Skill UI

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.

A structured framework designed for authors writing comprehensive psychological reviews or theoretical statements. It guides users to transform a collection of research papers into a single, cohesive, and provocative argument by imposing a unifying structure—such as a taxonomy, theoretical model, or question sequence—that defines the field's central claim.

A comprehensive guide for authors submitting to PR&AI, detailing how to truthfully declare and provide verifiable artifacts (code, data, models) for peer review. It emphasizes honesty, consistency, and structuring a reproducible repository to enhance reviewer trust.

This guide outlines comprehensive best practices for ensuring the reproducibility of Machine Learning and Pattern Recognition research. It covers fixing random seeds, locking hardware/software dependencies (Python, CUDA, etc.), managing data splits, tracking pre-trained model versions, and structuring the required artifact package for external review. It is essential for building trust in published scientific results.

Provides a comprehensive guide to the entire academic publication review lifecycle, from initial submission to acceptance. It models the key stages—including editorial screening, expert review, revision, and final check—identifying critical decision criteria, common rejection reasons, and actionable leverage points for authors to significantly improve manuscript quality and navigate the publishing process.

A comprehensive guide for positioning computer science manuscripts for the Privacy Enhancing Technologies Symposium (PETS). It helps authors assess if their research matches the venue's core focus (e.g., differential privacy, anonymity) and provides a detailed framework for structuring empirical evidence against a clear adversary model to maximize acceptance chances.

A comprehensive guide for authors writing for high-impact journals like Physical Review Letters (PRL). It addresses the strict 'deductible budget' model where figures, equations, references, and text all count against a single limit. Provides systematic steps to triage content—such as moving derivations to Supplemental Material, compressing figures, and tightening prose—to ensure the core scientific claims remain clear and within publication guidelines.

This skill helps authors determine the suitability of their research paper (analytical, empirical, or behavioral) for prestigious Operations Management journals like Production and Operations Management (POM). It provides detailed guidance on required scope, methodological rigor (e.g., identification strategies, model assumptions), structural requirements, and common pitfalls to ensure maximum publication potential.

A comprehensive skill for advanced meta-analysis in psychology, designed to assess the reliability and sources of heterogeneity. It performs moderator/subgroup analyses (meta-regression) to explain effect size variation and runs multiple publication-bias diagnostics (e.g., Funnel plot, Egger's test, p-curve, selection models). Essential for rigorous academic reporting requiring robust evidence and sensitivity checks.

This guide outlines the rigorous methodology for developing theoretical arguments in psychological review papers. Instead of presenting raw data, the focus is on deriving specific, falsifiable predictions from a model's core assumptions. Techniques involve confronting these predictions against existing published evidence and rigorously comparing them head-to-head with rival theoretical models, ensuring logical and quantitative soundness.

A comprehensive guide for defining the scope, limitations, and identifiability of a scientific theory. It instructs researchers on how to explicitly state what phenomena a theory explains (in scope), where it fails (out of scope), and how to address structural identifiability for formal models, thereby enhancing the theory's falsifiability and scientific rigor.