一种基于类不平衡学习的情感分析方法

针对网络评论中普遍存在的负面评论较少而影响力却较大的类不平衡问题, 提出一种基于类不平衡学习的情感分析方法. 该方法利用深度学习训练过程中的概率输出, 以计算样例的信息熵作为影响因子构建交叉信息熵损失函数. 在IMDB公开数据集上进行实验验证的结果表明, 基于集成信息熵损失函数的双向长短期记忆网络能处理类不平衡问题; 对数据的统计分析结果表明, 该策略能提升基于双向长短期记忆网络的评论情感极性分类性能. 针对AUC(area under curve)指标, 使用集成信息熵损失函数的双向长短期记忆网络模型比未考虑类不平衡的深度学习模型在中位数上最多提升15.3%.     

Methods for backcasting,nowcasting and forecasting using factor‐MIDAS: With an application to Korean GDP

We utilize mixed‐frequency factor‐MIDAS models for the purpose of carrying out backcasting, nowcasting, and forecasting experiments using real‐time data. We also introduce a new real‐time Korean GDP dataset, which is the focus of our experiments. The methodology that we utilize involves first estimating common latent factors (i.e., diffusion indices) from 190 monthly macroeconomic and financial series using various estimation strategies. These factors are then included, along with standard variables measured at multiple different frequencies, in various factor‐MIDAS prediction models. Our key empirical findings as follows. (i) When using real‐time data, factor‐MIDAS prediction models outperform various linear benchmark models. Interestingly, the “MSFE‐best” MIDAS models contain no autoregressive (AR) lag terms when backcasting and nowcasting. AR terms only begin to play a role in “true” forecasting contexts. (ii) Models that utilize only one or two factors are “MSFE‐best” at all forecasting horizons, but not at any backcasting and nowcasting horizons. In these latter contexts, much more heavily parametrized models with many factors are preferred. (iii) Real‐time data are crucial for forecasting Korean gross domestic product, and the use of “first available” versus “most recent” data “strongly” affects model selection and performance. (iv) Recursively estimated models are almost always “MSFE‐best,” and models estimated using autoregressive interpolation dominate those estimated using other interpolation methods. (v) Factors estimated using recursive principal component estimation methods have more predictive content than those estimated using a variety of other (more sophisticated) approaches. This result is particularly prevalent for our “MSFE‐best” factor‐MIDAS models, across virtually all forecast horizons, estimation schemes, and data vintages that are analyzed.     

基于变量选择-神经网络模型的复杂路网短时交通流预测

摘要： 针对传统交通流预测模型正在由单断面历史数据处理向多断面、多时刻历史数据处理转变,但在考虑各断面间的影响时,多变的交通状况往往会使预测模型复杂化的问题,引入一种多元线性回归最小绝对收缩和选择算子方法(Lasso),并利用其优秀的变量选择能力,在复杂路网多断面中选出相关性较高的断面；结合神经网络(NN)的非线性特性,提出了Lasso NN组合模型.结果表明：Lasso NN模型在路网交叉口对未来15 min交通流数据预测的误差率低于9.2%；在非交叉口的误差率低于6.7%,总体优于各自单独使用得出的结果.     

光引发丙烯酰胺沉淀聚合研究

以丙酮/水为溶剂，采用紫外光（UV）引发丙烯酰胺（AM）沉淀聚合，考察了聚合反应的特征以及溶剂、初始单体浓度、引发剂浓度、表面入射光强、液层厚度等参数对聚合物分子量的影响。结果表明，紫外光引发丙烯酰胺沉淀聚合过程中单体转化率和聚合物分子量都随反应时间的延长而增加，光照80min时，单体转化率可达90％以上。所得聚合物分子量为10⁵～10⁶，提高单体浓度可使聚合物分子量增加，增加引发剂浓度和提高光强导致聚合物分子量降低，液层厚度对聚合物的分子量影响不大。所得聚合物粒子粒径约为200～300nm左右，粒径分布较为均匀，溶剂组成对聚合物粒子相貌影响较大，增加水含量可使粒子粒经变大但分散性变差。     

纳米氧化锌粉的表面改性研究

以Zn(NO3)2·6H2O和CO(NH2)2为原料,采用均相沉淀法制备纳米氧化锌粉.用扫描电镜对产物粒度大小、形貌进行观察,并对其影响因素进行了探讨.结果表明,在反应温度为120℃,反应时间为2.5h时,所制的纳米氧化锌的产率最大.用不同的表面改性剂对纳米ZnO进行表面改性,粉体不再发生团聚.图6,表7,参5.     

