同分布NA列Jamison型加权和强稳定的充要条件

本文将Ｊａｍｉｓｏｎ等（１９６５）关于ｉｉｄ列加权和强稳定的充分性结果推广到ＮＡ列的场合并且得到了ＮＡ列Ｊａｍｉｓｏｎ型加权和强稳定的必要性结果。     

过硫酸盐活化高级氧化新技术

过硫酸盐在热、光、过渡金属催化等条件激活下产生强氧化性的硫酸根自由基·SO₄^-。基于·SO₄^-的过硫酸盐活化“高级氧化技术”在环境污染治理领域的应用,是刚刚发展起来的崭新的研究方向,具有广阔的应用前景。本文在分析其基本原理的基础上,综述了过硫酸盐活化技术在国内外土壤地下水有机污染原位修复、难降解有机废水处理等环境污染治理方面的研究进展,并就存在问题进行了研究展望。     

亚氯酸钠溶液同时脱硫脱硝的热力学研究

在自行设计的小型鼓泡反应器中, 以亚氯酸钠溶液作为吸收剂, 进行了模拟烟气同时脱硫脱硝实验研究, 得到反应的最佳实验条件以及在此条件下同时脱硫脱硝效率. 分析了反应产物, 推导出了亚氯酸钠溶液与硫氧化物、氮氧化物的反应历程以及总化学反应方程式. 利用热力学原理计算出亚氯酸钠溶液同时脱硫脱硝的摩尔反应吉布斯函数、摩尔反应焓变、化学反应平衡常数以及化学反应达到平衡时SO2和NO的分压力. 结果表明: 亚氯酸钠溶液同时脱硫脱硝是可行的, 且可以几乎100%的脱除烟气中的SO2和NO.     

陈皮黄酮的分离纯化研究

陈皮,又名橘皮,为芸香科植物橘及其栽培变种的成熟果皮,是我国传统中草药之一,具有行气健脾,和胃止呕,燥湿化痰的功效.陈皮中含有丰富的挥发油、黄酮类化合物以及多种人体生命必需的营养元素.     

选择性传输的仿生水通道的研究进展

水通道蛋白是对水分子具有高选择性和渗透性的跨膜蛋白。仿生水通道是由各种无机或有机材料,如碳纳米材料、有机化合物以及肽等分别自组装而成,旨在模仿天然水通道蛋白的结构和功能。本文介绍了水通道蛋白的种类、结构及其特异性透水机理,在此基础上分别对以碳纳米材料、有机及肽孔的仿生水通道的研究进展进行了综述。重点阐述了三类仿生水通道的材料特性及其对仿生水通道结构和功能的影响。最后针对现有仿生水通道的不足,提出了开发新型仿生水通道面临的挑战,并展望了仿生水通道的发展前景。     

AP Shadow Net: A Remote Sensing Shadow Removal Network Based on Atmospheric Transport and Poisson’s Equation

Shadow is one of the fundamental indicators of remote sensing image which could cause loss or interference of the target data. As a result, the detection and removal of shadow has already been the hotspot of current study because of the complicated background information. In the following passage, a model combining the Atmospheric Transport Model (hereinafter abbreviated as ATM) with the Poisson Equation, AP ShadowNet, is proposed for the shadow detection and removal of remote sensing images by unsupervised learning. This network based on a preprocessing network based on ATM, A Net, and a network based on the Poisson Equation, P Net. Firstly, corresponding mapping between shadow and unshaded area is generated by the ATM. The brightened image will then enter the Confrontation identification in the P Net. Lastly, the reconstructed image is optimized on color consistency and edge transition by Poisson Equation. At present, most shadow removal models based on neural networks are significantly data-driven. Fortunately, by the model in this passage, the unsupervised shadow detection and removal could be released from the data source restrictions from the remote sensing images themselves. By verifying the shadow removal on our model, the result shows a satisfying effect from a both qualitative and quantitative angle. From a qualitative point of view, our results have a prominent effect on tone consistency and removal of detailed shadows. From the quantitative point of view, we adopt the non-reference evaluation indicators: gradient structure similarity (NRSS) and Natural Image Quality Evaluator (NIQE). Combining various evaluation factors such as reasoning speed and memory occupation, it shows that it is outstanding among other current algorithms.     

On Fourier-Based Inequality Indices

Inequality indices are quantitative scores that take values in the unit interval, with a zero score denoting complete equality. They were originally created to measure the heterogeneity of wealth metrics. In this study, we focus on a new inequality index based on the Fourier transform that demonstrates a number of intriguing characteristics and shows great potential for applications. By extension, it is demonstrated that other inequality measures, such as the Gini and Pietra indices, can be usefully stated in terms of the Fourier transform, allowing us to illuminate characteristics in a novel and straightforward manner.     

Dynamics of Hopfield-Type Neural Networks with Modulo Periodic Unpredictable Synaptic Connections,Rates and Inputs

In this paper, we rigorously prove that unpredictable oscillations take place in the dynamics of Hopfield-type neural networks (HNNs) when synaptic connections, rates and external inputs are modulo periodic unpredictable. The synaptic connections, rates and inputs are synchronized to obtain the convergence of outputs on the compact subsets of the real axis. The existence, uniqueness, and exponential stability of such motions are discussed. The method of included intervals and the contraction mapping principle are applied to attain the theoretical results. In addition to the analysis, we have provided strong simulation arguments, considering that all the assumed conditions are satisfied. It is shown how a new parameter, degree of periodicity, affects the dynamics of the neural network.     

How to Secure Valid Quantizations

Canonical quantization has created many valid quantizations that require infinite-line coordinate variables. However, the half-harmonic oscillator, which is limited to the positive coordinate half, cannot receive a valid canonical quantization because of the reduced coordinate space. Instead, affine quantization, which is a new quantization procedure, has been deliberately designed to handle the quantization of problems with reduced coordinate spaces. Following examples of what affine quantization is, and what it can offer, a remarkably straightforward quantization of Einstein’s gravity is attained, in which a proper treatment of the positive definite metric field of gravity has been secured.     

Improved Information-Theoretic Generalization Bounds for Distributed,Federated, and Iterative Learning

We consider information-theoretic bounds on the expected generalization error for statistical learning problems in a network setting. In this setting, there are K nodes, each with its own independent dataset, and the models from the K nodes have to be aggregated into a final centralized model. We consider both simple averaging of the models as well as more complicated multi-round algorithms. We give upper bounds on the expected generalization error for a variety of problems, such as those with Bregman divergence or Lipschitz continuous losses, that demonstrate an improved dependence of 1/K on the number of nodes. These “per node” bounds are in terms of the mutual information between the training dataset and the trained weights at each node and are therefore useful in describing the generalization properties inherent to having communication or privacy constraints at each node.