A novel framework is proposed to obtain physiologically meaningful features for Alzheimer's disease(AD)classification based on sparse functional connectivity and non-negative matrix factorization.Specifically,the ...A novel framework is proposed to obtain physiologically meaningful features for Alzheimer's disease(AD)classification based on sparse functional connectivity and non-negative matrix factorization.Specifically,the non-negative adaptive sparse representation(NASR)method is applied to compute the sparse functional connectivity among brain regions based on functional magnetic resonance imaging(fMRI)data for feature extraction.Afterwards,the sparse non-negative matrix factorization(sNMF)method is adopted for dimensionality reduction to obtain low-dimensional features with straightforward physical meaning.The experimental results show that the proposed framework outperforms the competing frameworks in terms of classification accuracy,sensitivity and specificity.Furthermore,three sub-networks,including the default mode network,the basal ganglia-thalamus-limbic network and the temporal-insular network,are found to have notable differences between the AD patients and the healthy subjects.The proposed framework can effectively identify AD patients and has potentials for extending the understanding of the pathological changes of AD.展开更多
Sparse code multiple access(SCMA) is a novel non-orthogonal multiple access technology considered as a key component in 5G air interface design. In SCMA, the incoming bits are directly mapped to multi-dimensional cons...Sparse code multiple access(SCMA) is a novel non-orthogonal multiple access technology considered as a key component in 5G air interface design. In SCMA, the incoming bits are directly mapped to multi-dimensional constellation vectors known as SCMA codewords, which are then mapped onto blocks of physical resource elements in a sparse manner. The number of codewords that can be non-orthogonally multiplexed in each SCMA block is much larger than the number of resource elements therein, so the system is overloaded and can support larger number of users. The joint optimization of multi-dimensional modulation and low density spreading in SCMA codebook design ensures the SCMA receiver to recover the coded bits with high reliability and low complexity. The flexibility in design and the robustness in performance further prove SCMA to be a promising technology to meet the 5G communication demands such as massive connectivity and low latency transmissions.展开更多
Natural Language To SQL(NL2SQL)任务的目标是将自然语言查询转化为结构化查询语言。现有的大多数模型所使用的方法是将NL2SQL任务分解为多个子任务,为每个子任务构建一个专用的全连接神经网络解码器。这些方法存在一些问题,如模型设...Natural Language To SQL(NL2SQL)任务的目标是将自然语言查询转化为结构化查询语言。现有的大多数模型所使用的方法是将NL2SQL任务分解为多个子任务,为每个子任务构建一个专用的全连接神经网络解码器。这些方法存在一些问题,如模型设计与模型结构较为简单,在学习不同子任务之间的依赖关系的能力有限。为了解决这些问题,将多通道并行LSTM模型引入到NL2SQL任务中,并采用稀疏连接层联合不同的子任务解码器,提升神经网络表现能力和计算资源的使用效率。在WikiSQL数据集上的评估结果表明,与基线模型相比,文中提出的模型计算精度较好。展开更多
基金The Foundation of Hygiene and Health of Jiangsu Province(No.H2018042)the National Natural Science Foundation of China(No.61773114)the Key Research and Development Plan(Industry Foresight and Common Key Technology)of Jiangsu Province(No.BE2017007-3)
文摘A novel framework is proposed to obtain physiologically meaningful features for Alzheimer's disease(AD)classification based on sparse functional connectivity and non-negative matrix factorization.Specifically,the non-negative adaptive sparse representation(NASR)method is applied to compute the sparse functional connectivity among brain regions based on functional magnetic resonance imaging(fMRI)data for feature extraction.Afterwards,the sparse non-negative matrix factorization(sNMF)method is adopted for dimensionality reduction to obtain low-dimensional features with straightforward physical meaning.The experimental results show that the proposed framework outperforms the competing frameworks in terms of classification accuracy,sensitivity and specificity.Furthermore,three sub-networks,including the default mode network,the basal ganglia-thalamus-limbic network and the temporal-insular network,are found to have notable differences between the AD patients and the healthy subjects.The proposed framework can effectively identify AD patients and has potentials for extending the understanding of the pathological changes of AD.
基金supported by the National Basic Research Program of China(973 Program 2012CB316000)the National Major Projects of China(2015ZX03002010)
文摘Sparse code multiple access(SCMA) is a novel non-orthogonal multiple access technology considered as a key component in 5G air interface design. In SCMA, the incoming bits are directly mapped to multi-dimensional constellation vectors known as SCMA codewords, which are then mapped onto blocks of physical resource elements in a sparse manner. The number of codewords that can be non-orthogonally multiplexed in each SCMA block is much larger than the number of resource elements therein, so the system is overloaded and can support larger number of users. The joint optimization of multi-dimensional modulation and low density spreading in SCMA codebook design ensures the SCMA receiver to recover the coded bits with high reliability and low complexity. The flexibility in design and the robustness in performance further prove SCMA to be a promising technology to meet the 5G communication demands such as massive connectivity and low latency transmissions.
文摘Natural Language To SQL(NL2SQL)任务的目标是将自然语言查询转化为结构化查询语言。现有的大多数模型所使用的方法是将NL2SQL任务分解为多个子任务,为每个子任务构建一个专用的全连接神经网络解码器。这些方法存在一些问题,如模型设计与模型结构较为简单,在学习不同子任务之间的依赖关系的能力有限。为了解决这些问题,将多通道并行LSTM模型引入到NL2SQL任务中,并采用稀疏连接层联合不同的子任务解码器,提升神经网络表现能力和计算资源的使用效率。在WikiSQL数据集上的评估结果表明,与基线模型相比,文中提出的模型计算精度较好。
