Papers where the original authors have engaged with our plain-language explanation.
For every paper on this page, at least one of the original authors has seen our plain-language explanation and engaged with it — either confirming it reads accurately or requesting corrections that we then applied. An endorsement does not mean the authors formally approve every sentence, but it does mean the explanation has passed the eyes of the people who wrote the paper.
1336 papers reviewed by authors · 501–510 / 1336
Using a dynamic spatial model of 20 Sub-Saharan African countries from 2006 to 2019, this study demonstrates that improving transport infrastructure quality significantly boosts both domestic and neighboring intra-regional trade in the short and long term, prompting a recommendation for supranational institutions to manage infrastructure investment.
Using the Electrostatic Levitation Furnace aboard the International Space Station, researchers measured the densities and molar volumes of CaO–SiO2–FeO melts under microgravity, revealing that density increases with FeO content while molar volume generally aligns with additive-model predictions despite slight deviations at higher FeO concentrations.
This paper introduces UMA-Inverse, a compact, ligand-conditioned inverse folding model that utilizes a dense pair-representation encoder to propagate ligand information beyond the binding interface, achieving competitive performance against LigandMPNN while providing new insights into how all-pair encoders distribute ligand signals.
Using a novel administrative dataset and a quasi-experimental design based on Small Business Development Center closures and openings, this paper provides causal evidence that reducing geographic distance to publicly funded advisory services significantly increases firm engagement, which in turn leads to substantial gains in annual revenue and employment for US small businesses.
This paper proposes the Focus-Aware Adaptive Dense Subgraph (FAADS) framework, which efficiently mines high-quality dense subgraphs in dynamic multilayer networks through an incremental update mechanism, achieving significant speed improvements over state-of-the-art methods while maintaining near-optimal density quality.
This paper introduces CKTN, the first multilingual corpus and benchmark for Vietnam's Cham, Khmer, and Tay-Nung languages, demonstrating that standard adaptation methods fail due to script and contact differences and proposing a script-aware pretraining recipe that significantly improves encoder performance and semantic generalization.
This paper proposes a risk-oriented verification and validation framework that transforms hallucination detection in public-sector LLMs from a purely technical challenge into a governance issue by introducing a composite risk index to guide operational decisions like deferral or human escalation, thereby ensuring legal certainty and administrative accountability.
This paper proposes an intelligent, real-time drone detection and early warning framework for critical infrastructure surveillance using the YOLO26 deep learning model, which is trained on a custom multi-scenario database and evaluated against baseline models to demonstrate superior accuracy and speed in identifying small, distant, and challenging drone targets.
This study demonstrates that a LightGBM model trained on optical light curve features from GAIA and Pan-STARRS data, with dimensionality reduced via BoostBoruta, can effectively classify active galactic nuclei into blazars and non-blazars with approximately 86% accuracy, even when applied to unknown candidates through self-learning.
This study utilizes comparative transcriptomics of SARS-CoV-2, Influenza A, and RSV infections in human airway epithelial cells to reveal that these respiratory viruses elicit largely distinct, virus-specific host transcriptional responses with minimal shared gene signatures, suggesting a need for pathogen-targeted therapeutic and diagnostic strategies.