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
Two-dimensional(2D)semiconductors have emerged as promising candidates in next-generation nanoelectronics and sustainable energy technologies,particularly in photoelectrochemical water splitting,due to their exception...Two-dimensional(2D)semiconductors have emerged as promising candidates in next-generation nanoelectronics and sustainable energy technologies,particularly in photoelectrochemical water splitting,due to their exceptional quantum confinement effects and tunable optoelectronic properties.Accurate determination of electronic band gaps remains a critical prerequisite for rational material design in advanced optoelectronic applications.However,the commonly used density functional theory approach with conventional functionals suffers from intrinsic deficiencies in predicting semiconductor band gaps,while calculations with higher hierarchy of functionals like the HSE06 hybrid functional or based on higher level methodologies such as GW approximation incur prohibitive computational costs.To address this challenge,here we propose a reference-guided graph neural network(RG-GNN)framework that achieves HSE06-level accuracy through efficient machine learning.Our approach uniquely embeds an input reference value for the target property with minimal elementary descriptors encoding the structural information of the materials in the model,enabling high-accuracy band gap prediction at the HSE06 level.The model achieves a mean absolute error of 0.15 eV on unseen 2D semiconductor systems compared to HSE06 band gaps.Systematic ablation studies reveal that the reference-guided mechanism reduces prediction error by 83.3%and significantly decreases training dataset requirements for model convergence compared to conventional GNN architectures.Our results demonstrates that topological atomic descriptors from primitive cells,when combined with appropriate reference values,contain sufficient information for highly accurate band gap prediction in 2D materials.展开更多
具有多维属性的实体相互连接构成的网络(如社交网络)称为多维网络,在多维网络上支持联机分析处理具有重要的应用价值。现有方法大都从文件或数据库中逐条读取记录,当数据量很大时,需要多次读取磁盘,导致查询响应时间过长,效率较低。文...具有多维属性的实体相互连接构成的网络(如社交网络)称为多维网络,在多维网络上支持联机分析处理具有重要的应用价值。现有方法大都从文件或数据库中逐条读取记录,当数据量很大时,需要多次读取磁盘,导致查询响应时间过长,效率较低。文中提出了一种新的基于倒排索引的多维网络存储模型II-GC(Inverted Index based Graph Cube),通过将图的拓扑结构和顶点的多维属性存储在倒排索引列表中加快查询速度,并给出了在多维网络上进行聚集查询(cuboid)和交叉查询(crossboid)的算法。在DBLP数据集上的实验表明,该模型较Graph Cube的查询效率更高,扩展性更好。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Project supported by the National Natural Science Foundation of China(Grant No.22173052)。
文摘Two-dimensional(2D)semiconductors have emerged as promising candidates in next-generation nanoelectronics and sustainable energy technologies,particularly in photoelectrochemical water splitting,due to their exceptional quantum confinement effects and tunable optoelectronic properties.Accurate determination of electronic band gaps remains a critical prerequisite for rational material design in advanced optoelectronic applications.However,the commonly used density functional theory approach with conventional functionals suffers from intrinsic deficiencies in predicting semiconductor band gaps,while calculations with higher hierarchy of functionals like the HSE06 hybrid functional or based on higher level methodologies such as GW approximation incur prohibitive computational costs.To address this challenge,here we propose a reference-guided graph neural network(RG-GNN)framework that achieves HSE06-level accuracy through efficient machine learning.Our approach uniquely embeds an input reference value for the target property with minimal elementary descriptors encoding the structural information of the materials in the model,enabling high-accuracy band gap prediction at the HSE06 level.The model achieves a mean absolute error of 0.15 eV on unseen 2D semiconductor systems compared to HSE06 band gaps.Systematic ablation studies reveal that the reference-guided mechanism reduces prediction error by 83.3%and significantly decreases training dataset requirements for model convergence compared to conventional GNN architectures.Our results demonstrates that topological atomic descriptors from primitive cells,when combined with appropriate reference values,contain sufficient information for highly accurate band gap prediction in 2D materials.
文摘具有多维属性的实体相互连接构成的网络(如社交网络)称为多维网络,在多维网络上支持联机分析处理具有重要的应用价值。现有方法大都从文件或数据库中逐条读取记录,当数据量很大时,需要多次读取磁盘,导致查询响应时间过长,效率较低。文中提出了一种新的基于倒排索引的多维网络存储模型II-GC(Inverted Index based Graph Cube),通过将图的拓扑结构和顶点的多维属性存储在倒排索引列表中加快查询速度,并给出了在多维网络上进行聚集查询(cuboid)和交叉查询(crossboid)的算法。在DBLP数据集上的实验表明,该模型较Graph Cube的查询效率更高,扩展性更好。
