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Resolution scaling governs DINOv3 transfer performance in chest radiograph classification
Soroosh Tayebi Arasteh, Mina Shaigan, Christiane Kuhl, Jakob Nikolas Kather, Sven Nebelung, Daniel Truhn
https://arxiv.org/abs/2510.07191

Resolution scaling governs DINOv3 transfer performance in chest radiograph classification
Self-supervised learning (SSL) has advanced visual representation learning, but its value in chest radiography, a high-volume imaging modality with fine-grained findings, remains unclear. Meta's DINOv3 extends earlier SSL models through Gram-anchored self-distillation. Whether these design choices improve transfer learning for chest radiography has not been systematically tested. We benchmarked DINOv3 against DINOv2 and ImageNet initialization across seven datasets (n>814,000). Two representati…

A Formal Framework for Fluency-based Multi-Reference Evaluation in Grammatical Error Correction
Eitan Klinger, Zihao Huang, Tran Minh Nguyen, Emma Jayeon Park, Yige Chen, Yang Gu, Qingyu Gao, Siliang Liu, Mengyang Qiu, Jungyeul Park
https://arxiv.org/abs/2510.06749

A Formal Framework for Fluency-based Multi-Reference Evaluation in Grammatical Error Correction
Evaluating grammatical error correction requires metrics that reflect the diversity of valid human corrections rather than privileging a single reference. Existing frameworks, largely edit-based and English-centric, rely on rigid alignments between system and reference edits, limiting their applicability in multilingual and generative settings. This paper introduces a formal framework for \textit{fluency-based multi-reference evaluation}, framing $n$-gram similarity as an aggregation problem ov…

Regular Expression Indexing for Log Analysis. Extended Version
Ling Zhang, Shaleen Deep, Jignesh M. Patel, Karthikeyan Sankaralingam
https://arxiv.org/abs/2510.10348 https://

Regular Expression Indexing for Log Analysis. Extended Version
In this paper, we present the design and architecture of REI, a novel system for indexing log data for regular expression queries. Our main contribution is an $n$-gram-based indexing strategy and an efficient storage mechanism that results in a speedup of up to 14x compared to state-of-the-art regex processing engines that do not use indexing, using only 2.1% of extra space. We perform a detailed study that analyzes the space usage of the index and the improvement in workload execution time, un…