Uncertain educational returns in a developing economy

This paper estimates the distribution of educational returns by gender for India. While previous studies focus on mean returns, the variance of educational returns has important implications for policy-making and micro-level decision making with respect to education. If the variance of educational returns is large, it can leave large sections of the population with negative returns; if the variance of educational returns is gender specific, it can influence households’ decisions to educate girls versus boys. Our econometric results provide evidence that India's labor markets are characterized by significant uncertainty and that the uncertainty is systematically larger for women.     

显式评分的重启动随机游走推荐算法研究

针对目前重启动随机游走推荐算法偏重隐式评分而忽略显式评分的问题,采用监督重启动随机游走算法,使得用户喜爱的项目被访问的概率大于用户不喜爱的项目的概率,从而做出推荐。实验表明,该算法可以有效地提高推荐的准确性。     

随机变量的分布函数求解方法的讨论

结合实例归纳了求离散型、连续型、特殊型三种随机变量的分布函数求法,使问题易于理解,计算更为简便。并对这三种随机变量的分布函数进行了对比,讨论了其本质、表达形式等方面的异同。     

基于假设间隔的弱随机特征子空间生成算法

集成算法是机器学习领域的研究热点。随机子空间算法是集成算法的一个主要算法。随机子空间生成的特征子集可能含有冗余特征、噪声特征,影响算法的分类精度。为此,本文提出了一种基于假设间隔的弱随机特征子空间生成算法（WRSSimba）,有效去除了特征子集中冗余特征和噪声特征。在UCI数据集上的实验结果表明,WRSSimba的分类性能优于随机子空间算法和Simba算法。     

基于随机对照试验的小儿支原体肺炎中医药治疗研究述评

根据我国中医药治疗小儿支原体肺炎的现状和存在的问题,对我国中医药治疗小儿支原体肺炎的随机对照试验文献的质量和内容进行了评价和分析,以期进一步提高小儿支原体肺类的中医药临床治疗水平。     

Iron removal from waterlogged wood: Extraction by electrophoresis and chemical treatments

Abstract

The presence of iron oxides (lepidocrocite, goethite) in archeological wood may result in a degradation of the wood matrix. Extraction of these iron oxides is largely dependent on their solubility. In this study, balsa wood samples were impregnated with iron oxides to test extraction treatments. Additionally, archeological wood samples were also examined to determine treatment efficiency. Electrophoresis and simple immersion treatments were performed using various chemical solutions: a neutral and a conductive substance (potassium nitrate), an acid (acetic acid), three alkaline chelating agents (tri-ammonium and tri-sodium citrate and sodium oxalate), three acidic and slightly acidic chelating agents (ethylenediaminetetraacetic acid (EDTA), citric acid, and oxalic acid), and a reductant (sodium dithionite). Potassium nitrate did not extract sufficient amounts of iron, irrespective of whether the treatment was conducted by electrophoresis or simple immersion; any observable dissolution was attributed to protonation because of the acidic pH around the anode (as low as 3). Dissolution in acetic acid did not extract iron with either treatment. Strong chelating agents improved extraction, and these compounds gave the best results for simple immersion, particularly EDTA. This chemical is well adapted for use on archeological objects because of its chemical properties (stability constant, speciation based on pH). The addition of sodium dithionite to the solution improved dissolution. Even though electrophoresis improved extraction (in particular for tri-ammonium citrate), none of the tested chelating chemicals were suitable for electrophoresis because of a significant increase in temperature as well as high anode corrosion. The presence of iron sulfide in the archeological wood limited the effectiveness of the tested chemicals. A pre-treatment in sodium persulfate was expanded to include oxidized iron sulfide in oxy/hydroxide iron, which improved the extraction rate.     

Meta-Analysis of Meta-Analyses in Communication: Comparing Fixed Effects and Random Effects Analysis Models

Thirty-nine meta-analyses obtained from the past 10 years of communication research (1997–2007) were reanalyzed using fixed effects (FE), random effects (RE), and Hunter and Schmidt (HS) meta-analytic methods. The majority of studies (62%) reported use of the HS model in the original analysis. Differences identified between models include (a) greater propensity for Type 1 error under the FE approach, (b) episodes of inflated effect size (ES) under the RE approach, and (c) high levels of heterogeneity in population ESs across studies. Recommendations are made for scholars to appropriately choose and implement meta-analytic models in future research.     

基于步态特征提取方法的研究

对步态识别的国内外研究现状进行了详细的论述;介绍了基于步态识别的身份识别过程,阐述了在步态识别各阶段用到的一些方法;对步态识别的下一步工作进行了探讨。     

Feature-enriched matrix factorization for relation extraction

Relation extraction aims at finding meaningful relationships between two named entities from within unstructured textual content. In this paper, we define the problem of information extraction as a matrix completion problem where we employ the notion of universal schemas formed as a collection of patterns derived from open information extraction systems as well as additional features derived from grammatical clause patterns and statistical topic models. One of the challenges with earlier work that employ matrix completion methods is that such approaches require a sufficient number of observed relation instances to be able to make predictions. However, in practice there is often insufficient number of explicit evidence supporting each relation type that could be used within the matrix model. Hence, existing work suffer from a low recall. In our work, we extend the work in the state of the art by proposing novel ways of integrating two sets of features, i.e., topic models and grammatical clause structures, for alleviating the low recall problem. More specifically, we propose that it is possible to (1) employ grammatical clause information from textual sentences to serve as an implicit indication of relation type and argument similarity. The basis for this is that it is likely that similar relation types and arguments are observed within similar grammatical structures, and (2) benefit from statistical topic models to determine similarity between relation types and arguments. We employ statistical topic models to determine relation type and argument similarity based on their co-occurrence within the same topics. We have performed extensive experiments based on both gold standard and silver standard datasets. The experiments show that our approach has been able to address the low recall problem in existing methods, by showing an improvement of 21% on recall and 8% on f-measure over the state of the art baseline.     

Neumann展开Monte Carlo随机无网格伽辽金法的镗刀位移变异分析

本文用Neumann展开Monte Carlo随机无网格伽辽金法（NMC—SEFGM）进行了随机结构分析。在随机无网格伽辽金法中，所求解问题的域由分布的节点表示，并采用移动最小二乘函数近似试函数以及用罚函数法施加本质边界条件。同时利用Neumann展开法，建立了随机结构分析的Neumann展开Monte Carlo随机无网格伽辽金法，并应用于镗刀杆工作位移的变异分析。数值实例表明Neumann展开Monte Carlo随机无网格伽辽金法适用于材料变异系数大和要求精度高的随机结构分析。