城市功能是城市整体活动特点和类型的体现,识别城市内部功能区的空间分布结构,有利于城市结构优化,城市资源优化配置和城市发展规划等。以深圳市为例,对抓取的新浪微博位置签到数据进行了有效的数据筛选清洗,结合《城市用地分类与规划...城市功能是城市整体活动特点和类型的体现,识别城市内部功能区的空间分布结构,有利于城市结构优化,城市资源优化配置和城市发展规划等。以深圳市为例,对抓取的新浪微博位置签到数据进行了有效的数据筛选清洗,结合《城市用地分类与规划建设用地标准》和居民出行目的,对兴趣点（points of interest,POI）数据重分类,并针对分类完成的POI数据进行点要素空间多密度聚类,结合用户签到频率、POI数量比例以及土地利用混合程度构建了POI数据模型,以综合POI空间信息和语义信息,实现了城市空间自组织形态的功能区域主体功能识别。识别结果与深圳市城市布局结构规划图以及高德地图对比发现,该方法的聚类结果大体吻合深圳市空间布局的基本骨架,同时也能基本识别各大功能分区的主体功能和具有明显特征的功能区。展开更多
随着互联网上服务数量的急剧增长及类型的日益多样化,如何准确、高效地发现满足用户需求的服务成为服务计算领域的一大挑战.服务聚类是提高服务发现效率的重要技术.尽管已有很多服务聚类方面的相关工作,但是现有方法不仅局限于单一类型...随着互联网上服务数量的急剧增长及类型的日益多样化,如何准确、高效地发现满足用户需求的服务成为服务计算领域的一大挑战.服务聚类是提高服务发现效率的重要技术.尽管已有很多服务聚类方面的相关工作,但是现有方法不仅局限于单一类型的文档,而且鲜有考虑服务需求的功能语义.有鉴于此,文中提出一种基于需求功能语义的服务聚类方法SCFSR(Service Clustering based on the Functional Semantics of Requirements).该方法对文档类型没有要求,且采用自然语言处理技术提取服务需求中的所有有用功能信息集;根据服务功能信息集度量服务的功能语义相似度;使用k-means算法实现服务聚类;使用ProgrammableWeb上API服务的真实数据来验证SCFSR方法的有效性.文中用准确率和召回率评估信息集提取的效果,并用纯度指标(Purity of Cluster)评估聚类的效果.评估结果表明,该方法可以有效地实现对服务的聚类,整个聚类的纯度达到了57.5%,比同类方法略有提高.展开更多
Research on brain function after brachial plexus injury focuses on local cortical functional reorganization,and few studies have focused on brain networks after brachial plexus injury.Changes in brain networks may hel...Research on brain function after brachial plexus injury focuses on local cortical functional reorganization,and few studies have focused on brain networks after brachial plexus injury.Changes in brain networks may help understanding of brain plasticity at the global level.We hypothesized that topology of the global cerebral resting-state functional network changes after unilateral brachial plexus injury.Thus,in this cross-sectional study,we recruited eight male patients with unilateral brachial plexus injury（right handedness,mean age of 27.9±5.4years old）and eight male healthy controls（right handedness,mean age of 28.6±3.2）.After acquiring and preprocessing resting-state magnetic resonance imaging data,the cerebrum was divided into 90 regions and Pearson’s correlation coefficient calculated between regions.These correlation matrices were then converted into a binary matrix with affixed sparsity values of 0.1–0.46.Under sparsity conditions,both groups satisfied this small-world property.The clustering coefficient was markedly lower,while average shortest path remarkably higher in patients compared with healthy controls.These findings confirm that cerebral functional networks in patients still show smallworld characteristics,which are highly effective in information transmission in the brain,as well as normal controls.Alternatively,varied small-worldness suggests that capacity of information transmission and integration in different brain regions in brachial plexus injury patients is damaged.展开更多
随着互联网上Mashup服务数量及种类的急剧增长,如何从这些海量的服务集合中快速、精准地发现满足用户需求的Mashup服务,成为一个具有挑战性的问题.针对这一问题,本文提出一种融合功能语义关联计算与密度峰值检测的Mashup服务聚类方法,...随着互联网上Mashup服务数量及种类的急剧增长,如何从这些海量的服务集合中快速、精准地发现满足用户需求的Mashup服务,成为一个具有挑战性的问题.针对这一问题,本文提出一种融合功能语义关联计算与密度峰值检测的Mashup服务聚类方法,用于缩小服务的搜索空间,提升服务发现的精度与效率.首先,该方法对Mashup服务进行元信息提取和描述文本内容整理,并根据Web API组合的标签对相应Mashup服务标签进行扩充.然后,用基于功能语义关联计算方法(Functional Semantic Association Calculation Method,FSAC)提取出各服务描述的功能名词集合,并通过功能名词的语义权重来构造Mashup语义特征向量.最后,通过基于密度信息的聚类中心检测方法(Clustering Center Detection Method based on Density Information,CCD-DI)检测出最为合适的K个Mashup语义特征向量作为K-means算法的初始中心,进行聚类划分.基于ProgrammableWeb的真实数据实验表明,本文所提聚类方法在纯度、精准率、召回率、熵等指标上均有良好表现.展开更多
