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1. Improved Accessibility and Community Knowledge of Lidar and Radar Data Analysis

   https://doi.org/10.5281/zenodo.16987131  

   DeHart, Jennifer; Espinoza, Ana; Javornik, Brenda; Chastang, Julien (August 2025, Zenodo)

   To improve accessibility and community knowledge of applications in the Lidar Radar Open Software Environment (LROSE), a team from the National Science Foundation (NSF) National Center for Atmospheric Research, Colorado State University, and NSF Unidata has developed a lidar and radar meteorology science gateway deployed on the NSF Jetstream2 cloud. Utilizing the “Zero to JupyterHub with Kubernetes” workflow, the science gateway integrates LROSE with other lidar and radar meteorology software packages. This integration allows users to execute applications directly from the JupyterLab terminal, streamlining the creation of datasets for further analysis and visualization within Jupyter notebooks. By combining traditional command-line operations with modern Python-based tools for data analysis and visualization, this gateway provides a robust end-to-end solution that caters to both educational and research needs. The gateway has already facilitated LROSE instructional workshops and classroom exercises. Our work demonstrates the significant potential of merging established scientific computing techniques with advanced Python environments, opening new avenues for computational science education and research. The LROSE team has acquired successive allocations on the NSF Jetstream2 cloud at Indiana University through ACCESS. To develop the LROSE Science Gateway, we employed the “Zero to JupyterHub with Kubernetes” workflow ported to the NSF Jetstream2 cloud, enabling rapid and scalable deployment to accommodate a variable number of users. Authentication is managed through either GitHub OAuth or temporary credentials, depending on the situation. Since LROSE is a collection of C/C++ applications, we configured Docker containers based on the Jupyter Docker Stack to integrate the LROSE software, available via the JupyterLab terminal. These containers also include Conda package manager environments equipped with Python packages like Py-ART, CSU RadarTools, and Metpy for further data analysis. A shared drive accessible to all participants contains instructional datasets for lidar and radar data analysis. Tutorials take the form of Jupyter notebooks for use by individuals, in classroom exercises, or at instructional workshops. Some tutorials are complete with pre-loaded examples to quickly visualize workflows and results. Other tutorials guide students how to run the applications independently. All tutorials are hosted on the LROSE Science Gateway GitHub repository, which is open to contributions from colleagues and community members. Future plans include an "intermediate" level workshop on SAMURAI, one of the multi-Doppler wind applications of the LROSE suite. Additionally, work is currently underway to run GUI applications in the same browser-based JupyterLab environment. GUI applications for radar and lidar data visualization utilize the QT framework and present unique technical challenges. The techniques to accomplish GUI access have immediate applications for other GUI programs, such as NSF Unidata's IDV and their version of the AWIPS CAVE data visualization tools. Lastly, as demand for the resources found on the gateway increases, it becomes increasingly important to efficiently manage the Jetstream2 resources allocated by the ACCESS program. LROSE, NSF Unidata, San Diego Supercomputing Center (SDSC), and Indiana University staff are working together to deploy and evaluate Kubernetes cluster auto-scaling. With auto-scaling, resources will no longer sit idle while awaiting new logins and will instead be provisioned on-demand. 

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2. Modeling Utilization to Identify Shared-Memory Atomic Bottlenecks

   https://doi.org/10.1145/3725798.3725801  

   Dong, Rongcui; Pai, Sreepathi (March 2025, ACM)

   Performance analysis is critical for GPU programs with data-dependent behavior, but models like Roofline are not very useful for them and interpreting raw performance counters is tedious. In this work, we present an analytical model for shared memory atomics (fetch-and-op and compare-and-swap instructions on NVIDIA Volta and Ampere GPU) that allows users to immediately determine if shared memory atomic operations are a bottleneck for a program’s execution. Our model is based on modeling the architecture as a single-server queuing model whose inputs are performance counters. It captures load-dependent behavior such as pipelining, parallelism, and different access patterns. We embody this model in a tool that uses CUDA hardware counters as parameters to predict the utilization of the shared-memory atomic unit. To the best of our knowledge, no existing profiling tool or model provides this capability for shared-memory atomic operations. We used the model to compare two histogram kernels that use shared-memory atomics. Although nearly identical, their performance can be different by up to 30%. Our tool correctly identifies a bottleneck shift from shared-memory atomic unit as the cause of this discrepancy 

