高校图书馆日文古籍文献价值的开发与利用

高校图书馆拥有很多古旧文献、古籍资料,这些文献如果得到妥善的利用,将有很高的经济价值和研究价值。而这类文献中有部分日文原文资料,大部分都是1945年以前旧日文资料,本文以这些旧日文献资料为例,通过对这些珍贵文献资料的来源、出版地、作者和出版背景、文献类型、文献内容进行挖掘、整理、翻译、解析和研究,使其有价值的东西展现出来,旨在为读者提供系统、完整的外文文献检索,以便更好地服务于高校及社会的广大科研工作者,服务于教学与科研。     

Estimation of Sobol's sensitivity indices under generalized linear models

We derive explicit formulas for Sobol's sensitivity indices (SSIs) under the generalized linear models (GLMs) with independent or multivariate normal inputs. We argue that the main-effect SSIs provide a powerful tool for variable selection under GLMs with identity links under polynomial regressions. We also show via examples that the SSI-based variable selection results are similar to the ones obtained by the random forest algorithm but without the computational burden of data permutation. Finally, applying our results to the problem of gene network discovery, we identify through the SSI analysis of a public microarray dataset several novel higher-order gene–gene interactions missed out by the more standard inference methods. The relevant functions for SSI analysis derived here under GLMs with identity, log, and logit links are implemented and made available in the R package Sobol sensitivity.     

求凸二次函数极值的一种递推算法

本文通过讨论凸二次函数的几个结论,给出了求凸二次函数极值的一种递推算法．     

复杂网络社区结构问题综述

本文从社区的定义,社区结构评价指标,社区发现算法几个方面对复杂网络社区结构问题进行了综述。对现有的几种对社区的定义进行了较详细的阐述,介绍了几种比较有代表性的评价指标,并对复杂网络社区发现算法进行了分类总结,最后对复杂网络社区结构的发展做出展望。     

工作搜寻理论、匹配模型及其政策启示——2010年诺贝尔经济学奖获得者研究贡献综述

本文首先简要介绍了戴蒙德、莫藤森和皮萨里德斯这三位诺奖得主的主要研究贡献.随后对这三位经济学家的主要理论--工作搜寻理论和匹配模型进行了简单回顾和梳理.最后,对上述理论的政策启示进行了讨论.     

长株潭城市群水资源承载力的实证研究

在界定水资源承载力概念的基础上,构建了长株潭城市群水资源承载能力的指标体系,采用基于遗传算法的投影寻踪聚类模型对长株潭城市群水资源承载能力进行了实证分析,提出了提高长株潭城市群水资源承载能力水平的几点应对措施。     

Bridge regression: Adaptivity and group selection

In high-dimensional regression problems regularization methods have been a popular choice to address variable selection and multicollinearity. In this paper we study bridge regression that adaptively selects the penalty order from data and produces flexible solutions in various settings. We implement bridge regression based on the local linear and quadratic approximations to circumvent the nonconvex optimization problem. Our numerical study shows that the proposed bridge estimators are a robust choice in various circumstances compared to other penalized regression methods such as the ridge, lasso, and elastic net. In addition, we propose group bridge estimators that select grouped variables and study their asymptotic properties when the number of covariates increases along with the sample size. These estimators are also applied to varying-coefficient models. Numerical examples show superior performances of the proposed group bridge estimators in comparisons with other existing methods.     

大数据的整合分析方法

大数据具有数据来源差异性、高维性及稀疏性等特点,如何挖掘数据集间的异质性和共同性并降维去噪是大数据分析的目标与挑战之一。整合分析（Integrative Analysis）同时分析多个独立数据集,避免因地域、时间等因素造成的样本差异而引起模型不稳定,是研究大数据差异性的有效方法。它的特点是将每个解释变量在所有数据集中的系数视为一组,通过惩罚函数对系数组进行压缩,研究变量间的关联性并实现降维。本文从同构数据整合分析、异构数据整合分析以及考虑网络结构的整合分析三方面梳理了惩罚整合分析方法的原理、算法和研究现状。统计模拟发现,在弱相关、一般相关和强相关三种情形下, Group Bridge、 Group MCP、Composite MCP都表现良好,其中 Group Bridge的假阳数最低且最稳定。最后,将整合分析用于研究具有来源差异性的新农合家庭医疗支出,以及具有超高维、小样本等大数据典型特征的癌症基因数据,得到了一些有意义的结论。     

Inventory management with variable lead-time dependent procurement cost

A reduction in the inventory replenishment lead-time allows reducing safety stock requirements and improving customer service. However, it might be accompanied by increased procurement costs because of premium charges imposed by suppliers, or higher transportation costs. This paper studies a single-stage variable lead-time inventory system with lead-time dependent procurement cost. Selection of the lead-time value represents finding the trade-off between benefits of lead-time reduction and increase in the procurement cost. A model for joint optimization of inventory and procurement costs is developed. Numerical studies are conducted to identify conditions under which lead-time reduction is favorable compared to procuring at the lowest cost.