Simulation of the p-generalized Gaussian distribution

In this paper, we introduce the p-generalized polar methods for the simulation of the p-generalized Gaussian distribution. On the basis of geometric measure representations, the well-known Box–Muller method and the Marsaglia–Bray rejecting polar method for the simulation of the Gaussian distribution are generalized to simulate the p-generalized Gaussian distribution, which fits much more flexibly to data than the Gaussian distribution and has already been applied in various fields of modern sciences. To prove the correctness of the p-generalized polar methods, we give stochastic representations, and to demonstrate their adequacy, we perform a comparison of six simulation techniques w.r.t. the goodness of fit and the complexity. The competing methods include adapted general methods and another special method. Furthermore, we prove stochastic representations for all the adapted methods.     

基于RPi的面曝光快速成形机控制系统

为了简化面曝光快速成形机的控制系统,降低其成本,提出了一种基于树莓派( Raspberry Pi,RPi)卡片电脑的面 曝光快速成形机控制系统。系统以Raspberry Pi为控制核心,以Linux为操作系统,通过Pvthon编程控制,实现了对面曝 光快速成形机的掩模图形投影和工作台运动控制;利用接入以太网的Raspberry Pi,实现了对面曝光快速成形机的远程 登录、文件传输以及实时监控。控制系统大大减小了面曝光快速成形机的占用空间,降低了成本,且具备远程操作功能, 有利于面曝光快速成形机进入大众市场。     

Seeing the Forest for Trees: Tools for Analyzing Faculty Research Output

Abstract

For academic libraries, because budgetary pressures are nearly universal, it is imperative to evaluate journal packages regularly. This article presents an overview of the data and methods that the NC State University Libraries traditionally uses to evaluate journal packages and presents additional methods to expand our evaluation of publishing and editorial activity. We describe methods for downloading and analyzing Web of Science citation data to identify the most common publishers for NC State affiliated authors as well as the journals in which NC State authors publish most frequently. This article also demonstrates a custom Python web scraping application to harvest NC State affiliated editor data from publishers’ websites. Finally, this article discusses how these data elements are combined to provide a more comprehensive evaluative strategy for our journal investments.     

论古代滇文化的民族属性

古代滇人留下的光辉灿烂、独具风格的青铜文化并非部分学者认为的那样,是越人或氐羌民族先民创造的,而是以云南古代濮人(即莫-孟人)为主,并有较多氐羌和部分百越族系参与形成的。     

Facilitating the Calculation of the Efficient Score Using Symbolic Computing

The score statistic continues to be a fundamental tool for statistical inference. In the analysis of data from high-throughput genomic assays, inference on the basis of the score usually enjoys greater stability, considerably higher computational efficiency, and lends itself more readily to the use of resampling methods than the asymptotically equivalent Wald or likelihood ratio tests. The score function often depends on a set of unknown nuisance parameters which have to be replaced by estimators, but can be improved by calculating the efficient score, which accounts for the variability induced by estimating these parameters. Manual derivation of the efficient score is tedious and error-prone, so we illustrate using computer algebra to facilitate this derivation. We demonstrate this process within the context of a standard example from genetic association analyses, though the techniques shown here could be applied to any derivation, and have a place in the toolbox of any modern statistician. We further show how the resulting symbolic expressions can be readily ported to compiled languages, to develop fast numerical algorithms for high-throughput genomic analysis. We conclude by considering extensions of this approach. The code featured in this report is available online as part of the supplementary material.     

试论蒙古史诗蟒古思形象之“美”

蒙古族英雄史诗往往包含两个重要对立形象——"至善至美"的英雄和"至恶至丑"的蟒古思。然而"美"并不是一个永久不变的概念,不同时代、不同民族对"美"的理解千姿百态、因人而异。本文通过对蟒古思形象的内在美与外在美的分析,深入探讨了在人们心中根深蒂固成为"丑"与"恶"的存在的蟒古思,如何能成为"美",怎样成为了"美"。在充分认识了蟒古思形象的内在美与外在美之后,从两方面阐述了其由"美"到"丑"的因由。     

Python for Unified Research in Econometrics and Statistics

Python is a powerful high-level open source programming language that is available for multiple platforms. It supports object-oriented programming and has recently become a serious alternative to low-level compiled languages such as C + +. It is easy to learn and use, and is recognized for very fast development times, which makes it suitable for rapid software prototyping as well as teaching purposes. We motivate the use of Python and its free extension modules for high performance stand-alone applications in econometrics and statistics, and as a tool for gluing different applications together. (It is in this sense that Python forms a “unified” environment for statistical research.) We give details on the core language features, which will enable a user to immediately begin work, and then provide practical examples of advanced uses of Python. Finally, we compare the run-time performance of extended Python against a number of commonly-used statistical packages and programming environments.

Supplemental materials are available for this article. Go to the publisher's online edition of Econometric Reviews to view the free supplemental file.