Functional networks(FNs)hold significant promise in understanding brain function.Independent component analysis(ICA)has been applied in estimating FNs from functional magnetic resonance imaging(fMRI).However,determini...Functional networks(FNs)hold significant promise in understanding brain function.Independent component analysis(ICA)has been applied in estimating FNs from functional magnetic resonance imaging(fMRI).However,determining an optimal model order for ICA remains challenging,leading to criticism about the reliability of FN estimation.Here,we propose a SMART(splitting-merging assisted reliable)ICA method that automatically extracts reliable FNs by clustering independent components(ICs)obtained from multi-model-order ICA using a simplified graph while providing linkages among FNs deduced from different-model orders.We extend SMART ICA to multi-subject fMRI analysis,validating its effectiveness using simulated and real fMRI data.Based on simulated data,the method accurately estimates both group-common and group-unique components and demonstrates robustness to parameters.Using two age-matched cohorts of resting fMRI data comprising 1,950 healthy subjects,the resulting reliable group-level FNs are greatly similar between the two cohorts,and interestingly the subject-specific FNs show progressive changes while age increases.Furthermore,both small-scale and large-scale brain FN templates are provided as benchmarks for future studies.Taken together,SMART ICA can automatically obtain reliable FNs in analyzing multi-subject fMRI data,while also providing linkages between different FNs.展开更多
In this paper,we consider the clustering of bivariate functional data where each random surface consists of a set of curves recorded repeatedly for each subject.The k-centres surface clustering method based on margina...In this paper,we consider the clustering of bivariate functional data where each random surface consists of a set of curves recorded repeatedly for each subject.The k-centres surface clustering method based on marginal functional principal component analysis is proposed for the bivariate functional data,and a novel clustering criterion is presented where both the random surface and its partial derivative function in two directions are considered.In addition,we also consider two other clustering methods,k-centres surface clustering methods based on product functional principal component analysis or double functional principal component analysis.Simulation results indicate that the proposed methods have a nice performance in terms of both the correct classification rate and the adjusted rand index.The approaches are further illustrated through empirical analysis of human mortality data.展开更多
文摘城市功能是城市整体活动特点和类型的体现,识别城市内部功能区的空间分布结构,有利于城市结构优化,城市资源优化配置和城市发展规划等。以深圳市为例,对抓取的新浪微博位置签到数据进行了有效的数据筛选清洗,结合《城市用地分类与规划建设用地标准》和居民出行目的,对兴趣点（points of interest,POI）数据重分类,并针对分类完成的POI数据进行点要素空间多密度聚类,结合用户签到频率、POI数量比例以及土地利用混合程度构建了POI数据模型,以综合POI空间信息和语义信息,实现了城市空间自组织形态的功能区域主体功能识别。识别结果与深圳市城市布局结构规划图以及高德地图对比发现,该方法的聚类结果大体吻合深圳市空间布局的基本骨架,同时也能基本识别各大功能分区的主体功能和具有明显特征的功能区。
文摘随着互联网上服务数量的急剧增长及类型的日益多样化,如何准确、高效地发现满足用户需求的服务成为服务计算领域的一大挑战.服务聚类是提高服务发现效率的重要技术.尽管已有很多服务聚类方面的相关工作,但是现有方法不仅局限于单一类型的文档,而且鲜有考虑服务需求的功能语义.有鉴于此,文中提出一种基于需求功能语义的服务聚类方法SCFSR(Service Clustering based on the Functional Semantics of Requirements).该方法对文档类型没有要求,且采用自然语言处理技术提取服务需求中的所有有用功能信息集;根据服务功能信息集度量服务的功能语义相似度;使用k-means算法实现服务聚类;使用ProgrammableWeb上API服务的真实数据来验证SCFSR方法的有效性.文中用准确率和召回率评估信息集提取的效果,并用纯度指标(Purity of Cluster)评估聚类的效果.评估结果表明,该方法可以有效地实现对服务的聚类,整个聚类的纯度达到了57.5%,比同类方法略有提高.
