A new kind of combining forecasting model based on the generalized weighted functional proportional mean is proposed and the parameter estimation method of its weighting coefficients by means of the algorithm of quadr...A new kind of combining forecasting model based on the generalized weighted functional proportional mean is proposed and the parameter estimation method of its weighting coefficients by means of the algorithm of quadratic programming is given. This model has extensive representation. It is a new kind of aggregative method of group forecasting. By taking the suitable combining form of the forecasting models and seeking the optimal parameter, the optimal combining form can be obtained and the forecasting accuracy can be improved. The effectiveness of this model is demonstrated by an example.展开更多
Under some conditions on the functions and defined on I, the weighted Bajraktarević?mean is given by where . In this paper, we study the invariance of the weighted Bajraktarević?mean with respect to ...Under some conditions on the functions and defined on I, the weighted Bajraktarević?mean is given by where . In this paper, we study the invariance of the weighted Bajraktarević?mean with respect to Beckenbach-Gini means.展开更多
Tarnavas established mixed weighted power mean inequality in 1999. A separation of weighted power mean inequslity was derived in this paper. As its applications, some separations of other inequalities were given.
In this paper, we will use a method of sharp maximal function approach to show the boundedness of commutator [b,TR^δ] by Bochner-Riesz operators and the function b on weighted Morrey spaces L^p,k （ω） under appro...In this paper, we will use a method of sharp maximal function approach to show the boundedness of commutator [b,TR^δ] by Bochner-Riesz operators and the function b on weighted Morrey spaces L^p,k （ω） under appropriate conditions on the weight w, where b belongs to Lipschitz space or weighted Lipschitz space.展开更多
标记分布学习是近年来提出的一种新的机器学习范式,它能很好地解决某些标记多义性的问题。现有的标记分布学习算法均利用条件概率建立参数模型,但未能充分利用特征和标记间的联系。本文考虑到特征相似的样本所对应的标记分布也应当相似...标记分布学习是近年来提出的一种新的机器学习范式,它能很好地解决某些标记多义性的问题。现有的标记分布学习算法均利用条件概率建立参数模型,但未能充分利用特征和标记间的联系。本文考虑到特征相似的样本所对应的标记分布也应当相似,利用原型聚类的k均值算法(k-means),将训练集的样本进行聚类,提出基于kmeans算法的标记分布学习(label distribution learning based on k-means algorithm,LDLKM)。首先通过聚类算法kmeans求得每一个簇的均值向量,然后分别求得对应标记分布的均值向量。最后将测试集和训练集的均值向量间的距离作为权重,应用到对测试集标记分布的预测上。在6个公开的数据集上进行实验,并与3种已有的标记分布学习算法在5种评价指标上进行比较,实验结果表明提出的LDLKM算法是有效的。展开更多
Considering the legacy of plant functional composition can help assess ecosystem functions and ecosystem services across different spatial scales under land cover changes.Woody plants likely respond to natural and ant...Considering the legacy of plant functional composition can help assess ecosystem functions and ecosystem services across different spatial scales under land cover changes.Woody plants likely respond to natural and anthropogenic perturbations due to historical events(e.g.,agricultural development),thus contemporary plant functional composition may be explained by historical woodland change,a type of land cover change.We propose that historical woodland changes may have legacy effects on contemporary plant functional composition.Here,we used partial least squares regression and linear mixed model analyses to test this assumption by coupling data on community weighted means(CWM)and community weighted variance(CWV)of vegetation plots and calculating the time of woodland existence across different periods from AD 0 to 2017.We found that the legacy effects of historical land cover changes on CWM and CWV during the existence time of woodland,particularly from AD 0 to 