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Data-driven model discovery with Kolmogorov-Arnold networks

Physical Review Research 2025年第2期7卷 023037-023037页

作者： Shirin Panahi Mohammadamin Moradi Erik M. Bollt Ying-Cheng Lai School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona 85287 USA Department of Electrical and Computer Engineering Clarkson Center for Complex Systems Science Clarkson University Potsdam New York 13699 USA Department of Physics Arizona State University Tempe Arizona 85287 USA

Data-driven model discovery of complex dynamical systems can be done using sparse optimization, but it has a fundamental limitation: sparsity in that the underlying governing equations of the system contain only a small number of elementary mathematical functions. Examples where sparse optimization fails abound, including the classic Ikeda or optical-cavity map in nonlinear dynamics, as well as a wide variety of ecosystems. Another approach is based on machine learning, e.g., deep neural networks, which excels at capturing system behavior from data but functions as black boxes, offering little insight into how inputs influence the outputs. We propose a general model-discovery framework based on the Kolmogorov-Arnold networks (KANs) that are not constrained by the sparsity condition. The KAN framework with a simple structure is capable of accurately capturing the complex behavior of dynamical systems that do not meet the sparsity requirement while offering greater interpretability compared to conventional neural networks. This interpretability provides insights into the dynamics generating the data, which are typically inaccessible in traditional black-box function approximation methods. We demonstrate nonuniqueness in that a large number of approximate models of the system can be found that generate the same invariant set with the true statistics such as the Lyapunov exponents and Kullback–Leibler divergence. An analogy to shadowing of numerical trajectories in chaotic systems is pointed out.

关键词： Deep neural networks

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Impact of diameter on D-shaped POF sensors for salt detection

Impact of diameter on D-shaped POF sensors for salt detectio...

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International Laser Technology and Optics Symposium 2024, iLATOS2024

作者： Khan, Abdul Ali Thirunavakkarasu, Punithavathi M. bte Saidin, Norazlina Noor, Ahmad Shukri Muhammad Waqas, Nawaf Communication Technology Section Universiti Kuala Lumpur British Malaysian Institute Kuala lumpur53100 Malaysia Faculty of I.C.T BUITEMS Quetta87100 Pakistan Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur53100 Malaysia Department of Computer and Communication Systems Engineering Faculty of Engineering Universiti Putra Malaysia Selangor Malaysia Department of Instrumentation and Control Engineering Universiti Kuala Lumpur British Malaysian Institute Bt. 8 Jalan Sungai Pusu Selangor Gombak53100 Malaysia

This study investigates the relationship between the diameter of a D-shaped plastic optical fiber (POF) sensor and its optical response as a function of varying salt concentrations. A simple and efficient dry etching polishing technique was employed to create D-shaped POF sensors with a range of diameters. All the fabricated sensors were carefully developed to maintain a consistent length of 20 cm with a sensing region of 1cm. To evaluate the optical intensity response, each sensor, with its respective diameter, was examined with the salt concentration ranging from (0 to 5%) examination utilizing a spectrometer. The sensor response was characterized by quantifying the variations observed in the intensity of the optical signal propagating through the sensing region. The sensors having a diameter of 500 μm, 700 μm, and 900 μm had a sensitivity of 149 A.U/%salt, 66 A.U/%salt, and 11.2 A.U/%salt respectively. The sensitivity of the 500 μm diameter sensor increased 13 times approximately as compared to the sensor having diameter of 900 μm. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Dry etching

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SpikeSpec: An On-chip Learning Neuromorphic Accelerator for Spectrum Sensing With Triplet-Boosting and Hardware Friendly Loss Function

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IEEE Transactions on computer-Aided Design of Integrated Circuits and systems 2025年

作者： Azmine, Muhammad Farhan Li, Ruizhe Sharma, Gauri Yi, Yang Virginia Tech Department of electrical and computer engineering BlacksburgVa United States United States Multifunctional Integrated Circuits and Systems Lab United States

