High-dimensional and incomplete(HDI)data subject to the nonnegativity constraints are commonly encountered in a big data-related application concerning the interactions among numerous nodes.A nonnegative latent factor...High-dimensional and incomplete(HDI)data subject to the nonnegativity constraints are commonly encountered in a big data-related application concerning the interactions among numerous nodes.A nonnegative latent factor analysis(NLFA)model can perform representation learning to HDI data efficiently.However,existing NLFA models suffer from either slow convergence rate or representation accuracy loss.To address this issue,this paper proposes a proximal alternating-directionmethod-of-multipliers-based nonnegative latent factor analysis(PAN)model with two-fold ideas:(1)adopting the principle of alternating-direction-method-of-multipliers to implement an efficient learning scheme for fast convergence and high computational efficiency;and(2)incorporating the proximal regularization into the learning scheme to suppress the optimization fluctuation for high representation learning accuracy to HDI data.Theoretical studies verify that PAN converges to a Karush-KuhnTucker(KKT)stationary point of its nonnegativity-constrained learning objective with its learning scheme.Experimental results on eight HDI matrices from real applications demonstrate that the proposed PAN model outperforms several state-of-the-art models in both estimation accuracy for missing data of an HDI matrix and computational efficiency.展开更多
Finding crucial vertices is a key problem for improving the reliability and ensuring the effective operation of networks,solved by approaches based on multiple attribute decision that suffer from ignoring the correlat...Finding crucial vertices is a key problem for improving the reliability and ensuring the effective operation of networks,solved by approaches based on multiple attribute decision that suffer from ignoring the correlation among each attribute or the heterogeneity between attribute and structure. To overcome these problems, a novel vertex centrality approach, called VCJG, is proposed based on joint nonnegative matrix factorization and graph embedding. The potential attributes with linearly independent and the structure information are captured automatically in light of nonnegative matrix factorization for factorizing the weighted adjacent matrix and the structure matrix, which is generated by graph embedding. And the smoothness strategy is applied to eliminate the heterogeneity between attributes and structure by joint nonnegative matrix factorization. Then VCJG integrates the above steps to formulate an overall objective function, and obtain the ultimately potential attributes fused the structure information of network through optimizing the objective function. Finally, the attributes are combined with neighborhood rules to evaluate vertex's importance. Through comparative analyses with experiments on nine real-world networks, we demonstrate that the proposed approach outperforms nine state-of-the-art algorithms for identification of vital vertices with respect to correlation, monotonicity and accuracy of top-10 vertices ranking.展开更多
在全球气候变化和高强度人类活动的共同影响下,许多流域天然水循环过程受到破坏。径流序列呈现明显的非平稳特性,给水资源规划、管理、预测和调控带来一定的挑战。揭示径流序列的非平稳特性可以有效应对全球气候变化下的复杂水问题,对...在全球气候变化和高强度人类活动的共同影响下,许多流域天然水循环过程受到破坏。径流序列呈现明显的非平稳特性,给水资源规划、管理、预测和调控带来一定的挑战。揭示径流序列的非平稳特性可以有效应对全球气候变化下的复杂水问题,对降低水文分析难度和提高径流预测精度具有十分重要的意义。研究以汾河上游兰村站为研究对象,分析该站1958-2016年年径流和月径流序列是否平稳。首先从随机水文学角度,采用Mann-Kendall检验法和小波分析法识别径流序列的趋势、突变和周期特征。在此基础上,从统计水文学角度引入Ng-Perron单位根检验方法。通过Mann-Kendall趋势检验和散点图法选择合适的检验方程,对径流序列进行广义最小二乘法(Generalized Least Squares,GLS)退势,并利用修正的信息准则(Modified information criterion,MIC)计算最优时间滞后阶数,判别径流序列是否具有非平稳性。结果显示,径流序列存在趋势、突变和周期成分,为非平稳径流序列。同时Ng-Perron单位根检验表明,该站年、月径流序列在1%显著性水平上具有非平稳特性。相较传统单位根检验方法,Ng-Perron单位根检验采用更为稳健的修正检验统计量,显著调整小样本情况下水平扭曲的现象,具有更好检验水平和功效,因而可以得到更合理的检验结果。研究成果为径流序列非平稳性检验理论的进一步改进及径流预测模型发展与应用提供参考。展开更多
In this note,we prove a logarithmic Sobolev inequality which holds for compact submanifolds without a boundary in manifolds with asymptotically nonnegative sectional curvature.Like the Michale-Simon Sobolev inequality...In this note,we prove a logarithmic Sobolev inequality which holds for compact submanifolds without a boundary in manifolds with asymptotically nonnegative sectional curvature.Like the Michale-Simon Sobolev inequality,this inequality contains a term involving the mean curvature.展开更多
