Accurately identifying disaster-affected areas is crucial for data-driven disaster resilience. In response, we introduce Georelating, a task that infers affected areas from textual reports containing complex locative expressions, moving beyond traditional geoparsing approaches that rely on explicit point locations. Georelating instead combines resolving unnamed regions and reasoning about spatial relations to represent event-affected areas within standardized Discrete Global Grid Systems (DGGSs).

Colorectal cancer (CRC) is a significant global health concern, and early detection through screening plays a critical role in reducing mortality. While deep learning models have shown promise in improving polyp detection, classification, and segmentation, their generalization across diverse clinical environments, particularly with out-of-distribution (OOD) data, remains a challenge. Multi-center datasets like PolypGen have been developed to address these issues, but their collection is costly and time-consuming. Traditional data augmentation techniques provide limited variability, failing to capture the complexity of medical images. Diffusion models have emerged as a promising solution for generating synthetic polyp images, but the image generation process in current models mainly relies on segmentation masks as the condition, limiting their ability to capture the full clinical context. To overcome these limitations, we propose a Progressive Spectrum Diffusion Model (PSDM) that integrates diverse clinical annotations-such as segmentation masks, bounding boxes, and colonoscopy reports-by transforming them into compositional prompts. These prompts are organized into coarse and fine components, allowing the model to capture both broad spatial structures and fine details, generating clinically accurate synthetic images. By augmenting training data with PSDM-generated samples, our model significantly improves polyp detection, classification, and segmentation. For instance, on the PolypGen dataset, PSDM increases the F1 score by 2.12% and the mean average precision by 3.09%, demonstrating superior performance in OOD scenarios and enhanced generalization.

Microblog content (e.g., Tweets) is noisy due to its informal use of language and its lack of contextual information within each post.To tackle these challenges, state-of-the-art microblog classification models rely on pre-training language models (LMs).However, pre-training dedicated LMs is resource-intensive and not suitable for small labs.Supervised contrastive learning (SCL) has shown its effectiveness with small, available resources.In this work, we examine the effectiveness of fine-tuning transformer-based language models, regularized with a SCL loss for English microblog classification.Despite its simplicity, the evaluation on two English microblog classification benchmarks (TweetEval and Tweet Topic Classification) shows an improvement over baseline models.The result shows that, across all subtasks, our proposed method has a performance gain of up to 11.9 percentage points.All our models are open source.