Spectrum sensing is a pivotal function in next-generation (G) Multiple-Input-Multiple-Output (MIMO) communication systems, tasked with detecting and characterizing the occupancy or availability of frequency bands within the radio spectrum. Conventional spectrum sensing techniques are hindered by challenges such as hardware complexity and the Signal-to-Noise Ratio (SNR) wall, leading to suboptimal performance in environments with high noise levels. Recurrent Neural Networks (RNNs), particularly Liquid State Machines (LSMs), are highly effective for developing energy-efficient accelerators, as they efficiently capture temporal dependencies in primary user frequency spectrums with a reduced number of trainable parameters. This paper introduces an innovative FPGA accelerator based on Liquid State Machines (LSMs), featuring fully integrated on-chip learning capabilities for spectrum sensing. Our accelerator leverages Reward-based Spike Timing-Dependent Plasticity (R-STDP) to discern temporal correlations within the related frequency band. Although traditional R-STDP methods face convergence difficulties during on-chip learning, the introduced approach overcomes this challenge with a novel, hardware-efficient loss function. This mechanism which is also hardware friendly, facilitates accelerated convergence with reduction of training period of about 46.67% in spectrum sensing classification for hardware implementation. Moreover, we implemented a fully asynchronous, low-latency, unsupervised Triplet-based STDP learning mechanism in the LSM accelerator reservoir, which improves training accuracy by about 3.88% in high-noise channels while enhancing reconfigurability. Furthermore, we investigated various encoder mechanisms to identify the most efficient encoder architecture for our LSM, leading to an accuracy increase of 6.11%. Our optimized LSM architecture achieved 1.27 times and 2.01 times the LUT and register counts, respectively, compared to the basic fixed-reservoir LSM

关键词： Recurrent neural networks

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Interpretable CNN-Based Lithographic Hotspot Detection Through Error Marker Learning

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IEEE Transactions on computer-Aided Design of Integrated Circuits and systems 2025年第3期44卷 1031-1044页

作者： Sun, Haoyang Jiang, Cong Ye, Xun Feng, Dan Tan, Benjamin Ma, Yuzhe Liu, Kang Huazhong University of Science and Technology School of Computer Science and Technology Wuhan430074 China Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Wuhan430074 China University of Calgary Department of Electrical and Software Engineering CalgaryABT2N 1N4 Canada With Microelectronics Thrust Guangzhou510530 China

As the technology node develops toward its physical limit, lithographic hotspot detection has become increasingly important and ever-challenging in the computer-aided design (CAD) flow. In recent years, convolutional neural networks (CNNs) have achieved great success in hotspot detection. However, the interpretability of their hotspot prediction has yet to be considered. Compared with conventional lithography simulation and pattern matching-based methods, the black-box nature of CNNs wavers their practical applications with confidence. In this article, we propose the first interpretable CNN-based hotspot detector capable of providing high-detection accuracy and reliable explanations for hotspot identification. Specifically, we augment the training dataset with expanded error markers obtained and preprocessed from lithography simulation, which are then learned by an encoder-decoder architecture as intermediate features. We additionally introduce coordinate attention in the encoder to facilitate better-feature extraction. By learning these error markers and part of their surrounding metals as root cause hotspot features, our architecture achieves the highest-hotspot accuracy of 99.78% and the lowest-false positive rate of 5.29% compared to all prior work. Moreover, our method demonstrates the best visual and quantitative interpretability results when applying CNN interpretation methods. © 2024 IEEE.

关键词： Pattern matching

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MWRS: A MAB-based Worker Recruitment Scheme with Tripartite Stackelberg Game for Reliable Mobile Crowdsensing

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IEEE Transactions on Mobile Computing 2025年

作者： Ouyang, Yan Zeng, Feng Xiong, Neal N. Liu, Anfeng Pedrycz, Witold School of Computer Science and Engineering Central South University Changsha410083 China Department of Computer Science and Mathematics Sul Ross State University AlpineTX79830 United States Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Istinye University Istanbul Turkey Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia

Mobile Crowdsensing (MCS) has emerged as a compelling paradigm for data sensing and collection, leveraging the widespread adoption of mobile devices and the active participation of numerous users. Despite its potential, MCS faces critical challenges, particularly in recruiting reliable workers and acquiring high-quality sensing data. Most existing approaches assume prior information on worker quality and are vulnerable to collusion attacks, especially having not comprehensively considered workers' reliability and stability. To address these problems, we propose a Multi-Armed Bandit (MAB) based Worker Recruitment Scheme (MWRS) integrated with the Tripartite Stackelberg Game (TSG) for MCS. Specifically, a trust evaluation and truth inference mechanism is introduced to assess the trustworthiness of workers through active truth detection. To enhance recruitment quality, we employ a trust-aware worker selection mechanism that utilizes a modified Upper Confidence Bound (UCB) algorithm, achieving an optimal balance between exploration and exploitation. Furthermore, the interactions among participants are modeled using a TSG framework, which formulates their respective payoffs to determine optimal decision-making strategies, thus achieving mutually beneficial outcomes. Extensive evaluations on real-world datasets demonstrate that our proposed scheme improves total quality by up to 30.8% and reduces regret by up to 80.3% compared to existing methods. © 2025 IEEE.

关键词： Decision making

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Strategies for Effective EEG Preprocessing

Strategies for Effective EEG Preprocessing

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Learning and Technology Conference (L&T)

作者： Nema Salem Nora Alamoudi Lama Mustafa Fatima Bagais Electrical Research Laboratory College of Engineering Effat University Jeddah Saudi Arabia Department of Electrical and Computer Engineering College of Engineering Effat University Jeddah Saudi Arabia

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

The administration of anesthesia is critical for ensuring the safety of surgical procedures, yet it comes with inherent risks associated with medication mistakes. Monitoring EEG signals during anesthesia is crucial for understanding patient physiology; however, the accuracy of this information heavily relies on effectively eliminating noise. Decreasing noise in EEG signals is vital for accurately interpreting data, ultimately enhancing patient safety and refining anesthesia management. This research concentrates on an advanced preprocessing technique for EEG signals, employing segmentation, detrending, variational mode decomposition (VMD), and noise reduction through Gaussian filtering, moving averages, Independent Component Analysis (ICA), and Discrete Wavelet Transform (DWT). The study utilized EEG data collected during surgeries and applied these preprocessing approaches to reduce noise and artifacts. Our results indicate that employing the Discrete Wavelet Transform (DWT) for denoising is robust and produces superior outcomes compared to other techniques investigated, achieving a high Signal-To-Noise Ratio (SNR) of 43.23 dB.

关键词： Noise reduction Surgery Transforms Anesthesia Wavelet analysis Electroencephalography Discrete wavelet transforms Safety Monitoring Signal to noise ratio

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Z-Stack Scanning can Improve AI Detection of Mitosis: A Case Study of Meningiomas

Z-Stack Scanning can Improve AI Detection of Mitosis: A Case...

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IEEE International Symposium on Biomedical Imaging

作者： Hongyan Gu Ellie Onstott Wenzhong Yan Tengyou Xu Ruolin Wang Zida Wu Xiang Anthony Chen Mohammad Haeri Department of Electrical and Computer Engineering University of California Los Angeles Department of Pathology and Laboratory Medicine University of Kansas Medical Center

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Z-stack scanning is an emerging whole slide imaging technology that captures multiple focal planes alongside the z-axis of a glass slide. Because z-stacking can offer enhanced depth information compared to the single-layer whole slide imaging, this technology can be particularly useful in analyzing small-scaled histopathological patterns. However, its actual clinical impact remains debated with mixed results. To clarify this, we investigate the effect of z-stack scanning on artificial intelligence (AI) mitosis detection of meningiomas. With the same set of 22 Hematoxylin and Eosin meningioma glass slides scanned by three different digital pathology scanners, we tested the performance of three AI pipelines on both single-layer and z-stacked whole slide images (WSIs). Results showed that in all scanner-AI combinations, z-stacked WSIs significantly increased AI's sensitivity (+17.14%) on the mitosis detection with only a marginal impact on precision. Our findings provide quantitative evidence that highlights z-stack scanning as a promising technique for AI mitosis detection, paving the way for more reliable AI-assisted pathology workflows, which can ultimately benefit patient management.