Regenerative approaches towards neuronal loss following traumatic brain or spinal cord injury have long been considered a dogma in neuroscience and remain a cutting-edge area of research.This is reflected in a large d...Regenerative approaches towards neuronal loss following traumatic brain or spinal cord injury have long been considered a dogma in neuroscience and remain a cutting-edge area of research.This is reflected in a large disparity between the number of studies investigating primary and secondary injury as therapeutic to rgets in spinal co rd and traumatic brain injuries.Significant advances in biotechnology may have the potential to reshape the current state-of-the-art and bring focus to primary injury neurotrauma research.Recent studies using neural-glial factor/antigen 2(NG2)cells indicate that they may differentiate into neurons even in the developed brain.As these cells show great potential to play a regenerative role,studies have been conducted to test various manipulations in neurotrauma models aimed at eliciting a neurogenic response from them.In the present study,we systematically reviewed the experimental protocols and findings described in the scientific literature,which were peer-reviewed original research articles(1)describing preclinical experimental studies,(2)investigating NG2 cells,(3)associated with neurogenesis and neurotrauma,and(4)in vitro and/or in vivo,available in PubMed/MEDLINE,Web of Science or SCOPUS,from 1998 to 2022.Here,we have reviewed a total of 1504 papers,and summarized findings that ultimately suggest that NG2 cells possess an inducible neurogenic potential in animal models and in vitro.We also discriminate findings of NG2 neurogenesis promoted by different pharmacological and genetic approaches over functional and biochemical outcomes of traumatic brain injury and spinal co rd injury models,and provide mounting evidence for the potential benefits of manipulated NG2 cell ex vivo transplantation in primary injury treatment.These findings indicate the feasibility of NG2 cell neurogenesis strategies and add new players in the development of therapeutic alternatives for neurotrauma.展开更多
Structure of nonnegative nontrivial and positive solutions was precisely studied for some singularly perturbed p-Laplace equations. By virtue of sub- and supersolution method, it is shown that there are many nonnegati...Structure of nonnegative nontrivial and positive solutions was precisely studied for some singularly perturbed p-Laplace equations. By virtue of sub- and supersolution method, it is shown that there are many nonnegative nontrivial spike-layer solutions and positive intermediate spike-layer solutions. Moreover, the upper and lower bound on the measure of each spike-layer were estimated when the parameter is sufficiently small.展开更多
基金supported by the National Natural Science Foundation of China(62272078,U21A2019)the Hainan Province Science and Technology Special Fund of China(ZDYF2022SHFZ105)the CAAI-Huawei MindSpore Open Fund(CAAIXSJLJJ-2021-035A)。
文摘High-dimensional and incomplete(HDI)data subject to the nonnegativity constraints are commonly encountered in a big data-related application concerning the interactions among numerous nodes.A nonnegative latent factor analysis(NLFA)model can perform representation learning to HDI data efficiently.However,existing NLFA models suffer from either slow convergence rate or representation accuracy loss.To address this issue,this paper proposes a proximal alternating-directionmethod-of-multipliers-based nonnegative latent factor analysis(PAN)model with two-fold ideas:(1)adopting the principle of alternating-direction-method-of-multipliers to implement an efficient learning scheme for fast convergence and high computational efficiency;and(2)incorporating the proximal regularization into the learning scheme to suppress the optimization fluctuation for high representation learning accuracy to HDI data.Theoretical studies verify that PAN converges to a Karush-KuhnTucker(KKT)stationary point of its nonnegativity-constrained learning objective with its learning scheme.Experimental results on eight HDI matrices from real applications demonstrate that the proposed PAN model outperforms several state-of-the-art models in both estimation accuracy for missing data of an HDI matrix and computational efficiency.
基金Project supported by the National Natural Science Foundation of China (Grant Nos.62162040 and 11861045)。
文摘Finding crucial vertices is a key problem for improving the reliability and ensuring the effective operation of networks,solved by approaches based on multiple attribute decision that suffer from ignoring the correlation among each attribute or the heterogeneity between attribute and structure. To overcome these problems, a novel vertex centrality approach, called VCJG, is proposed based on joint nonnegative matrix factorization and graph embedding. The potential attributes with linearly independent and the structure information are captured automatically in light of nonnegative matrix factorization for factorizing the weighted adjacent matrix and the structure matrix, which is generated by graph embedding. And the smoothness strategy is applied to eliminate the heterogeneity between attributes and structure by joint nonnegative matrix factorization. Then VCJG integrates the above steps to formulate an overall objective function, and obtain the ultimately potential attributes fused the structure information of network through optimizing the objective function. Finally, the attributes are combined with neighborhood rules to evaluate vertex's importance. Through comparative analyses with experiments on nine real-world networks, we demonstrate that the proposed approach outperforms nine state-of-the-art algorithms for identification of vital vertices with respect to correlation, monotonicity and accuracy of top-10 vertices ranking.
