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arXiv 2025年

作者： Li, Chaojian Ye, Zhifan Pasini, Massimiliano Lupo Choi, Jong Youl Wan, Cheng Lin, Yingyan Balaprakash, Prasanna Computational Sciences and Engineering Division Oak Ridge National Laboratory United States Computer Science and Mathematics Division Oak Ridge National Laboratory United States Computing and Computational Sciences Directorate Oak Ridge National Laboratory United States Georgia Institute of Technology United States

Atomistic materials modeling is a critical task with wide-ranging applications, from drug discovery to materials science, where accurate predictions of the target material property can lead to significant advancements in scientific discovery. Graph Neural Networks (GNNs) represent the state-of-the-art approach for modeling atomistic material data thanks to their capacity to capture complex relational structures. While machine learning performance has historically improved with larger models and datasets, GNNs for atomistic materials modeling remain relatively small compared to large language models (LLMs), which leverage billions of parameters and terabyte-scale datasets to achieve remarkable performance in their respective domains. To address this gap, we explore the scaling limits of GNNs for atomistic materials modeling by developing a foundational model with billions of parameters, trained on extensive datasets in terabyte-scale. Our approach incorporates techniques from LLM libraries to efficiently manage large-scale data and models, enabling both effective training and deployment of these large-scale GNN models. This work addresses three fundamental questions in scaling GNNs: the potential for scaling GNN model architectures, the effect of dataset size on model accuracy, and the applicability of LLM-inspired techniques to GNN architectures. Specifically, the outcomes of this study include (1) insights into the scaling laws for GNNs, highlighting the relationship between model size, dataset volume, and accuracy, (2) a foundational GNN model optimized for atomistic materials modeling, and (3) a GNN codebase enhanced with advanced LLM-based training techniques. Our findings lay the groundwork for large-scale GNNs with billions of parameters and terabyte-scale datasets, establishing a scalable pathway for future advancements in atomistic materials modeling. Copyright © 2025, The Authors. All rights reserved.

关键词： Materials properties

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Multi-Agent Systems for Autonomous IoT Network Management Using Distributed Reinforcement Learning 3

Multi-Agent Systems for Autonomous IoT Network Management Us...

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3rd International Conference on Intelligent Systems, Advanced computing, and Communication, ISACC 2025

作者： Neelamegam, G. Venkatesan, Rajaram Ramya, S.R. Ramya, R.S. Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak VSB College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Builders Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Tirupur India Dr NGP Institute of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Tamil Nadu Coimbatore India SRM Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Networking and Communications Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India

ISBN: (纸本)9798331523893

The ever-growing complexity of IoT networks ignited by their wide scale adoption in applications such as smart cities, the industrial automation, and health care, compelled to develop sophisticated yet resource efficient and scalable network management solution. Centralized systems are traditional in architecture, which leads to higher latency and bottlenecks, while decentralized systems can distribute workloads however the lack of adaptability to real time, along with the difficulty of extracting maximum resource usage, are limitations. In recent years, reinforcement learning (RL) based approaches have taken shape as a promising alternative to facilitate the automated learning and optimisation of task management. However, there are two challenges for existing RL based methods: slow convergence, inefficient priority assignment of tasks, and poor coordination amongst agents in the case of dynamic, large scale IoT environment. To solve these challenges, in this research we propose a distributed reinforcement learning based multi agent framework for managing IoT network. The framework also integrates a task prioritization mechanism to dynamically choose an optimal task allocation solution. Further validation was performed in a simulated IoT environment where real world inspired datasets were used. The proposed framework demonstrated 30% more task throughput than centralized, 25% less latency than decentralized, and 40% more energy efficiency than current RL based systems. The system was further validated using advanced visualizations such as anomaly detection maps and resource allocation efficiency graphs, demonstrating its capacity to manage dynamic load of tasks and resource allocation. The proposed framework addresses the critical limitations of current methods, through scalable, adaptive, and energy efficient framework for managing IoT network, and accordingly helps advancing the intelligent and autonomous IoT systems. © 2025 IEEE.

