arXiv subjects

Shaoqing Chen

Publications and source records attributed to Shaoqing Chen.

4 recordsLinked to original sources

arXiv2026

Remarks on Linear Growth of Vorticity Gradients and Support Diameters for 2D Euler Flow in Half-Plane

It has been conjectured that generic smooth solutions of the two-dimensional Euler equation exhibit linear growth of vorticity gradients. We prove an elementary arbitrary-background perturbation principle in the odd symmetric setting. More precisely, for any compactly supported nonnegative function in the half-plane, one can find an arbitrarily small smooth nonnegative perturbation whose associated solution undergoes linear-in-time filamentation in the quadrant. The main ingredients are the lower bound of the center of mass given by Iftimie-Sideris-Gamblin, and the velocity estimate for the sparse part to capture those slowly moving particles.

math.AP↗

arXiv2025

Bistability and Noise-Induced Evasion in Tumor-Immune Dynamics with Antigen Accumulation and Immune Escape

Tumor-immune interactions are shaped by both antigenic heterogeneity and stochastic perturbations in the tumor microenvironment, yet the mathematical mechanisms underlying immune phase transitions remain poorly understood. We propose a four-compartment dynamical model that incorporates antigen accumulation and immune escape mutations. Bifurcation analysis reveals bistability between immune surveillance and immune escape states, providing a mechanistic explanation for heterogeneous immune outcomes during tumor progression. In the multistable regime, the stable manifold of a saddle point partitions the state space into distinct basins of attraction, determining the long-term fate of the system. We further analyze how stochastic fluctuations in the tumor microenvironment perturb these separatrices, potentially triggering irreversible state transitions. By characterizing the critical noise intensity and estimating the tipping time, we establish a mathematical framework for assessing noise-induced transitions. The model further predicts that increasing tumor cell death can improve system resilience to stochastic perturbations, whereas stronger immune pressure may facilitate immune escape-highlighting the nonlinear and non-monotonic nature of tumor-immune dynamics.

q-bio.PE↗

arXiv2025

Fixed-budget simulation method for growing cell populations

Investigating the dynamics of growing cell populations is crucial for unraveling key biological mechanisms in living organisms, with many important applications in therapeutics and biochemical engineering. Classical agent-based simulation algorithms are often inefficient for these systems because they track each individual cell, making them impractical for fast (or even exponentially) growing cell populations. To address this challenge, we introduce a novel stochastic simulation approach based on a Feynman-Kac-like representation of the population dynamics. This method, named the Feynman-Kac-inspired Gillespie's Stochastic Simulation Algorithm (FKG-SSA), always employs a fixed number of independently simulated cells for Monte Carlo computation of the system, resulting in a constant computational complexity regardless of the population size. Furthermore, we theoretically show the statistical consistency of the proposed method, indicating its accuracy and reliability. Finally, a couple of biologically relevant numerical examples are presented to illustrate the approach. Overall, the proposed FKG-SSA effectively addresses the challenge of simulating growing cell populations, providing a solid foundation for better analysis of these systems.

q-bio.QM↗

arXiv2023

Hierarchical Terrain Attention and Multi-Scale Rainfall Guidance For Flood Image Prediction

With the deterioration of climate, the phenomenon of rain-induced flooding has become frequent. To mitigate its impact, recent works adopt convolutional neural network or its variants to predict the floods. However, these methods directly force the model to reconstruct the raw pixels of flood images through a global constraint, overlooking the underlying information contained in terrain features and rainfall patterns. To address this, we present a novel framework for precise flood map prediction, which incorporates hierarchical terrain spatial attention to help the model focus on spatially-salient areas of terrain features and constructs multi-scale rainfall embedding to extensively integrate rainfall pattern information into generation. To better adapt the model in various rainfall conditions, we leverage a rainfall regression loss for both the generator and the discriminator as additional supervision. Extensive evaluations on real catchment datasets demonstrate the superior performance of our method, which greatly surpasses the previous arts under different rainfall conditions.

cs.CV↗