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arXiv 2025年

作者： Stein, Samuel Liu, Chenxu Kan, Shuwen Crane, Eleanor Ding, Yufei Mao, Ying Schuckert, Alexander Li, Ang Future Computing Research Group Pacific Northwest National Laboratory United States Department of Computer and Information Science Fordham University United States Department of Physics King’s College London Strand LondonWC2R 2LS United Kingdom Department of Computer Science & Engineering University of California San Diego United States Joint Quantum Institute Joint Center for Quantum Information and Computer Science University of Maryland and NIST United States

Multi-target gates offer the potential to reduce gate depth in syndrome extraction for quantum error correction. Although neutral-atom quantum computers have demonstrated native multi-qubit gates, existing approaches that avoid additional control or multiple atomic species have been limited to single-target gates. We propose single-control-multi-target C(Z⊗N) gates on a single-species neutral-atom platform that require no extra control and have gate durations comparable to standard CZ gates. Our approach leverages tailored interatomic distances to create an asymmetric blockade between the control and target atoms. Using a GPU-accelerated pulse synthesis protocol, we design smooth control pulses for CZZ and CZZZ gates, achieving fidelities of up to 99.55% and 99.24%, respectively, even in the presence of simulated atom placement errors and Rydberg-state decay. This work presents a practical path to implementing multi-target gates in neutral-atom systems, significantly reducing the resource overhead for syndrome extraction. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

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Prediction of High Heating Value Using ANN Technique Aftermath Natural Disasters

Prediction of High Heating Value Using ANN Technique Afterma...

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ACS/IEEE International Conference on computer Systems and Applications

作者： Disha Thakur Sanjay Kumar Bhisham Sharma Imed Ben Dhaou Department of Civil Engineering UIT Himachal Pradesh University Shimla India Department of Electrical Engineering UIT Himachal Pradesh University Shimla India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India Department of Computer Science Hekma School of Engineering Computing and Design Dar Al-Hekma University Jeddah Saudi Arabia

ISBN: (数字)9798331518240

ISBN: (纸本)9798331518257

Natural disasters, particularly earthquakes and floods, can generate substantial waste, presenting challenges for waste management and energy recovery. This study explores the application of machine learning, specifically Artificial Neural Networks (ANN), to optimize Waste-to-Energy (WTE) conversion processes for disaster-generated Municipal Solid Waste (MSW). Using data from Sundernagar City, India, we constructed and evaluated multiple ANN models (collectively named ANNSNAGAR) to estimate High Heating Value, HHV, based on ultimate/elemental analysis of MSW. The models were rigorously assessed using statistical error metrics. Our best-performing network, with a 9-50-1 architecture (ANNMS), achieved a mean absolute percentage error (MAPE) of 1.47%, demonstrating high predictive accuracy.

关键词： Waste management Heating systems Waste materials Accuracy Disasters Urban areas Artificial neural networks Machine learning Predictive models Data models

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Predicting the State of Synchronization of Financial Time Series using Cross Recurrence Plots

arXiv

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arXiv 2022年

作者： Shabani, Mostafa Magris, Martin Tzagkarakis, George Kanniainen, Juho Iosifidis, Alexandros Department of Electrical and Computer Engineering Aarhus University Denmark Foundation for Research and Technology Hellas Institute of Computer Science Greece University of Bordeaux France Department of Computing Sciences Tampere University Finland

Cross-correlation analysis is a powerful tool for understanding the mutual dynamics of time series. This study introduces a new method for predicting the future state of synchronization of the dynamics of two financial time series. To this end, we use the cross-recurrence plot analysis as a nonlinear method for quantifying the multidimensional coupling in the time domain of two time series and for determining their state of synchronization. We adopt a deep learning framework for methodologically addressing the prediction of the synchronization state based on features extracted from dynamically sub-sampled cross-recurrence plots. We provide extensive experiments on several stocks, major constituents of the S&P100 index, to empirically validate our approach. We find that the task of predicting the state of synchronization of two time series is in general rather difficult, but for certain pairs of stocks attainable with very satisfactory performance1 © 2022, CC BY.

关键词： Synchronization

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DOMINO++: Domain-Aware Loss Regularization for Deep Learning Generalizability 26th

DOMINO++: Domain-Aware Loss Regularization for Deep Learning...

