arXiv subjects

Qianrun Wu

Publications and source records attributed to Qianrun Wu.

2 recordsLinked to original sources

arXiv2026

On Partition Functions for Time-Inhomogeneous Branching Random Walks

We establish the phase transition and universality for the partition function of time inhomogeneous branching random walks (BRWs) with decreasing variance increment,a model related to two dimensional directed polymers. By modifying Berestycki's GMC framework (removing martingale property dependency) and adapting it to discrete BRWs, we prove that the critical value of time inhomogeneous BRWs coincides with that of time homogeneous ones, and the partition functions converge in L1 in the subcritical regime. We also extend the universality at the critical parameter, showing the same decay rate of partition functions. Our approach reveals the potential for such framework in GMC, which provides a new technical path for martingale free processes and random fields beyond log correlated. Finally, we raise some open problems related to GMC beyond log correlations, branching Brownian motions and directed polymers.

math.PR↗

arXiv2020

DA-RefineNet:A Dual Input Whole Slide Image Segmentation Algorithm Based on Attention

Automatic medical image segmentation has wide applications for disease diagnosing. However, it is much more challenging than natural optical image segmentation due to the high-resolution of medical images and the corresponding huge computation cost. The sliding window is a commonly used technique for whole slide image (WSI) segmentation, however, for these methods based on the sliding window, the main drawback is lacking global contextual information for supervision. In this paper, we propose a dual-inputs attention network (denoted as DA-RefineNet) for WSI segmentation, where both local fine-grained information and global coarse information can be efficiently utilized. Sufficient comparative experiments are conducted to evaluate the effectiveness of the proposed method, the results prove that the proposed method can achieve better performance on WSI segmentation compared to methods relying on single-input.

cs.CV↗