多视角子空间学习研究进展

大规模多源异构数据的产生为多视角学习算法提出了新的挑战。文中系统回顾了三类基于子空间学习的多视角聚类算法:最大相关子空间、低维子空间潜入和隐子空间,归纳了不同多视角学习算法的优缺点和适用范围,并在两个真实的多视角数据集上执行了大量对比实验,结果显示:用鲁棒的关联矩阵来刻画具有非线性、复杂结构关系的数据,有助于带来算法性能的显著提升;最后,总结了多视角学习领域出现的问题并展望了未来可以深入研究的方向。     

Personalized recommendation of learning material using sequential pattern mining and attribute based collaborative filtering

Material recommender system is a significant part of e-learning systems for personalization and recommendation of appropriate materials to learners. However, in the existing recommendation algorithms, dynamic interests and multi-preference of learners and multidimensional-attribute of materials are not fully considered simultaneously. Moreover, these algorithms cannot effectively use the learner’s historical sequential patterns of material accessing in recommendation. For addressing these problems and improving the accuracy and quality of recommendation, a new material recommender system framework based on sequential pattern mining and multidimensional attribute-based collaborative filtering (CF) is proposed. In the sequential pattern based approach, modified Apriori and PrefixSpan algorithms are implemented to discover latent patterns in accessing of materials and use them for recommendation. Leaner Preference Tree (LPT) is introduced to take into account multidimensional-attribute of materials, and learners’ rating and model dynamic and multi-preference of learners in the multidimensional attribute-based CF approach. Finally, the recommendation results of two approaches are combined using cascade, weighted and mixed methods. The proposed method outperforms the previous algorithms on the classification accuracy measures and the learner’s real learning preference can be satisfied accurately according to the real-time up dated contextual information.     

Assessing Effects of Schooling With Cross-Sectional Data: Between-Grades Differences Addressed as a Selection-Bias Problem

Abstract

This article details how cross-sectional data may be used to assess the effect of schooling. The methodology proposed requires that the data derive from two (or more) adjacent grades. Differences in learning outcomes between grades are assumed to be accounted for by differences in age, the education received, and unmeasured variables affecting both assignment to grades and learning outcomes. The approach presented includes an estimation of the impact of these unmeasured variables. It was applied to the TIMSS-95 data, which relate to student achievement and attitudes to both mathematics and science in 15 different countries. This research illustrated that the proposed approach yielded results that were very close to estimates based on an instrumental variables method.     

Random walk models for top-N recommendation task

Recently there has been an increasing interest in applying random walk based methods to recommender systems. We employ a Gaussian random field to model the top-N recommendation task as a semi-supervised learning problem, taking into account the degree of each node on the user-item bipartite graph, and induce an effective absorbing random walk （ARW） algorithm for the top-N recommendation task. Our random walk approach directly generates the top-N recommendations for individuals, rather than predicting the ratings of the recommendations. Experimental results on the two real data sets show that our random walk algorithm significantly outperforms the state-of-the-art random walk based personalized ranking algorithm as well as the popular item-based collaborative filtering method.     

Modeling of Nonrecursive Structural Equation Models With Categorical Indicators

Nonrecursive structural equation models generally take the form of feedback loops, involving 2 latent variables that are connected by 2 unidirectional paths, 1 starting with each variable and terminating in the other variable. Nonrecursive models belong to a larger class of path models that require the use of instrumental variables (IVs) to achieve model identification. Prior research has focused on SEM parameter estimation with IVs when indicators were continuous and normally distributed. Much less is known about how estimators function in the presence of categorical indicators, which are commonly used in the social sciences, such as with cognitive and affective instruments. In this study, there was specific interest in comparing the 2-stage least squares (2SLS) estimator and its categorical variant to other recommended estimators. This study compares the performance of several estimation approaches for fitting structural equation models with categorical indicator variables when IVs are necessary to obtain proper model estimates. Across conditions, 1 extension of the nonlinear 2SLS (N2SLS) approach, the nonlinear 3-stage least squares (N3SLS), which accounts for correlated errors among regressors within each model (as does the N2SLS), as well as correlations of errors across models, which N2SLS does not, appears to work the best among methods compared.     

Peer effects and measurement error: The impact of sampling variation in school survey data (evidence from PISA)

Investigation of peer effects on achievement with sample survey data on schools may mean that only a random sample of the population of peers is observed for each individual. This generates measurement error in peer variables similar in form to the textbook case of errors-in-variables, resulting in the estimated peer group effects in an OLS regression model being biased towards zero. We investigate the problem using survey data for England from the Programme for International Student Assessment (PISA) linked to administrative microdata recording information for each PISA sample member's entire year cohort. We calculate a peer group measure based on these complete data and compare its use with a variable based on peers in just the PISA sample. We also use a Monte Carlo experiment to show how the extent of the attenuation bias rises as peer sample size falls. On average, the estimated peer effect is biased downwards by about one third when drawing a sample of peers of the size implied by the PISA survey design.     

