Network-Initialized Monte Carlo Based on Generative Neural Networks

We design generative neural networks that generate Monte Carlo configurations with complete absence of autocorrelation from which only short Markov chains are needed before making measurements for physical observables,irrespective of the system locating at the classical critical point,fermionic Mott insulator,Dirac semimetal,or quantum critical point.We further propose a network-initialized Monte Carlo scheme based on such neural networks,which provides independent samplings and can accelerate t...     

FTIR噪声修正方法用于提高挥发性有机物定量精度

经典最小二乘法(CLS)是傅里叶红外光谱定量分析软件中的一种常用算法。统计学中,当待测信号中的误差为正态分布零均值等方差时,CLS法可以得到最优无偏估计,但对异方差误差则不然。傅里叶红外光谱仪(FTIR)吸光度谱中的噪声相当于异方差误差,通过对噪声源的理论分析和实验统计计算获得了噪声的方差分布,并以此对CLS方法进行加权修正。计算表明,在分析气体中挥发性有机化合物(VOCs)污染浓度时,相比CLS方法, 采用仪器噪声方差分布修正后的加权最小二乘法(WLS)可以显著降低噪声对定量精度的影响,提高了分析结果的准确性和可靠性。