基于深度学习的中医古文献临床经验抽取

中医古文献蕴藏着丰富的临床经验，是古代中医在行医过程中对临床诊疗的经验性总结，体现了中医学形成和发展的理论框架和思想基础.然而这些宝贵的临床经验不仅量大，而且分散在不同的文献中，使得中医从业者手工很难快速全面地获取它们，文献检索工具也只能提供文档级别的信息筛选，无法为这种细粒度的信息获取提供支持.此外，古汉语相对于现代汉语的不同特点也限制了主流文本分析工具的使用效果.为此本文提出面向临床经验获取的中医古文献信息抽取任务，用于识别古文献中描述临床经验的文本片段，手工标注了样本数据用于这种抽取模型的训练和测试，并设计了基于深度学习的序列标注器用于完成该任务.考虑到标注数据量小可能带来的过度拟合问题，本文引入对抗训练和虚拟对抗训练来增强模型的泛化能力.一系列充分的实验验证了模型的有效性，表明利用信息抽取技术从古文献获取中医临床经验具有可行性，为这一新的信息抽取任务提供了有希望的研究基线和可复用的标注数据集.     

打造新时代创新策源地——以湖北科技特派员制度为例

湖北省多年来深入推行科技特派员制度,在全国科技特派员制度推行和科技服务的伟大实践中拼搏奋进,从启动科技特派员地市试点工作,到全面开展科技特派员基层创业行动,从出台深入推行科技特派员制度实施意见,到开展湖北省科技特派员"百县千镇万人"工程实施,围绕产业链布局人才链的科技生态日臻完善.科技特派员个人、团队和法人单位点面结合,工作室与工作站交相辉映,他们立足湖北服务华中片区和秦巴山区,辐射服务全国各地,湖北科技特派员的服务足迹遍布荆楚星辰大地,谱写着新时代湖北科技特派员服务体系的新篇章.     

大学生童年受忽视经历对自尊的影响:应对方式的中介作用

为研究大学生在童年时受到忽视与自尊水平、应对方式之间的关系,采用方便抽样法,向430名学生发放童年受忽视量表、简易应对方式量表和自尊量表,使用统计软件SPSS17.0进行数据整理与分析,结果表明,大学生在童年时受到的忽视与自尊、处理应对的积极方式是负相关关系(r=-0.164,r=-0.246,P<0.01),忽视与消极应对方式呈正相关关系(r=0.152,P<0.01);自尊与大学生采用积极的应对方式呈正相关关系(r=0.398,P<0.01),与采用消极的应对方式呈负相关关系(r=-0.217,P<0.01);不同的应对方式在面临忽视与自尊之间起着协调作用,积极应对方式的协调效果有29.4%,而消极应对方式的协调效果则只有12.6%,差距十分明显。因此,大学生在童年时受到忽视与自尊、不同的应对方式会收到完全不同的协调效果。     

时标轴上二阶Dirichlet边值问题的弱解（英文）

采用变分方法和临界点理论研究一个时标轴上二阶Dirichlet边值问题弱解的存在性.     

高职院校图书馆新生入馆教育工作创新研究

对几种新生入馆教育方式的优缺点进行了比较分析,以山东商务职业学院图书馆为例,详细介绍了宣传片拍摄要领及技巧,提出了一种综合性的新生入馆教育新模式.     

关于二面体群的共轭类长素图

讨论二面体群的结构问题,利用GAP软件给出二面体群D2n的共轭类长素图,并完整给出当n以及n/2分别是奇数和偶数时的共轭类长素图情况.     

A Layer-Stripping Method for 3D Near-Surface Velocity Model Building Using Seismic First-Arrival Times

In order to improve the efficiency of 3D near-surface velocity model building, we develop a layer-stripping method using seismic first-arrival times. The velocity model within a Common Mid-Point (CMP) ...     

工程实践中的体系与系统

 介绍了国际上通行的体系概念，通过实例阐述了常见的体系类型，从多个维度描述了系统与体系的区别，根据实践总结，提出体系与系统的核心界定准则，并结合工程实践进行了详细描述。     

An Economic Model-Based Matching Approach Between Buyers and Sellers Through a Broker in an Open E-Marketplace

A broker in an open e-marketplace enables buyers and sellers to do business with each other. Although a broker plays an important role in e-marketplaces, theory and guidelines for matching between buyers and sellers in multi-attribute exchanges are limited. Therefore, a challenge for a broker’s responsibility is how to maximize a buyer’s total satisfaction degree as its goals under the consideration of trade-off between a buyer’s buying quantity and price paid to a seller, and other attributes. To solve this challenge, this paper proposes an economic model-based matching approach between a buyer’s requirements and a seller’s offers. The major contributions of this paper are that (i) a broker can model a seller’s price policy as per a buyer’s buying quantity through communication between a broker and a seller; (ii) due to each buyer’s different quantity demand, a broker models a buyer’s satisfaction degree as per a buyer’s buying quantity based on communication between a broker and a buyer; and (iii) to carry out a broker’s matching processes, an objective function and a set of constraints are generated to help a broker to maximize a buyer’s total satisfaction degree. Experimental results demonstrate the good performance of the proposed approach.     

Hierarchical Community Detection Based on Partial Matrix Convergence Using Random Walks

Random walks are a standard tool for modeling the spreading process in social and biological systems.But in the face of large-scale networks,to achieve convergence,iterative calculation of the transition matrix in random walk methods consumes a lot of time.In this paper,we propose a three-stage hierarchical community detection algorithm based on Partial Matrix Approximation Convergence(PMAC) using random walks.First,this algorithm identifies the initial core nodes in a network by classical measurement and then utilizes the error function of the partial transition matrix convergence of the core nodes to determine the number of random walks steps.As such,the PMAC of the core nodes replaces the final convergence of all the nodes in the whole matrix.Finally,based on the approximation convergence transition matrix,we cluster the communities around core nodes and use a closeness index to merge two communities.By recursively repeating the process,a dendrogram of the communities is eventually constructed.We validated the performance of the PMAC by comparing its results with those of two representative methods for three real-world networks with different scales.