矩阵方程AXB=C在对称矩阵类中有解的充要条件

对称矩阵是一类很重要的矩阵.矩阵微分方程、数学物理问题经离散后一般得到对称形式线性方程，文中讨论矩阵方程AXB=C在对称矩阵类中求解问题.     

对称群Sn的极小生成集

本文给出了Sn的几个子集，并证明了它们均可极小生成Sn。     

一类对称统计量的强相合性

考虑了一类有如下形式的对称统计量Tn=k(n-k) k(k-1)2-1∑n-ki=1∑i kj=i 1φ(Xi,Xj) ∑n-1i=n-k 1∑nj=i 1φ(Xi,Xj),这里φ(Xi,Xj)为一对称函数,其中k为不大于n的自然数.这一表达形式是一类统计量,当k=n-1时,它就是一U统计量.在假定E(φ2(Xi,Xj)≤M<∞下,研究了Tn的强相合性.     

基于Graph的铁道工程工期估算算法及实现

为有效帮助铁道工程技术管理人员进行工程工期估算,合理调度和控制各子工程的施工进度,运用Graph结构中的AOE网分析工期估算算法,可实现计算机辅助管理,有利于科学管理铁道工程。     

新时期我国软科学研究前沿热点研究——基于软科学文献的知识图谱分析(2008-2018)

运用科学计量方法对软科学领域的六大核心期刊(2008—2018年)现状和研究热点进行分析。研究发现,目前我国软科学以国家创新驱动发展战略为主要研究方向,围绕区域创新体系及创新链的热点焦点问题和关键环节开展研究,同时,发现各个参与研究者之间合作紧密。研究运用描述性分析的方法,管窥我国软科学领域研究现状,并对未来发展做出展望,为我国软科学繁荣发展提供参考。     

对称反循环矩阵的逆矩阵

讨论了求对称反循环矩阵逆矩阵的一般方法,并根据反循环矩阵与对称反循环矩阵的特殊关系给出几类特殊对称反循环矩阵的逆矩阵.     

隐私保护研究

伴随着数字化网络技术的发展和应用,许多社交网站被创建,使得关于个人的大量信息被收集和发布。为了能够安全地利用社交网站进行信息交流,社交网站在发布数据的同时也要对用户的个人隐私进行必要的保护,本文对社交网络发布的个人信息隐私保护进行了总结,阐述了个人隐私保护模型,指出了社交网络在数据发布时隐私保护存在的待解决的问题以及面临的挑战。     

基于ATML标准的测试流程表示方法研究

测试信息标准化描述是实现不同ATS平台之间数据交换的关键。针对地面测控设备种类多样及自动测试系统测试任务复杂的情况,对ATML标准进行了深入研究。分析了ATML标准族的背景、目的、模型结构及组件标准,说明了仪器和测试结果组件在实际应用中的信息描述方法,并结合有限自动机的状态转换图理论,提出了测试流程状态转换图模型。实验中设计了一种测试流程信息标准化描述方法,解决了自动测试系统中的数据交换及信息共享问题。实验结果表明ATML在自动测试领域具有良好的发展前景。     

A Dual-Pointer guided transition system for end-to-end structured sentiment analysis with global graph reasoning

Structured sentiment analysis is a newly proposed task, which aims to summarize the overall sentiment and opinion status on given texts, i.e., the opinion expression, the sentiment polarity of the opinion, the holder of the opinion, and the target the opinion towards. In this work, we investigate a transition-based model for end-to-end structured sentiment analysis task. We design a transition architecture which supports the recognition of all the possible opinion quadruples in one shot. Based on the transition backbone, we then propose a Dual-Pointer module for more accurate term boundary detection. Besides, we further introduce a global graph reasoning mechanism, which helps to learn the global-level interactions between the overlapped quadruples. The high-order features are navigated into the transition system to enhance the final predictions. Extensive experimental results on five benchmarks demonstrate both the prominent efficacy and efficiency of our system. Our model outperforms all baselines in terms of all metrics, especially achieving a 10.5% point gain over the current best-performing system only detecting the holder-target-opinion triplets. Further analyses reveal that our framework is also effective in solving the overlapping structure and long-range dependency issues.     

Contrastive and attentive graph learning for multi-view clustering

Graph-based multi-view clustering aims to take advantage of multiple view graph information to provide clustering solutions. The consistency constraint of multiple views is the key of multi-view graph clustering. Most existing studies generate fusion graphs and constrain multi-view consistency by clustering loss. We argue that local pair-view consistency can achieve fine-modeling of consensus information in multiple views. Towards this end, we propose a novel Contrastive and Attentive Graph Learning framework for multi-view clustering (CAGL). Specifically, we design a contrastive fine-modeling in multi-view graph learning using maximizing the similarity of pair-view to guarantee the consistency of multiple views. Meanwhile, an Att-weighted refined fusion graph module based on attention networks to capture the capacity difference of different views dynamically and further facilitate the mutual reinforcement of single view and fusion view. Besides, our CAGL can learn a specialized representation for clustering via a self-training clustering module. Finally, we develop a joint optimization objective to balance every module and iteratively optimize the proposed CAGL in the framework of graph encoder–decoder. Experimental results on six benchmarks across different modalities and sizes demonstrate that our CAGL outperforms state-of-the-art baselines.