arXiv subjects

Xinxiang Chen

Publications and source records attributed to Xinxiang Chen.

3 recordsLinked to original sources

arXiv2026

From real polymers to random graphs: percolation thresholds in associative polymer solutions

Sol-gel transitions are ubiquitous in soft matter and biological systems, yet their thresholds are often poorly captured by classical Flory-Stockmayer theory because spatial organization and loop formation are neglected. Here, we combine molecular dynamics simulations with random graph and random geometric graph models to determine the respective roles of topology and geometry in reversible associative polymer solutions. We show that a coordinate-free random graph recovers the mean-field Flory-Stockmayer limit, whereas a random geometric graph quantitatively reproduces the shifted percolation thresholds observed in molecular dynamics simulations when the detection radius is chosen according to the polymer conformational size. This geometric mapping remains quantitatively valid for linear chains with regularly spaced binding sites over a broad range of chain stiffness. At the microscopic level, we identify primary loops formed already in the pre-gel regime as the dominant source of the deviation from mean-field predictions. Near the gel point, the cluster-size statistics obtained from simulations and random geometric graphs are consistent with the universality class of three-dimensional percolation. These results establish random geometric graphs as a minimal predictive framework for describing topological transitions in reversible associative polymer solutions and show that gelation and network formation can be inferred directly from single-chain conformational information.

cond-mat.soft↗

arXiv2025

Sol-gel transition in heteroassociative RNA-protein solutions: A quantitative comparison of coarse-grained simulations and the Semenov-Rubinstein theory

Protein RNA-binding domains selectively interact with specific RNA sites, a key interaction that determines the emergent cooperative behaviors in RNA-protein mixtures. Through molecular dynamics simulations, we investigate the impact of the specific binding interactions on the phase transitions of an examplary RNA-protein system and compare it with predictions of the Semenov-Rubinstein theory of associative polymers. Our findings reveal a sol-gel (percolation) transition without phase separation, characterized by double reentrant behavior as the RNA or protein concentration increases. We highlight the crucial role of bridge formations in driving these transitions, particularly when binding sites are saturated. The theory quantitatively predicts the binding numbers at equilibrium in the semidilute regime, but it significantly overestimates the size of the concentration range where percolation is observed. This can partly be traced back to the fact that the mean-field assumption in the theory is not valid in the dilute regime, and that the theory neglects the existence of cycles in the connectivity graph of the percolating cluster at the sol-gel transition. Our study enriches the understanding of RNA-protein phase behaviors, providing valuable insights for the interpretation of experimental observations.

cond-mat.soft↗

arXiv2024

Contextuality Helps Representation Learning for Generalized Category Discovery

This paper introduces a novel approach to Generalized Category Discovery (GCD) by leveraging the concept of contextuality to enhance the identification and classification of categories in unlabeled datasets. Drawing inspiration from human cognition's ability to recognize objects within their context, we propose a dual-context based method. Our model integrates two levels of contextuality: instance-level, where nearest-neighbor contexts are utilized for contrastive learning, and cluster-level, employing prototypical contrastive learning based on category prototypes. The integration of the contextual information effectively improves the feature learning and thereby the classification accuracy of all categories, which better deals with the real-world datasets. Different from the traditional semi-supervised and novel category discovery techniques, our model focuses on a more realistic and challenging scenario where both known and novel categories are present in the unlabeled data. Extensive experimental results on several benchmark data sets demonstrate that the proposed model outperforms the state-of-the-art. Code is available at: https://github.com/Clarence-CV/Contexuality-GCD

cs.CV↗