The local field potential（LFP） is a signal reflecting the electrical activity of neurons surrounding the electrode tip. Synchronization between LFP signals provides important details about how neural networks are or...The local field potential（LFP） is a signal reflecting the electrical activity of neurons surrounding the electrode tip. Synchronization between LFP signals provides important details about how neural networks are organized. Synchronization between two distant brain regions is hard to detect using linear synchronization algorithms like correlation and coherence. Synchronization likelihood（SL） is a non-linear synchronization-detecting algorithm widely used in studies of neural signals from two distant brain areas. One drawback of non-linear algorithms is the heavy computational burden. In the present study, we proposed a graphic processing unit（GPU）-accelerated implementation of an SL algorithm with optional 2-dimensional time-shifting. We tested the algorithm with both artificial data and raw LFP data. The results showed that this method revealed detailed information from original data with the synchronization values of two temporal axes,delay time and onset time, and thus can be used to reconstruct the temporal structure of a neural network. Our results suggest that this GPU-accelerated method can be extended to other algorithms for processing time-series signals（like EEG and f MRI） using similar recording techniques.展开更多
Pilot contamination can bring up a grave impairment in the performance of massive multiple-input multiple-output(MIMO)systems.In this paper,an improved time-shifted pilot scheme is proposed to reduce the pilot contami...Pilot contamination can bring up a grave impairment in the performance of massive multiple-input multiple-output(MIMO)systems.In this paper,an improved time-shifted pilot scheme is proposed to reduce the pilot contamination,where orthogonal pilots are employed in the same group to eliminate the residual intragroup interference existing in the original time-shifted pilot scheme.Meanwhile,the rigorous closed-form expressions of both downlink and uplink transmission rates with a finite number of antennas are derived,and it is shown that the intra-group interference can be completely eliminated by the proposed scheme.Simulation results demonstrate that both downlink and uplink transmission rates are significantly improved by employing the proposed scheme.展开更多
时间序列预测在能源管理、交通流量和气象分析等多个实际场景中具有重要应用价值。然而,时间序列数据中存在的分布漂移(Distribution Shift)与长程依赖(Long-term Dependency)仍限制了传统方法与现有深度学习模型在长期预测中的表现。为...时间序列预测在能源管理、交通流量和气象分析等多个实际场景中具有重要应用价值。然而,时间序列数据中存在的分布漂移(Distribution Shift)与长程依赖(Long-term Dependency)仍限制了传统方法与现有深度学习模型在长期预测中的表现。为此,提出了一种名为D-LINet(Dual-Normalization and Linear Integration Network)的创新模型。该模型结合了Dish-TS(Distribution Shift in Time Series Forecasting)框架的分布归一化能力与线性映射的高效性,并采用双向归一化与双线性层的设计,有效缓解输入与输出空间的分布偏移,增强了对周期性与趋势性特征的捕捉能力。在多个真实数据集上对D-LINet的预测性能进行了全面评估。结果显示,在短期与长期预测中,D-LINet的均方误差和平均绝对误差均显著优于主流模型(如Transformer,Informer,Autoformer和DLinear)。此外,实验还探讨了输入窗口长度及先验知识的引入对预测性能的影响,为后续模型优化提供了重要指导。该研究针对复杂分布漂移问题提出了新的解决思路,并有助于提升时间序列预测的精度与稳健性。展开更多
基金supported by Grants from the National Natural Science Foundation of China(81230023,81571067,and 81521063)National Basic Research Development Program(973 Program)of China(2013CB531905)the‘‘111’’Project of China
文摘The local field potential（LFP） is a signal reflecting the electrical activity of neurons surrounding the electrode tip. Synchronization between LFP signals provides important details about how neural networks are organized. Synchronization between two distant brain regions is hard to detect using linear synchronization algorithms like correlation and coherence. Synchronization likelihood（SL） is a non-linear synchronization-detecting algorithm widely used in studies of neural signals from two distant brain areas. One drawback of non-linear algorithms is the heavy computational burden. In the present study, we proposed a graphic processing unit（GPU）-accelerated implementation of an SL algorithm with optional 2-dimensional time-shifting. We tested the algorithm with both artificial data and raw LFP data. The results showed that this method revealed detailed information from original data with the synchronization values of two temporal axes,delay time and onset time, and thus can be used to reconstruct the temporal structure of a neural network. Our results suggest that this GPU-accelerated method can be extended to other algorithms for processing time-series signals（like EEG and f MRI） using similar recording techniques.
基金Supported by Beijing Natural Science Foundation(4194087)。
文摘Pilot contamination can bring up a grave impairment in the performance of massive multiple-input multiple-output(MIMO)systems.In this paper,an improved time-shifted pilot scheme is proposed to reduce the pilot contamination,where orthogonal pilots are employed in the same group to eliminate the residual intragroup interference existing in the original time-shifted pilot scheme.Meanwhile,the rigorous closed-form expressions of both downlink and uplink transmission rates with a finite number of antennas are derived,and it is shown that the intra-group interference can be completely eliminated by the proposed scheme.Simulation results demonstrate that both downlink and uplink transmission rates are significantly improved by employing the proposed scheme.
文摘时间序列预测在能源管理、交通流量和气象分析等多个实际场景中具有重要应用价值。然而,时间序列数据中存在的分布漂移(Distribution Shift)与长程依赖(Long-term Dependency)仍限制了传统方法与现有深度学习模型在长期预测中的表现。为此,提出了一种名为D-LINet(Dual-Normalization and Linear Integration Network)的创新模型。该模型结合了Dish-TS(Distribution Shift in Time Series Forecasting)框架的分布归一化能力与线性映射的高效性,并采用双向归一化与双线性层的设计,有效缓解输入与输出空间的分布偏移,增强了对周期性与趋势性特征的捕捉能力。在多个真实数据集上对D-LINet的预测性能进行了全面评估。结果显示,在短期与长期预测中,D-LINet的均方误差和平均绝对误差均显著优于主流模型(如Transformer,Informer,Autoformer和DLinear)。此外,实验还探讨了输入窗口长度及先验知识的引入对预测性能的影响,为后续模型优化提供了重要指导。该研究针对复杂分布漂移问题提出了新的解决思路,并有助于提升时间序列预测的精度与稳健性。
