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arXiv 2025年

作者： Leng, Jiaqi Zheng, Yufan Jia, Zhiyuan Fan, Lei Zhao, Chaoyue Peng, Yuxiang Wu, Xiaodi Simons Institute for the Theory of Computing University of California Berkeley United States Department of Mathematics University of California Berkeley United States Department of Computer Science University of Maryland College Park United States Joint Center for Quantum Information and Computer Science University of Maryland United States Department of Applied Mathematics University of Washington United States Department of Engineering Technology University of Houston United States Department of Electrical and Computer Engineering University of Houston United States Department of Industrial and Systems Engineering University of Washington United States Artephi Computing Inc United States

Non-smooth optimization models play a fundamental role in various disciplines, including engineering, science, management, and finance. However, classical algorithms for solving such models often struggle with convergence speed, scalability, and parameter tuning, particularly in high-dimensional and non-convex settings. In this paper, we explore how quantum mechanics can be leveraged to overcome these limitations. Specifically, we investigate the theoretical properties of the Quantum Hamiltonian Descent (QHD) algorithm for non-smooth optimization in both continuous and discrete time. First, we propose continuous-time variants of the general QHD algorithm and establish their global convergence and convergence rate for non-smooth convex and strongly convex problems through a novel Lyapunov function design. Furthermore, we prove the finite-time global convergence of continuous-time QHD for non-smooth non-convex problems under mild conditions (i.e., locally Lipschitz). In addition, we propose discrete-time QHD, a fully digitized implementation of QHD via operator splitting (i.e., product formula). We find that discrete-time QHD exhibits similar convergence properties even with large time steps. Finally, numerical experiments validate our theoretical findings and demonstrate the computational advantages of QHD over classical non-smooth non-convex optimization algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Continuous time systems

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Fine-Tuned Yolo V-10X: Real-Time Object Detection for Autonomous Vehicles Under Multiple Weather Conditions

Fine-Tuned Yolo V-10X: Real-Time Object Detection for Autono...

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Safa Teboulbi Seifeddine Messaoud Mohamed Ali Hajjaji Mohamed Atri Abdellatif Mtibaa Higher Institute of Computer Sciences and Mathematics of Monastir Monastir University Monastir Tunisia Faculty of Sciences of Monastir Monastir University Monastir Tunisia Higher Institute of Applied Sciences and Technology of Sousse Sousse University Sousse Tunisia Computer Engineering Department College of Computer Science King Khalid University Abha Saudi Arabia Systems Integration & Emerging Energies Laboratory National Engineering School of Sfax Sfax University Sfax Tunisia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Detecting objects such as vehicles, buildings, pedestrians, and road signs is indispensable to advancing the concept of autonomous and self-driving cars. Furthermore, an autonomous vehicle (AV) must accurately detect its surrounding environment to operate reliably. Most object detection (OD) techniques perform adequately under typical weather conditions, including cloudy or sunny days. However, their efficiency decreases significantly when exposed to Adverse Weather Conditions (AWCs), including days with sandstorm, rain, fog or snow. Complex and computationally costly models are required to achieve high accuracy rates. In this study, we present an improved OD system in AWCs for autonomous vehicles (AVs) using the single-stage deep learning (DL) algorithm YOLO (You Only Look Once) version 10. To evaluate our system, Vehicle Detection in Adverse Weather Nature (DAWN) dataset is used. It comprises real-world images captured under various types of AWCs. The experimental findings confirm that the suggested method is effective and surpasses state-of-the-art OD approaches under AWCs.

关键词： YOLO Adaptation models Accuracy Rain Smart cities Snow Roads Real-time systems Reliability Meteorology

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The potential for electrifying industrial utility systems in existing chemical plants

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applied Energy 2025年 392卷

作者： Bielefeld, Svenja Cvetković, Miloš Ramírez, Andrea Engineering Systems and Services Department Faculty of Technology Policy and Management Delft University of Technology Delft Netherlands Electrical Sustainable Energy Department Faculty of Electrical Engineering Mathematics & Computer Science Delft University of Technology Delft Netherlands Department of Chemical Engineering Faculty of Applied Science Delft University of Technology Delft Netherlands

