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20212025
most citedTheoretical foundations of studying criticality in the brain

43 citations · 72 across the 17 of their papers we have counts for

collaborators

17 papers

stat.ME2025

Conformalized Multiple Testing under Unknown Null Distribution with Symmetric Errors

Yang Tian, Zinan Zhao, Wenguang Sun

This article addresses a fundamental concern, first raised by Efron (2004), regarding the selection of null distributions in large-scale multiple testing. In modern data-intensive…

physics.flu-dyn2024

Fluid-network relations: decay laws meet with spatial self-similarity, scale-invariance, and control scaling

Yang Tian, Pei Sun, Yizhou Xu

Diverse implicit structures of fluids are discovered lately, providing opportunities to study the physics of fluids applying network analysis. Although considerable works devote to…

cond-mat.stat-mech2024

Fast renormalizing the structures and dynamics of ultra-large systems via random renormalization group

Yang Tian, Yizhou Xu, Pei Sun

Criticality and symmetry, studied by the renormalization groups, lie at the heart of modern physics theories of matters and complex systems. However, surveying these properties wit…

q-bio.NC2023★ 43 cited

Theoretical foundations of studying criticality in the brain

Yang Tian, Zeren Tan, Hedong Hou +6

Criticality is hypothesized as a physical mechanism underlying efficient transitions between cortical states and remarkable information processing capacities in the brain. While co…

cond-mat.stat-mech2023★ 1 cited

Simplex path integral and simplex renormalization group for high-order interactions

Aohua Cheng, Yunhui Xu, Pei Sun +1

Modern theories of phase transitions and scale-invariance are rooted in path integral formulation and renormalization group (RG). Despite the applicability of these approaches on s…

cond-mat.stat-mech2023

Laplacian dynamics of convergent and divergent swarm behaviors

Yang Tian, Yunhui Xu, Pei Sun

Swarming phenomena are ubiquitous in various physical, biological, and social systems, where simple local interactions between individual units lead to complex global patterns. A c…

cond-mat.stat-mech2023

Thermodynamics of percolation in interacting systems

Yizhou Xu, Pei Sun, Yang Tian

Interacting systems can be studied as the networks where nodes are system units and edges denote correlated interactions. Although percolation on network is a unified way to model…

cs.LG2023★ 1 cited

Koopman neural operator as a mesh-free solver of non-linear partial differential equations

Wei Xiong, Xiaomeng Huang, Ziyang Zhang +3

The lacking of analytic solutions of diverse partial differential equations (PDEs) gives birth to a series of computational techniques for numerical solutions. Although numerous la…

cs.LG2023★ 1 cited

KoopmanLab: machine learning for solving complex physics equations

Wei Xiong, Muyuan Ma, Xiaomeng Huang +3

Numerous physics theories are rooted in partial differential equations (PDEs). However, the increasingly intricate physics equations, especially those that lack analytic solutions…

cs.LG2022★ 1 cited

Statistical Physics of Deep Neural Networks: Initialization toward Optimal Channels

Kangyu Weng, Aohua Cheng, Ziyang Zhang +2

In deep learning, neural networks serve as noisy channels between input data and its representation. This perspective naturally relates deep learning with the pursuit of constructi…

cs.CV2022★ 3 cited

Video Interpolation by Event-driven Anisotropic Adjustment of Optical Flow

Song Wu, Kaichao You, Weihua He +5

Video frame interpolation is a challenging task due to the ever-changing real-world scene. Previous methods often calculate the bi-directional optical flows and then predict the in…

q-bio.NC2022★ 15 cited

Bridging the information and dynamics attributes of neural activities

Yang Tian, Guoqi Li, Pei Sun

The brain works as a dynamic system to process information. Various challenges remain in understanding the connection between information and dynamics attributes in the brain. The…

cs.SI2022

Network comparison via encoding, decoding, and causality

Yang Tian, Hedong Hou, Guangzheng Xu +2

Quantifying the relations (e.g., similarity) between complex networks paves the way for studying the latent information shared across networks. However, fundamental relation metric…

cond-mat.stat-mech2022

A unified theory of information transfer and causal relation

Yang Tian, Hedong Hou, Yaoyuan Wang +2

Information transfer between coupled stochastic dynamics, measured by transfer entropy and information flow, is suggested as a physical process underlying the causal relation of sy…

q-bio.PE2022

Self-organized critical dynamics of RNA virus evolution

Xiaofei Ge, Kaichao You, Zeren Tan +3

RNA virus (e.g., SARS-CoV-2) evolves in a complex manner. Studying RNA virus evolution is vital for understanding molecular evolution and medicine development. Scientists lack, how…

physics.bio-ph2021★ 7 cited

Information Evolution in Complex Networks

Yang Tian, Guoqi Li, Pei Sun

Many biological phenomena or social events critically depend on how information evolves in complex networks. However, a general theory to characterize information evolution is yet…

physics.data-an2021

Fourier-domain transfer entropy spectrum

Yang Tian, Yaoyuan Wang, Ziyang Zhang +1

We propose the Fourier-domain transfer entropy spectrum, a novel generalization of transfer entropy, as a model-free metric of causality. For arbitrary systems, this approach syste…