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1. GRACE Downscaler: A Framework to Develop and Evaluate Downscaling Models for GRACE

   https://doi.org/10.3390/rs15092247  

   Pulla, Sarva T.; Yasarer, Hakan; Yarbrough, Lance D. (May 2023, Remote Sensing)

   Monitoring and managing groundwater resources is critical for sustaining livelihoods and supporting various human activities, including irrigation and drinking water supply. The most common method of monitoring groundwater is well water level measurements. These records can be difficult to collect and maintain, especially in countries with limited infrastructure and resources. However, long-term data collection is required to characterize and evaluate trends. To address these challenges, we propose a framework that uses data from the Gravity Recovery and Climate Experiment (GRACE) mission and downscaling models to generate higher-resolution (1 km) groundwater predictions. The framework is designed to be flexible, allowing users to implement any machine learning model of interest. We selected four models: deep learning model, gradient tree boosting, multi-layer perceptron, and k-nearest neighbors regressor. To evaluate the effectiveness of the framework, we offer a case study of Sunflower County, Mississippi, using well data to validate the predictions. Overall, this paper provides a valuable contribution to the field of groundwater resource management by demonstrating a framework using remote sensing data and machine learning techniques to improve monitoring and management of this critical resource, especially to those who seek a faster way to begin to use these datasets and applications. 

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2. First Impressions of the NVIDIA Grace CPU Superchip and NVIDIA Grace Hopper Superchip for Scientific Workloads

   https://doi.org/10.1145/3636480.3637097  

   Simakov, Nikolay A.; Jones, Matthew D.; Furlani, Thomas R.; Siegmann, Eva; Harrison, Robert J. (January 2024, ACM)

   The engineering samples of the NVIDIA Grace CPU Superchip and NVIDIA Grace Hopper Superchips were tested using different benchmarks and scientific applications. The benchmarks include HPCC and HPCG. The real application-based benchmark includes AI-Benchmark-Alpha (a TensorFlow benchmark), Gromacs, OpenFOAM, and ROMS. The performance was compared to multiple Intel, AMD, ARM CPUs and several x86 with NVIDIA GPU systems. A brief energy efficiency estimate was performed based on TDP values. We found that in HPCC benchmark tests, the per-core performance of Grace is similar to or faster than AMD Milan cores, and the high core count often allows NVIDIA Grace CPU Superchip to have per-node performance similar to Intel Sapphire Rapids with High Bandwidth Memory: slower in matrix multiplication (by 17%) and FFT (by 6%), faster in Linpack (by 9%)). In scientific applications, the NVIDIA Grace CPU Superchip performance is slower by 6% to 18% in Gromacs, faster by 7% in OpenFOAM, and right between HBM and DDR modes of Intel Sapphire Rapids in ROMS. The combined CPU-GPU performance in Gromacs is significantly faster (by 20% to 117% faster) than any tested x86-NVIDIA GPU system. Overall, the new NVIDIA Grace Hopper Superchip and NVIDIA Grace CPU Superchip Superchip are high-performance and most likely energy-efficient solutions for HPC centers. 

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3. GRACE: Generalizing Robot-Assisted Caregiving with User Functionality Embeddings

   Liu, Ziang; Ju, Yuanchen; Da, Yu; Silver, Tom; Thakkar, Pranav N; Li, Jenna; Guo, Justin; Dimitropoulou, Katherine; Bhattacharjee, Tapomayukh (March 2025, HRI 2025)
4. Failing with Grace: Learning Neural Network Controllers that are Boundedly Unsafe

   Vlantis, Panagiotis; Bridgeman, Leila; Zavlanos, Michael (June 2023, 5th Annual Learning for Dynamics and Control Conference)
5. GRACE: A Granular Benchmark for Evaluating Model Calibration against Human Calibration

   Sung, Yoo Yeon; Fleisig, Eve; Hope, Yu; Upadhyay, Ishan; Boyd-Graber, Jordan (July 2025, Association for Computational Linguistics)

   As AI use becomes more common, it's important to measure not just whether the systems are correct but whether they know when they're incorrect. We propose a new metric to measure this mismatch between correctness and confidence, compare computer ability with human ability, and show that computers have a long way to go before they're well-calibrated. 

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