The electrification of utility systems in energy-intensive plants is a promising measure for decarbonising the chemical industry in the short term. However, with the increasing deployment of renewable energy sources, the variability of electricity prices will become a challenge for plants with continuous and constant energy demand. It is thus uncertain whether electrification can become financially viable. This work models the electrification of utility systems in combination with storage technologies for five chemical plants with existing fossil fuel-based utility generation and uses historical data as energy price scenarios. The results show that partial electrification is cost-effective when using electricity is cheaper than natural gas for more than 600 h. Regarding the portfolio of technologies, electric boilers are installed first, followed by thermal energy storage and batteries. Hydrogen is not cost-effective in any of the scenarios explored. This is independent of the type of plant, the available grid connection capacity, and the minimal load of existing fossil fuel-based utility generation. This work thus highlights the potential for electrifying industrial utility systems and the role that electric boilers and energy storage units can play in electrification. © 2025 The Author(s)

关键词： Chemical plants

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Subgrid informed neural networks for high-resolution flood mapping

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Journal of Hydrology 2025年 660卷

作者： Herath, Herath Mudiyanselage Viraj Vidura Marshall, Lucy Saha, Abhishek Rasnayaka, Sanka Seneviratne, Sachith Faculty of Science and Engineering Macquarie University SydneyNSW Australia Faculty of Engineering The University of Sydney SydneyNSW Australia Delft Institute of Applied Mathematics Delft University of Technology Delft Netherlands Hydroinformatics Institute Singapore Department of Computer Science School of Computing National University of Singapore Singapore Transport Health and Urban Systems Research Lab Melbourne School of Design The University of Melbourne MelbourneVIC Australia Faculty of Engineering and Information Technology The University of Melbourne MelbourneVIC Australia

Physics-based hydrodynamic models are essential for accurate flood prediction but are computationally expensive, limiting their applicability for real-time forecasting and probabilistic analyses. Conversely, pure machine learning (ML) models offer both computational efficiency and accuracy but often lack interpretability. To address this gap, we propose SGUnet, a physics-informed ML model and a hybrid theory-guided data science approach, for rapid, high-resolution flood mapping. It utilizes a neural network with U-Net architecture and integrates subgrid-based coarse-grid hydrodynamic model predictions as initial estimates, upskilling them to achieve fine-grid model accuracy. Unlike traditional hydrodynamic models, the subgrid method embeds fine-scale topographic details within coarse-grid cells, enhancing both computational efficiency and predictive accuracy. SGUnet processes flood depth raster patches (512 × 512 pixels) and corresponding digital elevation models as inputs. It functions as a deep learning-based corrector, refining flood predictions from numerical simulators. Trained through supervised learning, SGUnet learns to correct deviations in coarse-grid predictions using fine-grid model outputs as target values. The model is evaluated across three large Australian watersheds—Wollombi, Chowilla, and Burnett River—using HEC-RAS flood simulations with subgrid formulation. SGUnet reduces root mean squared error by a factor of 4.5–5.3 compared to coarse-grid models, achieves a critical success index exceeding 0.9 for flood extent mapping, and delivers a 50x speed-up over fine-grid hydrodynamic models. Furthermore, SGUnet outperforms a state-of-the-art ML-based upskilling model in depth and extent predictions. By effectively correcting flood artifacts from coarse-grid models, SGUnet achieves near fine-grid accuracy with significantly reduced computational cost, demonstrating its potential for real-time flood risk assessment. © 2025 The Authors

关键词： Deep learning

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Automatic position and shape optimization of flexible TMS coils to improve coil placement during electric field simulations

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Brain Stimulation 2025年第1期18卷 293-294页

作者： Rumi, Bianka Worbs, Torge Madsen, Kristoffer H. Thielscher, Axel Danish Research Centre for Magnetic Resonance Centre for Functional and Diagnostic Imaging and Research Copenhagen University Hospital - Amager and Hvidovre Copenhagen Denmark Section for Cognitive Systems Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs Lyngby Denmark Sino-Danish Center for Education and Research Aarhus Denmark Section for Magnetic Resonance DTU Health Tech Technical University of Denmark Kgs Lyngby Denmark

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A secure and trustworthy blockchain-assisted edge computing architecture for industrial internet of things

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Scientific Reports 2025年第1期15卷 1-17页

作者： Asaithambi, Sasikumar Nallusamy, Senthilkumar Yang, Jing Prajapat, Sunil Kumar, Gyanendra Rathore, Pramod Singh Department of Data Science and Business Systems Faculty of Engineering and Technology SRM Institute of Science & Technology Tamil Nadu Kattankulathur 603203 India Centre for Applied Nanomaterials Chennai Institute of Technology Tamil Nadu Chennai 600069 India Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur 50603 Malaysia Department of Mathematics Central University of Himachal Pradesh Dharamshala 176215 India Department of IoT and Intelligent Systems Manipal University Jaipur Jaipur 303007 India Department of Computer and Communication Engineering Manipal University Jaipur Jaipur 303007 India

