427 papers
This study demonstrates that the sharp rise in retraction metadata tagged as computer-generated content from 2010 to 2025 is primarily driven by a few publishers' cleanup programs rather than reflecting the actual prevalence of AI in manuscripts, rendering these counts unsuitable for inferring causal effects of generative AI.
This paper proposes a blockwise joint conformal prediction framework that calibrates prediction sets over operational blocks rather than individual components, demonstrating on hydraulic system data that this approach achieves significantly higher simultaneous coverage for multi-component states compared to conventional marginal methods, albeit with backbone-dependent set sizes and strict reliance on exchangeability assumptions.
This paper describes the systematic development of the AD-DELTA2 checklist, an expert-endorsed extension of the DELTA2 guidance created through a multi-stage process of evidence review, Delphi surveys, and consensus meetings to improve the transparency and reproducibility of sample size reporting in adaptive design trial grant applications and protocols.
This paper presents an exact polynomial-time algorithm that computes the minimum cost of binary modifier-label changes required to alter the sign of an empirical additive treatment-by-modifier interaction, while explicitly clarifying that the method addresses a specific computational instance without establishing broader causal robustness or runtime superiority.
This paper argues that novelty prediction requires explicitly separating category absorption from category opening within a well-specified context, demonstrating that a simple combinatorial null model often outperforms complex rich-get-richer models like the Pitman-Yor process when the task structure is properly accounted for.
This study introduces a bias-aware spatial statistical framework using 273,566 geotagged Sina Weibo posts to quantify and map the uneven digital visibility of Guangzhou's urban categories, revealing that while emotional visibility is scale-dependent, architectural visibility exhibits consistent spatial clustering across modern and historic districts.
This study introduces the ratio-metric BC-IRR effect size as a method to bridge single-case and group experimental designs, demonstrating through a reanalysis of technology-aided interventions for students with autism that BC-IRR offers distinct interpretive advantages over traditional metrics while enabling the synthesis of diverse evidence types.
The paper introduces SynthGuard-ReleaseBench, a reproducible audit framework that establishes pre-locked criteria for evaluating synthetic tabular data releases by comparing real and synthetic workflows to provide finite-sample bounds and distinct evidence for specific use cases, populations, and threat models rather than making blanket claims about privacy or safety.
This paper introduces the four-parameter Inverse Lomax Frechet (ILF) distribution by compounding the Frechet and Inverse Lomax Generalized families, derives its key statistical properties and maximum likelihood estimators, and demonstrates its superior fitting performance through applications to two real-life datasets.
This paper presents a data-driven modeling framework for tracking and forecasting COVID-19 dispersion using minimax measurements on occurrence position data, with a focus on model verification, validation, and robustness to improve predictive capabilities.