38 citations · 44 across the 7 of their papers we have counts for
7 papers
Utilizing Causal Network Markers to Identify Tipping Points ahead of Critical Transition
Shirui Bian, Zezhou Wang, Siyang Leng +2
Early-warning signals of delicate design are always used to predict critical transitions in complex systems, which makes it possible to render the systems far away from the catastr…
Non-Bayesian Social Learning with Multiview Observations
Dongyan Sui, Weichen Cao, Stefan Vlaski +2
Non-Bayesian social learning enables multiple agents to conduct networked signal and information processing through observing environmental signals and information aggregating. Tra…
Deciphering interventional dynamical causality from non-intervention complex systems
Jifan Shi, Yang Li, Juan Zhao +5
Detecting and quantifying causality is a focal topic in the fields of science, engineering, and interdisciplinary studies. However, causal studies on non-intervention systems attra…
UniG-Encoder: A Universal Feature Encoder for Graph and Hypergraph Node Classification
Minhao Zou, Zhongxue Gan, Yutong Wang +4
Graph and hypergraph representation learning has attracted increasing attention from various research fields. Despite the decent performance and fruitful applications of Graph Neur…
Embedding Theory of Reservoir Computing and Reducing Reservoir Network Using Time Delays
Xing-Yue Duan, Xiong Ying, Si-Yang Leng +3
Reservoir computing (RC), a particular form of recurrent neural network, is under explosive development due to its exceptional efficacy and high performance in reconstruction or/an…
Continuity scaling: A rigorous framework for detecting and quantifying causality accurately
Xiong Ying, Si-Yang Leng, Huan-Fei Ma +3
Data based detection and quantification of causation in complex, nonlinear dynamical systems is of paramount importance to science, engineering and beyond. Inspired by the widely u…
Data-based prediction and causality inference of nonlinear dynamics
Huanfei Ma, Siyang Leng, Luonan Chen
Natural systems are typically nonlinear and complex, and it is of great interest to be able to reconstruct a system in order to understand its mechanism, which can not only recover…