900,were drivers of contemporary plant functional composition at large spatial scales.Furthermore,historical woodland changes can affect contemporary plant functional composition,depending on the biome type.Particularly,the CWM of plant height,seed mass,and seed length showed the strongest correlations with woodland changes from AD 1910 to 2010 in tropics with year-round rain,and the CWM of leaf traits correlated with woodland changes from AD 0 to 1700 in tropics with summer rain.Our study provides the effective evidence on the legacy of historical woodland changes and the effects on contemporary plant functional composition,which is crucial with respect to effective management of plant diversity and assessing ecosystem functions and services from local to global scales over time.展开更多
工业数据由于技术故障和人为因素通常导致数据异常,现有基于约束的方法因约束阈值设置的过于宽松或严格会导致修复错误,基于统计的方法因平滑修复机制导致对时间步长较远的异常值修复准确度较低.针对上述问题,提出了基于奖励机制的最小...工业数据由于技术故障和人为因素通常导致数据异常,现有基于约束的方法因约束阈值设置的过于宽松或严格会导致修复错误,基于统计的方法因平滑修复机制导致对时间步长较远的异常值修复准确度较低.针对上述问题,提出了基于奖励机制的最小迭代修复和改进WGAN混合模型的时序数据修复方法.首先,在预处理阶段,保留异常数据,进行信息标注等处理,从而充分挖掘异常值与真实值之间的特征约束.其次,在噪声模块提出了近邻参数裁剪规则,用于修正最小迭代修复公式生成的噪声向量.将其传递至模拟分布模块的生成器中,同时设计了一个动态时间注意力网络层,用于提取时序特征权重并与门控循环单元串联组合捕捉不同步长的特征依赖,并引入递归多步预测原理共同提升模型的表达能力;在判别器中设计了Abnormal and Truth奖励机制和Weighted Mean Square Error损失函数共同反向优化生成器修复数据的细节和质量.最后,在公开数据集和真实数据集上的实验结果表明,该方法的修复准确度与模型稳定性显著优于现有方法.展开更多
设f∈L^p(R^n),1≤p≤2(n+1)/n+3,以及δ>n/p-(n+1)/2.本文证明了f在R^n上的Bochner-Riesz平均σR(f;x)满足关系式其中权函数w满足条件w(u)≥0以及1≤1/t integral from 0 to t(w(u)du≤C)(C为一绝对常数)。结论对周期情形也成立。
Weighted Lp mean convergence of Extended Hermite-Fejer operators based on the zeros of orthogonal polynomials with respct to the general weight and Jacobi weight is investigated. Suf ficient conditions for such conve...Weighted Lp mean convergence of Extended Hermite-Fejer operators based on the zeros of orthogonal polynomials with respct to the general weight and Jacobi weight is investigated. Suf ficient conditions for such convergence for all continuous functions are given.展开更多
文摘A new kind of combining forecasting model based on the generalized weighted functional proportional mean is proposed and the parameter estimation method of its weighting coefficients by means of the algorithm of quadratic programming is given. This model has extensive representation. It is a new kind of aggregative method of group forecasting. By taking the suitable combining form of the forecasting models and seeking the optimal parameter, the optimal combining form can be obtained and the forecasting accuracy can be improved. The effectiveness of this model is demonstrated by an example.
基金Funded by Longshan academic talent research supporting program of SWUST(17LZXY12)Doctoral fund of SWUST(18zx7166,15zx7142).
文摘Under some conditions on the functions and defined on I, the weighted Bajraktarević?mean is given by where . In this paper, we study the invariance of the weighted Bajraktarević?mean with respect to Beckenbach-Gini means.
基金Project supported by National Natural Science Foundation of China （Grant No. 10271071）
文摘Tarnavas established mixed weighted power mean inequality in 1999. A separation of weighted power mean inequslity was derived in this paper. As its applications, some separations of other inequalities were given.
基金supported by NSFC (NO. 11201003)Education Committee of Anhui Province (NO. KJ2011A138 and NO. KJ2012A133)
文摘In this paper, we will use a method of sharp maximal function approach to show the boundedness of commutator [b,TR^δ] by Bochner-Riesz operators and the function b on weighted Morrey spaces L^p,k （ω） under appropriate conditions on the weight w, where b belongs to Lipschitz space or weighted Lipschitz space.