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26th International Conference on Medical Image computing and computer-Assisted Intervention, MICCAI 2023

作者： Stolte, Skylar E. Volle, Kyle Indahlastari, Aprinda Albizu, Alejandro Woods, Adam J. Brink, Kevin Hale, Matthew Fang, Ruogu Gainesville United States Torch Technologies LLC ShalimarFL United States Center for Cognitive Aging and Memory McKnight Brain Institute UF Gainesville United States Department of Clinical and Health Psychology College of Public Health and Health Professions UF Gainesville United States Department of Neuroscience College of Medicine UF Gainesville United States United States Air Force Research Laboratory Eglin Air Force Base ValparaisoFL United States Department of Electrical and Computer Engineering Herbert Wertheim College of Engineering UF Gainesville United States Department of Computer Information and Science and Engineering Herbert Wertheim College of Engineering UF Gainesville United States Department of Mechanical and Aerospace Engineering Herbert Wertheim College of Engineering UF Gainesville United States

ISBN: (纸本)9783031439001

Out-of-distribution (OOD) generalization poses a serious challenge for modern deep learning (DL). OOD data consists of test data that is significantly different from the model’s training data. DL models that perform well on in-domain test data could struggle on OOD data. Overcoming this discrepancy is essential to the reliable deployment of DL. Proper model calibration decreases the number of spurious connections that are made between model features and class outputs. Hence, calibrated DL can improve OOD generalization by only learning features that are truly indicative of the respective classes. Previous work proposed domain-aware model calibration (DOMINO) to improve DL calibration, but it lacks designs for model generalizability to OOD data. In this work, we propose DOMINO++, a dual-guidance and dynamic domain-aware loss regularization focused on OOD generalizability. DOMINO++ integrates expert-guided and data-guided knowledge in its regularization. Unlike DOMINO which imposed a fixed scaling and regularization rate, DOMINO++ designs a dynamic scaling factor and an adaptive regularization rate. Comprehensive evaluations compare DOMINO++ with DOMINO and the baseline model for head tissue segmentation from magnetic resonance images (MRIs) on OOD data. The OOD data consists of synthetic noisy and rotated datasets, as well as real data using a different MRI scanner from a separate site. DOMINO++’s superior performance demonstrates its potential to improve the trustworthy deployment of DL on real clinical data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Magnetic resonance

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Design Method for Series-Fed Omnidirectional Filtenna Based on Full-Metal 3-D Printing

Design Method for Series-Fed Omnidirectional Filtenna Based ...

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Cross Strait Quad-Regional Radio science and Wireless Technology Conference (CSRWTC)

作者： Shu-Qing Zhang Shu-Ting Wu Sai-Wai Wong Yejun He Zhonghe Zhang Xuzhou Yu State Key Laboratory of Radio Frequency Heterogeneous Integration Shenzhen Key Laboratory of Antennas and Propagation Guangdong Engineering Research Center of Base Station Antennas and Propagation College of Electronics and Information Engineering Shenzhen University China The Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau SAR China

ISBN: (数字)9798331507794

ISBN: (纸本)9798331507800

This paper proposed a design approach for a series-fed omnidirectional antenna based on 3D full-metal printing. A major issue when designing series-fed antennas is the radiation pattern lobing caused by the unique feeding mechanism. In this paper, necessary adjustments are made based on the phase relationships between the currents and electric fields of each radiating element, resulting in improved radiation characteristics for the series-fed antenna. To validate this idea, the paper designs a rod-shaped full-metal omnidirectional antenna, achieving a wide operating bandwidth and high gain. Additionally, a filtering antenna with omnidirectional radiation is designed, which achieves a high level of out-of-band suppression as well as good omnidirectional radiation performance.

