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Abstract The northern Indian Ocean is a hotspot of nitrous oxide (O) emission to the atmosphere. Yet, the direct link between production and emission of O in this region is still poorly constrained, in particular the relative contributions of denitrification, nitrification and ocean transport to the O efflux. Here, we implemented a mechanistically based O cycling module into a regional ocean model of the Indian Ocean to examine how the biological production and transport of O control the spatial variation of O emissions in the basin. The model captures the upper ocean physical and biogeochemical dynamics of the northern Indian Ocean, including vertical and horizontal O distribution observed in situ and regionally integrated O emissions of 286 152 Gg N (annual mean seasonal range) in the lower range of the observation‐based reconstruction (391 237 Gg N ). O emissions are primarily fueled by nitrification in or right below the surface mixed layer (57%, including 26% in the mixed layer and 31% right below), followed by denitrification in the oxygen minimum zones (30%) and O produced elsewhere and transported into the region (13%). Overall, 74% of the emitted O is produced in subsurface and transported to the surface in regions of coastal upwelling, winter convection or turbulent mixing. This spatial decoupling between O production and emissions underscores the need to consider not only changes in environmental factors critical to O production (oxygen, primary productivity etc.) but also shifts in ocean circulation that control emissions when evaluating future changes in global oceanic O emissions. 

ABSTRACT In modern statistical applications, identifying critical features in high‐dimensional data is essential for scientific discoveries. Traditional best subset selection methods face computational challenges, while regularization approaches such as Lasso, SCAD and their variants often exhibit poor performance with ultrahigh‐dimensional data. Sure screening methods, widely used for dimensionality reduction, have been developed as popular alternatives, but few target heavy‐tailed characteristics in modern big data. This paper introduces a new sure screening method, based on robust distance correlation (‘RDC’), designed for heavy‐tailed data. The proposed method inherits the benefits of the original model‐free distance correlation‐based screening while robustly estimating distance correlation in the presence of heavy‐tailed data. We further develop an FDR control procedure by incorporating the Reflection via Data Splitting (REDS) method. Extensive simulations demonstrate the method's advantage over existing screening procedures under different scenarios of heavy‐tailedness. Its application to high‐dimensional heavy‐tailed RNA‐seq data from The Cancer Genome Atlas (TCGA) pancreatic cancer cohort showcases superior performance in identifying biologically meaningful genes predictive of MAPK1 protein expression critical to pancreatic cancer. 

Inaccurate spatial tracking in extended reality (XR) headsets can cause virtual object jitter, misalignment, and user discomfort, limiting the headsets’ potential for immersive content and natural interactions. We develop a modular testbed to evaluate the tracking performance of commercial XR headsets, incorporating system calibration, tracking data acquisition, and result analysis, and allowing the integration of external cameras and IMU sensors for comparison with opensource VI-SLAM algorithms. Using this testbed, we quantitatively assessed spatial tracking accuracy under various user movements and environmental conditions for the latest XR headsets, Apple Vision Pro and Meta Quest 3. The Apple Vision Pro outperformed the Meta Quest 3, reducing relative pose error (RPE) and absolute pose error (APE) by 33.9% and 14.6%, respectively. While both headsets achieved sub-centimeter RPE in most cases, they exhibited APE exceeding 10 cm in challenging scenarios, highlighting the need for further improvements in reliability and accuracy.