硅、铋离子鉴定方法的探讨

本文对现用叙材中硅、铋离子的鉴定方法进行了改进,改进后的鉴定方法既简单快速,又灵敏可靠.     

The performance of non‐linear exchange rate models: a forecasting comparison

In recent years there has been a considerable development in modelling non‐linearities and asymmetries in economic and financial variables. The aim of the current paper is to compare the forecasting performance of different models for the returns of three of the most traded exchange rates in terms of the US dollar, namely the French franc (FF/$), the German mark (DM/$) and the Japanese yen (Y/$). The relative performance of non‐linear models of the SETAR, STAR and GARCH types is contrasted with their linear counterparts. The results show that if attention is restricted to mean square forecast errors, the performance of the models, when distinguishable, tends to favour the linear models. The forecast performance of the models is evaluated also conditional on the regime at the forecast origin and on density forecasts. This analysis produces more evidence of forecasting gains from non‐linear models. Copyright © 2002 John Wiley & Sons, Ltd.     

泡沫分离,带溶剂萃取和沉淀法提取庆大霉素

研究了用文题法从水相中分离砂大霉素的各种操作因素对分离过程的影响。在最适条件下，种方法对庆大霉素配制液的收率分别为７２％，８８％，８７％，无机盐能使沉淀收率提高。将上述３法用于发酵液时，泡沫法和萃取法的收率均有显著降低，而沉淀法依然能达到很高的收率，ψ＝捍沉淀率约为１００％．     

Artificial Neural Network for Combining Forecasts

ＡｒｔｉｆｉｃｉａｌＮｅｕｒａｌＮｅｔｗｏｒｋｆｏｒＣｏｍｂｉｎｉｎｇＦｏｒｅｃａｓｔｓ￥ＳｈａｎｍｉｎｇＳｈｉ，ＬｉＤ．Ｘｕ＆ＢａｏＬｉｕ（ＤｅｐａｒｔｍｅｎｔｏｆＣｏｍｐｕｔｅｒＳｃｉｅｎｃｅ，ＵｎｉｖｅｒｓｉｔｙｏｆＣｏｌｏｒａｄｏａｔＢｏｕｌ...     

云南两高辐合背景下低涡强降水过程对比分析

基于气象观测和欧洲中期天气预报中心第五代大气再分析资料（ERA5）,选取2018—2020年发生在云南的3次两高压辐合区（以下简称两高辐合）背景下低涡连续性强降水过程进行对比分析,探究其环流形势、动热力因子及水汽辐合的相似性和差异性.结果显示：(1)夏半年西太平洋副热带高压（以下简称西太副高）西伸时,云南常受两高辐合影响,当辐合区中有低涡生成,易发生连续性强降水,且强降水落区并不完全分布在整个狭长的两高辐合区内.(2)两高辐合区低涡降水具有相似的雨带分布和移动特征,但雨带的集中程度、分布范围、小时雨强和总雨量存在差异.强降水开始时,落区偏东,随着西太副高西伸及东北引导气流的引导,低涡向南向西移动,强降水落区也随之向南向西移动.低涡在某一地区徘徊导致降水集中在该区域,且西太副高西伸较强时,两高辐合形势更强,低涡降水大雨及以上量级雨带也更窄.(3)低涡造成的降水主要分布在低涡中心及低涡切变附近,且降水发生在低层暖湿的环境中,低层及中高层有冷空气侵入时,小时雨强的极端性更强,降水落区也相对偏北.两高辐合区低涡降水落区与水汽辐合区对应较好,水汽辐合强度较弱时,对应的总降水量较小.