关键词： Wireless communication Phased arrays Filtering Design methodology Omnidirectional antennas Bandwidth Three-dimensional printing Antenna feeds Electric fields Antenna radiation patterns

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Secure and Private Over-The-Air Federated Learning: Biased and Unbiased Aggregation Design

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IEEE Transactions on Wireless Communications 2025年

作者： Yan, Na Wang, Kezhi Zhi, Kangda Pan, Cunhua Chai, Kok Keong Vincent Poor, H. King's College London Department of Engineering LondonE1 4NS United Kingdom Department of Computer Science Brunel University London Uxbridge MiddlesexUB8 3PH United Kingdom National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Over-The-Air federated learning (OTA-FL) presents a promising paradigm that improves the efficiency of local update aggregation by leveraging the superposition property of wireless multiple access channels (MACs). However, it faces significant security and privacy concerns that demand careful consideration. To address these threats associated with OTA-FL, we develop a secure and private over-The-Air federated learning (SP-OTA-FL) framework, which can realize the secure and private aggregation for both OTA-FL with unbiased aggregation (UB-OTA-FL) and OTA-FL with biased aggregation (B-OTA-FL). In this framework, a subset of devices participate in training, while another subset functions as jammers, emitting jamming signals to enhance the security and privacy of the OTA-FL process. In particular, we measure the privacy leakage of users' data using differential privacy (DP) and introduce an innovative application of mean squared error security (MSE-security) to evaluate the security of the OTA-FL system. We conduct convergence analyses for both convex and non-convex loss functions. Building on these analytical results, we separately formulate optimization problems for UB-OTA-FL and B-OTA-FL to enhance the learning performance of SP-OTA-FL by strategically optimizing the scheduling of training participants and jammers. The effectiveness of the proposed schemes is verified through simulations. © 2002-2012 IEEE.

关键词： Federated learning

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Continuity Scaling: A Rigorous Framework for Detecting and Quantifying Causality Accurately

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research 2022年第4期2022卷 137-146页

作者： Xiong Ying Si-Yang Leng Huan-Fei MaD Qing Nie Ying-Cheng Lai Wei Lin School of Mathematical Sciences SCMSand SCAMFudan UniversityShanghai 200433China Research Institute for Inteligent Complex Systems CCSBand LCNBIFudan UniversityShanghai 200433China State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Institutes of Brain ScienceFudan UniversityShanghai 200032China Institute of AI and Robotics Academy for Engineering and TechnologyFudan UniversityShanghai 200433China School of Mathematical Sciences Soochow UniversitySuzhou 215006China Department of Mathematics Department of Developmental and Cll BiologyAnd NSF-Simons Center for Multiscale Cell Fate ResearchUniversity of CaliforniaIrvineCA 92697-3875USA School of Electrical ComputerAnd Energy EngineeringArizona State UniversityTempeArizona 85287-5706USA Shanghai Artificial Intelligence Laboratory Shanghai 200232China

Data-based detection and quantification of causation in complex,nonlinear dynamical systems is of paramount importance to science,engineering,and *** by the widely used methodology in recent years,the cross-map-based techniques,we develop a general framework to advance towards a comprehensive understanding of dynamical causal mechanisms,which is consistent with the natural interpretation of *** particular,instead of measuring the smoothness of the cross-map as conventionally implemented,we define causation through measuring the scaling law for the continuity of the investigated dynamical system *** uncovered scaling law enables accurate,reliable,and eficient detection of causation and assessment of its strength in general complex dynamical systems,outperforming those existing representative *** continuity scaling-based framework is rigorously established and demonstrated using datasets from model complex systems and the real world.

关键词： dynamical scaling continuity

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Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systems

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IEEE Communications Surveys and Tutorials 2024年第3期27卷 2085-2128页

作者： Wang, Xiaofei Zhao, Yunfeng Qiu, Chao Hu, Qinghua Leung, Victor C. M. Tianjin University College of Intelligence and Computing Tianjin300072 China Shenzhen518000 China Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverV6T 1Z4 Canada

Amidst the robust impetus from artificial intelligence (AI) and big data, edge intelligence (EI) has emerged as a nascent computing paradigm, synthesizing AI with edge computing (EC) to become an exemplary solution for unleashing the full potential of AI services. Nonetheless, challenges in communication costs, resource allocation, privacy, and security continue to constrain its proficiency in supporting services with diverse requirements. In response to these issues, this paper introduces socialized learning (SL) as a promising solution, further propelling the advancement of EI. SL is a learning paradigm predicated on social principles and behaviors, aimed at amplifying the collaborative capacity and collective intelligence of agents within the EI system. SL not only enhances the system's adaptability but also optimizes communication, and networking processes, essential for distributed intelligence across diverse devices and platforms. Therefore, a combination of SL and EI may greatly facilitate the development of collaborative intelligence in the future network. This paper presents the findings of a literature review on the integration of EI and SL, summarizing the latest achievements in existing research on EI and SL. Subsequently, we delve comprehensively into the limitations of EI and how it could benefit from SL. Special emphasis is placed on the communication challenges and networking strategies and other aspects within these systems, underlining the role of optimized network solutions in improving system efficiency. Based on these discussions, we elaborate in detail on three integrated components: socialized architecture, socialized training, and socialized inference, analyzing their strengths and weaknesses. Finally, we identify some possible future applications of combining SL and EI, discuss open problems and suggest some future research, with the hope of arousing the research community's interest in this emerging and exciting interdisciplinary field. © 1

