activity
19952022
most citedFlocks, herds, and schools: A quantitative theory of flocking

1.6k citations · 3.1k across the 31 of their papers we have counts for

collaborators

31 papers

cond-mat.stat-mech2022

Free energy dissipation enhances spatial accuracy and robustness of Turing pattern in small reaction-diffusion systems

Dongliang Zhang, Chenghao Zhang, Qi Ouyang +1

Accurate and robust spatial orders are ubiquitous in living systems. In 1952, Alan Turing proposed an elegant mechanism for pattern formation based on spontaneous breaking of the s…

cond-mat.stat-mech2022★ 23 cited

The energy cost for flocking of active spins: the cusped dissipation maximum at the flocking transition

Qiwei Yu, Yuhai Tu

We study the energy cost of flocking in the active Ising model (AIM) and show that besides the energy cost for self-propelled motion, an additional energy dissipation is required t…

cond-mat.soft2022

Modeling bacterial flagellar motor with new structure information: Rotational dynamics of two interacting protein nano-rings

Yuansheng Cao, Tairan Li, Yuhai Tu

In this article, we develop a mathematical model for the rotary bacterial flagellar motor (BFM) based on the recently discovered structure of the stator complex (MotAMotB).…

cond-mat.stat-mech2022★ 10 cited

State-space renormalization group theory of nonequilibrium reaction networks: Exact solutions for hypercubic lattices in arbitrary dimensions

Qiwei Yu, Yuhai Tu

Nonequilibrium reaction networks (NRNs) underlie most biological functions. Despite their diverse dynamic properties, NRNs share the signature characteristics of persistent probabi…

physics.bio-ph2021★ 1 cited

Multiplicative noise underlies Taylor's law in protein concentration fluctuations in single cells

Alberto Stefano Sassi, Mayra Garcia-Alcala, Philippe Cluzel +1

Protein concentration in a living cell fluctuates over time due to noise in growth and division processes. From extensive single-cell experiments by using E. coli strains with diff…

cs.LG2021★ 1 cited

Phases of learning dynamics in artificial neural networks: with or without mislabeled data

Yu Feng, Yuhai Tu

Despite tremendous success of deep neural network in machine learning, the underlying reason for its superior learning capability remains unclear. Here, we present a framework base…