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3. Generalizations of the R-Matrix Method to the Treatment of the Interaction of Short-Pulse Electromagnetic Radiation with Atoms

   https://doi.org/10.3390/atoms10010026  

   Schneider, Barry I.; Hamilton, Kathryn R.; Bartschat, Klaus (March 2022, Atoms)

   Since its initial development in the 1970s by Phil Burke and his collaborators, the R-matrix theory and associated computer codes have become the method of choice for the calculation of accurate data for general electron–atom/ion/molecule collision and photoionization processes. The use of a non-orthogonal set of orbitals based on B-splines, now called the B-spline R-matrix (BSR) approach, was pioneered by Zatsarinny. It has considerably extended the flexibility of the approach and improved particularly the treatment of complex many-electron atomic and ionic targets, for which accurate data are needed in many modelling applications for processes involving low-temperature plasmas. Both the original R-matrix approach and the BSR method have been extended to the interaction of short, intense electromagnetic (EM) radiation with atoms and molecules. Here, we provide an overview of the theoretical tools that were required to facilitate the extension of the theory to the time domain. As an example of a practical application, we show results for two-photon ionization of argon by intense short-pulse extreme ultraviolet radiation. 

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4. Filming and viewing ultrafast motion inside molecules: What do we see and what can we learn?

   Bucksbaum, P. (January 2023, Bulletin of the American Physical Society)

   Liwendowski, H. (Ed.)

   The electrons and atoms inside molecules can rearrange rapidly during photoexcitation or collisions, moving angstroms in a few femtoseconds or less. This non-classical many-body quantum evolution is far too small and too fast to be resolved in any imaging microscope, but if we could film it, what should we expect to see? New tools based on ultrafast lasers, electron accelerators, and x-ray free-electron lasers have now begun to record this motion with increasing detail, and for a growing array of atomic and molecular systems. Here I will attempt to answer the question, "So what?" What have we learned, and how are molecular movies guiding us toward future discoveries in AMO physics? *Much of this work is supported by the U.S. Department of Energy (DOE), Office of Science, Office of Basic Energy Sciences (BES), Chemical Sciences, Geosciences, and Biosciences Division (CSGB). Other work described here has been supported by the National Science Foundation 

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5. Advances and Applications of Atomic-Resolution Scanning Transmission Electron Microscopy

   https://doi.org/10.1017/S1431927621012125  

   Liu, Jingyue (August 2021, Microscopy and Microanalysis)

   null (Ed.)

   Although scanning transmission electron microscopy (STEM) images of individual heavy atoms were reported 50 years ago, the applications of atomic-resolution STEM imaging became wide spread only after the practical realization of aberration correctors on field-emission STEM/TEM instruments to form sub-Ångstrom electron probes. The innovative designs and advances of electron optical systems, the fundamental understanding of electron–specimen interaction processes, and the advances in detector technology all played a major role in achieving the goal of atomic-resolution STEM imaging of practical materials. It is clear that tremendous advances in computer technology and electronics, image acquisition and processing algorithms, image simulations, and precision machining synergistically made atomic-resolution STEM imaging routinely accessible. It is anticipated that further hardware/software development is needed to achieve three-dimensional atomic-resolution STEM imaging with single-atom chemical sensitivity, even for electron-beam-sensitive materials. Artificial intelligence, machine learning, and big-data science are expected to significantly enhance the impact of STEM and associated techniques on many research fields such as materials science and engineering, quantum and nanoscale science, physics and chemistry, and biology and medicine. This review focuses on advances of STEM imaging from the invention of the field-emission electron gun to the realization of aberration-corrected and monochromated atomic-resolution STEM and its broad applications. 

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