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QUANTITATIVE DERIVATION OF THE TWO-COMPONENT GROSS–PITAEVSKII EQUATION WITH UNIFORM-IN-TIME CONVERGENCE RATE

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arXiv 2025年

作者： Chong, Jacky Lee, Jinyeop Sun, Zhiwei School of Mathematical Sciences Peking University Beijing China Department of Mathematics and Computer Science University of Basel Spiegelgasse 1 BaselCH-4051 Switzerland Institute of Analysis and Scientific Computing TU Wien Wiedner Hauptstraße 8–10 Wien1040 Austria

We derive the time-dependent two-component Gross–Pitaevskii equation as an effective description of the dynamics of a dilute two-component Bose gas near its ground state, which exhibits a two-component mixture Bose–Einstein condensate, in the Gross–Pitaevskii limit regime. Our main result establishes a uniform-in-time bound on the convergence rate between the many-body dynamics and the effective description, explicitly quantified in terms of the particle number N. This improves upon the works of Michelangeli and Olgliati [73, 85] by providing a sharper, N-dependent, time-independent convergence rate. Our approach also extends the framework of Benedikter, de Oliveira, and Schlein [10] to the multi-component Bose gas setting. More specifically, we develop the necessary Bogoliubov theory to analyze the dynamics of multi-component Bose gases in the Gross–Pitaevskii *** Codes 82C10(Primary), 35Q55(Secondary) Copyright © 2025, The Authors. All rights reserved.

关键词： Statistical mechanics

The World is Not Enough: Growing Waste in HPC-enabled Academic Practice 25

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The World is Not Enough: Growing Waste in HPC-enabled Academ...

Proceedings of the 2025 CHI Conference on Human Factors in computing Systems

作者： Carolynne Lord Adrian Friday Adrian Jackson Caroline Bird Chris Preist Simon Lambert Gabin Kayumbi Kelly Widdicks UK Centre for Ecology & Hydrology Lancaster United Kingdom and School of Computing and Communications Lancaster University Lancaster United Kingdom School of Computing and Communications Lancaster University Lancaster United Kingdom EPCC The University of Edinburgh Edinburgh United Kingdom School of Computer Science University of Bristol Bristol United Kingdom Scientific Computing Department Science and Technology Facilities Council (STFC) Didcot United Kingdom The Alan Turing Institute London United Kingdom UK Centre for Ecology & Hydrology Lancaster United Kingdom

Recorder: Comprehensive Parallel I/O Tracing and Analysis

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arXiv 2025年

作者： Wang, Chen Yildirim, Izzet Devarajan, Hariharan Mohror, Kathryn Snir, Marc Center for Applied Scientific Computing Lawrence Livermore National Laboratory 7000 E. Ave LivermoreCA94550 United States Department of Computer Science Illinois Institute of Technology 10 W 35th St ChicagoIL60616 United States Department of Computer Science University of Illinois Urbana-Champaign 201 N Goodwin Ave UrbanaIL61801 United States

This paper presents Recorder, a parallel I/O tracing tool designed to capture comprehensive I/O information on HPC applications. Recorder traces I/O calls across various I/O layers, storing all function parameters for each captured call. The volume of stored information scales linearly the application’s execution scale. To address this, we present a sophisticated pattern-recognition-based compression algorithm. This algorithm identifies and compresses recurring I/O patterns both within individual processes and across multiple processes, significantly reducing space and time overheads. We evaluate the proposed compression algorithm using I/O benchmarks and real-world applications, demonstrating that Recorder can store more information while requiring approximately 12× less storage space compared to its predecessor. Notably, for applications with typical parallel I/O patterns, Recorder achieves a constant trace size regardless of execution scale. Additionally, a comparison with the profiling tool Darshan shows that Recorder captures detailed I/O information without incurring substantial overhead. The richer data collected by Recorder enables new insights and facilitates more in-depth I/O studies, offering valuable contributions to the I/O research community. Copyright © 2025, The Authors. All rights reserved.

关键词： Benchmarking

ENSEMBLE SCORE FILTER WITH IMAGE INPAINTING FOR DATA ASSIMILATION IN TRACKING SURFACE QUASI-GEOSTROPHIC DYNAMICS WITH PARTIAL OBSERVATIONS

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arXiv 2025年

作者： Liang, Siming Tran, Hoang Bao, Feng Chipilski, Hristo G. van Leeuwen, Peter Jan Zhang, Guannan Department of Mathematics Florida State University TallahasseeFL United States Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN United States Department of Scientific Computing Florida State University TallahasseeFL United States Department of Atmospheric Science Colorado State University Fort CollinsCO United States

