Oriented total variation l1/2 regularization

Total Variation (TV) is a widely used image restoration/decomposition model. It is observed that the classical TV l1 and TV l2 regularization, on the one hand, do not favor higher-gradient structures over lower-gradient details as expected for structure preserving image processing, and on the other hand, tend to reduce the horizontal and vertical gradients, and thus inevitably blur the oblique edges in images. In this paper, we address these two problems by defining Oriented Total Variation l1/2 (OTV l1/2). It is theoretically and experimentally demonstrated that applying l1/2 regularization to the directional derivatives of images leads to superior structure preservation. OTV l1/2 regularization can be applied to image denoising and video compression, and the experimental results verify that OTV l1/2 outperforms other similar models.     

Solution for the general integral‐type nonlocal plasticity model with Tikhonov regularization

A new solution approach, based on Tikhonov regularization on the Fredholm integral equations of the first kind, is proposed to find the approximate solutions of the strain softening problems. In this approach, the consistency condition is regularized with the Tikhonov stabilizers along with a regularization parameter, and the internal variable increments are solved from the resulting Euler's equations. It is shown that, as the regularization parameter is increased, the solutions converge to a unique one. A nonlocal yield condition and a nonlocal return mapping algorithm are proposed to carry out the integration of constitutive equations in the time and spatial domains. A global plastic dissipation principle is proposed to relax the classical local plastic dissipation postulate. Numerical examples show that the proposed approach leads to objective, mesh‐independent solutions of the softening‐induced localization problems. A comparison of the results from the proposed approach with those from the gradient‐dependent plasticity model shows that the two models give close solutions.     

基于Citespace的我国稀土出口研究进展与热点分析

随着战略性新兴产业的兴起发展，稀土作为战略资源的重要地位不断凸显，作为公共产品供给，稀土出口问题备受国内外关注。文章运用CiteSpace软件并基于文献计量与知识图谱分析方法，以CNKI文献资料为数据源，对1993~2019年稀土出口研究相关文献从发文量、作者群体、发文机构、关键词、突现词及时间线方面进行分析，以探讨国内稀土出口领域研究的演进趋势与发展热点。结果表明：近年来稀土出口领域文献整体呈增长趋势，但是文献发表具有明显的波动性和政策关联性。作者群体和发文机构主要集中在具有科研优势或资源优势地区的高校，研究团队之间合作程度较低。稀土产业、出口管制、出口配额、出口定价、出口政策等成为文献关键词和高频词。未来的稀土出口领域的研究方向主要集中在出口定价、可持续发展、本土稀土产业链发展和稀土产业资本出口四个方面。     

Estimating posterior inference quality of the relational infinite latent feature model for overlapping community detection

Overlapping community detection has become a very hot research topic in recent decades, and a plethora of methods have been proposed. But, a common challenge in many existing overlapping community detection approaches is that the number of communities K must be predefinedmanually. We propose a flexible nonparametric Bayesian generative model for count-value networks, which can allow K to increase as more and more data are encountered instead of to be fixed in advance. The Indian buffet process was used to model the community assignment matrix Z, and an uncollapsed Gibbs sampler has been derived.However, as the community assignment matrix Z is a structured multi-variable parameter, how to summarize the posterior inference results and estimate the inference quality about Z, is still a considerable challenge in the literature. In this paper, a graph convolutional neural network based graph classifier was utilized to help to summarize the results and to estimate the inference quality about Z. We conduct extensive experiments on synthetic data and real data, and find that empirically, the traditional posterior summarization strategy is reliable.     

A skeletal n-butane mechanism with integrated simplification method

A new skeletal mechanism of n-butane is developed for describing its ignition and combustion characteristics applicable over a wide range of conditions: initial temperature 690–1430 K, pressure 1–30 atm, and equivalence ratio 0.5–2.0. Starting with a detailed chemical reaction kinetic model of 230 species and 1328 reactions (Healy et al., Combust. Flame, 2010), the directed relation graph method is applied as the first step to derive a semi-detailed mechanism with 134 species. Then, the reaction path analysis in conjunction with temperature sensitivity analysis is used to remove the redundant species and reaction paths simultaneously under the condition of low-temperature and moderate-to-high temperatures, respectively. Finally, a skeletal n-butane mechanism consisting of 86 species and 373 reactions can be obtained. Mechanism validation indicates that the new developed skeletal mechanism is in good agreement with the detailed mechanism in predicting the global ignition and combustion characteristics. The new skeletal mechanism is further validated using extensive available literature data including rapid pressure machine ignition delay time, shock-tube ignition delay time, laminar flame speed, and jet-stirred reaction oxidation, covering a large range of temperatures, pressures, and equivalence ratios. The comparison results demonstrate that a satisfactory agreement between predictions and experimental measurements is achieved.     

