Skill UI

A comprehensive guide for finalizing academic manuscripts in finance and economics. It enforces strict formatting rules for regression tables (three-line style, standard error presentation), requires detailed variable definition tables with calculation formulas, mandates specific data source citation, and ensures high-resolution, professionally designed figures, moving beyond simple copy-pasting of results.

A comprehensive guide on structuring and writing the 'Related Work' section for elite computer vision conferences like CVPR. It addresses the high velocity of academic literature, teaching advanced methods like defining explicit technical deltas, structuring arguments by research question, maintaining citation hygiene, and handling complex reviewer feedback on originality and concurrency.

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.

A comprehensive skill guide designed to help researchers assess the suitability of their economics manuscripts for submission to 《Economic Review》. It covers journal positioning, required empirical rigor, structural expectations (abstract, citation style), common rejection pitfalls, and advice on necessary revisions and alternative journals. This tool is crucial for navigating academic publishing standards in Chinese economics.

This skill guides researchers on how to position a new econometric theorem by rigorously comparing it against the existing theoretical literature frontier. Instead of a general survey, it requires defining the 'delta'—the specific element (e.g., weaker assumptions, broader scope, generalization) that distinguishes the new result from prior work, using formal APA author-date citations. Essential for high-level academic manuscripts.

A comprehensive guide detailing how to structure the 'Related Work' section for submissions to top data mining conferences like ICDM. It emphasizes positioning the research based on core mechanisms and mining tasks, rather than simply listing results. The guide also provides critical advice on maintaining triple-blind anonymity, verifying citation venues (avoiding misattribution traps), and ensuring the novelty claim is distilled into a single, quotable mechanism-contrast sentence.

Provides strict guidelines for writing the Related Work section in theoretical database papers. It teaches authors how to precisely position their novel results against existing theorems (ICDT/PODS canon), covering complex topics like finite model theory, query complexity, and proper academic citation etiquette, ensuring genuine novelty is highlighted.

This guide provides an advanced framework for positioning empirical research findings against established academic literature, particularly for high-impact journals. It instructs authors to move beyond simple citation by quantitatively reconciling differences in estimates (magnitude, sign, significance). By explicitly identifying disparities in sample design, population, or underlying mechanisms, this methodology significantly strengthens the paper's academic rigor and perceived contribution.

Provides comprehensive guidelines for authors submitting to the Journal of Econometrics (JoE). It details the required best practices for data citation (using the Elsevier `[dataset]` format), ensuring methodological reproducibility (e.g., Monte Carlo simulations), and preparing estimators as runnable code packages (R, Python, etc.). This guide is essential for meeting rigorous academic publication standards and passing peer review.

A comprehensive pre-deadline audit checklist for submitting papers to the MLSys conference. It guides authors through track-specific requirements (Research vs. Industrial), formatting compliance (10-page limit, two-column layout), maintaining double-blind anonymity, and correctly handling appendices and citations to ensure readiness before the submission deadline.

A comprehensive guide for maintaining consistency and completeness of references for PNAS Nexus submissions. It details the critical differences between the format-neutral requirements at submission time and the final numbered style required for publication, emphasizing the proper citation of datasets, software, and preprints.

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.