论当代大学生道德知行分离与协调

随着改革开放和社会主义市场经济建设不断深入 ,新观念、新思想层出不穷 ,许多落后陈旧的道德观念得到更新 ,同时也有一些腐朽、没落的道德观念沉渣泛起 ,还有一些似是而非的错误思想观念披着新观念的外衣 ,模糊了人们的视野 ,动摇了人们的善恶是非标准 ,这些都对传统道德提出了严峻的挑战。因此构建当代大学生的价值观和认识评价标准 ,既要弘扬和维系那些优秀的传统道德规范和观念 ,又要与时俱进 ,高扬时代精神 ,进行道德观念的创新 ,使爱国奉献、服务社会、爱岗敬业、诚实守信、创新进取等一系列基本道德规范和行为规范深入每一位大学生的日常生活和精神世界     

Real nonparametric regression using complex wavelets

Summary.  Wavelet shrinkage is an effective nonparametric regression technique, especially when the underlying curve has irregular features such as spikes or discontinuities. The basic idea is simple: take the discrete wavelet transform of data consisting of a signal corrupted by noise; shrink or remove the wavelet coefficients to remove the noise; then invert the discrete wavelet transform to form an estimate of the true underlying curve. Various researchers have proposed increasingly sophisticated methods of doing this by using real-valued wavelets. Complex-valued wavelets exist but are rarely used. We propose two new complex-valued wavelet shrinkage techniques: one based on multiwavelet style shrinkage and the other using Bayesian methods. Extensive simulations show that our methods almost always give significantly more accurate estimates than methods based on real-valued wavelets. Further, our multiwavelet style shrinkage method is both simpler and dramatically faster than its competitors. To understand the excellent performance of this method we present a new risk bound on its hard thresholded coefficients.     

On Combining Wavelets Expansion and Sparse Linear Models for Regression on Metabolomic Data and Biomarker Selection

Wavelet thresholding of spectra has to be handled with care when the spectra are the predictors of a regression problem. Indeed, a blind thresholding of the signal followed by a regression method often leads to deteriorated predictions. The scope of this article is to show that sparse regression methods, applied in the wavelet domain, perform an automatic thresholding: the most relevant wavelet coefficients are selected to optimize the prediction of a given target of interest. This approach can be seen as a joint thresholding designed for a predictive purpose. The method is illustrated on a real world problem where metabolomic data are linked to poison ingestion. This example proves the usefulness of wavelet expansion and the good behavior of sparse and regularized methods. A comparison study is performed between the two-steps approach (wavelet thresholding and regression) and the one-step approach (selection of wavelet coefficients with a sparse regression). The comparison includes two types of wavelet bases, various thresholding methods, and various regression methods and is evaluated by calculating prediction performances. Information about the location of the most important features on the spectra was also obtained and used to identify the most relevant metabolites involved in the mice poisoning.     

Wavelet Estimator in Nonparametric Regression Model with Dependent Error’s Structure

In this article, we consider a nonparametric regression model with replicated observations based on the dependent error’s structure, for exhibiting dependence among the units. The wavelet procedures are developed to estimate the regression function. The moment consistency, the strong consistency, strong convergence rate and asymptotic normality of wavelet estimator are established under suitable conditions. A simulation study is undertaken to assess the finite sample performance of the proposed method.     

工业取用水监测奇异数据挖掘与重构方法

提高工业取用水监测数据质量是目前国家水资源监控能力建设的重要内容,而奇异值问题已成为影响监测数据质量的关键短板。本文在解析现阶段工业取用水监测数据奇异值主要类型基础上,以国家水资源管理系统数据库中工业取用水监测数据为样本,利用小波变换模极大值模型提取工业取用水监测数据时频变化特征,并利用傅里叶函数对其残差序列进行修正,进而运用相对误差控制方法挖掘监测数据奇异值。在此基础上,采用混沌粒子群优化的最小二乘支持向量机模型重构填补奇异值数据。研究结果表明：小波变换模极大值模型能够较好地提取工业取用水监测数据序列的时频变化特征,但是同时容易导致监测数据的信息损失,利用傅里叶函数对小波变换进行残差修正则可进一步提升取用水监测数据序列的特征提取效果;以小波变换模极大值特征序列为基础,通过相对误差控制可实现对监测数据奇异值的高效挖掘;对于挖掘出的奇异值重构填补问题,可选取混沌粒子群优化的最小二乘支持向量机模型,其重构精度要优于多项式曲线拟合等传统统计学方法和普通最小二乘支持向量机模型。上述工业取用水监测数据奇异值挖掘重构策略为现阶段国家水资源监控能力建设的推进提供了重要技术方法支持。     

