基于MBP算法和深度学习的人脸识别

为了解决在深度学习提取人脸图像特征时,易忽略其局部结构特征和缺乏对其旋转不变性学习的问题,提出了一种基于单演局部二值模式(MBP)与深度学习相结合的高效率人脸识别方法。首先,用多尺度单演滤波器对图像进行滤波,得到幅值和方向信息;其次,用LBP算法和象限比特的方法进行编码,分块计算组合其直方图特征;然后,将提取的单演特征作为深度信念网络(DBN)的输入,逐层训练优化网络参数,得到优异的网络模型;最后,将训练好的DBN网络在ORL人脸数据库上进行人脸识别实验,进行识别率计算,其识别率为98.75%。所提出的方法使用无监督的贪婪算法,隐藏层设定为2层,使用反向传播算法优化网络。相较于已知的人脸识别方法,MBP+DBN算法对光照、表情和部分遮挡变化具有较好的鲁棒性,在人脸识别中识别率较高,具有一定的优势,为图像特征提供了一种新的识别方法。     

Forecasting dynamic return distributions based on ordered binary choice

We present a simple approach to the forecasting of conditional probability distributions of asset returns. We work with a parsimonious specification of ordered binary choice regressions that imposes a connection on sign predictability across different quantiles. The model forecasts the future conditional probability distributions of returns quite precisely when using a past indicator and a past volatility proxy as predictors. The direct benefits of the model are revealed in an empirical application to the 29 most liquid U.S. stocks. The forecast probability distribution is translated to significant economic gains in a simple trading strategy. Our approach can also be useful in many other applications in which conditional distribution forecasts are desired.     

LT码新型度分布及其在电力线通信中的应用

Luby变换(Luby Transform,LT)码作为信道编码应用于电力线通信(Power Line Communication,PLC)，可实现电信号的可靠传输。度分布对LT码编译码性能的影响至关重要。为了得到更优的度分布，首先调整二进制指数分布(Binary Exponential Distribution,BED)中的度数比例，获得一种译码性能更优的改进的二进制指数分布(Improved BED,IBED)。然后，根据IBED在冗余度较小时译码成功率高，而冗余度增大后鲁棒孤子分布(Robust Soliton Distribution,RSD)的译码性能表现更佳的特点，通过求和归一化的方式将IBED与RSD两种度分布的优势进行有机结合，提出一种新型二进制鲁棒孤子分布(Binary RSD,BRSD)。仿真结果表明，与其他方法及传统的RSD相比，采用新度分布进行LT编码，可明显降低译码开销，并节约编译码耗时。将新型度分布应用于基于LT码的PLC系统中，能有效地抑制PLC信道中各种噪声对电信号的干扰，并提高通信效率。     

基于政策情景的家庭乘用车能源回弹效应研究 ——来自长株潭城市群的调查数据

基于长株潭城市群的调查数据,测算在燃料消耗量为5.0L/100km情景下家庭乘用车的直接能源回弹效应,并采用二分类Logistic法分析影响家庭乘用车直接能源回弹效应的因素.结果表明:长株潭城市群家庭乘用车平均直接能源回弹效应为20%;不同居民家庭乘用车的直接能源回弹效应差别较大,存在超级节能、零回弹、部分回弹和回火效应;户主受教育水平、收入水平、购买新车意愿、车辆行驶里程、出行偏好等因素对家庭乘用车超级节能和零回弹效应影响显著.     

英语读写素质研究中的二元理论分析

英语读写素质是习得读写能力并形成读写体验的过程。读写素质主要由分析能力、结合社会文化能力、话语分类能力、读写实践能力、记忆能力、情感反应、思辨能力和隐喻理解能力等因素构成。本文首先应用二元理论中的集对分析理论分析了句子、语法和词语,英语句子具有同异性,英语语法中时态的相关性均有特定的语法表征手段,英语词语是一种约定俗成的语言单位常常附衍了一串同义词、近义词和反义词。如果用联系数来描述词语的相关度,它同时也反映出了词语的思想丰度。本文还运用博弈理论和悖论分析文章的含义及价值,最后应用模糊二元对比排序法对英语读写素质各因素进行分析,通过分析确定了各因素的重要度,将其应用于读写素质研究中具有积极作用并具一定的发展潜力。     

