Cross-lingual prompt exploration improves factual recall and consistency in LLMs across 17 languages more efficiently than native-language scaling.
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The paper establishes a reproducible retrospective benchmark for ranking daily active-fire detections in Cerrado conservation units by comparing atmospheric, surface, static spatial, and short-term memory covariates with standard ML models under time-series cross-validation and held-out AOI tests.
A multimodal transformer-based generic mixture density network estimates FRB scattering timescale τ with 94% R² on measurable events and 90% recall for unresolvable cases on CHIME/FRB data.
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A hybrid deep learning plus classical ML pipeline for waste image classification reaches up to 100% accuracy on TrashNet and a corrected household dataset while cutting feature dimensionality by over 95%.
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A Retrospective Benchmark of Spatiotemporal Covariates for Daily Active-Fire Detection in Cerrado Conservation Units
The paper establishes a reproducible retrospective benchmark for ranking daily active-fire detections in Cerrado conservation units by comparing atmospheric, surface, static spatial, and short-term memory covariates with standard ML models under time-series cross-validation and held-out AOI tests.