基于复杂网络的国民经济动员链风险传播研究

国民经济动员链的目标是在危态下保证应战应急资源的超常规供给,在其运行过程中的风险传播会降低动员链整体稳定性甚至破坏动员链的连通结构。以国民经济动员链为研究对象,依托复杂网络理论与方法系统地研究动员链风险传播问题。首先,构建国民经济动员链的复杂网络模型作为风险传播研究的载体。其次,改进SIRS疾病传播模型,建立具备动员特征的国民经济动员链风险传播模型。最后,以汶川特大地震医用物资动员任务为实际案例,模拟风险传播演化过程,探索相关变量对于动员链风险传播的影响。结果表明,不同的状态转换概率会对风险传播范围和风险传播时长造成影响。     

Horseshoe Regularisation for Machine Learning in Complex and Deep Models1

Since the advent of the horseshoe priors for regularisation, global–local shrinkage methods have proved to be a fertile ground for the development of Bayesian methodology in machine learning, specifically for high-dimensional regression and classification problems. They have achieved remarkable success in computation and enjoy strong theoretical support. Most of the existing literature has focused on the linear Gaussian case; for which systematic surveys are available. The purpose of the current article is to demonstrate that the horseshoe regularisation is useful far more broadly, by reviewing both methodological and computational developments in complex models that are more relevant to machine learning applications. Specifically, we focus on methodological challenges in horseshoe regularisation in non-linear and non-Gaussian models, multivariate models and deep neural networks. We also outline the recent computational developments in horseshoe shrinkage for complex models along with a list of available software implementations that allows one to venture out beyond the comfort zone of the canonical linear regression problems.     

一种基于“四域特征”的复杂电磁环境多层次评估方法

针对复杂电磁环境难以评定,尤其是电磁环境与电子信息系统相互影响、紧密耦合情况下复杂电磁环境难以度量的问题,提出了一种从复杂电磁环境复杂度、威胁度两个层面对复杂电磁环境进行度量评估的方法。在构建复杂电磁环境“四域”（时、空、频、能域）评价指标基础上,建立了基于“四域”特征占有度指标的电磁环境复杂度评估以及基于“四域”特征相关性指标的电磁环境威胁度评估方法,并给出了相应的仿真评估算例。仿真结果表明了评估方法的可行性,可为复杂电磁环境评估和模拟等相关问题提供方法和依据。     

Linear mixed-effects model for longitudinal complex data with diversified characteristics

The increasing richness of data encourages a comprehensive understanding of economic and financial activities, where variables of interest may include not only scalar (point-like) indicators, but also functional (curve-like) and compositional (pie-like) ones. In many research topics, the variables are also chronologically collected across individuals, which falls into the paradigm of longitudinal analysis. The complicated nature of data, however, increases the difficulty of modeling these variables under the classic longitudinal framework. In this study, we investigate the linear mixed-effects model (LMM) for such complex data. Different types of variables are first consistently represented using the corresponding basis expansions so that the classic LMM can then be conducted on them, which generalizes the theoretical framework of LMM to complex data analysis. A number of simulation studies indicate the feasibility and effectiveness of the proposed model. We further illustrate its practical utility in a real data study on Chinese stock market and show that the proposed method can enhance the performance and interpretability of the regression for complex data with diversified characteristics.     

兰州市李家庄田园综合体农户满意度分析

田园综合体的农户满意度研究是把握田园综合体发展的基本方向及制定相关政策的重要参考因素。以甘肃省国家级田园综合体试点素朴·李家庄为例,从农户满意度视角出发,在实地调研的基础上通过Excel和SPSS软件分析对其在建设发展过程中农户满意度进行定量分析,从而探讨李家庄田园综合体建设过程中存在的问题及对策。     

MOOC网络的行为交互及影响因素研究

大规模开放课程已成为一种在线学习的新途径,探索学习者-课程交互关系网络的拓扑结构,对于提高学习者参与度和MOOC平台的活跃程度具有重要作用。研究随机爬取“中国大学MOOC”的学习者数据,利用复杂网络分析工具探究学习者的度分布演变,基于学习者的学习行为建模,提出学习者学习增益产出模型,并利用Pajek软件将二模网映射至学习者一模关系网;同时结合学习者的属性数据,构建属性数据各分量影响学习者参与课程度的回归模型。