二叉树期权定价模式评估林业碳汇项目的价值

基于黑龙江省林业局和中国林业统计年鉴的数据,运用修正的Faustmann模型和二叉树期权定价方法分析黑龙江省黑河造林林区的碳汇价值及该项目的经济可行性。研究结果表明:经营成本负向影响项目的最终价值;碳汇价格和木材价格正向影响项目的最终价值;初始价值正向影响项目的最终价值;黑龙江省黑河造林林区项目具有较高的经济效益和生态效益。因此,相关部门应建立完善的碳汇交易市场,实施激励措施和制定林地产权制度。     

农户收入分化对林地流入与流出意愿影响研究

以辽宁省409户样本农户问卷调查为基础,采用描述性统计分析农户收入分化在农户年龄层次、受教育程度、家庭劳动力数量等方面的差异化特征,并运用二元Logistic回归模型分别讨论收入水平分化、收入结构分化、二者交互项对农户林地流转综合意愿、流入意愿、流出意愿的差异化影响。结果表明:农户收入水平分化对林地综合流转意愿、林地流入意愿具有显著正向影响;农户收入结构分化对农户林地流出意愿具有显著正向影响;农户收入水平分化与结构分化交互项对农户林地综合流转意愿、林地流出意愿具有显著正向影响。因此,需要关注农户收入分化因素;做好收入分化因素的有效识别;充分认识收入分化因素对农户林地流入与流出意愿的差异影响;把握分化特征,有针对性提高林地流转意愿。     

Predicting Corporate Bankruptcy: An Evaluation of Alternative Statistical Frameworks

Corporate bankruptcy prediction has attracted significant research attention from business academics, regulators and financial economists over the past five decades. However, much of this literature has relied on quite simplistic classifiers such as logistic regression and linear discriminant analysis (LDA). Based on a large sample of US corporate bankruptcies, we examine the predictive performance of 16 classifiers, ranging from the most restrictive classifiers (such as logit, probit and linear discriminant analysis) to more advanced techniques such as neural networks, support vector machines (SVMs) and “new age” statistical learning models including generalised boosting, AdaBoost and random forests. Consistent with the findings of Jones et al. ( 2015 ), we show that quite simple classifiers such as logit and LDA perform reasonably well in bankruptcy prediction. However, we recommend the use of “new age” classifiers in corporate bankruptcy modelling because: (1) they predict significantly better than all other classifiers on both the cross‐sectional and longitudinal test samples; (2) the models may have considerable practical appeal because they are relatively easy to estimate and implement (for instance, they require minimal researcher intervention for data preparation, variable selection and model architecture specification); and (3) while the underlying model structures can be very complex, we demonstrate that “new age” classifiers have a reasonably good level of interpretability through such metrics as relative variable importances (RVIs).     

Maximum likelihood estimation of a binomial proportion using one‐sample misclassified binary data

In this article, we construct two likelihood‐based confidence intervals (CIs) for a binomial proportion parameter using a double‐sampling scheme with misclassified binary data. We utilize an easy‐to‐implement closed‐form algorithm to obtain maximum likelihood estimators of the model parameters by maximizing the full‐likelihood function. The two CIs are a naïve Wald interval and a modified Wald interval. Using simulations, we assess and compare the coverage probabilities and average widths of our two CIs. Finally, we conclude that the modified Wald interval, unlike the naïve Wald interval, produces close‐to‐nominal CIs under various simulations and, thus, is preferred in practice. Utilizing the expressions derived, we also illustrate our two CIs for a binomial proportion parameter using real‐data example.     

基于生态文明理念下化解城乡生态环境二元化的路径选择

改革开放30多年以来,我国城乡经济发生了翻天覆地的变化,城乡居民经济水平和生活质量都得到了普遍提升。但随着城市化、工业化进程加快,城乡经济也随之拉开差距,在城乡经济结构二元化的影响下,城乡生态环境也出现了严重的二元化趋势。介于此,本文以"天人合一、人地和谐"的生态文明理念为指导思想,在挖掘生态文明与生态环境保护两者内在关系的基础上,结合实际以分析我国城乡生态环境严重二元化的现况为出发点,剖析导致城市与乡村生态环境二元化的三大矛盾,并提出六大破解城乡生态环境二元结构可行之路,为共同构建美好生态环境提供良策。