Skill UI

A comprehensive guide detailing the strict structural constraints for submitting papers to INFOCOM. It covers crucial rules regarding the 9-page text budget (including appendices), the prohibition of external, decision-critical material, and best practices for organizing proofs, algorithms, and results to ensure the paper is fully judgeable from the PDF alone. Essential for academic researchers.

This skill helps authors evaluate whether their research—particularly in algorithms, optimization, or machine learning for Operations Research (OR)—meets the high computational standards required by the INFORMS Journal on Computing (IJOC). It acts as a fit/venue-selection tool, guiding authors on methodological rigor, computational evidence, and structuring their submission to maximize acceptance chances.

This guide helps researchers determine the primary focus of their work—whether it lies in Information Processing (algorithms, estimation) or Sensor Platforms, Tools, and Design Methods (hardware, deployment). It provides a structured methodology for selecting the most appropriate academic venue (e.g., SenSys, MobiSys, INFOCOM) based on the project's technical contribution and maturity.

This comprehensive guide details the rigorous methodological standards required for validating new statistical estimators, tests, or algorithms. It moves beyond simple causal inference, emphasizing foundational proofs of identification, regularity conditions, asymptotic consistency, and finite-sample robustness. It serves as a checklist for assuring the credibility and reliability of methods in academic statistical journals.

This guide assists authors in preparing research materials for submission to the Journal of Financial Intermediation (JFI), adhering to Elsevier Option C. It provides detailed instructions on managing, depositing, citing, and linking research data, code, and algorithms—whether the data is public, proprietary, or restricted (e.g., central bank registers). The goal is to ensure methodological rigor and reproducibility through the Data Statement.

A specialized tool designed to assess the suitability and refine the structure of a manuscript targeting the Journal of Industrial Engineering and Engineering Management (IJEM). It helps authors understand the journal's specific scope, preferred methodologies (modeling, algorithms, simulation), structural requirements, and common rejection pitfalls, optimizing the paper before submission.

This guide details the rigorous requirements for publishing in top-tier quantitative management journals. It specifically targets research outputs that utilize mathematical models, theoretical proofs, and computational algorithms (e.g., operations research, decision theory). It strongly advises against purely empirical surveys or case studies, emphasizing methodological contribution over policy implication.

A comprehensive guide for authors writing mathematical finance manuscripts. It specifies the rigorous standards for presenting figures, numerical tables, algorithms, and dependency maps, ensuring every visual exhibit supports a formal theorem or proposition. It advises on reporting numerical settings, avoiding empirical claims, and structuring evidence to aid theoretical comprehension rather than serving as standalone proof.

This comprehensive guide outlines the rigorous methodology required for designing high-impact evaluations of parallel systems and algorithms for top-tier conferences. It stresses that contributions must demonstrate the 'twin bar': both concurrency correctness (e.g., linearizability) and measurable scalability (e.g., strong vs. weak scaling). It covers best practices for establishing strong baselines, managing measurement hygiene (variance, pinning), and anticipating advanced reviewer scrutiny in HPC.

This guide helps researchers determine the correct academic venue for their work in parallel and concurrent computing. It clarifies the scope of PPoPP versus related conferences (CGO, PLDI, ASPLOS, HPCA, POPL, etc.) by asking: 'Is the parallelism the point, or is it the setting?' Use this framework to distinguish contributions focused on parallel algorithms, compiler optimization, concurrency theory, or hardware microarchitecture.

A comprehensive guide for researchers developing systems or algorithms, detailing the necessary level of evidence required for performance claims (e.g., bounds, real-world performance, learning guarantees). It emphasizes scientific rigor, statistical reporting (confidence intervals), assumption testing, and data provenance to ensure findings are robust and reproducible in academic settings.

This guide helps researchers determine the most appropriate venue (e.g., SIGMETRICS, NSDI, IMC, NeurIPS) for their computer systems performance evaluation research. It emphasizes that contributions must be rigorous performance-evaluation results—such as proven models, principled measurements, or guaranteed learning algorithms—rather than merely built systems or pure network measurements.