Skill UI

This guide outlines advanced methodologies for achieving high reproducibility in networked-systems research. It details capturing crucial provenance ledgers—including topology, traffic traces, system configuration, and run details—to ensure that published results are verifiable and not dependent on ephemeral experimental conditions. It emphasizes characterizing inherent variance rather than demanding absolute determinism, providing a rigorous framework for academic publication.

This guide provides a framework for designing and auditing the experimental rigor of academic papers, particularly in systems and programming languages. It emphasizes the crucial link between a claim type (e.g., 'Faster') and the required evidence type (e.g., benchmarks, user studies, formal proofs). Learn how to establish robust baselines, define clear workload rules, size experiments for conference cycles, and ensure statistical compliance for maximum impact.

A comprehensive skill for Particle Image Velocimetry (PIV) analysis. It processes pairs of images to measure 2D fluid velocity fields. Functions include cross-correlation, vector validation, outlier replacement, and calculating derived metrics like vorticity and strain rate. Essential for experimental fluid dynamics visualization and analysis.

This guide helps researchers evaluate whether their high-impact optics or photonics manuscript meets the stringent criteria of Optica. It covers ideal scope (e.g., nanophotonics, quantum optics), required experimental rigor (quantitative measurements, state-of-the-art benchmarking), article structure (concise, load-bearing figures), and official submission guidelines, ensuring the work is framed for broad scientific interest.

This guide is a comprehensive tool for assessing the suitability of academic manuscripts for high-impact journals like Organization Science. It details the journal's scope, methodological expectations (including computational, qualitative, and experimental rigor), and the requirement for a clear, cross-disciplinary theoretical contribution. It helps authors refine their work, re-frame their arguments, and select the optimal academic venue before submission.

A comprehensive guide for selecting and defending appropriate research designs—including qualitative, quantitative, experimental, and computational methods—for organizational science manuscripts. It emphasizes matching the method to the theoretical contribution and level of analysis, rather than solely focusing on causal identification. Learn how to structure your argument, ensure transparency, and build robust mechanism evidence.

A comprehensive guide for structuring academic system evaluation papers (e.g., OSDI). It dictates how to frame experiments around core research questions, select robust baselines, utilize realistic workloads (including adversarial tests), and apply rigorous measurement disciplines (e.g., reporting distributions, p99s) while maintaining conciseness within strict page limits.

Provides a rigorous framework for ensuring the reproducibility of systems research presented at conferences like OSDI. It mandates maintaining a comprehensive, machine-readable provenance ledger that records hardware specifications, software dependencies, workload traces, and measurement policies throughout the entire experimental lifecycle, preventing 'artifact drift' and ensuring verifiable results.

A comprehensive guide for drafting a structured, double-blind academic rebuttal for conferences (like PerCom). This skill focuses on addressing explicit reviewer questions using only evidence already present in the submitted paper, thereby avoiding the promise of new experiments. It covers maintaining anonymity, structuring arguments, and focusing on core technical dimensions like cross-subject evaluation and metric soundness.

A comprehensive guide for designing and auditing rigorous empirical evaluations for sensing systems, particularly Human Activity Recognition (HAR). It emphasizes best practices such as leave-one-subject-out cross-validation, using real human subjects under diverse conditions, reporting event-level metrics (e.g., Macro-F1) over raw accuracy, establishing fair baselines, and mitigating data leakage (e.g., subject or session leakage). This ensures that reported results are robust, generalizable, and defensible against academic skepticism.

This comprehensive guide assists authors in assessing whether their manuscript, focusing on condensed matter or materials physics, is appropriate for Physical Review B (PRB). It details the journal's specific scope, the required scientific rigor (e.g., computational transparency, experimental characterization), and the house style for maximizing submission success within this specialized academic domain.

This guide provides best practices for designing and auditing academic evaluations of programming language features and compilers, following standards used in venues like PLDI. It covers selecting defensible benchmark suites, establishing strong baselines, performing targeted ablations, and measuring multiple performance metrics (runtime, compile time, memory) to ensure research claims are reproducible and scientifically sound.