Enabling Quantitative Polarimetry for Keck/NIRC2: Preliminary Mueller Matrix Model Calibration

Kavli Affiliate: Dimitri Mawet| Summary:The Keck/NIRC2 infrared imager was upgraded in 2025 with dual-beam polarimetric observing modes spanning approximately 1.1–4.1 microns (JHKL’ bands). We present a preliminary JHK calibration of NIRC2 Polarimetry using a wavelength-dependent Mueller matrix model of the Keck tertiary mirror (M3), half-wave plate (HWP), image rotator (IMR), downstream optics, and Wollaston prism. […]


Continue.. Enabling Quantitative Polarimetry for Keck/NIRC2: Preliminary Mueller Matrix Model Calibration

Rollplex: Cross-Phase GPU Spatial Sharing for Vision Language Model Post-Training

Kavli Affiliate: Wei Gao| Summary:Vision-language models (VLMs) enable embodied agents to reason and act from visual observations and language instructions. Reinforcement learning (RL) post-training enhances these capabilities using task feedback, but current on-policy RL runtimes execute rollout, reference scoring, and actor training in strict serial phases. While effective for text-only RL, this phase-granular execution is […]


Continue.. Rollplex: Cross-Phase GPU Spatial Sharing for Vision Language Model Post-Training

CoDS: Robust Collaborative Perception via Expert-driven Detection and BEV Segmentation

Kavli Affiliate: Wei Gao| Summary:Collaborative perception breaks through single-view limitations via multi-agent information exchange. However, multi-source noise such as pose errors and communication delays degrades fusion feature quality, constraining perception performance. Joint training of detection and BEV segmentation provides a natural remedy, where segmented road regions help constrain target distributions and detection bounding boxes help […]


Continue.. CoDS: Robust Collaborative Perception via Expert-driven Detection and BEV Segmentation

Energy Partitioning in Dust-catalyzed $mathrmH_2$ and HD Formation Revealed by Molecular Simulations Considering Nuclear Quantum Effects

Kavli Affiliate: Lile Wang |Summary:Molecular hydrogen formation on interstellar dust grains is a key surface process in the interstellar medium, but the redistribution of the recombination energy between the substrate and the nascent molecule remains poorly understood. Here, we use ring-polymer molecular dynamics (RPMD) with a machine-learning force field to investigate energy partitioning during $mathrmH_2$ […]


Continue.. Energy Partitioning in Dust-catalyzed $mathrmH_2$ and HD Formation Revealed by Molecular Simulations Considering Nuclear Quantum Effects