Skill UI

A comprehensive guide for formulating rigorous Operations Research (OR) manuscripts. It covers defining mathematical models (optimization, stochastic, simulation), structuring claims (theorems, propositions), and rigorously justifying assumptions. Essential for transforming practical problems into mathematically provable, publication-ready academic work.

This guide helps researchers determine if their problem constitutes a rigorous Operations Research (OR) or Management Science (MS) methodological contribution. It provides a comprehensive fit test covering method novelty, mathematical rigor (proofs, guarantees), and significance to the OR community. Furthermore, it guides users on selecting the precise editorial area (e.g., Optimization, Stochastic Models, Simulation) for submission.

This guide outlines the rigorous standards for presenting 'evidence' in highly theoretical computing venues. It emphasizes that results must be supported by deep mathematical proofs, establishing matching upper and lower bounds (tightness), and meticulously auditing all underlying model assumptions (e.g., timing, failure modes). It shifts focus from empirical benchmarks to formal correctness.

A comprehensive audit checklist for authors preparing submissions for top-tier academic conferences like PODC. This guide covers critical submission stages, including the two-step abstract registration, selecting the appropriate paper track (Regular vs. Brief Announcement), adhering to the 10-page merits rule, maintaining double-blind anonymity, and ensuring the mathematical rigor of the proof. Use this before final submission.

This comprehensive guide provides a rigorous framework for auditing the technical and mathematical evidence in theoretical computer science papers. It instructs authors on how to ensure that every claim—especially those regarding optimality or complexity bounds—is supported by matching lower and upper bounds, explicit assumptions, and consistent cost models. It emphasizes that the evidence must be robust proofs, not merely empirical benchmarks.

This guide details the rigorous and multi-stage academic review process for submissions to the PODS conference. It moves beyond simple accept/reject decisions, explaining the importance of multi-cycle revisions, shepherded rounds, and the strict focus on mathematical proofs and theoretical correctness. It helps authors understand how to strategically strengthen their work for high-impact, theory-based venues.

This guide details the specific rules for assembling supplementary material for PODS submissions. It covers how to correctly partition content between the main 15-page body and the appendix, ensuring all mathematical proofs are complete, self-contained, and included within a single PDF file. It also addresses double-anonymous hygiene and page budget discipline.

A rigorous guide detailing the advanced writing standards required for top-tier database theoretical papers (e.g., ACM PODS). It emphasizes precision in defining models, stating theorems, matching upper and lower bounds, and structuring the narrative to ensure mathematical completeness and verifiability for peer review.

A comprehensive style guide and checklist for writing and refining long-form academic articles for 'Pattern Recognition and Artificial Intelligence' (PR&AI). It covers the entire publishing workflow, from mastering the structured introduction (Problem-Gap-Contribution) and writing four-element abstracts, to adhering to rigorous standards for citations (GB/T 7714), figures, and mathematical notation in AI research.

This guide assists researchers in the rigorous construction of psychological theories and formal models for academic manuscripts. It provides a step-by-step methodology to move from initial intuition to an explicit, testable theoretical structure. Users learn to define core assumptions, specify underlying causal mechanisms, and formalize models using mathematical equations or precise conceptual frameworks, ensuring the derived model behavior is the central contribution.

This skill guides authors on strategically positioning data mining research for the SIAM International Conference on Data Mining (SDM). It helps adjust manuscript framing, emphasizing mathematical rigor, statistical depth, and algorithmic contribution suitable for top Computer Science venues. Use it for venue selection, understanding reviewer expectations, and structuring evidence to meet the high standards of SIAM-style research.

This skill provides a comprehensive framework for ensuring the mathematical verifiability of advanced algorithms papers (like those submitted to SODA). It guides authors to establish a complete, checkable argument, documenting every claim, explicit model assumption, constant status, and providing machine-checkable steps. It moves beyond simple claims of accuracy to mandate full, reproducible proof structures.