Tootfinder

Transfer Learning on Edge Connecting Probability Estimation under Graphon Model
Yuyao Wang, Yu-Hung Cheng, Debarghya Mukherjee, Huimin Cheng
https://arxiv.org/abs/2510.05527 htt…

Transfer Learning on Edge Connecting Probability Estimation under Graphon Model
Graphon models provide a flexible nonparametric framework for estimating latent connectivity probabilities in networks, enabling a range of downstream applications such as link prediction and data augmentation. However, accurate graphon estimation typically requires a large graph, whereas in practice, one often only observes a small-sized network. One approach to addressing this issue is to adopt a transfer learning framework, which aims to improve estimation in a small target graph by leveragi…

Experimental Characterization and Dynamic Modeling of THz Channels Under Fog Conditions
Jiaobiao Zhao, Kefeng Huang, Xiaoxiang Li, Mingxia Zhang, Peian Li, Jie Yang, Wenbo Liu, Yiming Zhao, Weidong Hu, Jianjun Ma
https://arxiv.org/abs/2510.09906

Experimental Characterization and Dynamic Modeling of THz Channels Under Fog Conditions
The terahertz (THz) band is a promising candidate for sixth-generation wireless networks, but its deploymen in outdoor environments is challenged by meteorological phenomena, particularly fog, which imposes variable and difficult-to-predict channel degradation. This article introduces dynamic channel model for the THz band explicitly driven by the time-evolving droplet size distribution (DSD) of fog, integrating real-time microphysical sensing to capture variations in the fog microstructure. Ex…