The impact of homework on student achievement

Summary Utilizing parametric and nonparametric techniques, we assess the role of a heretofore relatively unexplored ‘input’ in the educational process, homework, on academic achievement. Our results indicate that homework is an important determinant of student test scores. Relative to more standard spending related measures, extra homework has a larger and more significant impact on test scores. However, the effects are not uniform across different subpopulations. Specifically, we find additional homework to be most effective for high and low achievers, which is further confirmed by stochastic dominance analysis. Moreover, the parametric estimates of the educational production function overstate the impact of schooling related inputs. In all estimates, the homework coefficient from the parametric model maps to the upper deciles of the nonparametric coefficient distribution and as a by‐product the parametric model understates the percentage of students with negative responses to additional homework.     

Quantitative susceptibility mapping (QSM): Decoding MRI data for a tissue magnetic biomarker

In MRI, the main magnetic field polarizes the electron cloud of a molecule, generating a chemical shift for observer protons within the molecule and a magnetic susceptibility inhomogeneity field for observer protons outside the molecule. The number of water protons surrounding a molecule for detecting its magnetic susceptibility is vastly greater than the number of protons within the molecule for detecting its chemical shift. However, the study of tissue magnetic susceptibility has been hindered by poor molecular specificities of hitherto used methods based on MRI signal phase and T2* contrast, which depend convolutedly on surrounding susceptibility sources. Deconvolution of the MRI signal phase can determine tissue susceptibility but is challenged by the lack of MRI signal in the background and by the zeroes in the dipole kernel. Recently, physically meaningful regularizations, including the Bayesian approach, have been developed to enable accurate quantitative susceptibility mapping (QSM) for studying iron distribution, metabolic oxygen consumption, blood degradation, calcification, demyelination, and other pathophysiological susceptibility changes, as well as contrast agent biodistribution in MRI. This paper attempts to summarize the basic physical concepts and essential algorithmic steps in QSM, to describe clinical and technical issues under active development, and to provide references, codes, and testing data for readers interested in QSM. Magn Reson Med 73:82–101, 2015. © 2014 The Authors. Magnetic Resonance in Medicine Published by Wiley Periodicals, Inc. on behalf of International Society of Medicine in Resonance. This is an open access article under the terms of the Creative commons Attribution License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited.     

Application of nonparametric quantile regression to body mass index percentile curves from survey data

Increasing rates of overweight among children in the U.S. stimulated interest in obtaining national percentile curves of body size to serve as a benchmark in assessing growth development in clinical and population settings. In 2000, the U.S. Centers for Disease Control and Prevention (CDC) developed conditional percentile curves for body mass index (BMI) for ages 2–20 years. The 2000 CDC BMI‐for‐age curves are partially parametric and only partially incorporated the survey sample weights in the curve estimation. As a result, they may not fully reflect the underlying pattern of BMI‐for‐age in the population. This motivated us to develop a nonparametric double‐kernel‐based method and automatic bandwidth selection procedure. We include sample weights in the bandwidth selection, conduct median correction to reduce small‐sample smoothing bias, and rescale the bandwidth to make it scale invariant. Using this procedure we re‐estimate the national percentile BMI‐for‐age curves and the prevalence of high‐BMI children in the U.S. Copyright © 2009 John Wiley & Sons, Ltd.     

舒康贴膏中愈创木酚GC鉴别的研究

目的建立舒康贴膏中愈创木酚GC鉴别方法。方法采用GC对舒康贴膏中的愈创木酚进行定性鉴别,色谱柱以聚5%二苯基-95%二甲基硅烷为固定相,起始温度50℃,保持5 min,以每分钟10℃程序升温至220℃。结果愈创木酚在GC色谱图中无干扰,分离良好。结论该方法简单、重复性好,可作为该制剂的鉴别方法。     

白果仁化学成分研究

目的研究白果仁的化学成分。方法应用溶剂法进行提取,采用硅胶柱色谱分离和纯化化合物,通过理化性质和波谱数据鉴定化合物结构。结果从体积分数95%乙醇提取物中分离并鉴定了19个化合物,分别为二十六烷酸(Ⅰ)、棕榈酸(Ⅱ)、白果醇(Ⅲ)、β-谷甾醇(Ⅳ)、正十六烷酸-1-甘油酯(Ⅴ)、熊果酸(Ⅵ)、金松双黄酮(Ⅶ)、银杏黄素(Ⅷ)、异银杏黄素(Ⅸ)、胡萝卜苷(Ⅹ)、银杏内酯A(Ⅺ)、银杏内酯B(Ⅻ)、银杏内酯C(ⅩⅢ)、尿嘧啶(ⅩⅣ)、松柏苷(ⅩⅤ)、甘草苷(ⅩⅥ)、腺苷(ⅩⅦ)、D-葡萄糖(ⅩⅧ)和蔗糖(ⅪⅩ)。结论化合物Ⅵ、ⅩⅤ～ⅩⅦ为首次从该植物中分离得到,化合物Ⅰ～Ⅴ、Ⅶ～Ⅹ、ⅩⅣ为首次从白果仁中分离得到。     