关键词： Distributed Reinforcement Learning IoT Network Management Multi-Agent Systems Resource Optimization

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Computable Bounds on the Solution to Poisson’s Equation for General Harris Chains

arXiv

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arXiv 2025年

作者： Glynn, Peter W. Lin, Na Liu, Yuanyuan Department of Management Science and Engineering Institute for Computational and Mathematical Engineering Stanford University United States School of Mathematics and Statistics HNP-LAMA Central South University China

Poisson’s equation is fundamental to the study of Markov chains, and arises in connection with martingale representations and central limit theorems for additive functionals, perturbation theory for stationary distributions, and average reward Markov decision process problems. In this paper, we develop a new probabilistic representation for the solution of Poisson’s equation, and use Lyapunov functions to bound this solution representation explicitly. In contrast to most prior work on this problem, our bounds are computable. Our contribution is closely connected to recent work of Hervé and Ledoux [2025], in which they focus their study on a special class of Harris chains satisfying a particular small set condition. However, our theory covers general Harris chains, and often provides a tighter bound. In addition to the new bound and representation, we also develop a computable uniform bound on marginal expectations for Harris chains, and a computable bound on the potential kernel representation of the solution to Poisson’s equation. Copyright © 2025, The Authors. All rights reserved.

关键词： Poisson equation

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The mathematics of mathematics: Using mathematics and Data science to Analyze the Mathematical sciences Community and Enhance Social Justice

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Matematica 2025年第1期4卷 110-125页

作者： Buckmire, Ron Hibdon, Joseph E. Lewis, Drew Ortega, Omayra Pabón, José L. Roca, Rachel Vindas-Meléndez, Andrés R. Mathematics Department Occidental College Los Angeles CA United States Department of Mathematics Northeastern Illinois University Chicago IL United States Independent Mathematician and Education Researcher Palo Alto CA United States Department of Mathematics & Statistics Sonoma State University Rohnert Park CA United States Mathematical Sciences Department New Jersey Institute of Technology Newark NJ United States Department of Computational Mathematics Science and Engineering Michigan State University East Lansing MI United States Department of Mathematics University of California Berkeley CA United States Department of Mathematics Harvey Mudd College Claremont CA United States

We present and discuss a curated selection of recent literature related to the application of quantitative techniques, tools, and topics from mathematics and data science that have been used to analyze the mathematical sciences community. We engage in this project with a focus on including research that highlights, documents, or quantifies inequality that exists in the mathematical sciences, specifically, and STEM (science, technology, engineering, and mathematics) more broadly. We seek to enhance social justice in the mathematics and data science communities by providing numerous examples of the ways in which the mathematical sciences fails to meet standards of equity, equal opportunity, and inclusion. We introduce the term “mathematics of mathematics” for this project, explicitly building upon the growing, interdisciplinary field known as “science of science” to interrogate, investigate, and identify the nature of the mathematical sciences itself. We aim to promote, provide, and posit sources of productive collaborations and we invite interested researchers to contribute to this developing body of work. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Data science quantitative justice Diversity Inequities Mathematical sciences community Social justice

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Statistical Multiplexing Gain Analysis of Centralized Open Radio Access Networks with Limited Cells and Heterogeneous Traffics

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IEEE Transactions on Vehicular Technology 2025年

作者： Wang, Lu Zhou, Yiqing Liu, Ya-Feng Liu, Ling Shi, Ningzhe Tian, Lin Shi, Jinglin National Key Laboratory of Transient Impact Beijing102202 China 208 Research Institute of China Ordnance Industries Beijing102202 China Chinese Academy of Sciences State Key Lab of Processors Institute of Computing Technology Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Beijing Key Laboratory of Mobile Computing and Pervasive Device Beijing100190 China Chinese Academy of Sciences State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Beijing100190 China Chinese Academy of Sciences Institute of Computing Technology Beijing100190 China Nanjing Institute of InforSuperBahn Nanjing211100 China

Centralized open radio access networks (O-RANs) aggregate computing resources of multiple cells to improve resource utilization and achieve statistical multiplexing gain (SMG), i.e., the ratio of the number of deployed resources (DepR) for distributed radio access network (D-RAN) to that for O-RAN. SMG of O-RAN has been investigated with infinite number of cells with homogeneous bursty-temporal traffic features. However, in practice, the number of cells is limited, and in the era of Internet of Things (IoT), cells with heterogeneous bursty-temporal traffics (het-traffics), should be considered. This paper analyzes the SMG of O-RAN with limited het-traffic cells. First of all, given traffic models of het-traffic cells following different bursty lognormal distribution and periodic time-varying features, required resources (ReqR) for D-RAN can be derived. Then, to obtain the distribution of ReqR for O-RAN with limited het-traffic cells, a Gaussian mixture model (GMM) based resource distribution approximation (GMM-RDA) is proposed. Thus SMG can be obtained for any given service rate. In simulations, the effectiveness of GMM-RDA is verified by Kolmogorov-Smirnov (K-S) test, and the accuracy of achieved SMG can be up to 99.7%, which is about 33% higher than that obtained by the law of large numbers (LLN) for infinite cells. Moreover, het-traffic cells resulting from service diversification can enhance SMG in O-RAN by 34.7%. © 1967-2012 IEEE.

