Skill UI

This guide provides guidelines for ensuring that academic proofs are independently verifiable, particularly for proofs-focused conferences. It covers creating self-contained proof appendices, meticulously defining all underlying assumptions in a 'model box' contract, and ensuring perfect synchronization between the short conference proceedings and the full arXiv version.

This guide details the rigorous and single-shot review process for ACM's Principles of Distributed Computing (PODC). Unlike venues with major revision rounds, PODC focuses entirely on the strength of the proof, the significance of the result, and the honesty of the model. It outlines the criteria (correctness, novelty, tightness) and emphasizes that all necessary work must be completed pre-submission, as there are no post-acceptance revision cycles.

This guide helps researchers navigate the complex landscape of academic publishing in distributed computing. It provides detailed criteria to differentiate between major specialized venues (like PODC, DISC, SPAA) and general systems conferences (ICDCS, OSDI). By analyzing the model, cost measure, and nature of the proof (e.g., provable guarantees, impossibility results), users can determine the best fit for their theoretical contribution, thereby avoiding common submission mistakes and maximizing impact.

This guide outlines the stringent structural requirements for writing papers on distributed systems theory, specifically for top-tier conferences like PODC. It details how to front-load the core arguments, define the model box rigorously (network, timing, fault model), structure proofs logically, and ensure the key results are visible within the critical first 10 pages.

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.

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.

This guide assists researchers in selecting the most appropriate and rigorous methodology for Production and Operations Management (POM) manuscripts. It covers various research tracks, including analytical modeling, stochastic process theory, game theory, causal empirical identification (DiD, IV, RD), behavioral experiments, simulation, and operations data science (ML/forecasting). It ensures the research method is tightly linked to the core operational question and possesses significant practical value.

Guides the rigorous development of theoretical models and research frameworks for Production and Operations Management (POM) manuscripts. It covers analytical, empirical, and behavioral tracks, ensuring that all assumptions and mechanisms are deeply grounded in real-world operational constraints and decision-making processes, thereby building a defensible academic argument.

This skill serves as a comprehensive router for developing academic manuscripts in Production and Operations Management (POM). It does not replace specialized skills but guides users through the entire scholarly process—from initial topic selection and theoretical modeling to data analysis, contribution framing, polishing, and submission. It ensures users maintain structural integrity and identify the correct next specialized skill to invoke, guiding the research journey from concept to publication.

This skill guides the rigorous defense of research design for demographic studies, particularly those linking population dynamics to development outcomes. It assists in selecting and justifying appropriate methods—such as life tables, decomposition, event-history modeling, and age-period-cohort analysis—and critically addresses the identification of causal links between population structure and development progress. It focuses on strengthening the argument by confronting rival hypotheses and establishing methodological rigor, without writing code.

A comprehensive guide for researchers detailing how to structure the 'Related Work' section for top-tier parallel computing conferences like PPoPP. It emphasizes 'delta-first' comparison, covering specific technical lanes (concurrent data structures, runtimes, GPU acceleration, memory models), and ensuring proper academic positioning against major parallel systems venues.

A comprehensive deep dive into the PPoPP conference review pipeline. This guide models the single-round, double-blind review process, detailing how submissions are evaluated by the Program Committee and External Review Committee. It covers critical aspects such as the author response rebuttal, automatic poster consideration, and the specific technical axes reviewers prioritize (e.g., scalability, concurrency correctness, baseline honesty) for parallel programming research.