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arXiv 2024年

作者： Hu, Zheyuan Daryakenari, Nazanin Ahmadi Shen, Qianli Kawaguchi, Kenji Em Karniadakis, George Department of Computer Science National University of Singapore Singapore119077 Singapore Center for Biomedical Engineering School of Engineering Brown University ProvidenceRI02912 United States Division of Applied Mathematics Brown University ProvidenceRI02912 United States Advanced Computing Mathematics and Data Division Pacific Northwest National Laboratory RichlandWA United States

Physics-informed machine learning (PIML) has emerged as a promising alternative to classical methods for predicting dynamical systems, offering faster and more generalizable solutions. However, existing models, including recurrent neural networks (RNNs), transformers, and neural operators, face challenges such as long-time integration, long-range dependencies, chaotic dynamics, and extrapolation, to name a few. To this end, this paper introduces state-space models implemented in Mamba for accurate and efficient dynamical system operator learning. Mamba addresses the limitations of existing architectures by dynamically capturing long-range dependencies and enhancing computational efficiency through reparameterization techniques. To extensively test Mamba and compare against another 11 baselines, we introduce several strict extrapolation testbeds that go beyond the standard interpolation benchmarks. We demonstrate Mamba’s superior performance in both interpolation and challenging extrapolation tasks. Mamba consistently ranks among the top models while maintaining the lowest computational cost and exceptional extrapolation capabilities. Moreover, we demonstrate the good performance of Mamba for a real-world application in quantitative systems pharmacology for assessing the efficacy of drugs in tumor growth under limited data scenarios. Taken together, our findings highlight Mamba’s potential as a powerful tool for advancing scientific machine learning in dynamical systems modeling. (The code will be available at https://***/zheyuanhu01/State_ Space_Model_Neural_Operator upon acceptance.) © 2024, CC BY.

关键词： Recurrent neural networks

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Deep Learning-based Physical Layer Authentication in LiFi Networks Under Multi - User Mobility

Deep Learning-based Physical Layer Authentication in LiFi Ne...

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IEEE Southeastcon

作者： Elmahedi Mahalal Eslam Hasan Muhammad Ismail Zi-Yang Wu Mostafa M. Fouda Zubair Md Fadlullah Department of Computer Science University of New Haven West Haven CT USA Department of Computer Science Tennessee Technological University Cookeville TN USA Cybersecurity Education Research and Outreach Center Cookeville TN USA College of Information Science and Engineering Northeastern University Shenyang China Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA Department of Computer Science Western University London ON Canada

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

The open nature of wireless networks poses significant security challenges. Upper-layer authentication (ULA) methods face limitations, including vulnerability to cryptanalysis and replay attacks. Physical-layer authentication (PLA) has emerged as a promising technique to complement the ULA, offering robust two-factor authentication. PLA leverages physical-layer characteristics to authenticate transmitters with reduced complexity and latency. In this paper, we focus on passive PLA schemes for indoor Light Fidelity (LiFi) networks, where approximately 80% of data traffic originates indoors. Existing studies overlook channel impulse response (CIR) similarity under multi-user mobility and varying user densities, resulting in a lack of robust PLA solutions. To address this gap, we investigate CIR similarity in indoor LiFi environments. Our simulation results demonstrated that the CIR similarity is significant under multiuser mobility, where the attacker can share more than 30% of the CIR of a legitimate user. We propose a deep learning-based Long Short- Term Memory (LSTM) model that predicts the next CIR value based on historical data to enhance authentication reliability. Authentication is performed if the predicted CIR matches the actual CIR at the access point. Our numerical results show that the proposed LSTM-based PLA method effectively distinguishes legitimate users, achieving a detection probability exceeding 94% even under high multi-user mobility. Furthermore, the proposed LSTM-based PLA method can maintain the missed detection and the false alarm under 6% and 3% in the worst-case scenario, respectively.

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Physical-Layer Authentication Over Spatially Correlated MIMO Channels

Physical-Layer Authentication Over Spatially Correlated MIMO...

