Skill UI

A comprehensive guide for positioning novel research in elite computer vision and AI conferences (e.g., ECCV). It details how to structure the related work section by tracing academic lineage, handling concurrent work, verifying citation hygiene across venues (CVPR, ICCV, ECCV), and transforming simple bibliographies into strong, argumentative prose. Essential for improving novelty visibility.

Provides a comprehensive framework for conducting and reporting robustness checks in health economics studies. It guides authors to organize findings by addressing specific threats to the core claim (e.g., selection bias, concurrent policy changes, measurement error) rather than simply appending multiple specifications. Essential for ensuring results are stable and defensible for top-tier journal submissions.

This guide directs authors on how to structure the related work section for academic papers in database theory (e.g., for venues like PODS, ICDT). It emphasizes a 'delta-first' approach, requiring authors to compare their specific *results* (theorems, bounds, dichotomies) directly against the nearest prior *results*, rather than simply cataloging related citations. It also covers complex rules regarding self-citation, anonymity, and overlap declaration with concurrent or prior-version work.

A comprehensive guide for authors submitting to top-tier database conferences (like SIGMOD) on how to position their research. It covers deep-history diligence, managing concurrent work, and structuring the related-work section using contrast-form writing to establish clear novelty claims against multiple literature lines, including industrial systems and theoretical boundaries.