DataView: a computational visualisation system for multidisciplinary design and analysis

Rapidly processing raw data and effectively extracting underlining information from huge volumes of multivariate data become essential to all decision-making processes in sectors like finance, government, medical care, climate analysis, industries, science, etc. Remarkably, visualisation is recognised as a fundamental technology that props up human comprehension, cognition and utilisation of burgeoning amounts of heterogeneous data. This paper presents a computational visualisation system, named DataView, which has been developed for graphically displaying and capturing outcomes of multiphysics problem-solvers widely used in engineering fields. The DataView is functionally composed of techniques for table/diagram representation, and graphical illustration of scalar, vector and tensor fields. The field visualisation techniques are implemented on the basis of a range of linear and non-linear meshes, which flexibly adapts to disparate data representation schemas adopted by a variety of disciplinary problem-solvers. The visualisation system has been successfully applied to a number of engineering problems, of which some illustrations are presented to demonstrate effectiveness of the visualisation techniques.     

无向简单图与无向连通图自动识别系统

近年来,图论越来越受到全世界数学界和其它科学界的广泛重视。图的理论及其在物理、化学、运筹学、计算机科学、电子学、信息论、控制论、网络理论、社会科学及经济管理等几乎所有学科领域中各方面的应用研究都迅速发展。无向图作为图论的重要组成部分,研究无向图的连通性问题具有很重要的意义。本文介绍了无向简单图与无向连通图自动识别系统的设计与实现过程。     

Hierarchical models for independence structures of networks

We introduce a new family of network models, called hierarchical network models, that allow us to represent in an explicit manner the stochastic dependence among the dyads (random ties) of the network. In particular, each member of this family can be associated with a graphical model defining conditional independence clauses among the dyads of the network, called the dependency graph. Every network model with dyadic independence assumption can be generalized to construct members of this new family. Using this new framework, we generalize the Erdös–Rényi and the β models to create hierarchical Erdös–Rényi and β models. We describe various methods for parameter estimation, as well as simulation studies for models with sparse dependency graphs.     

纺织服装类学生信息素养教育课程体系的构建

信息素养教育是终身学习能力培养的核心要素,也是终身教育需要研究的一个重要课题。通过对高职院校信息素养现状的调查和分析,结合纺织服装行业信息化应用现状及行业发展趋势,构建了适合纺织服装类学生信息素养教育的"层次模块化"课程体系。     

城市出行信息环境分层规划方法

认为出行信息环境的服务水平是影响城市交通效率的重要因素之一,有效的出行信息传递能够显著提高路网的整体效率。系统分析了我国城市出行信息环境现状,在出行信息需求分析的基础上,建立了包含城市路网、交通方式与路线三个层面的出行信息环境分层规划方法,并提出了具体的出行信息环境优化对策。     

Accessible Conceptions of Statistical Inference: Pulling Ourselves Up by the Bootstraps

With the rapid, ongoing expansions in the world of data, we need to devise ways of getting more students much further, much faster. One of the choke points affecting both accessibility to a broad spectrum of students and faster progress is classical statistical inference based on normal theory. In this paper, bootstrap‐based confidence intervals and randomisation tests conveyed through dynamic visualisation are developed as a means of reducing cognitive demands and increasing the speed with which application areas can be opened up. We also discuss conceptual pathways and the design of software developed to enable this approach.     

基于土木结构递阶分散控制的稳定性研究

采用递阶分散控制的方法对土木结构进行振动控制,同时结构满足稳定性要求。控制作用分为局部和全局控制两级,局部控制器对子结构的振动控制,用李雅普诺夫方法来获得子结构稳定,而全局控制器来减小子结构之间相互作用而保证结构全局稳定性。对悬臂梁结构进行了控制稳定性仿真分析,将振动仿真结果与集中控制的结果进行对比分析。结果表明,基于递阶分散的控制方法保证大型土木结构的稳定性。     

也谈S_n与A_n的群图——质疑《群图的基本理论及置换群图的构造》中某些结论

详细研究了Sn与An的群图,修正了《群图的基本理论及置换群图的构造》一文中某些错误的结论,并用计算机作出了S4与A4的部分群图。     

The Hierarchical Erosion Effect: A New Perspective on Perceptual Differences and Business Performance

Organizations are coalitions of individuals with heterogeneous interests and perceptions (March and Simon, 1958/1993). We examine an important source of heterogeneity, namely the different perceptions individuals hold across hierarchical levels. We introduce the notion of a hierarchical erosion effect whereby individual perceptions about specific practices become less favourable the lower one goes in the hierarchy. Using data from 4,243 employees across four levels in 38 business units, we provide evidence that this effect exists, controlling for other factors, including the overall favourability of the business unit culture across eight practices. We show how the size of this hierarchical erosion effect varies depending on the nature of the organizational practice being evaluated and the extent to which executives share strategic information widely, and we also show that a lower hierarchical erosion effect is correlated with higher business unit growth. In doing so, we enrich understanding of two aspects of March and Simon's work, their notion of intra‐organizational heterogeneity and their distinctive view of the nature of hierarchy.     