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International Symposium on Wireless Personal Multimedia Communications (WPMC)

作者： Kenta Miwa Kengo Ando Giuseppe Thadeu Freitas de Abreu Koji Ishibashi Advanced Wireless and Communication Research Center The University of Electro-Communications Tokyo Japan School of Computer Science and Engineering Constructor University Bremen Germany

We propose a novel physical-layer authentication (PLA) method that exploits spatio-temporal correlation features of multiple-input multiple-output (MIMO) channels. In particular, in the proposed method, the correlation due to the spatial structure and aging of the channel between two legitimate nodes (Alice and Bob), captured in the corresponding covariance matrix, is incorporated into the Mahalanobis distance between consecutive channel estimates and utilized to detect the attack by an impersonator. The authentication decision is carried out via hypothesis testing, with distributions and threshold derived analytically. computer simulations demonstrate that, for a given false alarm probability, the proposed method can detect an attacker with a higher probability than a conventional method based on Euclidean distances between channels.

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RadonPy:automated physical property calculation using all-atom classical molecular dynamics simulations for polymer informatics

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npj Computational Materials 2022年第1期8卷 2155-2169页

作者： Yoshihiro Hayashi Junichiro Shiomi Junko Morikawa Ryo Yoshida The Institute of Statistical Mathematics(ISM) Research Organization of Information and Systems10-3 Midori-choTachikawaTokyo190-8562Japan Department of Mechanical Engineering The University of Tokyo7-3-1 HongoBunkyoTokyo113-8656Japan Institute of Engineering Innovation The University of Tokyo2-11 YayoiBunkyo-kuTokyo113-0032Japan Department of Materials Science and Engineering School of Materials and Chemical TechnologyTokyo Institute of Technology2-12-1 OokayamaMeguro-kuTokyo152-8552Japan Research and Services Division of Materials Data and Integrated System(MaDIS) National Institute for Materials Science(NIMS)1-2-1 SengenTsukubaIbaraki305-0047Japan Department of Statistical Science School of Multidisciplinary ScienceThe Graduate University of Advanced Studies(SOKENDAI)10-3 Midori-choTachikawaTokyo190-8562Japan

The spread of data-driven materials research has increased the need for systematically designed materials property ***,the development of polymer databases has lagged far behind other material *** present RadonPy,an open-source library that can automate the complete process of all-atom classical molecular dynamics(MD)simulations applicable to a wide variety of polymeric ***,15 different properties were calculated for more than 1000 amorphous *** MD-calculated properties were systematically compared with experimental data to validate the calculation conditions;the bias and variance in the MD-calculated properties were successfully calibrated by a machine learning *** the high-throughput data production,we identified eight amorphous polymers with extremely high thermal conductivity(>0.4 W∙m^(–1)∙K^(–1))and their underlying *** to the advancement of materials informatics since the advent of computational property databases for inorganic crystals,database construction using RadonPy will promote the development of polymer informatics.

关键词： property calculation dynamics

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Gold-Price Forecasting Method Using Long Short-Term Memory and the Association Rule

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Journal of Mobile Multimedia 2023年第1期19卷 165-186页

作者： Boongasame, Laor Viriyaphol, Piboonlit Tassanavipas, Kriangkrai Temdee, Punnarumol Department of Mathematics Faculty of Science King Mongkut’s Institute of Technology Ladkrabang Bangkok 10520 Thailand Business Innovation and Investment Laboratory: B2I-Lab Faculty of Science King Mongkut’s Institute of Technology Ladkrabang Bangkok 10520 Thailand Gold Research Center Gold Traders Association Bangkok Thailand Faculty of Engineering and Technology Panyapiwat Institute of Management Nonthaburi 11120 Thailand Computer and Communication Engineering for Capacity Building Research Center School of Information Technology Mae Fah Luang University Chiang Rai 57100 Thailand

Since gold prices influence international economic and monetary systems, numerous studies have been conducted to forecast gold prices. Nonetheless, studies employing the linear relationship method usually fail to explain the change in the pattern of the gold price. This study introduces a new paradigm that incorporates association rules and long short-term memory (LSTM) as a nonlinear-based method. For simulation, the proposed method was analyzed with data from Yahoo Finance from January 2010 to December 2020. The association rule was used to choose features relevant to the gold spot (GS) in the US Dollar Index (DXY). The LSTM forecast the gold price with a range of hyperparameter settings. The simulation results showed that the proposed method—the LSTM with GS and DXY, or LSTM-GS-DXY—resulted in low mean absolute percentage error (MAPE) metrics. In addition, the proposed LSTM-GS-DXY system outperformed the simple moving average (SMA), weight moving average (WMA), exponential moving average (EMA), and auto-regressive integrated moving average (ARIMA). © 2022 River Publishers.

