计算机抽站法在青海巴州沟流域雨量站网密度分析中的应用

本文应用计算机随机抽站和组合抽站两方法,对青海湟水支流巴州沟流域配套雨量站网按６、１２、２４小时三种时段,四种误差分别进行计算后,确定了该流域的合理布站密度。     

统计实验方法浅析

随着计算机的产生,许多的实际问题都借用计算机来解决,从中产生了计算方法,概率统计法就是其中的一种.本文把握概率统计法的基本思想是:根据所要解决的问题设计模型,使这个模型的某个模型恰好就是所要计算的量,而这个特征值可以通过实验方法求出.本文针对不同形式的随机变量设计模型,进行随机变量的模拟,从而来解决实际问题.     

《随想录》意义探析

巴金的《随想录》，充溢着这位世纪作家的善良、悲悯与博爱。《随想录》之所以被誉为“中国当代文坛的高峰”之作，在于它是“力透纸背、情透纸背、热透纸背”的“讲真话”的大书。     

两两NQD列的LP收敛性

本文研究了两两NQD列的LP收敛性,通过减弱条件,改进了前人的相应结果.     

Searching for short-term motivational alignment and spillover effects: A random intercept cross-lagged analysis of students’ expectancies and task values in math-intensive study programs

Students’ expectancy and value beliefs about math influence their academic choices and success in math-intensive study programs. Short-term declines in these motivational beliefs can serve as early warning signs of academic difficulties and dropout. However, such short-term motivational changes are underresearcherd. Based on Eccles et al.’s (2020) situated expectancy-value theory, this study analyzed within-person changes in the associations among students’ course-specific (summative) or week-specific (situated) expectancies and task values in gateway math courses for students in physics, math, or math teacher education majors (N = 773). Random intercept cross-lagged panel models showed increasing within-person alignment between students’ course-specific expected success and intrinsic/utility values (but not costs) over one semester. This alignment was linked to unidirectional spillover (i.e., cross-lagged) effects from expectancy to intrinsic/utility values. Students’ week-specific expectancy-value beliefs, reported at the beginning of the semester, showed no significant alignment and spillover effects. Differences in students’ course- or week-specific expectancy-value beliefs favored male and higher-achieving students and were largely time-invariant. Alignment between course-specific expectancy and value beliefs was higher for students who failed or dropped out of their math courses compared to those who succeeded. Greater motivational alignment can thus indicate greater disengagement from (math) coursework in challenging academic contexts. These findings highlight the importance of differentiating between-person and within-person motivational processes, suggest that summative versus situation-specific assessments of motivational beliefs may show different developmental patterns, and demonstrate that motivational alignment and spillover effects can be a sign of maladaptive motivational processes concerning students’ persistence in challenging STEM contexts.     

Our Digital Future

This article introduces a selection of presentations from the 21st International Rights Directors Meeting held at the 2007 Frankfurt Book Fair It offers an overview of a variety of issues related to negotiating digital rights, in particular, and the development of digital publishing, in general. Topics covered include current business models, opportunities for licensing, and methods for negotiating contracts. Publishers are sanguine about the possibilities; though acknowledge that the digital rights business is still in its formative stages. Agents and author advocates are concerned that digital advances may lead to devaluation of writer’s works. Downloadable audiobooks have already created a new market, though e-books have yet to gain traction and are awaiting a device or software application that make them available to a wide audience.     

The use of minimization to form comparison groups in educational research

Randomized controlled trials in educational research tend to be small. Small trials can have large, chance, imbalances in important covariates. For studies with sample sizes greater than 50, chance imbalances can be corrected using analysis of covariance; for small trials, however, statistical power is maximized if the trial is balanced and analysis of covariance is used in the analysis. The aim of the present study was to discuss methods of improving covariate balance in trial design and to demonstrate the method of minimization. Using an exemplar of a cluster or class‐randomized trial with 29 classes, we employed minimization to achieve covariate balance. Minimization achieved good balance on four prognostic variables. Many trialists in education use restricted forms of allocation, including pairing or stratified randomization. These approaches have disadvantages. Another approach rarely used in educational research is minimization. Minimization uses a simple arithmetic algorithm to produce balanced groups across a number of important covariates and should be more widely used in educational and psychological research.     

Random quadralinear forms and schur product on tensors

INTRODUCTIONWeadoptthestandardnotation(1)Nplp%forthecomplexvectorspaceNCequippedwiththenorm11||||:(||)NpppnnXx==.TheusualmodificationsaremadetodefineNlandtheinfinitedimensionalsequencespace(1)plp%.AlloftheseareBanachspaces.LetA:(1,)NMpqllpq?alinearmapanddefinetheoperatornormby||||:sup{||||:||||1}pqqpxx==AA.ThemapAcanberepresentedasanMNmatrix()ijawithrespecttothestandardbases.Motivatedbyproblemsonabsolutelysummingoperators,Bennettetal.(1975)andBennett(1977)obtainedestimatesforthep…     

基于页面链接结构Page Rank算法的改进--有向访问模型

互联网上的信息每天都以指数量级的速度爆炸性增长,面对如此浩瀚的资源,从web中的大量信息中准确并且有效的提取用户所需要的信息成为了Internet的用户的迫切需要。web信息检索系统可以利用web页面的这种特殊的链接结构关系来改进检索的算法,以提高检索的精度。链接结构分析显著地提高了检索结果的相关性。在充分分析基于链接结构的算法的基础上,本文提出了一个更接近真实情形的模型——有向访问模型,它假定访问者将根据与查询相关的概率模型来指导下一步的访问,它能够真实地描述用户在浏览网页时的行为。     

A comparative study of automated legal text classification using random forests and deep learning

Automated legal text classification is a prominent research topic in the legal field. It lays the foundation for building an intelligent legal system. Current literature focuses on international legal texts, such as Chinese cases, European cases, and Australian cases. Little attention is paid to text classification for U.S. legal texts. Deep learning has been applied to improving text classification performance. Its effectiveness needs further exploration in domains such as the legal field. This paper investigates legal text classification with a large collection of labeled U.S. case documents through comparing the effectiveness of different text classification techniques. We propose a machine learning algorithm using domain concepts as features and random forests as the classifier. Our experiment results on 30,000 full U.S. case documents in 50 categories demonstrated that our approach significantly outperforms a deep learning system built on multiple pre-trained word embeddings and deep neural networks. In addition, applying only the top 400 domain concepts as features for building the random forests could achieve the best performance. This study provides a reference to select machine learning techniques for building high-performance text classification systems in the legal domain or other fields.