169 citations · 196 across the 10 of their papers we have counts for
11 papers
Information transfer as a new indicator of approaching local bifurcations: saddle-node, pitchfork and Hopf bifurcations
Stéphane Vannitsem, Carlos A. Pires, X. San Liang
Early warning signals are key tools for anticipating qualitative changes in the dynamics of complex systems. In this work, we investigate whether an information-theoretic measure d…
Quantitative causality, causality-guided scientific discovery, and causal machine learning
X. San Liang, Dake Chen, Renhe Zhang
It has been said, arguably, that causality analysis should pave a promising way to interpretable deep learning and generalization. Incorporation of causality into artificial intell…
Quantitative causality analysis with coarsely sampled time series
X. San Liang
The information flow-based quantitative causality analysis has been widely applied in different disciplines because of its origin from first principles, its concise form, and its c…
An overview of the quantitative causality analysis and causal graph reconstruction based on a rigorous formalism of information flow
X. San Liang
Inference of causal relations from data now has become an important field in artificial intelligence. During the past 16 years, causality analysis (in a quantitative sense) has bee…
The causal interaction between the subnetworks of a complex network
X. San Liang
Information flow provides a natural measure for the causal interaction between dynamical events. This study extends our previous rigorous formalism of componentwise information flo…
El Niño Modoki thus far can be mostly predicted more than 10 years ahead of time
X. San Liang, Fen Xu, Yineng Rong
The 2014-2015 "Monster"/"Super" El Niño failed to be predicted one year earlier due to the growing importance of a new type of El Niño, El Niño Modoki, which reportedly has much lo…
Measuring the importance of individual units in producing the collective behavior of a complex network
X. San Liang
A quantitative evaluation of the contribution of individual units in producing the collective behavior of a complex network can allow us to understand the potential damage to the s…
Information flow and causality as rigorous notions ab initio
X. San Liang
Information flow (or information transfer as may be called) the widely applicable general physics notion can be rigorously derived from first principles, rather than axiomatically…
Normalizing the causality between time series
X. San Liang
Recently, a rigorous yet concise formula has been derived to evaluate the information flow, and hence the causality in a quantitative sense, between time series. To assess the impo…
Causality between time series
X. San Liang
Given two time series, can one tell, in a rigorous and quantitative way, the cause and effect between them? Based on a recently rigorized physical notion namely information flow, w…
Remarkable evolutionary laws of absolute and relative entropies with dynamical systems
X. San Liang
The evolution of entropy is derived with respect to dynamical systems. For a stochastic system, its relative entropy evolves in accordance with the second law of thermodynamics…