关键词： artificial neural network association rule gold forecasting LSTM

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Optimizing IoT-based Human Activity Recognition on Extreme Edge Devices

Optimizing IoT-based Human Activity Recognition on Extreme E...

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International Conference on Smart Computing (SMARTCOMP)

作者： Angelo Trotta Federico Montori Giacomo Vallasciani Luciano Bononi Marco Di Felice Department of Computer Science and Engineering University of Bologna Italy Advanced Research Center on Electronic Systems "Ercole De Castro" University of Bologna Italy

Wearable Internet of Things (IoT) devices with inertial sensors can enable personalized and fine-grained Human Activity Recognition (HAR). While activity classification on the Extreme Edge (EE) can reduce latency and maximize user privacy, it must tackle the unique challenges posed by the constrained environment. Indeed, Deep Learning (DL) techniques may not be applicable, and data processing can become burdensome due to the lack of input systems. In this paper, we address those issues by proposing, implementing, and validating an EE-aware HAR system. Our system incorporates a feature selection mechanism to reduce the data dimensionality in input, and an unsupervised feature separation and classification technique based on Self-Organizing Maps (SOMs). We developed the system on an M5Stack IoT prototype board and implemented a new SOM library for the Arduino SDK. Experimental results on two HAR datasets show that our proposed solution is able to overcome other unsupervised approaches and achieve performance close to state-of-art DL techniques while generating a model small enough to fit the limited memory capabilities of EE devices.

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Designing a Hybrid Push-Pull Architecture for Mobile Crowdsensing using the Web of Things

Designing a Hybrid Push-Pull Architecture for Mobile Crowdse...

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International Conference on Smart Computing (SMARTCOMP)

作者： Luca Sciullo Federico Montori Ivan Zyrianoff Lorenzo Gigli Davide Tinti Marco Di Felice Department of Computer Science and Engineering University of Bologna Italy Advanced Research Center on Electronic Systems "Ercole De Castro" University of Bologna Italy

Mobile crowdsensing (MCS) is an emerging paradigm that leverages the pervasive presence of mobile devices to collect and analyze data from the environment. However, the choice of a push- or pull-based architecture for MCS can result in a loss of flexibility and limitations for the creators of the campaigns (crowdsourcers). To address this issue, we propose a hybrid push-pull architecture for MCS campaigns that leverages the W3C Web of Things (WoT) to standardize the interfaces and interactions of devices through well-consolidated Web technologies. Furthermore, we present the design and implementation of a WoT-enabled Android application for MCS. We evaluate our proposal through simulations in a vehicular scenario based on a real dataset, showing that the hybrid architecture provides greater flexibility to crowdsourcers, supporting simultaneously the push and pull paradigms.

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Quantum Technologies for Beyond 5G and 6G Networks: Applications, Opportunities, and Challenges

arXiv

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arXiv 2025年

作者： Zeydan, Engin De Alwis, Chamitha Khan, Rabia Turk, Yekta Aydeger, Abdullah Gadekallu, Thippa Reddy Liyanage, Madhusanka Barcelona08860 Spain School of Computer Science and technology University of Bedfordshire United Kingdom Transmission Perth United Kingdom Aselsan Corp. Istanbul34380 Turkey Department of Electrical Engineering and Computer Science Florida Institute of Technology United States College of Mathematics and Computer Science Zhejiang A&F University Hangzhou311300 China Division of Research and Development Lovely Professional University Phagwara India Center of Research Impact and Outcome Chitkara University Punjab India School of Computer Science University College Dublin Ireland

