Characterizing the trajectory of the healthy aging brain and exploring age-related structural changes in the brain can help deepen our understanding of the mechanism of brain aging.Currently,most structural magnetic r...Characterizing the trajectory of the healthy aging brain and exploring age-related structural changes in the brain can help deepen our understanding of the mechanism of brain aging.Currently,most structural magnetic resonance imaging literature explores brain aging merely from the perspective of morphological features,which cannot fully utilize the grayscale values containing important intrinsic information about brain structure.In this study,we propose the construction of two-dimensional horizontal visibility graphs based on the pixel intensity values of the gray matter slices directly.Normalized network structure entropy(NNSE)is then introduced to quantify the overall heterogeneities of these graphs.The results demonstrate a decrease in the NNSEs of gray matter with age.Compared with the middle-aged and the elderly,the larger values of the NNSE in the younger group may indicate more homogeneous network structures,smaller differences in importance between nodes and thus a more powerful ability to tolerate intrusion.In addition,the hub nodes of different adult age groups are primarily located in the precuneus,cingulate gyrus,superior temporal gyrus,inferior temporal gyrus,parahippocampal gyrus,insula,precentral gyrus and postcentral gyrus.Our study can provide a new perspective for understanding and exploring the structural mechanism of brain aging.展开更多
The information rate is an important metric of the performance of a secret-sharing scheme. In this paper we consider 272 non-isomorphic connected graph access structures with nine vertices and eight or nine edges, and...The information rate is an important metric of the performance of a secret-sharing scheme. In this paper we consider 272 non-isomorphic connected graph access structures with nine vertices and eight or nine edges, and either determine or bound the optimal information rate in each case. We obtain exact values for the optimal information rate for 231 cases and present a method that is able to derive information-theoretical upper bounds on the optimal information rate. Moreover, we apply some of the constructions to determine lower bounds on the information rate. Regarding information rate, we conclude with a full listing of the known optimal information rate （or bounds on the optimal information rate） for all 272 graphs access structures of nine participants.展开更多
图数据增强是一种通过变换和扩充图结构和节点特征来增加训练数据多样性、提高图神经网络性能的技术。为了应对图数据增强面临的难以综合信息完整性、特征平滑性、图多样性和局部依赖关系的挑战,缓解图神经网络的过平滑和过拟合问题,提...图数据增强是一种通过变换和扩充图结构和节点特征来增加训练数据多样性、提高图神经网络性能的技术。为了应对图数据增强面临的难以综合信息完整性、特征平滑性、图多样性和局部依赖关系的挑战,缓解图神经网络的过平滑和过拟合问题,提高其性能,提出了一种基于物理热力学中的熵理论的图数据增强模型(Neighbor Replacement Based on Graph Entropy,NRGE)。首先,引入了一种新的图熵定义,用于度量数据流形平滑度;基于减少图熵损失的思想,提出了一种新的数据增强策略,用于生成更多合适的训练数据。然后,通过增强节点的采样邻居,以保证数据增强的一致性;采用随机替换节点的一阶邻居为二阶邻居的方式,增加了数据增强的多样性。最后,引入了邻居约束正则化方法,通过约束增强后的邻居之间的预测一致性来提高模型性能。消融实验结果表明,通过保持三角形图案的信息结构,NRGE模型能够有效降低图熵损失,从而改善学习效果。在Cora,Citeseer和Pubmed 3个公开数据集上进行了节点分类实验,相较于基准模型,NRGE模型在Cora数据集上提升了1.1%,在Citeseer数据集上提升了0.8%,在Pubmed数据集上略微降低了0.4%。结果表明,NRGE模型有效改善了图神经网络的性能,提高了其泛化能力。展开更多
基金Project supported by the Natural Science Foundation of Jiangsu Province,China(Grant No.BK20190736)the Young Scientists Fund of the National Natural Science Foundation of China(Grant Nos.81701346 and 61603198)Qinglan Team of Universities in Jiangsu Province(Jiangsu Teacher Letter[2020]10 and Jiangsu Teacher Letter[2021]11).
文摘Characterizing the trajectory of the healthy aging brain and exploring age-related structural changes in the brain can help deepen our understanding of the mechanism of brain aging.Currently,most structural magnetic resonance imaging literature explores brain aging merely from the perspective of morphological features,which cannot fully utilize the grayscale values containing important intrinsic information about brain structure.In this study,we propose the construction of two-dimensional horizontal visibility graphs based on the pixel intensity values of the gray matter slices directly.Normalized network structure entropy(NNSE)is then introduced to quantify the overall heterogeneities of these graphs.The results demonstrate a decrease in the NNSEs of gray matter with age.Compared with the middle-aged and the elderly,the larger values of the NNSE in the younger group may indicate more homogeneous network structures,smaller differences in importance between nodes and thus a more powerful ability to tolerate intrusion.In addition,the hub nodes of different adult age groups are primarily located in the precuneus,cingulate gyrus,superior temporal gyrus,inferior temporal gyrus,parahippocampal gyrus,insula,precentral gyrus and postcentral gyrus.Our study can provide a new perspective for understanding and exploring the structural mechanism of brain aging.
基金Acknowledgements This work was supported by the National Natural Science Foundation of China （Grant No. 61373150） and the Key Technologies R ＆ D Program of Shaanxi Province （2013k0611）.
文摘The information rate is an important metric of the performance of a secret-sharing scheme. In this paper we consider 272 non-isomorphic connected graph access structures with nine vertices and eight or nine edges, and either determine or bound the optimal information rate in each case. We obtain exact values for the optimal information rate for 231 cases and present a method that is able to derive information-theoretical upper bounds on the optimal information rate. Moreover, we apply some of the constructions to determine lower bounds on the information rate. Regarding information rate, we conclude with a full listing of the known optimal information rate （or bounds on the optimal information rate） for all 272 graphs access structures of nine participants.
文摘图数据增强是一种通过变换和扩充图结构和节点特征来增加训练数据多样性、提高图神经网络性能的技术。为了应对图数据增强面临的难以综合信息完整性、特征平滑性、图多样性和局部依赖关系的挑战,缓解图神经网络的过平滑和过拟合问题,提高其性能,提出了一种基于物理热力学中的熵理论的图数据增强模型(Neighbor Replacement Based on Graph Entropy,NRGE)。首先,引入了一种新的图熵定义,用于度量数据流形平滑度;基于减少图熵损失的思想,提出了一种新的数据增强策略,用于生成更多合适的训练数据。然后,通过增强节点的采样邻居,以保证数据增强的一致性;采用随机替换节点的一阶邻居为二阶邻居的方式,增加了数据增强的多样性。最后,引入了邻居约束正则化方法,通过约束增强后的邻居之间的预测一致性来提高模型性能。消融实验结果表明,通过保持三角形图案的信息结构,NRGE模型能够有效降低图熵损失,从而改善学习效果。在Cora,Citeseer和Pubmed 3个公开数据集上进行了节点分类实验,相较于基准模型,NRGE模型在Cora数据集上提升了1.1%,在Citeseer数据集上提升了0.8%,在Pubmed数据集上略微降低了0.4%。结果表明,NRGE模型有效改善了图神经网络的性能,提高了其泛化能力。