Data assimilation plays a pivotal role in understanding and predicting turbulent systems within geoscience and weather forecasting, where data assimilation is used to address three fundamental challenges, i.e., high-dimensionality, nonlinearity, and partial observations. Recent advances in machine learning (ML)-based data assimilation methods have demonstrated encouraging results. In this work, we develop an ensemble score filter (EnSF) that integrates image inpainting to solve the data assimilation problems with partial observations. The EnSF method, proposed in our previous work [3], exploits an exclusively designed training-free diffusion models to solve high-dimensional nonlinear data assimilation problems. Its performance has been successfully demonstrated in the context of having full observations, i.e., all the state variables are directly or indirectly observed. However, because the EnSF does not use a covariance matrix to capture the dependence between the observed and unobserved state variables, it is nontrivial to extend the original EnSF method to the partial observation scenario. In this work, we incorporate various image inpainting techniques into the EnSF to predict the unobserved states during data assimilation. At each filtering step, we first use the diffusion model to estimate the observed states by integrating the likelihood information into the score function. Then, we use image inpainting methods to predict the unobserved state variables. We demonstrate the performance of the EnSF with inpainting by tracking the Surface Quasi-Geostrophic (SQG) model dynamics under a variety of scenarios. The successful proof of concept paves the way to more in-depth investigations on exploiting modern image inpainting techniques to advance data assimilation methodology for practical geoscience and weather forecasting *** Codes 68Q25, 68R10, 68U05 Copyright © 2025, The Authors. All rights reserved.

关键词： Weather forecasting

Cheesemap: A High-Performance Point-Indexing Data Structure for Neighbor Search in LiDAR Data

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arXiv 2025年

作者： Laso, Ruben Yermo, Miguel Research Group for Scientific Computing Faculty of Computer Science University of Vienna Währinger Straße 29 Vienna Vienna1090 Austria Universidad de Santiago de Compostela Rúa de Jenaro de la Fuente Domínguez Galicia Santiago de Compostela15782 Spain

Point cloud data, as the representation of three-dimensional spatial information, is a fundamental piece of information in various domains where indexing and querying these point clouds efficiently is crucial for tasks such as object recognition, autonomous navigation, and environmental modeling. In this paper, we present a comprehensive comparative analysis of various data structures combined with neighboring search methods across different types of point clouds. Additionally, we introduce a novel data structure, cheesemap, to handle 3D LiDAR point clouds. Exploring the sparsity and irregularity in the distribution of points, there are three flavors of the cheesemap: dense, sparse, and mixed. Results show that the cheesemap can outperform state-of-the-art data structures in terms of execution time per query, particularly for ALS (Aerial Laser Scanning) point clouds. Memory consumption is also minimal, especially in the sparse and mixed representations, making the cheesemap a suitable choice for applications involving three-dimensional point clouds. © 2025, CC BY.

关键词： Indexing (of information)

A hybrid healthy diet recommender system based on machine learning techniques

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computers in Biology and Medicine 2025年 184卷 109389-109389页

作者： Sweidan, Sara Askar, S.S. Abouhawwash, Mohamed Badr, Elsayed Department of Artificial Intelligence Faculty of Computer and Artificial Intelligence Benha University Benha Egypt Faculty of Computer Science and Engineering New Mansoura University New Mansoura Egypt Department of Statistics and Operations Research College of Science King Saud University P.O. Box 2455 Riyadh11451 Saudi Arabia College of Engineering Michigan State University East LansingMI48824 United States Department of Mathematics Faculty of Science Mansoura University Mansoura35516 Egypt Department of Scientific Computing Faculty of Computer and Artificial Intelligence Benha University Benha Egypt Department of Information Systems College of Information Technology Misr University for Science & Technology Giza Egypt

Obesity is a chronic disease correlated with numerous risk factors that not only negatively affect all body functions but also increase the chances of developing chronic diseases and the associated morbidity and mortality rates. This study proposes a novel system that bridges the gap between healthcare providers and patients by offering both parties some tools for navigating the intricacies of dietary planning. In this system, machine learning techniques are used to determine the required calories before starting an obesity treatment. A hybrid precision model with minimal parameters is also developed to estimate the appropriate number of calories for losing weight and to formulate a healthy diet plan. A real dataset of 15 anthropometric measurements is analyzed using SVR, LR, and DTR regression models, and all the data are preprocessed before analysis to enhance model performance. Results show that the required calories can be estimated with a high correlation (R = 0.985) from independent measurements. The proposed model also calculates the healthy daily percentages of fats, proteins, and carbohydrates based on a knowledge base of medical rules and functions, thus facilitating the sequential treatment of obese patients. In sum, this study applies different models to design a practical, cost-effective approach for accurately determining the required calories and formulating valuable diet plans for obesity treatment and management. © 2024 Elsevier Ltd

关键词： Calorie Machine learning Regression Obesity treatment Expert system

Kilometer-Scale E3SM Land Model Simulation over North America

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arXiv 2025年

作者： Wang, Dali Wang, Chen Cao, Qinglei Schwartz, Peter Yuan, Fengming Krishna, Jayesh Wu, Danqing Ricciuto, Danial Thornton, Peter Kao, Shih-Chieh Thornton, Michele Mohror, Kathryn Environmental Sciences Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA United States Department of Computer Science Saint Louis University Saint LouisMO United States Mathematics and Computer Sciences Argonne National Laboratory ArgonneIL United States