小麦纤维素颗粒治疗中国南方功能性便秘患者的有效性与安全性

目的观察小麦纤维素颗粒对中国南方功能性便秘患者的疗效及安全性。方法采用随机自身对照研究的方法,对100例采用传统中国南方食谱的功能性便秘患者进行研究,给予小麦纤维素颗粒连续治疗14d,观察患者服药前后便秘症状及粪便性状的改变,并对其进行评分量化比较。结果应用小麦纤维素颗粒治疗后第7天、第14天的便秘症状和粪便性状与治疗前相比较,各项症状的评分均显著下降,差异有统计学意义（P〈0.05）。服药期间未发现腹痛、腹胀等不良反应。结论对采用中国南方饮食的功能性便秘患者,小麦纤维素颗粒是一种安全有效的良好药物。     

Mean shift方法在图像处理中的研究与应用

目的：Mean shift方法在模式检测、聚类、图像分割、图像滤波以及目标实时跟踪等方面的应用非常广泛。本文主要对Mean shift基本方法进行理论分析并对其在图像处理与模式识别领域的应用进行综述和展望。方法：首先根据非参数密度估计理论推导Mean shift方法的一般公式与表达形式,并对算法的基本步骤和收敛性进行了分析和论述;然后对核函数的选择以及窗口带宽矩阵的计算等关键技术进行讨论。结果：在综述mean shift基本原理基础上对该方法的特点、发展及应用进行了展望。结论：Mean shift算法具有计算简单、收敛速度快和对噪声的鲁棒性强等优点。作为一种高效的非参统计迭代算法,Mean shift方法在很多领域都得到了广泛的应用,未来还会在图像处理和模式识别领域得到更大的发展及应用。由于该算法的计算复杂度严重依赖于Mean shift公式中核函数的选择和带宽矩阵的计算,该方法对于多变量、多模态数据的处理速度和效果尤其有待提高。因此,该算法未来的主要研究方向为对多模态和多变量数据的快速处理方法研究。     

Spectral methods in machine learning and new strategies for very large datasets

Spectral methods are of fundamental importance in statistics and machine learning, because they underlie algorithms from classical principal components analysis to more recent approaches that exploit manifold structure. In most cases, the core technical problem can be reduced to computing a low-rank approximation to a positive-definite kernel. For the growing number of applications dealing with very large or high-dimensional datasets, however, the optimal approximation afforded by an exact spectral decomposition is too costly, because its complexity scales as the cube of either the number of training examples or their dimensionality. Motivated by such applications, we present here 2 new algorithms for the approximation of positive-semidefinite kernels, together with error bounds that improve on results in the literature. We approach this problem by seeking to determine, in an efficient manner, the most informative subset of our data relative to the kernel approximation task at hand. This leads to two new strategies based on the Nyström method that are directly applicable to massive datasets. The first of these—based on sampling—leads to a randomized algorithm whereupon the kernel induces a probability distribution on its set of partitions, whereas the latter approach—based on sorting—provides for the selection of a partition in a deterministic way. We detail their numerical implementation and provide simulation results for a variety of representative problems in statistical data analysis, each of which demonstrates the improved performance of our approach relative to existing methods.     

Cross-validation-based kernel support selection for improved GRAPPA reconstruction.

The extended version of the generalized autocalibrating partially parallel acquisition (GRAPPA) technique incorporates multiple lines and multiple columns of measured k-space data to estimate missing data. For a given accelerated dataset, the selection of the measured data points for fitting a missing datum (i.e., the kernel support) that provides optimal reconstruction depends on coil array configuration, noise level in the acquired data, imaging configuration, and number and position of autocalibrating signal lines. In this work, cross-validation is used to select the kernel support that best balances the conflicting demands of fit accuracy and stability in GRAPPA reconstruction. The result is an optimized tradeoff between artifacts and noise. As demonstrated with experimental data, the method improves image reconstruction with GRAPPA. Because the method is simple and applied in postprocessing, it can be used with GRAPPA routinely.