Application of honey-bee mating optimization on state estimation of a power distribution system including distributed generators

We present a new approach based on honey-bee mating optimization to estimate the state variables in distribution networks including distributed generators. The proposed method considers practical models of electrical equipments such as static var compensators, voltage regulators, and under-load tap changer transformers, which have usually nonlinear and discrete characteristics. The feasibility of the proposed approach is demonstrated by comparison with the methods based on neural networks, ant colony optimization, and genetic algorithms for two test systems, a network with 34-bus radial test feeders and a realistic 80-bus 20 kV network.     

Development and validation of a path analytic model of students' performance in chemistry

This article reports the development and validation of an integrated model of performance on a chemical concept - volumetric analysis. From the chemical literature a path-analytic model of performance on volumetric analysis calculation was postulated based on studies utilizing the proportional reasoning schema of Piaget and the Cumulative learning theory of Gagne. This integrated model hypothesized some relationships among the variables: direct proportional reasoning, inverse proportional reasoning, prerequisite concepts (content) and performance on volumetric analysis calculations. This model was postulated for the two groups of students involved in the study - that is those who use algorithms with understanding and those who use algorithms without understanding. Two hundred and sixty-five grade twelve chemistry students in eight schools (14 classes) in the lower mainland of British Columbia, Canada participated fully in the study. With the exception of the test on volumetric analysis calculations all the other tests were administered prior to the teaching of the unit on volumetric analysis. The results of the study indicate that for subjects using algorithms without understanding, their performance on VA problems is not influenced by proportional reasoning strategies while for those who use algorithms with understanding, their performance is influenced by proportional reasoning strategies.     

Forward and backward models for fault diagnosis based on parallel genetic algorithms

In this paper, a mathematical model consisting of forward and backward models is built on parallel genetic algorithms （PGAs） for fault diagnosis in a transmission power system. A new method to reduce the scale of fault sections is developed in the forward model and the message passing interface （MPI） approach is chosen to parallel the genetic algorithms by global single-population master-slave method （GPGAs）. The proposed approach is applied to a sample system consisting of 28 sections, 84 protective relays and 40 circuit breakers. Simulation results show that the new model based on GPGAs can achieve very fast computation in online applications of large-scale power systems.     

Determinants of international students’ adaptation: examining effects of integrative motivation,instrumental motivation and second language proficiency

This study examined the influence of integrative motivation, instrumental motivation and second language (L2) proficiency on socio-cultural/academic adaptation in a sample of two groups of international students studying Chinese in China. Results revealed that the non-Asian student group reported higher levels of integrative motivation, socio-cultural adaptation and Chinese language proficiency than the Asian student group, who reported a higher level of instrumental motivation. No significant difference was found in academic adaptation between the two groups. Moreover, the relationships between study variables are the same across Asian and non-Asian student groups. Further evidence from this research indicated that socio-cultural adaptation was influenced more by integrative motivation and less by L2 proficiency. Integrative motivation was found to be the only significant predictor of academic adaptation. Implications for future research are discussed.     

Comparative study of response surface designs with errors-in-variables model

This paper investigates the scaled prediction variances in the errors-in-variables model and compares the performance with those in classic model of response surface designs for three factors.The ordinary least squares estimators of regression coefficients are derived from a second-order response surface model with errors in variables.Three performance criteria are proposed.The first is the difference between the empirical mean of maximum value of scaled prediction variance with errors and the maximum value of scaled prediction variance without errors.The second is the mean squared deviation from the mean of simulated maximum scaled prediction variance with errors.The last performance measure is the mean squared scaled prediction variance change with and without errors.In the simulations,1 000 random samples were performed following three factors with 20 experimental runs for central composite designs and 15 for Box-Behnken design.The independent variables are coded variables in these designs.Comparative results show that for the low level errors in variables,central composite face-centered design is optimal;otherwise,Box-Behnken design has a relatively better performance.     

The Effects of Sex and Gender Role Orientation on Approach-Based Coping Strategies Across Cultures: A Moderated Mediation Model

This study explores the mediation effect of gender role orientation on the influence of sex on coping strategies and how the mediation effects differ across the two cultures of the United States and China. A total of 478 respondents (243 Americans and 235 resident Chinese) participated in this study. Employing Hayes’ (2013) PROCESS for testing moderated mediation models using conditional process analysis, we found that femininity mediated the influence of sex on both emotional and instrumental coping, and the mediation effects were significantly different between American participants and Chinese participants. We also found that national culture moderated the influence of sex on coping strategies when gender role orientation was being held constant. This study adopts an integrated model of sex, gender role orientation, culture, and coping. This model allows us to examine both general and specific relationships among interested variables, which contributes to existing literature in several ways.     

