Currently,functional connectomes constructed from neuroimaging data have emerged as a powerful tool in identifying brain disorders.If one brain disease just manifests as some cognitive dysfunction,it means that the di...Currently,functional connectomes constructed from neuroimaging data have emerged as a powerful tool in identifying brain disorders.If one brain disease just manifests as some cognitive dysfunction,it means that the disease may affect some local connectivity in the brain functional network.That is,there are functional abnormalities in the sub-network.Therefore,it is crucial to accurately identify them in pathological diagnosis.To solve these problems,we proposed a sub-network extraction method based on graph regularization nonnegative matrix factorization(GNMF).The dynamic functional networks of normal subjects and early mild cognitive impairment(eMCI)subjects were vectorized and the functional connection vectors(FCV)were assembled to aggregation matrices.Then GNMF was applied to factorize the aggregation matrix to get the base matrix,in which the column vectors were restored to a common sub-network and a distinctive sub-network,and visualization and statistical analysis were conducted on the two sub-networks,respectively.Experimental results demonstrated that,compared with other matrix factorization methods,the proposed method can more obviously reflect the similarity between the common subnetwork of eMCI subjects and normal subjects,as well as the difference between the distinctive sub-network of eMCI subjects and normal subjects,Therefore,the high-dimensional features in brain functional networks can be best represented locally in the lowdimensional space,which provides a new idea for studying brain functional connectomes.展开更多
针对移动自组织网络中设备性能的异构性和网络拓扑的动态变化,提出了一种面向移动自组织网络的分布式机器学习优化方法(A Distributed Machine Learning Optimization Method for Mobile Adhoc Networks,MOCHA),MOCHA创新性地引入了基...针对移动自组织网络中设备性能的异构性和网络拓扑的动态变化,提出了一种面向移动自组织网络的分布式机器学习优化方法(A Distributed Machine Learning Optimization Method for Mobile Adhoc Networks,MOCHA),MOCHA创新性地引入了基于设备计算能力、电量等性能指标的通信概率机制,并通过目标值子矩阵法完成最优适配,最后以模型参数的传递完成设备间信息的交互。理论分析与试验结果表明,相比传统的分布式学习方法,MOCHA在移动环境下展现出了显著的性能提升优势,为移动分布式机器学习实践提供了新的思路。展开更多
基金supported by the National Natural Science Foundation of China(No.51877013),(ZJ),(http://www.nsfc.gov.cn/)the Natural Science Foundation of Jiangsu Province(No.BK20181463),(ZJ),(http://kxjst.jiangsu.gov.cn/)sponsored by Qing Lan Project of Jiangsu Province(no specific grant number),(ZJ),(http://jyt.jiangsu.gov.cn/).
文摘Currently,functional connectomes constructed from neuroimaging data have emerged as a powerful tool in identifying brain disorders.If one brain disease just manifests as some cognitive dysfunction,it means that the disease may affect some local connectivity in the brain functional network.That is,there are functional abnormalities in the sub-network.Therefore,it is crucial to accurately identify them in pathological diagnosis.To solve these problems,we proposed a sub-network extraction method based on graph regularization nonnegative matrix factorization(GNMF).The dynamic functional networks of normal subjects and early mild cognitive impairment(eMCI)subjects were vectorized and the functional connection vectors(FCV)were assembled to aggregation matrices.Then GNMF was applied to factorize the aggregation matrix to get the base matrix,in which the column vectors were restored to a common sub-network and a distinctive sub-network,and visualization and statistical analysis were conducted on the two sub-networks,respectively.Experimental results demonstrated that,compared with other matrix factorization methods,the proposed method can more obviously reflect the similarity between the common subnetwork of eMCI subjects and normal subjects,as well as the difference between the distinctive sub-network of eMCI subjects and normal subjects,Therefore,the high-dimensional features in brain functional networks can be best represented locally in the lowdimensional space,which provides a new idea for studying brain functional connectomes.
文摘针对移动自组织网络中设备性能的异构性和网络拓扑的动态变化,提出了一种面向移动自组织网络的分布式机器学习优化方法(A Distributed Machine Learning Optimization Method for Mobile Adhoc Networks,MOCHA),MOCHA创新性地引入了基于设备计算能力、电量等性能指标的通信概率机制,并通过目标值子矩阵法完成最优适配,最后以模型参数的传递完成设备间信息的交互。理论分析与试验结果表明,相比传统的分布式学习方法,MOCHA在移动环境下展现出了显著的性能提升优势,为移动分布式机器学习实践提供了新的思路。