文摘在全球气候变化和高强度人类活动的共同影响下,许多流域天然水循环过程受到破坏。径流序列呈现明显的非平稳特性,给水资源规划、管理、预测和调控带来一定的挑战。揭示径流序列的非平稳特性可以有效应对全球气候变化下的复杂水问题,对降低水文分析难度和提高径流预测精度具有十分重要的意义。研究以汾河上游兰村站为研究对象,分析该站1958-2016年年径流和月径流序列是否平稳。首先从随机水文学角度,采用Mann-Kendall检验法和小波分析法识别径流序列的趋势、突变和周期特征。在此基础上,从统计水文学角度引入Ng-Perron单位根检验方法。通过Mann-Kendall趋势检验和散点图法选择合适的检验方程,对径流序列进行广义最小二乘法(Generalized Least Squares,GLS)退势,并利用修正的信息准则(Modified information criterion,MIC)计算最优时间滞后阶数,判别径流序列是否具有非平稳性。结果显示,径流序列存在趋势、突变和周期成分,为非平稳径流序列。同时Ng-Perron单位根检验表明,该站年、月径流序列在1%显著性水平上具有非平稳特性。相较传统单位根检验方法,Ng-Perron单位根检验采用更为稳健的修正检验统计量,显著调整小样本情况下水平扭曲的现象,具有更好检验水平和功效,因而可以得到更合理的检验结果。研究成果为径流序列非平稳性检验理论的进一步改进及径流预测模型发展与应用提供参考。
基金Supported by the NSFC(11771087,12171091 and 11831005)。
文摘In this note,we prove a logarithmic Sobolev inequality which holds for compact submanifolds without a boundary in manifolds with asymptotically nonnegative sectional curvature.Like the Michale-Simon Sobolev inequality,this inequality contains a term involving the mean curvature.
基金supported by funding from FAPERGS under Grant No.1010267FAPERGS/PPSUS+8 种基金No.17/2551-0001FAPERGS/PRONEXNo.16/2551-0000499-4FAPERGS/CAPES under Grant No.19/25510000717-5Conselho Nacional de Desenvolvimento Científico e Tecnologico under Grants Nos.4011645/2012-6 and#5465346/2014-6Irish Research Council Government of Ireland Postdoctoral FellowshipNo.GOIPD/2022/792Irish Research Council Enterprise Postdoctoral FellowshipNo.EPSPD/2022/112。
文摘Regenerative approaches towards neuronal loss following traumatic brain or spinal cord injury have long been considered a dogma in neuroscience and remain a cutting-edge area of research.This is reflected in a large disparity between the number of studies investigating primary and secondary injury as therapeutic to rgets in spinal co rd and traumatic brain injuries.Significant advances in biotechnology may have the potential to reshape the current state-of-the-art and bring focus to primary injury neurotrauma research.Recent studies using neural-glial factor/antigen 2(NG2)cells indicate that they may differentiate into neurons even in the developed brain.As these cells show great potential to play a regenerative role,studies have been conducted to test various manipulations in neurotrauma models aimed at eliciting a neurogenic response from them.In the present study,we systematically reviewed the experimental protocols and findings described in the scientific literature,which were peer-reviewed original research articles(1)describing preclinical experimental studies,(2)investigating NG2 cells,(3)associated with neurogenesis and neurotrauma,and(4)in vitro and/or in vivo,available in PubMed/MEDLINE,Web of Science or SCOPUS,from 1998 to 2022.Here,we have reviewed a total of 1504 papers,and summarized findings that ultimately suggest that NG2 cells possess an inducible neurogenic potential in animal models and in vitro.We also discriminate findings of NG2 neurogenesis promoted by different pharmacological and genetic approaches over functional and biochemical outcomes of traumatic brain injury and spinal co rd injury models,and provide mounting evidence for the potential benefits of manipulated NG2 cell ex vivo transplantation in primary injury treatment.These findings indicate the feasibility of NG2 cell neurogenesis strategies and add new players in the development of therapeutic alternatives for neurotrauma.
文摘Structure of nonnegative nontrivial and positive solutions was precisely studied for some singularly perturbed p-Laplace equations. By virtue of sub- and supersolution method, it is shown that there are many nonnegative nontrivial spike-layer solutions and positive intermediate spike-layer solutions. Moreover, the upper and lower bound on the measure of each spike-layer were estimated when the parameter is sufficiently small.