关键词： Deep learning Image segmentation Sensitivity Microscopy Pipelines Glass Reliability Artificial intelligence Tumors Testing

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Optimal coordination of Production, Logistics, and Maintenance 4.0: A framework for Multi-Site Stamping Plants

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Procedia computer Science 2025年 253卷 2899-2908页

作者： Raoua Zerrad Kamar Diaz Meriem Hayani Mechkouri Naoufal Sefiani Department of Mechanical Engineering Research Laboratory in Engineering Innovation and Management of Industrial Systems Faculty of Sciences and Techniques Abdelmalek Essaâdi University Tangier Morocco Department of industrial Engineering and Maintenance Computer Engineering Production and Maintenance Laboratory University of Lorraine Metz France

This paper presents a comprehensive framework for optimal coordination of production, logistics, and maintenance in multi-site stamping plants within the context of Industry 4.0. By integrating key technologies such as the Internet of Things, cloud computing and big data analytics, we developed a system that would enable real-time data sharing, predictive analytics, and intelligent decision-making. This framework aims to enhance operational efficiency, reduce downtime, and improve service rate. Additionally, our approach aims to optimize spare parts inventory management across different sites, minimizing inventory costs and lead times by ensuring timely availability and efficient sourcing of spare parts.

关键词： Industry 4.0 Maintenance 4.0 Production 4.0 Logistics 4.0 Stamping machine Spare parts Internet of things Prognostics Health Management

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Transcoding-Enabled Edge Caching Strategy Optimization: A Dual-Timescale Meta-Learning Based Stackelberg Game Approach

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IEEE Internet of Things Journal 2025年

作者： Wang, Dan Zhu, Keke Song, Bin Yu, F. Richard Xidian University State Key Laboratory of Integrated Services Networks 710071 China Carleton University Department of Systems and Computer Engineering Ottawa Canada

The explosive growth in video services has significantly strained current mobile network infrastructure, leading to spectrum scarcity, backhaul congestion, and degraded quality of experience. While edge caching has emerged as a promising solution to address these challenges and deliver seamless video playback experience, multi-version edge caching for heterogeneous clients remains challenging due to varying client requirements and network conditions. This paper proposes a transcoding enabled edge caching framework for mobile edge-cloud computing networks. Specifically, we combine video transcoding with edge caching to support both "direct cache hits" and "soft cache hits" for reducing transmission latency. We model this joint caching and resource allocation problem as a Stackelberg game to minimize video transmission latency. To solve this problem, we develop a novel dual timescale Model Agnostic Meta-Learning (MAML) based Stackelberg game (DTMSG) optimization approach that determines the delay-optimal Stackelberg equilibrium (SE) and accelerates convergence. Simulation results demonstrate that our DTMSG optimization algorithm efficiently converges to the SE point, maximizing the utility function of the MVNO and BSs while reducing the average video transmission delay. © 2014 IEEE.

关键词： Mobile edge computing

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Onboard Perception-Assisted High Fidelity Simulation Framework for Autonomous Planetary Soft-Landing

Onboard Perception-Assisted High Fidelity Simulation Framewo...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Alibekov, Ulugbek Banerjee, Avijit Satpute, Sumeet Gajanan Nikolakopoulos, George ERASMUS MUNDUS JOINT MASTER in Intelligent Field Robotic Systems Faculty of Informatics Eötvös Loránd University Budapest Hungary Robotics and AI research group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This article presents an onboard perception-assisted high-fidelity simulation framework for autonomous planetary soft-landing, enabling visual information processing tightly integrated with advanced onboard guidance system in a realistic simulation environments. Utilizing the open source Unity game engine, a physics-based simulation toolbox integrated with the Robotic Operating System (ROS2), we effectively emulate the motion of spacecraft approaching planetary terrain. The simulation environment features a realistic 3D terrain model and a lander spacecraft equipped with onboard camera to effectively capture detailed local terrain images. The simulation engine accurately captures the spacecraft’s 6-degrees of freedom motion, seamlessly integrating space craft orientation with the associated camera feed while incorporating various illumination conditions using a light source representing the Sun. To demonstrate the efficacy of the framework, we implement a recently developed attitude-constrained minimum jerk guidance algorithm to emulate spacecraft motion. Concurrently, the visual feed from the onboard RGB camera is processed through a gradient extraction-based perception system, providing the visual odometer. This system identifies provably safe landing sites, effectively avoiding potential risks of landing on uneven terrain. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Planetary landers

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