Functional variability in both gray matter (GM) and white matter (WM) is closely associated with human brain cognitive and developmental processes, and is commonly assessed using functional connectivity (FC). However, as a correlationbased approach, FC captures the co-fluctuation between brain regions rather than the intensity of neural activity in each region. Consequently, FC provides only a partial view of functional variability, and this limitation is particularly pronounced in WM, where functional signals are weaker and more susceptible to noise. To tackle this limitation, we introduce fractional amplitude of low-frequency fluctuation (fALFF) to measure the intensity of spontaneous neural activity and analyze functional variability in WM. Specifically, we propose a novel method to quantify WM functional variability by estimating the differential identifiability of fALFF. Higher differential identifiability is observed in WM fALFF compared to FC, which indicates that fALFF is more sensitive to WM functional variability. Through fALFF differential identifiability, we evaluate the functional variabilities of both WM and GM, and find the overall functional variability pattern is similar although WM shows slightly lower variability than GM. The regional functional variabilities of WM are associated with structural connectivity, where commissural fiber regions generally exhibit higher variability than projection fiber regions. Furthermore, we discover that WM functional variability demonstrates a spatial gradient ascending from the brainstem to the cortex by hypothesis testing, which aligns well with the evolutionary expansion. The gradient of functional variability in WM provides novel insights for understanding WM function. To the best of our knowledge, this is the first attempt to investigate WM functional variability via fALFF. Our code is available at https://github.com/Xinle-Chang/WM-fALFF-Idiff-Gradient.

In this paper, we analyze the covert UAV-to-UAV (U2U) communication with the ground warden under a Poisson field of ground interferers and the blockage effect of air-to-ground propagations. With the aid of stochastic geometry, we derive the average covert probability and connection outage probability to quantify the covert communication performance for the scenarios with and without interference. By comparing the two scenarios, the introduction of interference increases the minimum average covert probability. Increasing both the density and transmission power of interferers can improve the average covert probability. However, the improvement of covertness is achieved by increasing the connection outage probability. To capture the competing requirements of covertness and reliability, we analyze the effective covert communication rate defined as the product of the average covert probability, connection success probability, and data transmission rate. The UAV transceiver can also flexibly raise its flight altitude to improve effective covert communication rate. Moreover, our results reveal that covert U2U communication performs better in high blockage environments such as dense urban. In summary, this study provides theoretical guidance for designing covert U2U communication systems.

Graph convolutional networks (GCNs) have been the predominant methods in skeleton-based human action recognition, including human-human interaction recognition. However, when dealing with interaction sequences, current GCN-based methods simply split the two-person skeleton into two discrete graphs and perform graph convolution separately as done for single-person action classification. Such operations ignore rich interactive information and hinder effective spatial inter-body relationship modeling. To overcome the above shortcoming, we introduce a novel unified two-person graph to represent inter-body and intra-body correlations between joints. Experimental results show accuracy improvements in recognizing both interactions and individual actions when utilizing the proposed two-person graph topology. In addition, several graph labeling strategies are designed to supervise the model to learn discriminant spatial-temporal interactive features. Finally, we propose a two-person graph convolutional network (2P-GCN). Our model outperforms state-of-the-art methods on four benchmarks of three interaction datasets: SBU, interaction subsets of NTU-RGB+D and NTU-RGB+D 120.