Adaptive density estimation: A curse of support?

This paper deals with the classical problem of density estimation on the real line. Most of the existing papers devoted to minimax properties assume that the support of the underlying density is bounded and known. But this assumption may be very difficult to handle in practice. In this work, we show that, exactly as a curse of dimensionality exists when the data lie in R^d, there exists a curse of support as well when the support of the density is infinite. As for the dimensionality problem where the rates of convergence deteriorate when the dimension grows, the minimax rates of convergence may deteriorate as well when the support becomes infinite. This problem is not purely theoretical since the simulations show that the support-dependent methods are really affected in practice by the size of the density support, or by the weight of the density tail. We propose a method based on a biorthogonal wavelet thresholding rule that is adaptive with respect to the nature of the support and the regularity of the signal, but that is also robust in practice to this curse of support. The threshold, that is proposed here, is very accurately calibrated so that the gap between optimal theoretical and practical tuning parameters is almost filled.     

从“新闻纸”走向“文化纸” —— 试论互联网时代高校校报的转型

高校校报长期被作为学校最主要的新闻宣传媒介.随着互联网的兴起和校园网的普遍应用,受众的信息需求发生变化,而校报存在着自身难以克服的 局限性,不能适应这种变化,因而其作为新闻媒介的信息传播优势地位将被网络取代.校报 的出路在于实现从"新闻纸"向"文化纸"的转型,向传播先进大学文化的地位复归.     

股指期货信息内含股价变动信息的挖掘 ――小波框架与支持向量回归的金融建模应用

 内容提要：中国股指期货的推出指日可待，交易者多了一种投资工具的同时也带来了新的风险。建立准确的金融时间序列预测模型是逐利及避险的方法之一，一直是学者专家研究的热点。本研究结合小波转换与支持向量回归，提出一个二阶段时间序列预测模型。先以离散小波框架将预测变量分解成不同尺度的多个子序列，揭示隐藏在预测变量内的信息，再以支持向量回归为工具，以这些子序列为预测变量建构SVR模型。本研究以日经225指数开盘价为预测目标，以期货开盘价为预测变量对模型进行实证研究，结果显示，该模型的预测绩效比单纯SVR模型及随机漫步模型好。未来可尝试以不同的基底函数作进一步研究。     

On the distribution of the maximum of bivariate normal random variables with general means and variances

A great amount of effort has been devoted to achieving exact expressions for moments of order statistics of independent normal random variables, as well as the dependent case with the same correlation coefficients, means and variances. It does not seem as if there are handy formulae for the order statistics of even the simple bivariate normal random variables when the means and variances are allowed to be different. In this paper we give an explicit formula for the Lanl ace-Stielties Transform of the maximum of bivariate normal random variables by which we obtain formulae for the first two moments in the standard way.     

机构改革背景下的中国计划生育新去向

结合实践对机构改革背景下的中国计划生育未来走向进行了探讨。认为:中国人口形势的根本性变化、计划生育负效应逐渐显现、法外职能拓展过度及职能交叉与计划生育工作特色渐失、自我封闭和思想僵化、未对变化了的人口形势适时作出政策回应是导致人口计生部门被撤并的主要原因。人口计生部门多对突然降临的机构改革缺少必要的心理准备,思想上出现一时混乱实属必然。伴随着机构改革,人口计生工作被弱化、卫生与计生冲突不可避免。而计划生育政策调整以及伴随而来的历史遗留问题的消解是国家卫生计生委与国家发改委将面临的无法回避的新问题。