IEEE

TriDGNet: Triple Feature Encoder-Based Dual Granularity Graph Learning Network for Enhanced Travel Time Estimation

The contemporary urban intelligent transportation system (ITS) generates an enormous amount of trajectory data daily, serving as an essential reflection of traffic dynamics. Accurate estimation of ...
GitHub

Depth Anything V2

This work presents Depth Anything V2. It significantly outperforms V1 in fine-grained details and robustness. Compared with SD-based models, it enjoys faster inference speed, fewer parameters, and ...
IEEE

Trajectory-based Region of Attraction Estimation on Higher-Order Networked Nonlinear Systems

Abstract: This letter presents a theoretical framework for estimating the trajectory-based region of attraction in nonlinear higher-order networks. A Lyapunov-based criterion is established for ...
GitHub

An extensive evaluation of single-cell RNA-Seq contrastive learning generative networks for intrinsic cell types distribution estimation

This is a python implementation of the scRCL-G framework that evaluates the performance of scRNA-Seq contrastive learning generative encoder networks in terms of their performance on learning the ...