关键词： Resource allocation

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Multi-objective Aerial IRS-assisted ISAC Optimization via Generative AI-enhanced Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Xie, Wenwen Sun, Geng Wang, Jiacheng Du, Hongyang Kang, Jiawen Huang, Kaibin Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering The University of Hong Kong 999077 Hong Kong School of Automation Guangdong University of Technology Guangzhou510006 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Integrated sensing and communication (ISAC) has garnered substantial research interest owing to its pivotal role in advancing the development of next-generation (6G) wireless networks. However, achieving a performance balance between communication and sensing in the dual-function radar communication (DFRC)-based ISAC system remains a significant challenge. In this paper, an aerial intelligent reflecting surface (IRS)-assisted ISAC system is explored, where a base station (BS) supports dual-functional operations, enabling both data transmission for multiple users and sensing for a blocked target, with the channel quality enhanced by an IRS mounted on the unmanned aerial vehicle (UAV). Moreover, we formulate an integrated communication, sensing, and energy efficiency multi-objective optimization problem (CSEMOP), which aims to maximize the communication rate of the users and the echo rate of the target, while minimizing UAV propulsion energy consumption by jointly optimizing the BS beamforming matrix, IRS phase shifts, the flight velocity and angle of the UAV. Considering the non-convexity, trade-off, and dynamic nature of the formulated CSEMOP, we propose a generative diffusion model-based deep deterministic policy gradient (GDMDDPG) method to solve the problem. Specifically, the diffusion model is incorporated into the actor network of DDPG to improve the action quality, with noise perturbation mechanism for better exploration and recent prioritized experience replay (RPER) sampling mechanism for enhanced training efficiency. Simulation results indicate that the GDMDDPG method delivers superior performance compared to the existing methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Lightweight AI for Drones: A Survey

Lightweight AI for Drones: A Survey

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International Youth Conference on Radio Electronics, electrical and Power engineering (REEPE)

作者： Moez Krichen Mohamed S. Abdalzaher Mostafa Shaaban Raafat Aburukba Al-Baha University Al-Baha Saudi Arabia ReDCAD Laboratory University of Sfax Sfax Tunisia Electrical Engineering Department American University of Sharjah Sharjah UAE Seismology Department National Research Institute of Astronomy and Geophysics Cairo Egypt Computer Science and Engineering Department American University of Sharjah Sharjah UAE

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

This paper explores the transformative potential of Lightweight AI techniques in enhancing the capabilities of Unmanned Aerial Vehicles (UAVs). As the demand for real-time processing and efficient data management grows, traditional AI models face significant limitations due to their high computational and resource requirements. We discuss key Lightweight AI techniques, including Tiny Machine Learning (TinyML), model compression methods, edge computing, transfer learning, and federated learning, which collectively enable more efficient UAV operations. The paper further examines various applications of Lightweight AI in drones, highlighting its role in pandemic response, object detection, vegetation segmentation, forest-fire detection, real-time path planning, and autonomous landing. Each application demonstrates how lightweight models can improve decision-making and operational efficiency in dynamic environments. We also address the challenges faced in implementing these technologies, such as resource constraints and the need for robust real-time processing. Finally, we outline future directions for research and development in Lightweight AI, emphasizing the necessity for continued innovation to fully leverage UAV potential across diverse sectors. This comprehensive analysis underscores the critical role of Lightweight AI in advancing UAV technology and enhancing their functionalities in various practical applications.

关键词： Surveys Tiny machine learning Computational modeling Transfer learning Vegetation mapping Object detection Autonomous aerial vehicles Real-time systems Artificial intelligence Drones

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