Critical nodes in temporal networks play more significant role than other nodes on the structure and function of networks. The research on identifying critical nodes in temporal networks has attracted much attention since the real-world systems can be illustrated more accurately by temporal networks than static networks. Considering the topological information of networks, the algorithm MLI based on network embedding and machine learning are proposed in this paper. we convert the critical node identification problem in temporal networks into regression problem by the algorithm. The effectiveness of proposed methods is evaluated by SIR model and compared with well-known existing metrics such as temporal versions of betweenness, closeness, k-shell, degree deviation and dynamics-sensitive centralities in one synthetic and five real temporal networks. Experimental results show that the proposed method outperform these well-known methods in identifying critical nodes under spreading dynamic.
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http://dx.doi.org/10.1038/s41598-020-69379-z | DOI Listing |
The hippocampus forms memories of our experiences by registering processed sensory information in coactive populations of excitatory principal cells or ensembles. Fast-spiking parvalbumin-expressing inhibitory neurons (PV INs) in the dentate gyrus (DG)-CA3/CA2 circuit contribute to memory encoding by exerting precise temporal control of excitatory principal cell activity through mossy fiber-dependent feed-forward inhibition. PV INs respond to input-specific information by coordinating changes in their intrinsic excitability, input-output synaptic-connectivity, synaptic-physiology and synaptic-plasticity, referred to here as experience-dependent PV IN plasticity, to influence hippocampal functions.
View Article and Find Full Text PDFClin EEG Neurosci
January 2025
Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, India.
Motor Imagery (MI) electroencephalographic (EEG) signal classification is a pioneer research branch essential for mobility rehabilitation. This paper proposes an end-to-end hybrid deep network "Spatio Temporal Inception Transformer Network (STIT-Net)" model for MI classification. Discrete Wavelet Transform (DWT) is used to derive the alpha (8-13) Hz and beta (13-30) Hz EEG sub bands which are dominant during motor tasks to enhance the performance of the proposed work.
View Article and Find Full Text PDFWe present a widefield fluorescence microscope that integrates an event-based image sensor (EBIS) with a CMOS image sensor (CIS) for ultra-fast microscopy with spectral distinction capabilities. The EBIS achieves a temporal resolution of ∼10s (∼ 100,000 frames/s), while the CIS provides diffraction-limited spatial resolution. A diffractive optical element encodes spectral information into a diffractogram, which is recorded by the CIS.
View Article and Find Full Text PDFAtmospheric turbulence introduces random disturbances that degrade and distort images of observed targets as light propagates through the atmosphere. Although numerous algorithms have been developed to restore images degraded by turbulence, most of these algorithms lack sufficient generalization and are limited to specific application scenarios or fixed atmospheric turbulence intensities. In this paper, we propose an Atmospheric Turbulence Restoration Network (ATRN), a two-stage algorithm based on multi-frame information fusion.
View Article and Find Full Text PDFIn this paper, a demodulation method based on a temporal-convolutional feature fusion network (TCFFN) is proposed for the non-line-of-sight (NLOS) ultraviolet communication (UVC) system. The TCFFN extracts the temporal features and the local features of the signals, offering strong adaptability to inter-symbol interference (ISI) caused by channel scattering. By evaluating a single-user and dual-user UVC on-off keying non-orthogonal multiple access (OOK-NOMA) systems, the results demonstrate that the TCFFN demodulator supports the higher rate transmission of NLOS UVC system compared with the static threshold (ST) demodulator and the minimum mean square error (MMSE) equalizer.
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