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Hyperedge overlap drives synchronizability of systems with higher-order interactions

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Physical Review E 2025年第3期111卷 034302-034302页

作者： Santiago Lamata-Otín Federico Malizia Vito Latora Mattia Frasca Jesús Gómez-Gardeñes Department of Condensed Matter Physics University of Zaragoza 50009 Zaragoza Spain GOTHAM Laboratory Institute of Biocomputation and Physics of Complex Systems (BIFI) University of Zaragoza 50018 Zaragoza Spain Department of Physics and Astronomy University of Catania 95125 Catania Italy School of Mathematical Sciences Queen Mary University of London London E1 4NS United Kingdom Complexity Science Hub Vienna A-1080 Vienna Austria Department of Electrical Electronics and Computer Science Engineering University of Catania 95125 Catania Italy

The microscopic organization of dynamical systems coupled via higher-order interactions plays a pivotal role in understanding their collective behavior. In this paper, we introduce a framework for systematically investigating the impact of the interaction structure on dynamical processes. Specifically, we develop an hyperedge overlap matrix whose elements characterize the two main aspects of the microscopic organization of higher-order interactions: the inter-order hyperedge overlap (nondiagonal matrix elements) and the intra-order hyperedge overlap (encapsulated in the diagonal elements). In this way, the first set of terms quantifies the extent of superposition of nodes among hyperedges of different orders, while the second focuses on the number of nodes in common between hyperedges of the same order. Our findings indicate that large values of both types of hyperedge overlap hinder synchronization stability, and that the larger is the order of interactions involved, the more important is their role. Our findings also indicate that the two types of overlap have qualitatively distinct effects on the dynamics of coupled chaotic oscillators. In particular, large values of intra-order hyperedge overlap hamper synchronization by favoring the presence of disconnected sets of hyperedges, while large values of inter-order hyperedge overlap hinder synchronization by increasing the number of shared nodes between groups converging on different trajectories, without necessarily causing disconnected sets of hyperedges.

关键词： Synchronization

Fault Analysis of Microgrids with Inverter Interfaced Resources in Grid-Connected and Islanded Modes

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Fault Analysis of Microgrids with Inverter Interfaced Resour...

On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Jigyesh Sharma Kushan Tharuka Lulbadda Anindita Golder Tarlochan Sidhu Sheldon S. Williamson Department of Electrical and Computer Engineering Advanced Storage Systems and Electric Transportation (ASSET) Laboratory Ontario Tech-Automotive Center of Excellence (ACE) Faculty of Engineering and Applied Science Smart Transportation Electrification and Energy Research (STEER) Group Ontario Tech University Oshawa ON Canada

The increasing penetration of inverter-based generation such as solar, wind and battery energy storage systems (BESS) has an impact on the fault currents in a microgrid. Also, if the microgrid is grid-connected or islanded affects the fault current. As such, it is important to conduct fault analysis of microgrids in grid-connected and islanded modes with different penetration levels of inverter-based generation sources to understand the impact of increasing levels of renewable energy on the fault currents. Different types of faults in a microgrid with inverter-based resources are simulated and the results are discussed and analyzed in this paper.

关键词： Wind energy generation Industrial electronics Renewable energy sources Wind energy Microgrids Inverters Batteries

EEGUnity: Open-Source Tool in Facilitating Unified EEG Datasets Towards Large-Scale EEG Model

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arXiv 2024年

作者： Qin, Chengxuan Yang, Rui You, Wenlong Chen, Zhige Zhu, Longsheng Huang, Mengjie Wang, Zidong School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou215123 China School of Electrical Engineering Electronics and Computer Science University of Liverpool LiverpoolL69 3BX United Kingdom Department of Computing The Hong Kong Polytechnic University Hong Kong Design School Xi’an Jiaotong-Liverpool University Suzhou215123 China Department of Computer Science Brunel University London Uxbridge MiddlesexUB8 3PH United Kingdom

The increasing number of dispersed EEG dataset publications and the advancement of large-scale Electroencephalogram (EEG) models have increased the demand for practical tools to manage diverse EEG datasets. However, the inherent complexity of EEG data, characterized by variability in content data, metadata, and data formats, poses challenges for integrating multiple datasets and conducting large-scale EEG model research. To tackle the challenges, this paper introduces EEGUnity, an open-source tool that incorporates modules of "EEG Parser", "Correction", "Batch Processing", and "Large Language Model Boost". Leveraging the functionality of such modules, EEGUnity facilitates the efficient management of multiple EEG datasets, such as intelligent data structure inference, data cleaning, and data unification. In addition, the capabilities of EEGUnity ensure high data quality and consistency, providing a reliable foundation for large-scale EEG data research. EEGUnity is evaluated across 25 EEG datasets from different sources, offering several typical batch processing workflows. The results demonstrate the high performance and flexibility of EEGUnity in parsing and data processing. The project code is publicly available at ***/Baizhige/EEGUnity. Copyright © 2024, The Authors. All rights reserved.

