CoGCN: Combining co‐attention with graph convolutional network for entity linking with knowledge graphs

Entity linking is a fundamental task in natural language processing. The task of entity linking with knowledge graphs aims at linking mentions in text to their correct entities in a knowledge graph like DBpedia or YAGO2. Most of existing methods rely on hand‐designed features to model the contexts of mentions and entities, which are sparse and hard to calibrate. In this paper, we present a neural model that first combines co‐attention mechanism with graph convolutional network for entity linking with knowledge graphs, which extracts features of mentions and entities from their contexts automatically. Specifically, given the context of a mention and one of its candidate entities' context, we introduce the co‐attention mechanism to learn the relatedness between the mention context and the candidate entity context, and build the mention representation in consideration of such relatedness. Moreover, we propose a context‐aware graph convolutional network for entity representation, which takes both the graph structure of the candidate entity and its relatedness with the mention context into consideration. Experimental results show that our model consistently outperforms the baseline methods on five widely used datasets.     

Ensuring reliability in B2B services: Fault tolerant inter-organizational workflows

In the age of Business-to-Business (B2B) collaboration, ensuring reliability of workflows underlying inter-organizational business processes is of significant importance. There are, however, quite a few challenges towards achieving seamless operation. Such challenges arise from heterogeneity in infrastructure and coordination mechanism at participant organizations, as well as time and cost associated with recovery from failure. Our research presents foundations for a reliable scheme for recovery from failure of workflow processes spanning through multiple business entities. First, a system model is adapted from the mobile computing literature that serves to establish the requirements to be enforced by each participating organization. In our model, we adopt the Maximal Sequence Path (MSP) approach from Yoo et al. (Lecture Notes in Artificial Intelligence 2132:222–236, 2001), as a means of decomposing workflows into mobile agent-driven processes that communicate via web services at each organization. This decomposition ensures defining logical points within the dynamics of a workflow instance for locating accurate and consistent states of the system for recovery in case of a failure. Then, a set of algorithms for various business scenarios are developed and presented as practical solutions. These algorithms are shown to create checkpoints such that the system is always in a globally consistent state. As such, these algorithms constitute a set of standards that can be incorporated in business process management suites that support reliable inter-organizational collaboration.     

I-convergence in probabilistic n-normed space

In this article we introduce the notion of I-Cauchy sequence and I-convergent sequence in probabilistic n-normed space. The concept of I*-Cauchy sequence and I*-convergence in probabilistic n-normed space are also introduced and some of their properties related to these notions have been established.     

Entanglement mean field theory: Lipkin–Meshkov–Glick Model

Entanglement mean field theory is an approximate method for dealing with many-body systems, especially for the prediction of the onset of phase transitions. While previous studies have concentrated mainly on applications of the theory on short-range interaction models, we show here that it can be efficiently applied also to systems with long-range interaction Hamiltonians. We consider the (quantum) Lipkin–Meshkov–Glick spin model, and derive the entanglement mean field theory reduced Hamiltonian. A similar recipe can be applied to obtain entanglement mean field theory reduced Hamiltonians corresponding to other long-range interaction systems. We show, in particular, that the zero temperature quantum phase transition present in the Lipkin–Meshkov–Glick model can be accurately predicted by the theory.     

红外弱小目标的匹配方法研究与仿真

研究红外图像中的目标匹配问题。针对传统的红外图像中目标匹配效果不理想,匹配不准确。当红外图像中是弱小目标时,背景灰度值与目标灰度值非常相近,灰度直方图均衡化无法实现目标与背景的完整分离,导致漏匹配率较高的问题。为提高目标的匹配精度,提出一种形态学预处理的红外目标匹配方法,通过形态学算法提取出图像中的形状信息,利用形状信息将红外图像中的目标与背景完整分离,避免了只依靠灰度图像信息造成的不能完整分离目标与背景带来的漏匹配问题。进行仿真的结果证明,采用的形态学与处理的匹配方法能够完整将目标与背景分离,成功完成目标匹配,取得了满意的结果。     

空心铝型材挤压过程计算机仿真系统

试开发了一套基于计算机辅助设计和数值模拟的空心铝型材挤压过程计算机仿真系统,它涵盖了模具工艺设计的全过程,可以集成原有的和新建立的工艺设计知识。该系统包括空心型材挤压模具及坯料的参数化几何造型模块,并能分析分流桥的截面形状、分流孔的布置、焊合室的高度、工作带长度和阻流或者助流结构对金属流速分布的影响,模具的弹性变形对工作带有效长度和模孔尺寸的影响。     

操作参数对煤泥脱水效果的影响

探究不同操作参数对煤泥脱水效果的影响,通过改变操作参数进行单因素试验,分别探究入料浓度、入料流量、转鼓转速对煤泥脱水效果的影响,并通过正交试验确定三者的主次序列.结果表明,随着入料浓度的增加,煤泥的脱水效果越来越好,产品的含水率逐渐降低;在一定的入料流量范围内,随着入料流量的增加,产品的含水率降低,但是超过某一流量后,含水率会有所增加;在转鼓最大允许范围内,产品水分随着转速的增加而降低.通过正交试验可以得知影响脱水效果的主次因素依次为转鼓转速、入料流量、入料浓度.     

高压交流线路避雷器劣化状态数值仿真

高压交流线路避雷器的劣化分析是保障其运行安全的重要评估技术.针对避雷器出现的劣化问题,提取其伏安特性中的小电流区间,建立数学表征模型,并以模型中的时间常数表征氧化锌的劣化程度.结合晶粒物理结构、Voronoi网格分析晶粒及晶粒间的电气连接特性,推演RC等效电路模型并简化电路.开展数值仿真计算,获得不同劣化程度下避雷器的伏安特性曲线以及交流电压作用下的阻性电流变化规律,分析了容性电流分量与谐波电压对总泄漏电流的影响.泄漏电流波形的畸变率可判断避雷器的劣化程度,模型可辅助优化基于泄漏电流的避雷器在线监测与诊断技术.     

依托数学建模竞赛,提高大学生综合素质——以常州工学院为例

数学建模竞赛是目前规模最大的全国性学科竞赛活动,受到越来越多的学校和学生的重视,近年来,参加比赛的学生人数逐年增加.文章首先分析了数学建模竞赛的特点以及其对大学生综合素质培养的促进作用,然后以常州工学院为例剖析了数学建模活动的现状,最后从教学体系、指导团队师资建设、竞赛激励制度等三方面对进一步推进数学建模竞赛给出了建议.     

Analysis of D.C Parameters of Short-Channel Heterostructure Double Gate Junction-Less MOSFET Circuits Considering Quantum Mechanical Effects

Silicon - In this article, the electrical behavior of short channel SiGe Heterostructure Junction-Less DG-MOSFET have been studied by incorporating the quantum mechanical effect and short channel...