关键词： Radio access networks

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HiGP: A high-performance Python package for Gaussian Process

arXiv

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arXiv 2025年

作者： Huang, Hua Xu, Tianshi Xi, Yuanzhe Chow, Edmond School of Computational Science and Engineering Georgia Institute of Technology AtlantaGA United States Department of Mathematics Emory University AtlantaGA United States

Gaussian Processes (GPs) [14, 11, 12] are flexible, nonparametric Bayesian models widely used for regression and classification tasks due to their ability to capture complex data patterns and provide uncertainty quantification (UQ). Traditional GP implementations often face challenges in scalability and computational efficiency, especially with large datasets. To address these challenges, HiGP, a high-performance Python package, is designed for efficient Gaussian Process regression (GPR) and classification (GPC) across datasets of varying sizes. HiGP combines multiple new iterative methods to enhance the performance and efficiency of GP computations. It implements various effective matrix-vector (MatVec) and matrix-matrix (MatMul) multiplication strategies specifically tailored for kernel matrices [20, 7, 2]. To improve the convergence of iterative methods, HiGP also integrates the recently developed Adaptive Factorized Nyström (AFN) preconditioner [21] and employs precise formulas for computing the gradients. With a user-friendly Python interface, HiGP seamlessly integrates with PyTorch and other Python packages, allowing easy incorporation into existing machine learning and data analysis workflows. © 2025, CC BY-SA.

关键词： Python

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Enhancing healthcare system for patient data with HEO-PRAFT consensus mechanism using SPS authentication based EPRSS encryption

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Biomedical Signal Processing and Control 2025年 109卷

作者： Chakraverti, Ashish Kumar Saini, Kavita Raj, Gaurav Sharad, Shwetav Singh, Murari Kumar Department of Mathematics and Computing Noida Institute of Engineering and Technology UP Greater Noida India Galgotias University NCR Delhi India Department of Computer Science and Engineering Sharda School of Computing Science & Engineering Sharda University UP Greater Noida India UP Ghaziabad India Department of Computer Science and Engineering Sharda School of Computing Science & Engineering Sharda University UP Greater Noida India

Owing to the advantages of immutability, blockchain has been proposed as a potential solution to facilitate the transmission of personal health data while maintaining anonymity and security. With its decentralized, unchangeable, and transparent characteristics, blockchain technology presents a viable way for addressing these issues. In proposed sophisticated EP (Elliptic curve-Proxy-Re-encryption) based RSS encryption (Ring Signature Scheme) system and Human Evolutionary Optimization (HEO) based effective Practical Byzantine Fault Tolerance (PBFT) enabled RAFT (HEO-PRAFT) is developed for secured EMR sharing and storing in blockchain. The suggested approach is to obtain patient information from standard hospitals’ Electronic Medical Records (EMRs). Subsequently, the gathered data are verified by the use of a structural preserving scheme (SPS), and the verified data are encrypted through the EP-RSS scheme. A public–private key pair for the user is generated in advance EPRSS scheme. The encrypted EMR data is stored in the cloud. Then HEO-PRAFT based consensus process is used to hash the encrypted data and add it to the blockchain. The healthcare data is safely transferred to the blockchain for effective and secure patient data storage based on this enhanced PRAFT. The method uses EPRSS-based access control to ensure privacy protection and offer customized access control. The obtained experimental outcomes, including transaction delay, block creation time, encryption time, decryption times, jitter, and throughput, are 4.51 ms, 3.71 ms, 4.56 ms, 4.85 ms, 4.09 ms, and 539mb/s. Ultimately, EPRSS and HEO-PRAFT based encryption and consensus methods enhance the security of the healthcare system. © 2025

关键词： Authentication

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On the temporal sensitivity of the reference data in bias correction techniques for improving metocean datasets 6th

On the temporal sensitivity of the reference data in bias co...

