Using simultaneous modeling of continuum lag-spectrum and broadband SED of Fairall 9 with the H0RIZON-AGN model, the authors obtain H0 = 72.4_{-3.7}^{+3.4} km s^{-1} Mpc^{-1}.
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6 Pith papers cite this work. Polarity classification is still indexing.
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Vines-DB is a new annotated RGB image dataset of 1,218 original field photos from seven vine species, expanded to 2,307 images via augmentation, for multi-class instance segmentation.
Requiring thermal stability and single-valuedness in the thin-disk Ṁ-Σ plane produces a viscosity law α(X) with X = P_gas/P_rad that eliminates the radiation-pressure dominated instability while preserving the effective-temperature profile.
MoEIoU is a mixture-of-experts IoU loss using log-sum-exp aggregation and curriculum weighting that reports consistent gains over prior IoU losses on PASCAL VOC, HRIPCB, and MS COCO with YOLO models.
A Bayesian framework produces relevance attribution distributions for power quality disturbance classifiers so experts can select explanations by confidence percentiles.
Review summarizing radio searches for WIMP and ALP dark matter with SKA precursors and forecasts for SKA-Low and SKA-Mid telescopes.
citing papers explorer
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HALO II: Constraining Hubble constant $H_{0}$ through continuum delay fitting of Fairall 9
Using simultaneous modeling of continuum lag-spectrum and broadband SED of Fairall 9 with the H0RIZON-AGN model, the authors obtain H0 = 72.4_{-3.7}^{+3.4} km s^{-1} Mpc^{-1}.
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Vines-DB: An RGB image dataset for multi-species ornamental vine segmentation
Vines-DB is a new annotated RGB image dataset of 1,218 original field photos from seven vine species, expanded to 2,307 images via augmentation, for multi-class instance segmentation.
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Radiation-pressure instability is an artifact of constant-$\alpha$ closure
Requiring thermal stability and single-valuedness in the thin-disk Ṁ-Σ plane produces a viscosity law α(X) with X = P_gas/P_rad that eliminates the radiation-pressure dominated instability while preserving the effective-temperature profile.
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MoEIoU: Rethinking Bounding-Box Regression as a Mixture of Experts
MoEIoU is a mixture-of-experts IoU loss using log-sum-exp aggregation and curriculum weighting that reports consistent gains over prior IoU losses on PASCAL VOC, HRIPCB, and MS COCO with YOLO models.
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A Bayesian Framework for Uncertainty-Aware Explanations in Power Quality Disturbance Classification
A Bayesian framework produces relevance attribution distributions for power quality disturbance classifiers so experts can select explanations by confidence percentiles.
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Probing the Fundamental Nature of Particle Dark Matter
Review summarizing radio searches for WIMP and ALP dark matter with SKA precursors and forecasts for SKA-Low and SKA-Mid telescopes.