Skill UI

This guide provides rigorous advice for researchers on how to appropriately incorporate computation, case analysis, and constructions into top-tier theoretical computer science papers. It distinguishes between proof-bearing computations, honestly reported discoveries (including those found by AI), and mere illustrations, ensuring submissions maintain academic rigor while avoiding the pitfalls of empirical benchmarking.

This skill guides authors in determining the suitability of their manuscripts for the journal Genome Biology. It details the journal's scope, which requires integrating large-scale genomic or epigenomic data with novel computational methods or statistical frameworks. It emphasizes that successful submissions must provide both methodological advances and meaningful biological insights, adhering strictly to standards like open-source code, public data deposition (GEO, SRA, ENCODE), and rigorous benchmarking.

A comprehensive guide for positioning and refining computer vision manuscripts for the IEEE/CVF International Conference on Computer Vision (ICCV). This skill helps authors assess conference fit, structure evidence, perform benchmarking, and adopt the necessary narrative for a flagship CV submission, covering topics from foundation models to video recognition.

This comprehensive guide instructs researchers on how to design rigorous and credible evaluations for Machine Learning systems. It covers critical areas such as workload selection, baseline comparison discipline, reporting four key coordinates (throughput, latency tails, memory, cost), structuring ablations, and generating scaling evidence. It ensures measurements meet academic benchmarking standards.

Provides comprehensive guidelines for achieving high reproducibility in Machine Learning Systems (MLSys) performance claims. It details how to rigorously control sources of non-determinism (e.g., thermal noise, random seeds) and pin the entire technical stack—from hardware and drivers to frameworks and serving policies—to ensure results can be validated by third parties.

This comprehensive skill guides authors on whether their enabling technology, platform, or method is suitable for Nature Biotechnology. It analyses the journal's specific focus on translational potential, broad applied impact, and industrial relevance. It covers best practices for advanced topics including genome editing, synthetic biology, biomanufacturing, and AI applications, emphasizing the requirement for robust in vivo proof and comprehensive benchmarking.

A comprehensive guide for authors submitting new methods, tools, or technologies to Nature Methods. It details the journal's specific requirements, including the mandatory criteria for generalizability, rigorous benchmarking against state-of-the-art alternatives, and the required structure and house style for achieving publication success in life sciences.

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 comprehensive guide outlines best practices for designing data systems evaluations, ensuring results are robust, reproducible, and convincing to expert reviewers. It covers workload realism, baseline fairness, necessary ablation studies, and advanced metric visualization techniques (e.g., sweeping curves, failure analysis) required for top-tier data systems research.

A comprehensive guide for authors submitting to high-impact scientific journals. It outlines the essential principles for structuring experiments and presenting evidence, emphasizing claims-evidence matching, fair benchmarking, differentiating result sources (simulation, hardware, theory), and ensuring statistical rigor. Covers best practices across computer science, control, communication, and microelectronics.

This guide clarifies the role of computation in theoretical computer science submissions, particularly for venues that prioritize theorems over empirical data. It distinguishes between legitimate forms of evidence—such as certified proof steps, constructive object discoveries, and illustrative examples—and inappropriate performance benchmarking. It helps researchers structure their work to align with theorem-based research, ensuring computational elements enhance the core theoretical contribution.

This skill guides authors on positioning information retrieval (IR) work for the Text REtrieval Conference (TREC). It functions as a venue-selection and manuscript re-framing tool, ensuring the submission aligns with TREC's focus on shared evaluation tracks, test collections, and NIST-style benchmarking. It helps authors structure evidence, understand reviewer expectations, and distinguish the work from general CS conference submissions.