快速交替方向乘子法求解基于全变分的图像重建问题

采用一种快速的新型算法,即交替方向乘子法求解图像重建的全变分模型.首先,对全变分模型进行等价变形,使之转化成带有等式约束的可分的凸优化问题.然后,通过引入增广拉格朗日函数,并采用Gauss-Seidel迭代的思想,对问题中2块变量交替极小化,最后更新乘子.因为该方法充分利用了问题的特殊结构,将原问题分解成一系列容易求解...     

Cooperative Chemistry Tests

Abstract

In spite of the abundance of research reports in the field of instructor and course evaluation by students, the application of major theoretical psychological and sociological orientations to this field has been virtually ignored. This paper adopts the Ohio State University approach to leadership theory as an analytical tool for comprehending students’ ratings. Two main determinants of instructors’ scores are their instrumental contribution to the achievement of their classes’ goals and their success in enhancing their students’ interests. These two variables are claimed to resemble Initiating Structure and Consideration (14). The identification of leadership-bound components in faculty evaluation lends powerful theoretical integrity to the presently nontheoretical research of students’ evaluations. This paper expands such an approach which was developed at the Ben Gurion University of the Negev, Beer Sheva, Israel, and tests its applicability to another university, the University of Hawaii.     

Mixed Gl2/GH2 multi-channel multi-objective control synthesis for discrete time systems

This paper proposes a new approach for multi-objective robust control.The approach extends the standard generalized l2(Gl2)and generalized H2(GH2)conditions to a set of new linear matrix inequality(LMI)constraints based on a new stability condition.A technique for variable parameterization is introduced to the multi-objective control problem to preserve the linearity of the synthesis variables.Consequently,the multi-channel multi-objective mixed Gl2/GH2 control problem can be solved less conservatively using computationally tractable algorithms developed in the paper.     

基于网络平台的个性化知识推荐系统设计

本文利用学科知识点之间的层次关系图设计知识表示模型,而用户模型则以＂用户的认知水平＂和＂用户兴趣＂为中心而构建,在此基础上提出组合过滤推荐算法.该算法保留了内容过滤推荐算法和协同过滤推荐算法的优点,又弥补了两种算法的不足,通过测试评估,验证了组合过滤推荐算法的有效性和准确性.     

Mixed Gl_2/GH_2 multi-channel multi-objective control synthesis for discrete time systems

INTRODUCTION In the past two decades, a large number ostrategies for control systems analysis and synthesis such as H2, H∞, l1 and μ-synthesis had beendeveloped. In H∞design, all disturbances arelumped into a single norm rather than boundedseparately by the size of each disturbance as ||d||2=||d1||2 … ||dm||2. This certainly leads to some conservatism (D’Andrea, 1999). In contrast, theμ-synthesis technique overcomes the conservatismby introducing structured uncertainty block…     

教育对中国经济增长的影响效应——基于元回归分析方法的分析

基于60篇教育与中国经济增长实证研究中626个回归方程的元分析发现,教育对中国经济增长的作用估计受到不同研究特征的影响。尤其是,"显著效应"的实证结果受到估计方法、教育变量和经济增长变量的设定形式、教育变量的测量指标、样本起始年份、区域和控制变量选择等研究特征的显著影响。使用变量增长率形式、运用工具变量法、使用入学率和教育经费数据估计会降低显著性效应的概率;样本区域覆盖面越小、样本起始年份越早、使用受教育年限数据估计会增加显著性效应的概率。漏斗不对称检验显示样本文献存在显著的正向偏误,即回归结果越显著的研究越有可能被发表。因此,在分析教育对我国经济增长的作用时,必须考虑研究之间的异质性和可能存在的发表偏误。未来应重点关注的是,改进测量和估计方法,通过更严谨的研究设计精确识别教育对我国经济增长的因果效应;结合中国经济发展的阶段特征,分行业和部门讨论教育的作用,同时拓展非线性模型以考察教育的收益递增的外部效应;持续关注对教育质量的监测及其对经济增长的影响,为高质量教育体系的建设提供科学的理论支持。     

未定权益分析中的鞅方法

将证券、证券组合、期权、衍生证券、金融资产等的未定权益用随机变量来描述,就时间水平为有限的多期完全证券市场模型以及连续时间的Black-Scholes模型论述了鞅方法的应用．     

New predictive control algorithms based on Least Squares Support Vector Machines

INTRODUCTION Model predictive control (MPC), based on pre-dictive model and receding horizon optimization, has become an attractive feedback control strategy, be-cause it has found successful applications, especially in the process industry. For this kind of control strategy, the predictive model is a crucial component because the essence of MPC is to optimize the fore-cast of process behavior (Rawlings, 2000), and the forecast is accomplished with the predictive model. If the controll…