Tootfinder

A Two-Stage Strategy for Mitosis Detection Using Improved YOLO11x Proposals and ConvNeXt Classification
Jie Xiao, Mengye Lyu, Shaojun Liu
https://arxiv.org/abs/2509.02627 https:…

A Two-Stage Strategy for Mitosis Detection Using Improved YOLO11x Proposals and ConvNeXt Classification
MIDOG 2025 Track 1 requires mitosis detection in whole-slide images (WSIs) containing non-tumor, inflamed, and necrotic regions. Due to the complicated and heterogeneous context, as well as possible artifacts, there are often false positives and false negatives, thus degrading the detection F1-score. To address this problem, we propose a two-stage framework. Firstly, an improved YOLO11x, integrated with EMA attention and LSConv, is employed to generate mitosis candidates. We use a low confidenc…

The Influence of Code Comments on the Perceived Helpfulness of Stack Overflow Posts
Kathrin Figl, Maria Kirchner, Sebastian Baltes, Michael Felderer
https://arxiv.org/abs/2508.19610

The Influence of Code Comments on the Perceived Helpfulness of Stack Overflow Posts
Question-and-answer platforms such as Stack Overflow have become an important way for software developers to share and retrieve knowledge. However, reusing poorly understood code can lead to serious problems, such as bugs or security vulnerabilities. To better understand how code comments affect the perceived helpfulness of Stack Overflow answers, we conducted an online experiment simulating a Stack Overflow environment (n=91). The results indicate that both block and inline comments are percei…

Learning Decomposed Contextual Token Representations from Pretrained and Collaborative Signals for Generative Recommendation
Yifan Liu, Yaokun Liu, Zelin Li, Zhenrui Yue, Gyuseok Lee, Ruichen Yao, Yang Zhang, Dong Wang
https://arxiv.org/abs/2509.10468

Learning Decomposed Contextual Token Representations from Pretrained and Collaborative Signals for Generative Recommendation
Recent advances in generative recommenders adopt a two-stage paradigm: items are first tokenized into semantic IDs using a pretrained tokenizer, and then large language models (LLMs) are trained to generate the next item via sequence-to-sequence modeling. However, these two stages are optimized for different objectives: semantic reconstruction during tokenizer pretraining versus user interaction modeling during recommender training. This objective misalignment leads to two key limitations: (i) …

Migration and spreading of a droplet driven by a chemical step
Zhuo Long, Peng Gao
https://arxiv.org/abs/2510.12514 https://arxiv.org/pdf/2510.12514…

Migration and spreading of a droplet driven by a chemical step
The chemical step is an elementary pattern in chemically heterogeneous substrates, featuring two regions of different wettability separated by a sharp border. Within the framework of lubrication theory, we investigate droplet motion and the contact-line dynamics driven by a chemical step, with the contact-line singularity addressed by the Navier slip condition. For both two-dimensional (2D) and three-dimensional (3D) droplets, two successive stages are identified: the migration stage, when the …