The development of a kilometer-scale E3SM Land Model (km-scale ELM) is an integral part of the E3SM project, which seeks to advance energy-related Earth system science research with state-of-the-art modeling and simulation capabilities on exascale computing systems. Through the utilization of high-fidelity data products, such as atmospheric forcing and soil properties, the km-scale ELM plays a critical role in accurately modeling geographical characteristics and extreme weather occurrences. The model is vital for enhancing our comprehension and prediction of climate patterns, as well as their effects on ecosystems and human activities. This study showcases the first set of full-capability, km-scale ELM simulations over various computational domains, including simulations encompassing 21.6 million land gridcells, reflecting approximately 21.5 million square kilometers of North America at a 1 km × 1 km resolution. We present the largest km-scale ELM simulation using up to 100,800 CPU cores across 2,400 nodes. This continental-scale simulation is 300 times larger than any previous studies, and the computational resources used are about 400 times larger than those used in prior efforts. Both strong and weak scaling tests have been conducted, revealing exceptional performance efficiency and resource utilization. The km-scale ELM uses the common E3SM modeling infrastructure and a general data toolkit known as KiloCraft. Consequently, it can be readily adapted for both fully-coupled E3SM simulations and data-driven simulations over specific areas, ranging from a single gridcell to the entire North America. © 2025, CC BY.

关键词： Digital elevation model

Unsupervised Training Framework for 3D Point Cloud Object Detection Model 25

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Unsupervised Training Framework for 3D Point Cloud Object De...

Proceedings of the 40th ACM/SIGAPP Symposium on Applied computing

作者： Min-Jyun Wu Hsiang-Jui Lin Shih-Hao Hung Chi-Sheng Shih Dept. of Computer Science and Information Engineering National Taiwan University Taipei Taiwan High Performance and Scientific Computing (HPSC) Center National Taiwan University Taipei Taiwan Graduate Institute of Networking and Multimedia National Taiwan University Taipei Taiwan Center of Artificial Intelligence and Advanced Robotics National Taiwan University Taipei Taiwan

ISBN: (纸本)9798400706295

Point cloud data frames are critical, if not indispensable, for precise robot navigation and localization, but training the object detection models for them remains challenging. Many models require labeled 3D objects to train the model. However, the sparse and occluded 3D point cloud data make it difficult, if not impossible, to automate the labeling process. This work proposes a training framework to generate 3D labels on point clouds to tackle the aforementioned challenges. The proposed method takes advantage of the consecutive presence of the same object on different frames to automate the labeling process. The experimental results show that the unsupervised framework trains a robust model for 3D object detection. On the roadside data, the model archives 90.27% AP for scooters and 91.33% AP for cars. On nuScenes dataset, the framework demonstrates the detection precision doubles on IoU 0.25 and IoU 0.5 when the recalls remain similar, compared to the model trained by the MODEST framework.

关键词： computer systems organization

A Scalable Real-Time Data Assimilation Framework for Predicting Turbulent Atmosphere Dynamics 24

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A Scalable Real-Time Data Assimilation Framework for Predict...

Proceedings of the SC '24 Workshops of the International Conference on High Performance computing, Network, Storage, and Analysis

作者： Junqi Yin Siming Liang Siyan Liu Feng Bao Hristo G. Chipilski Dan Lu Guannan Zhang National Center for Computational Science Oak Ridge National Laboratory Oak Ridge TN USA Department of Mathematics Florida State University Tallahassee FL USA Computational Science and Engineering Division Oak Ridge National Laboratory Oak Ridge TN USA Department of Scientific Computing Florida State University Tallahassee FL USA Computer Science and Mathematics Division Oak Ridge National Laboratory Oak Ridge TN USA

ISBN: (纸本)9798350355543

AI-based foundation models like FourCastNet, GraphCast, ClimaX, and Pangu-Weather are revolutionizing weather and climate predictions but are not yet ready for operational use. Their limitation lies in the absence of a data assimilation system to incorporate real-time Earth system observations, crucial for accurately forecasting events like tropical cyclones and atmospheric rivers. To overcome these obstacles, we introduce a generic real-time data assimilation framework and demonstrate its end-to-end performance on the Frontier supercomputer. This framework comprises two primary modules: an ensemble score filter (EnSF), which significantly outperforms the state-of-the-art data assimilation method, e.g., the Local Ensemble Transform Kalman Filter (LETKF); and a vision transformer-based surrogate capable of real-time adaptation through the integration of observational data. We demonstrate both the strong and weak scaling of our framework up to 1024 GPUs on the Exascale supercomputer, Frontier. Our results not only illustrate the framework's exceptional scalability on high-performance computing systems, but also demonstrate the importance of supercomputers in real-time data assimilation for weather and climate predictions. Even though the proposed framework is tested only on a benchmark surface quasi-geostrophic (SQG) turbulence system, it has the potential to be combined with existing AI-based foundation models, making it suitable for future operational implementations.

关键词： Diffusion models