The Industrial Internet of Things (IIoT) seeks to improve smart factory productivity by leveraging automation and scalability. For automation in industry, optimization, collaboration and protection, and scalability, the IoTs paradigm, technology for communication and information, and intelligent systems are integrated as a single organism. This article presents a blockchain-assisted safe data-sharing mechanism that provides security requirements in the industry using IoT. End-to-end authentication is developed based on the blockchain’s reputation, and the smart contract is used to validate nodes’ security measures. Through integrity verification and categorization in node terminals and industry, the blockchain paradigm manages data collection and dissemination. An efficient proof of authentication (PoAh) consensus mechanism is created using the blockchain network to build a collaborative network to preserve logs and verification data in the industrial IoT. It accomplishes trustworthy authentication and endpoint activity tracing, and edge computing is used in blockchain nodes to offer device authentication processes that utilize smart contracts and PoAh. According to an experimental study, the proposed architecture lowers the authentication time and obtains a high response rate. The proposed system’s service time shows the efficiency of PoAh-based blockchain architecture for industrial IoT compared with existing works. Finally, we evaluated various block sizes to ensure an efficient transaction rate. The outcomes demonstrate the practicality of the suggested and put-into-practice architecture, distinguished by enhanced data audibility, device data ownership, security, and privacy while utilizing decentralized storage. © The Author(s) 2025.

关键词： Authentication Blockchain Edge computing Industrial internet of things PoAh

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Dynamic thermo-mechanical coupled response of random particulate composites:A statistical two-scale method

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Chinese Physics B 2014年第7期23卷 605-616页

作者：杨自豪陈云杨志强马强 Department of Applied Mathematics Northwestern Polytechnical University College of Computer Science Fujian Agriculture and Forestry University LSEC ICMSECAcademy of Mathematics and Systems ScienceChinese Academy of Sciences

This paper focuses on the dynamic thermo-mechanical coupled response of random particulate composite materials. Both the inertia term and coupling term are considered in the dynamic coupled problem. The formulation of the problem by a statistical second-order two-scale （SSOTS） analysis method and the algorithm procedure based on the finite-element difference method are presented. Numerical results of coupled cases are compared with those of uncoupled cases. It shows that the coupling effects on temperature, thermal flux, displacement, and stresses are very distinct, and the micro- characteristics of particles affect the coupling effect of the random composites. Furthermore, the coupling effect causes a lag in the variations of temperature, thermal flux, displacement, and stresses.

关键词： random particulate composites statistical second-order two-scale （SSOTS） analysis method,thermo-mechanical coupling effect numerical algorithm

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Random Normed k-Means: A Paradigm-Shift in Clustering within Probabilistic Metric Spaces

arXiv

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arXiv 2025年

作者： Hemdanou, Abderrafik Laakel Achtoun, Youssef Sefian, Mohammed Lamarti Tahiri, Ismail Afia, Abdellatif El Applied Mathematics and computer science team Higher normal school Martil Morocco National Higher School for Computer Science and Systems Analysis Mohammed V University in Rabat Rabat Morocco

Existing approaches remain largely constrained by traditional distance metrics, limiting their effectiveness in handling random data. In this work, we introduce the first k-means variant in the literature that operates within a probabilistic metric space, replacing conventional distance measures with a well-defined distance distribution function. This pioneering approach enables more flexible and robust clustering in both deterministic and random datasets, establishing a new foundation for clustering in stochastic environments. By adopting a probabilistic perspective, our method not only introduces a fresh paradigm but also establishes a rigorous theoretical framework that is expected to serve as a key reference for future clustering research involving random data. Extensive experiments on diverse real and synthetic datasets assess our model’s effectiveness using widely recognized evaluation metrics, including Silhouette, Davies-Bouldin, Calinski-Harabasz, the adjusted Rand index, and distortion. Comparative analyses against established methods such as k-means++, fuzzy c-means, and kernel probabilistic k-means demonstrate the superior performance of our proposed random normed k-means (RNKM) algorithm. Notably, RNKM exhibits a remarkable ability to identify nonlinearly separable structures, making it highly effective in complex clustering scenarios. These findings position RNKM as a groundbreaking advancement in clustering research, offering a powerful alternative to traditional techniques while addressing a long-standing gap in the literature. By bridging probabilistic metrics with clustering, this study provides a foundational reference for future developments and opens new avenues for advanced data analysis in dynamic, data-driven applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Distribution functions

