we construct the equilibrium distribution of CVs from biased distributions obtained from independent MTD simulations with umbrella potentials. Reweighting is carried out by a procedure that combines US reweighting and Tiwary–Parrinello MTD reweighting within the Weighted Histogram Analysis Method (WHAM). The approach is ideal for a controlled sampling of a CV in a MTD simulation, making it computationally efficient in sampling flat, broad, and unbound free energy surfaces. This technique also allows for a distributed sampling of a high‐dimensional free energy surface, further increasing the computational efficiency in sampling. We demonstrate the application of this technique in sampling high‐dimensional surface for various chemical reactions using ab initio and QM/MM hybrid molecular dynamics simulations. Further, to carry out MTD bias reweighting for computing forward reaction barriers in ab initio or QM/MM simulations, we propose a computationally affordable approach that does not require recrossing trajectories. © 2016 Wiley Periodicals, Inc.     

BayesWHAM: A Bayesian approach for free energy estimation,reweighting, and uncertainty quantification in the weighted histogram analysis method

The weighted histogram analysis method (WHAM) is a powerful approach to estimate molecular free energy surfaces (FES) from biased simulation data. Bayesian reformulations of WHAM are valuable in proving statistically optimal use of the data and providing a transparent means to incorporate regularizing priors and estimate statistical uncertainties. In this work, we develop a fully Bayesian treatment of WHAM to generate statistically optimal FES estimates in any number of biasing dimensions under arbitrary choices of the Bayes prior. Rigorous uncertainty estimates are generated by Metropolis‐Hastings sampling from the Bayes posterior. We also report a means to project the FES and its uncertainties into arbitrary auxiliary order parameters beyond those in which biased sampling was conducted. We demonstrate the approaches in applications of alanine dipeptide and the unthreading of a synthetic mimic of the astexin‐3 lasso peptide. Open‐source MATLAB and Python implementations of our codes are available for free public download. © 2017 Wiley Periodicals, Inc.     

Mass Transport Based Multi-Model Color Transfer

Image editing is a hot research topic in the image processing region, and one of the most basic problems in this field is the color transfer. It modifies the color style of the target image to match the color palette of the source while preserving the content of the target. Although there exist many algorithms to deal with this problem, none of existing methods can handle all kinds of images, especially complex landscape images. In this paper, we propose a Mass Transport based color transfer method in the CIELab color space, which can generate natural transfer results for different kinds of images including landscapes. Different from previous methods using the same transfer model for all the channels, we adopt different models for the luminance channel and color channels. For the luminance channel, a global transfer method is adopted for keeping the consistence in the visual perception. For the color channels, we construct a model based on histogram and Mass Transport, which perfectly transfers the colors of all the pixels from the source to the target and simultaneously preserves the content. What is more, experiments on real images demonstrate that our approach performs favorably against most of the existing methods.     

一种基于H直方图变换的白细胞图像分割方法

对检出的大量样本细胞图像进行统计分析,发现白细胞在Wright染色后着色效果不一致时色彩分量差异的规律。通过建立数学表达模型,提出了一种基于H直方图变换的白细胞图像自动分割算法。对细胞图像的色度分量直方图进行适当变换,有效削弱了血液染色的不一致性对白细胞分割的影响,实现了白细胞胞浆区域的良好分割,实验结果验证了所提分割方法的可行性和实用性。     

一种结合小波分析与直方图的红外图像增强方法

针对传统红外图像增强算法中存在的问题,提出一种结合小波分析与直方图的红外图像增强方法.采用正交小波变换对红外图像进行处理,得到小波各层的分解系数;运用双向直方图均衡法对红外图像的低频子带小波系数进行处理,利用阈值滤波的细节系数增强法对红外图像的高频子带小波系数进行处理,经小波逆变换图像重构得到增强后的红外图像.实验结果...     

快速均值漂移图像分割算法研究

Mean shift算法是一种搜索与样本点分布最接近模式的非参数统计方法.但它是一种迭代统计方法,要保证较高的数值计算精度需要较多的迭代次数,耗费较长的计算时间.为克服这一缺点,提出快速均值漂移图像分割算法.该算法在每次迭代时以前一次的聚类中心集合T动态地更新样本集S,并通过使用直方图缩小样本点的搜索范围进一步加快算法的收敛速度.实验结果表明该方法在保证图像分割质量的同时具有较快的收敛速度.     

基于CLAHE和开闭运算的绿色苹果图像分割

依据绿色苹果图像的自身特点,设计了一种分区域提取而后合并的图像分割方法。首先对图像进行限制对比度自适应直方图均衡化(contrast limited adaptive histogram equalization,CLAHE),增大果实和背景的颜色差,而后获取R-B色差图像,得到以光常区域为主的区域;然后用CLAHE处理后的图像进行开闭运算,提取局部极大值,得到以高亮区域为主的区域;最后将两区域合并获得完整的果实目标区域。为了验证该方法的有效性,运用Hough算法检测圆,并用相对偏差、圆心相对误差及半径相对误差三个评价指标来定量评价。试验结果表明,提取区域与果实目标区域相比,顺光下,相对偏差、圆心和半径相对误差平均值分别为3.59%,4.76%和2.60%;逆光下,三个指标的平均值分别为10.77%、16.77%、11.49%。无论是顺光还是逆光都有很好的识别效果,能满足机器人采摘果实的精确定位的要求。