基于双向位图的CSR大规模图存储优化

大数据时代,Graph500是评测超级计算机处理数据密集型应用能力的重要工具,E级验证系统的图遍历处理能力主要受限于内存空间和访存带宽,尤其是内存空间利用率直接决定了图的测试规模和测试性能.针对天河E级验证系统小内存特征,提出了基于双向位图的大规模图数据压缩存储方法(bidirectional-bitmap based CSR,Bi-CSR),Bi-CSR在CSR矩阵压缩的基础上引入行方向位图和列方向位图协同完成稀疏矩阵压缩存储,行方向位图主要负责行方向位图的压缩存储与索引,列方向位图除了进一步压缩图存储空间,还负责为顶点遍历向量并行优化提供加速空间.Bi-CSR大幅度减少了稀疏矩阵存储空间.面向天河E级验证系统,当图输入规模为237时,Graph500的图存储空间节约效率接近70%,全系统稳定测试性能为2.131E+12TEPS,性能最大加速比超过100倍.     

Optimization and regularization of complex task decomposition for blind removal of multi-factor degradation

Most existing image restoration methods based on deep neural networks are developed for images which only degraded by a single degradation mode and imaging under an ideal condition. They cannot be directly used to restore the images degraded by multi-factor coupling. A complex task decomposition regularization optimization strategy (TDROS) is proposed to solve the problem. The restoration of images degraded by multi-factor coupling is a complex task that can be solved by separating these multiple factors, that is, breaking the complex task into numbers of simpler tasks to make the entire complex problem be overcome more easily. Motivated by this idea, the TDROS decomposes the complex task of image restoration into two sub-task: the potential task constrained by regularization and the main task for reconstructing high-definition images. In TDROS, the front of the neural network is focused on the restoration of images degraded by additive noise, while the other part of the network is focused mainly on the restoration of images degraded by blur. We applied the TDROS to an 11-layer convolutional neural network (CNN) and compared it with initial CNNs from the aspects of restoration accuracy and generalization ability. Based on these results, we used TDROS to design a novel network model for the restoration of atmospheric turbulence-degraded images. The experimental results demonstrate that the proposed TDROS can improve the generalization ability of the existing network more effectively than current popular methods, offering a better solution for the problem of severely degraded image restoration. Moreover, the TDROS concept provides a flexible framework for low-level visual complex tasks and can be easily incorporated into existing CNNs.     

Dynamic modelling of PEM fuel cell system for simulation and sizing of marine power systems

This article presents a model of a proton exchange membrane fuel cell (PEMFC) system for marine power systems. PEMFC in marine hybrid power sources can have various power ranges and capacities in contrast with vehicle applications. Investigating PEMFCs behaviour and performance for various conditions and configurations is demanded for proper sizing and feasibility studies. Hence, modelling and simulation facilitate understanding the performance of the PEMFC behaviour with various sizes and configurations in power systems. The developed model in this work has a system level fidelity with real time capabilities, which can be utilized for simulator approaches besides quasi-static studies with a power-efficiency curve. Moreover, the model can be used for scaling the PEMFC power range by considering transient responses and corresponding efficiencies. The Bond graph approach as a multi-disciplinary energy based modelling strategy is employed for the PEMFC as a multi domains system. In the end, various PEMFC cell numbers and compressor sizes have been compared with power-efficiency curves and transient responses in a benchmark.     

基于节点1-邻居图相似性的社会网络匿名技术

利用传统的k匿名技术在社会网络中进行隐私保护时会存在聚类准则单一、图中数据信息利用不足等问题. 针对该问题, 提出了一种利用Kullback-Leibler (KL)散度衡量节点1-邻居图相似性的匿名技术(anonymization techniques for measuring the similarity of node 1-neighbor graph based on Kullback-Leibler divergence, SNKL). 根据节点1-邻居图分布的相似性对原始图节点集进行划分, 按照划分好的类进行图修改, 使修改后的图满足k匿名, 完成图的匿名发布. 实验结果表明, SNKL方法与HIGA方法相比在聚类系数上的改变量平均降低了17.3%, 同时生成的匿名图与原始图重要性节点重合度保持在95%以上. 所提方法在有效保证隐私的基础上, 可以显著的降低对原始图结构信息的改变.     

基于人脸卡口数据的行人共现关系图谱构建

随着人脸识别技术的不断进步以及人脸卡口的大范围且密集的部署, 本文针对团伙犯罪案件侦察这一应用场景, 对人脸卡口数据进行深入挖掘, 探究其中行人间的共现关系, 获取所关注的嫌疑人的现实社交网络, 锁定团伙其余人员. 经过实验比对和论证, 本文使用Chinese Whispers聚类算法对行人节点进行识别, 通过Faiss加速邻接边的构建, 加速图的初始化步骤, 解决其聚类效率低下的问题. 在此基础上, 使用共现频次和Apriori算法中的置信度挖掘行人间的共现关系, 构建行人共现关系图谱.