As the world prepares for the advent of 6G networks, quantum technologies are becoming critical enablers of the next generation of communication systems. This survey paper investigates the convergence of quantum technologies and 6G networks, focusing on their applications, opportunities and challenges. We begin with an examination of the motivations for integrating quantum technologies into 6G, investigating the potential to overcome the limits of classical computing and cryptography. We then highlight key research gaps, particularly in quantum communication, quantum computing integration and security enhancement. A comprehensive overview of quantum technologies relevant to 6G, including quantum communication devices, quantum computing paradigms, and hybrid quantum-classical approaches is provided. A particular focus is on the role of quantum technologies in enhancing 6G Radio Access Networks (RAN), 6G core and edge network optimization, and 6G security. The survey paper also explores the application of quantum cryptography with a focus on Quantum Key Distribution (QKD), Quantum Secure Direct Communication (QSDC) and quantum-resistant cryptographic algorithms and assesses their implementation challenges and potential impact on 6G networks. We also discuss the significant challenges associated with integrating quantum technologies into existing communications infrastructures, including issues of technological maturity, standardization, and economic considerations. Finally, we summarize the lessons learned from current research and outline future research directions to guide the ongoing development of quantum-enabled 6G networks. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

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Ultrafast dynamics of exciton-polaritons in 2D semiconductor monolayers

Ultrafast dynamics of exciton-polaritons in 2D semiconductor...

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2023 Optica Nonlinear Optics Topical Meeting, NLO 2023

作者： Liu, Bin Michail, Evripidis He, Guiying Sfeir, Matthew Y. Forrest, Stephen R. Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States Department of Physics Graduate Center City University of New York New YorkNY10016 United States Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Department of Physics University of Michigan Ann ArborMI48109 United States Department of Material Science and Engineering University of Michigan Ann ArborMI48109 United States

We observe ultrafast dynamics of exciton-polaritons in 2D semiconductor monolayers with sub-wavelength dielectric photonic structures via non-resonant and resonant pumps, which results from distinct relaxation mechani... 详细信息

ISBN: (纸本)9781957171234

关键词： Monolayers

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Evaluating the performance of metaheuristic-tuned weight agnostic neural networks for crop yield prediction

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Neural Computing and Applications 2024年第24期36卷 14727-14756页

作者： Jovanovic, Luka Zivkovic, Miodrag Bacanin, Nebojsa Dobrojevic, Milos Simic, Vladimir Sadasivuni, Kishor Kumar Tirkolaee, Erfan Babaee Faculty of Technical Sciences Singidunum University Danijelova 32 Belgrade11000 Serbia Faculty of Informatics and Computing Singidunum University Danijelova 32 Belgrade11000 Serbia Department of Mathematics Saveetha School of Engineering SIMATS Tamilnadu Thandalam Chennai602105 India MEU Research Unit Middle East University Amman Jordan Faculty of Transport and Traffic Engineering University of Belgrade Vojvode Stepe 305 Belgrade11010 Serbia Department of Industrial Engineering and Management College of Engineering Yuan Ze University Yuandong Rd. Zhongli Dist. Taoyuan City320315 Taiwan Department of Computer Science and Engineering College of Informatics Korea University Seoul02841 Korea Republic of Center for Advanced Materials Qatar University Doha Qatar Department of Industrial Engineering Istinye University Istanbul34396 Turkey Department of Industrial and Mechanical Engineering Lebanese American University Byblos Lebanon

This study explores crop yield forecasting through weight agnostic neural networks (WANN) optimized by a modified metaheuristic. WANNs offer the potential for lighter networks with shared weights, utilizing a two-layer cooperative framework to optimize network architecture and shared weights. The proposed metaheuristic is tested on real-world crop datasets and benchmarked against state-of-the-art algorithms using standard regression metrics. While not claiming WANN as the definitive solution, the model demonstrates significant potential in crop forecasting with lightweight architectures. The optimized WANN models achieve a mean absolute error (MAE) of 0.017698 and an R-squared (R2) score of 0.886555, indicating promising forecasting performance. Statistical analysis and Simulator for Autonomy and Generality Evaluation (SAGE) validate the improvement significance and feature importance of the proposed approach. © The Author(s) 2024.

关键词： Crops

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