Bayesian Model Selection of Gaussian Directed Acyclic Graph Structures

During the last years, graphical models have become a popular tool to represent dependencies among variables in many scientific areas. Typically, the objective is to discover dependence relationships that can be represented through a directed acyclic graph (DAG). The set of all conditional independencies encoded by a DAG determines its Markov property. In general, DAGs encoding the same conditional independencies are not distinguishable from observational data and can be collected into equivalence classes, each one represented by a chain graph called essential graph (EG). However, both the DAG and EG space grow super exponentially in the number of variables, and so, graph structural learning requires the adoption of Markov chain Monte Carlo (MCMC) techniques. In this paper, we review some recent results on Bayesian model selection of Gaussian DAG models under a unified framework. These results are based on closed-form expressions for the marginal likelihood of a DAG and EG structure, which is obtained from a few suitable assumptions on the prior for model parameters. We then introduce a general MCMC scheme that can be adopted both for model selection of DAGs and EGs together with a couple of applications on real data sets.     

从递阶控制理论看系统运行的动态衔接管理

在递阶控制系统的理论分析的基础上 ,结合海尔公司采用递阶控制理论进行系统运作的实践 ,分析了递阶控制的6个优点 ;提出递阶控制体现了动态衔接的管理思想 ;对提高企业竞争力能起到有力的推动作用。     

Multilevel Analysis of Repeated Measures Data

Hierarchically structured data are common in many areas of scientific research. Such data are characterized by nested membership relations among the units of observation. Multilevel analysis is a class of methods that explicitly takes the hierarchical structure into account. Repeated measures data can be considered as having a hierarchical structure as well: measurements are nested within, for instance, individuals. In this paper, an overview is given of the multilevel analysis approach to repeated measures data. A simple application to growth curves is provided as an illustration. It is argued that multilevel analysis of repeated measures data is a powerful and attractive approach for several reasons, such as flexibility, and the emphasis on individual development.     

分层教学模式优势分析及功能评价

消防部队院校由于学生英语基础参差不齐,分化差异明显。英语分层教学模式从一定程度上解决了教与学之间的矛盾,本文就分层教学实施过程中的教学成效和出现的问题进行了分析和研究,并就下一步如何进行有效改进提出了建设性的意见,对消防部队院校的英语教学改革起到了一定的指导意义和作用。     

新经济条件下企业的组织结构创新研究

产生于工业经济时代的的传统科层组织结构曾是人类组织结构的伟大创新,曾促进了工业企业的巨大成功。但在以信息、知识、网络为时代背景的新经济条件下,科层组织赖以成功的条件正在或已经发生变化,已不能适应新经济时代的要求。交易成本低、管理效率高且变化灵活的网络型组织结构是一种理想的组织结构模式,这是一种发展趋势。但在中国目前的情况下,由于各方面条件的限制,网络化的科层组织结构可能会是一种更为合适的组织结构形式。本文对科层组织的贡献和局限性进行了分析,阐述了新经济对企业组织结构的影响,进而提出了企业组织结构发展的趋势,对企业组织结构创新进行了探讨。     

浙江高等教育层次结构与科类结构的优化研究

本文分析浙江高等教育层次结构和科类结构的现状及存在的问题,由此提出优化层次结构和科类结构的对策:构建梯度合理、职责明晰的层次结构,打造调整与需求适应、改造与发展并举的科类结构。     

以职业能力本位构建高职计算机应用基础课程新模式

信息基础的不断发展,软件的日新月异,对计算机应用基础教学提出了更高的要求。针对教学中存在的内容陈旧、层次不分明、与专业知识脱节等弊端,探讨了职业能力内涵视角下的课程模式,提出了"层次模块化"的计算机应用基础课程体系、"分层次"教学方案和任务驱动教学方式,从而构建职业能力本位教育理念指导下高职计算机应用基础课程新模式。     

Data visualization and forecast combination for probabilistic load forecasting in GEFCom2017 final match

This paper describes the methods used by Team Cassandra, a joint effort between IBM Research Australia and the University of Melbourne, in the GEFCom2017 load forecasting competition. An important first phase in the forecasting effort involved a deep exploration of the underlying dataset. Several data visualisation techniques were applied to help us better understand the nature and size of gaps, outliers, the relationships between different entities in the dataset, and the relevance of custom date ranges. Improved, cleaned data were then used to train multiple probabilistic forecasting models. These included a number of standard and well-known approaches, as well as a neural-network based quantile forecast model that was developed specifically for this dataset. Finally, model selection and forecast combination were used to choose a custom forecasting model for every entity in the dataset.     

基于分层遗传算法的一类非线性规划问题的新解法

关于非线性规划的问题的诸多传统解法中都存在效率较低、容易达到局部最优、甚至找不到最优解的局限性。而基本遗传算法也常常由于本身算法的局限性,在搜索最优解的过程中易早熟,局部搜索能力弱,后期收敛过慢。源于这些缺陷,本论文提出使用分层遗传算法来求解一类非线性规划问题,并通过数值实验,结果表明基于分层遗传算法求解一类非线性规划问题是非常有效的。     

工程造价专业“分层次递进式”认识实习体系构建

高职院校工程造价专业认识实习是学生形成对建筑安装工程的感性认识和获得基本的工程专业实践知识的必要环节,构建"分层次递进式"认识实习体系,根据学生不同学习阶段的特点和需求分层次(主要分为三个层次)、多次性、多样化开展认识实习能切实提高实习效果,有助于学生对专业知识的掌握和专业能力的形成。