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6th International Conference on Renewable Energies Offshore, RENEW 2024

作者： Callea, F. Martinez-Perurena, A. Zarketa-Astigarraga, A. Penalba, M. Cervelli, G. Giorgi, G. Robertson, B. Iglesias, G. Fluid Mechanics Department Mondragon University Gipuzkoa Arrasate Spain Ikerbasque - Basque Foundation for Science Bizkaia Bilbao Spain Politecnico di Torino Torino Italy Civil and Construction Engineering Oregon State University CorvallisOR United States UCC MaREI Environmental Research Institute and School of Engineering Cork Ireland School of Engineering Computing and Mathematics University of Plymouth Plymouth United Kingdom

ISBN: (纸本)9781032905570

The paper presents a preliminary study limited to the use of wave height for the data corresponding to the Gulf of Biscay, a location with a very dominant North-West wave rose. It benchmarks different bias correction (BC) techniques and evaluates their performance, as well as the sensitivity of the BC to the reference dataset employed for the identification of the BC parameters. More precisely, the amount of data (number of years) and the selected period (exact years) are analysed. Overall, the results demonstrate that the Gumbel-based BC techniques overperform the linearly-spaced BC techniques, the directional-adjusted Gumbel Quantile Mapping technique showing the lowest bias. With respect to the sensitivity of the reference data, BC seems to provide satisfactory results even when only 1 year of data are used. However, the dispersion among the different selected periods is large, resulting in large uncertainties. This dispersion reduces significantly once 3 or more years of data are used, independently of the selected period. © 2025 The Author(s).

关键词： Dispersion (waves)

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Two-plasmon-decay instability stimulated by dual laser beams in inertial confinement fusion

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Matter and Radiation at Extremes 2025年第1期10卷 48-56页

作者： C.-W.Lian Y.Ji R.Yan J.Li L.-F.Wang Y.-K.Ding J.Zheng Department of Modern Mechanics University of Science and Technology of ChinaHefeiAnhui 230026China Collaborative Innovation Center of IFSA Shanghai Jiao Tong UniversityShanghai 200240China Department of Plasma Physics and Fusion Engineering University of Science and Technology of ChinaHefei 230026China Institute of Applied Physics and Computational Mathematics Beijing 100088China

Two-plasmon-decay instability(TPD)poses a critical target preheating risk in direct-drive inertial confinement *** this paper,TPD collectively driven by dual laser beams consisting of a normal-incidence laser beam(Beam-N)and a large-angle-incidence laser beam(Beam-L)is investigated via particle-in-cell *** is found that significant TPD growth can develop in this regime at previously unexpected low laser intensities if the intensity of Beam-L exceeds the large-angle-incidence *** beams contribute to the growth of TPD in a“seed-amplification”manner in which the absolute instability driven by Beam-L provides the seeds that are convectively amplified by Beam-N,making TPD energetically important and causing significant pump depletion and hot-electron generation.

关键词： laser pump decay

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Formation mechanism of multiple spallation and its penetration induced by shear localization in NiTi alloy under implosion loading

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Matter and Radiation at Extremes 2025年第1期10卷 81-97页

作者： Xianye Wu Xiaoyang Pei Xiang Chen Hao Zhang Jin Wang Xin Yang Meizhen Xiang Shang Gao Fang Wang School of Materials and Energy Southwest UniversityChongqing 400715China Institute of Fluid Physics China Academy of Engineering PhysicsMianyang 621999China State Key Laboratory of Nuclear Physics and Technology Center for Applied Physics and TechnologyPeking UniversityBeijing 100871China School of Environment and Resource Southwest University of Science and TechnologyMianyang 621010China Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsBeijing 100088China

This study uses nonequilibrium molecular dynamics simulations to explore the dynamic failures and deformation mechanisms of a cylindrical shell composed of nanocrystalline nickel-titanium alloy under implosion *** discover that some individual spall planes are sequentially generated in the material along the propagation of a radial stress wave,indicative of the formation of multiple *** larger grain sizes,void nucleation at the first spallation occurs in a coexisting intergranular/transgranular manner,whereas with decreasing grain size,voids tend to nucleate along the grain ***,the spall strength exhibits a transition from an inverse Hall-Petch to a Hall-Petch *** larger grain sizes,at the secondary spallation,localized shearing zones and grain boundaries provide potential void-nucleated ***,the formation of shear deformation bands promotes grain refinement,contributing to a reduction in the dislocation-induced strengthening ***,a lower spall strength is produced,in contrast to the first *** the grain size becomes smaller,voids nucleate mostly along grain boundaries,and plastic deformation is dominated by dense grain ***,the high temperature caused by shear localization leads to material weakening,and in turn there is a significant decrease in the spall strength for the secondary spallation,compared with the ***,significant penetration between two spall planes is observed for large grain size,which can be attributed to the nucleation of voids on linking grain boundaries,with temperatures exceeding the melting point of the material.

关键词： grain alloy deformation

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