文摘标记分布学习是近年来提出的一种新的机器学习范式,它能很好地解决某些标记多义性的问题。现有的标记分布学习算法均利用条件概率建立参数模型,但未能充分利用特征和标记间的联系。本文考虑到特征相似的样本所对应的标记分布也应当相似,利用原型聚类的k均值算法(k-means),将训练集的样本进行聚类,提出基于kmeans算法的标记分布学习(label distribution learning based on k-means algorithm,LDLKM)。首先通过聚类算法kmeans求得每一个簇的均值向量,然后分别求得对应标记分布的均值向量。最后将测试集和训练集的均值向量间的距离作为权重,应用到对测试集标记分布的预测上。在6个公开的数据集上进行实验,并与3种已有的标记分布学习算法在5种评价指标上进行比较,实验结果表明提出的LDLKM算法是有效的。
基金We acknowledge support from the National Natural Science Foundation of China(NSFC,32060385 and 31860668)the Project of Qinghai Science&Technology Department(2020-ZJ-733).
文摘Considering the legacy of plant functional composition can help assess ecosystem functions and ecosystem services across different spatial scales under land cover changes.Woody plants likely respond to natural and anthropogenic perturbations due to historical events(e.g.,agricultural development),thus contemporary plant functional composition may be explained by historical woodland change,a type of land cover change.We propose that historical woodland changes may have legacy effects on contemporary plant functional composition.Here,we used partial least squares regression and linear mixed model analyses to test this assumption by coupling data on community weighted means(CWM)and community weighted variance(CWV)of vegetation plots and calculating the time of woodland existence across different periods from AD 0 to 2017.We found that the legacy effects of historical land cover changes on CWM and CWV during the existence time of woodland,particularly from AD 0 to 900,were drivers of contemporary plant functional composition at large spatial scales.Furthermore,historical woodland changes can affect contemporary plant functional composition,depending on the biome type.Particularly,the CWM of plant height,seed mass,and seed length showed the strongest correlations with woodland changes from AD 1910 to 2010 in tropics with year-round rain,and the CWM of leaf traits correlated with woodland changes from AD 0 to 1700 in tropics with summer rain.Our study provides the effective evidence on the legacy of historical woodland changes and the effects on contemporary plant functional composition,which is crucial with respect to effective management of plant diversity and assessing ecosystem functions and services from local to global scales over time.
文摘工业数据由于技术故障和人为因素通常导致数据异常,现有基于约束的方法因约束阈值设置的过于宽松或严格会导致修复错误,基于统计的方法因平滑修复机制导致对时间步长较远的异常值修复准确度较低.针对上述问题,提出了基于奖励机制的最小迭代修复和改进WGAN混合模型的时序数据修复方法.首先,在预处理阶段,保留异常数据,进行信息标注等处理,从而充分挖掘异常值与真实值之间的特征约束.其次,在噪声模块提出了近邻参数裁剪规则,用于修正最小迭代修复公式生成的噪声向量.将其传递至模拟分布模块的生成器中,同时设计了一个动态时间注意力网络层,用于提取时序特征权重并与门控循环单元串联组合捕捉不同步长的特征依赖,并引入递归多步预测原理共同提升模型的表达能力;在判别器中设计了Abnormal and Truth奖励机制和Weighted Mean Square Error损失函数共同反向优化生成器修复数据的细节和质量.最后,在公开数据集和真实数据集上的实验结果表明,该方法的修复准确度与模型稳定性显著优于现有方法.
文摘设f∈L^p(R^n),1≤p≤2(n+1)/n+3,以及δ>n/p-(n+1)/2.本文证明了f在R^n上的Bochner-Riesz平均σR(f;x)满足关系式其中权函数w满足条件w(u)≥0以及1≤1/t integral from 0 to t(w(u)du≤C)(C为一绝对常数)。结论对周期情形也成立。
文摘Weighted Lp mean convergence of Extended Hermite-Fejer operators based on the zeros of orthogonal polynomials with respct to the general weight and Jacobi weight is investigated. Suf ficient conditions for such convergence for all continuous functions are given.