文摘Research on brain function after brachial plexus injury focuses on local cortical functional reorganization,and few studies have focused on brain networks after brachial plexus injury.Changes in brain networks may help understanding of brain plasticity at the global level.We hypothesized that topology of the global cerebral resting-state functional network changes after unilateral brachial plexus injury.Thus,in this cross-sectional study,we recruited eight male patients with unilateral brachial plexus injury（right handedness,mean age of 27.9±5.4years old）and eight male healthy controls（right handedness,mean age of 28.6±3.2）.After acquiring and preprocessing resting-state magnetic resonance imaging data,the cerebrum was divided into 90 regions and Pearson’s correlation coefficient calculated between regions.These correlation matrices were then converted into a binary matrix with affixed sparsity values of 0.1–0.46.Under sparsity conditions,both groups satisfied this small-world property.The clustering coefficient was markedly lower,while average shortest path remarkably higher in patients compared with healthy controls.These findings confirm that cerebral functional networks in patients still show smallworld characteristics,which are highly effective in information transmission in the brain,as well as normal controls.Alternatively,varied small-worldness suggests that capacity of information transmission and integration in different brain regions in brachial plexus injury patients is damaged.
文摘随着互联网上Mashup服务数量及种类的急剧增长,如何从这些海量的服务集合中快速、精准地发现满足用户需求的Mashup服务,成为一个具有挑战性的问题.针对这一问题,本文提出一种融合功能语义关联计算与密度峰值检测的Mashup服务聚类方法,用于缩小服务的搜索空间,提升服务发现的精度与效率.首先,该方法对Mashup服务进行元信息提取和描述文本内容整理,并根据Web API组合的标签对相应Mashup服务标签进行扩充.然后,用基于功能语义关联计算方法(Functional Semantic Association Calculation Method,FSAC)提取出各服务描述的功能名词集合,并通过功能名词的语义权重来构造Mashup语义特征向量.最后,通过基于密度信息的聚类中心检测方法(Clustering Center Detection Method based on Density Information,CCD-DI)检测出最为合适的K个Mashup语义特征向量作为K-means算法的初始中心,进行聚类划分.基于ProgrammableWeb的真实数据实验表明,本文所提聚类方法在纯度、精准率、召回率、熵等指标上均有良好表现.
基金supported by the National Natural Science Foundation of China(62076157 and 61703253)the Fund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shanxi Province(20210033)the National Institutes of Health(R01MH123610 and R01EB006841).
文摘Functional networks(FNs)hold significant promise in understanding brain function.Independent component analysis(ICA)has been applied in estimating FNs from functional magnetic resonance imaging(fMRI).However,determining an optimal model order for ICA remains challenging,leading to criticism about the reliability of FN estimation.Here,we propose a SMART(splitting-merging assisted reliable)ICA method that automatically extracts reliable FNs by clustering independent components(ICs)obtained from multi-model-order ICA using a simplified graph while providing linkages among FNs deduced from different-model orders.We extend SMART ICA to multi-subject fMRI analysis,validating its effectiveness using simulated and real fMRI data.Based on simulated data,the method accurately estimates both group-common and group-unique components and demonstrates robustness to parameters.Using two age-matched cohorts of resting fMRI data comprising 1,950 healthy subjects,the resulting reliable group-level FNs are greatly similar between the two cohorts,and interestingly the subject-specific FNs show progressive changes while age increases.Furthermore,both small-scale and large-scale brain FN templates are provided as benchmarks for future studies.Taken together,SMART ICA can automatically obtain reliable FNs in analyzing multi-subject fMRI data,while also providing linkages between different FNs.
基金supported by National Natural Science Foundation of China (Grant Nos.12261007)Natural Science Foundation of Guangxi Province (Grant No.2020GXNSFAA297225)。
文摘In this paper,we consider the clustering of bivariate functional data where each random surface consists of a set of curves recorded repeatedly for each subject.The k-centres surface clustering method based on marginal functional principal component analysis is proposed for the bivariate functional data,and a novel clustering criterion is presented where both the random surface and its partial derivative function in two directions are considered.In addition,we also consider two other clustering methods,k-centres surface clustering methods based on product functional principal component analysis or double functional principal component analysis.Simulation results indicate that the proposed methods have a nice performance in terms of both the correct classification rate and the adjusted rand index.The approaches are further illustrated through empirical analysis of human mortality data.