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Structure in conversation: Evidence for the vocabulary, semantics, and syntax of prosody

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Proceedings of the National Academy of sciences of the United States of America 2025年第17期122卷 e2403262122页

作者： Matalon, Nadav Weinreb, Eyal Freche, Dominik Volk, Erez Biron, Tirza Moses, Elisha Biron, David Department of Physics of Complex Systems Weizmann Institute of Science Rehovot 7610001 Israel Department of Linguistics Hebrew University of Jerusalem Jerusalem 9190501 Israel NeuraLight Inc. Tel Aviv 6713818 Israel Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot 7610001 Israel The Data Science Institute The University of Chicago Chicago 60637 IL United States The Department of Statistics The University of Chicago Chicago 60637 IL United States

Prosody, the musical facet of speech, is pivotal in human communication, and its structure and meaning remain subjects of ongoing research. In this study, we introduce a data-driven model for English prosody, based on large-scale analysis of spontaneous conversations. As a first step, we identify approximately 200 discernible prosodic patterns—which we view as building blocks of the prosodic vocabulary—and outline their properties and range of meanings. Next, we reveal a Markovian logic, akin to a syntax, for concatenating these elementary building blocks into coherent utterances. We identify distinct compound functions associated with pairs of consecutive patterns and show that the Markovian syntax is more prevalent in spontaneous prosody, as compared to scripted speech. These findings offer invaluable insights into the underlying mechanisms of conversational prosody: They empirically inform and refine existing theoretical concepts. The methodology we present, combining unsupervised analysis of large datasets of spontaneous speech with manual sampling of the results, could guide future research aimed at refining our model and expanding it to other languages. Copyright © 2025 the Author(s).

关键词： language prosody speech unsupervised analysis

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Quantum Dynamics Simulation of the Advection-Diffusion Equation

arXiv

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arXiv 2025年

作者： Alipanah, Hirad Zhang, Feng Yao, Yongxin Thompson, Richard Nguyen, Nam Liu, Junyu Givi, Peyman McDermott, Brian J. Mendoza-Arenas, Juan José Department of Mechanical Engineering and Materials Science University of Pittsburgh PittsburghPA15261 United States Ames National Laboratory U.S. Department of Energy AmesIA50011 United States Department of Physics and Astronomy Iowa State University AmesIA50011 United States Integrated Vehicle Systems Applied Mathematics Boeing Research & Technology HuntsvilleAL35824 United States Integrated Vehicle Systems Applied Mathematics Boeing Research & Technology Huntington BeachCA92647 United States Department of Computer Science University of Pittsburgh PittsburghPA15261 United States Department of Physics and Astronomy University of Pittsburgh PittsburghPA15261 United States Naval Nuclear Laboratory SchenectadyNY12301 United States

The advection-diffusion equation is simulated on a superconducting quantum computer via several quantum algorithms. Three formulations are considered: (1) Trotterization, (2) variational quantum time evolution (VarQTE), and (3) adaptive variational quantum dynamics simulation (AVQDS). These schemes were originally developed for the Hamiltonian simulation of many-body quantum systems. The finite-difference discretized operator of the transport equation is formulated as a Hamiltonian and solved without the need for ancillary qubits. Computations are conducted on a quantum simulator (IBM Qiskit Aer) and an actual quantum hardware (IBM Fez). The former emulates the latter without the noise. The predicted results are compared with direct numerical simulation (DNS) data with infidelities of the order 10−5. In the quantum simulator, Trotterization is observed to have the lowest infidelity and is suitable for fault-tolerant computation. The AVQDS algorithm requires the lowest gate count and the lowest circuit depth. The VarQTE algorithm is the next best in terms of gate counts, but the number of its optimization variables is directly proportional to the number of qubits. Due to current hardware limitations, Trotterization cannot be implemented, as it has an overwhelming large number of operations. Meanwhile, AVQDS and VarQTE can be executed, but suffer from large errors due to significant hardware noise. These algorithms present a new paradigm for computational transport phenomena on quantum computers. © 2025, CC BY.

关键词： Hamiltonians

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