关键词： Data integration

Sequential Bayesian Neural Subnetwork Ensembles

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arXiv 2022年

作者： Jantre, Sanket Bhattacharya, Shrijita Urban, Nathan M. Yoon, Byung-Jun Maiti, Tapabrata Balaprakash, Prasanna Madireddy, Sandeep Computational Science Initiative Brookhaven National Laboratory Department of Statistics and Probability Michigan State University United States Department of Electrical and Computer Engineering Texas A&M University United States Computing and Computational Sciences Directorate Oak Ridge National Laboratory United States Mathematics and Computer Science Division Argonne National Laboratory United States

Deep ensembles have emerged as a powerful technique for improving predictive performance and enhancing model robustness across various applications by leveraging model diversity. However, traditional deep ensemble methods are often computationally expensive and rely on deterministic models, which may limit their flexibility. Additionally, while sparse subnetworks of dense models have shown promise in matching the performance of their dense counterparts and even enhancing robustness, existing methods for inducing sparsity typically incur training costs comparable to those of training a single dense model, as they either gradually prune the network during training or apply thresholding post-training. In light of these challenges, we propose an approach for sequential ensembling of dynamic Bayesian neural subnetworks that consistently maintains reduced model complexity throughout the training process while generating diverse ensembles in a single forward pass. Our approach involves an initial exploration phase to identify high-performing regions within the parameter space, followed by multiple exploitation phases that take advantage of the compactness of the sparse model. These exploitation phases quickly converge to different minima in the energy landscape, corresponding to high-performing subnetworks that together form a diverse and robust ensemble. We empirically demonstrate that our proposed approach outperforms traditional dense and sparse deterministic and Bayesian ensemble models in terms of prediction accuracy, uncertainty estimation, out-of-distribution detection, and adversarial robustness. © 2022, CC BY.

关键词： Prediction models

Comment on "Amplified emission and lasing in photonic time crystals"

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arXiv 2022年

作者： Park, Jagang Park, Hee Chul Lee, Kyungmin Shin, Jonghwa Ryu, Jung-Wan Jeon, Wonju Park, Namkyoo Min, Bumki Department of Mechanical Engineering Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Daejeon34126 Korea Republic of Department of Physics Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Department of Material Sciences and Engineering Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of

Lyubarov et al. (Research Articles, 22 July 2022, p. 425) claim that the spontaneous emission rate of an atom vanishes at the momentum gap edges of photonic Floquet media. We show that their theoretical prediction is based on assumptions that result in misleading interpretations on the spontaneous emission rate in photonic Floquet media. © 2022, CC BY.

关键词： Spontaneous emission

2D Ge₂Se₂P₄ Monolayer: A Versatile Photocatalyst for Sustainable Water Splitting

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Journal of Physical Chemistry C 2024年第10期128卷 4245-4257页

作者： Vu, Tuan V. Hieu, Nguyen N. Vo, Dat D. Kartamyshev, A.I. Tong, Hien D. Trinh, Thuat T. Khuong Dien, Vo Haman, Zakaryae Dey, Poulumi Khossossi, Nabil Laboratory for Computational Physics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City 70000 Viet Nam Faculty of Mechanical─Electrical and Computer Engineering School of Technology Van Lang University Ho Chi Minh City 70000 Viet Nam Institute of Research and Development Duy Tan University Da Nang 550000 Viet Nam Faculty of Natural Sciences Duy Tan University Da Nang 550000 Viet Nam Faculty of Engineering Vietnamese-German University Binh Duong 820000 Viet Nam Porelab Department of Chemistry Norwegian University of Science and Technology Høgskoleringen 5 Trondheim 7491 Norway Department of Physics National Cheng Kung University 1 University Road Tainan 70101 Taiwan Laboratoire de Physique des Matèriaux et Modélisations des Systèmes LP2MS Faculty of Sciences Department of Physics Moulay Ismail University Meknes 50050 Morocco Department of Materials Science and Engineering Faculty of Mechanical Engineering Delft University of Technology Mekelweg 2 Delft 2628 CD Netherlands

This study aims to identify photo-/electrocatalysts that can enhance the oxygen evolution reaction (OER), hydrogen evolution reaction (HER), and oxygen reduction reaction (ORR), which are of utmost importance in electro-/photochemical energy systems, such as solar energy, fuel cells, water electrolyzers, or metal-air batteries. Our study focused on investigating the 2D Ge₂Se₂P₄ monolayer and found that it exhibits a bifunctional photocatalyst with a very high solar-to-hydrogen efficiency. The two-dimensional (2D) Ge₂Se₂P₄ monolayer has superior HER activity compared to that of most 2D materials, and it also outperforms the reference catalysts IrO₂(110) and Pt(111) in terms of low overpotential values for ORR and OER mechanisms. Such superior catalytic performance in the 2D Ge₂Se₂P₄ monolayer can be attributed to its electron states, charge transfer process, and suitable band alignments referring to normal hydrogen electrodes. Overall, the study suggests that the Ge₂Se₂P₄ monolayer could be an excellent bifunctional catalyst for advancing photo-/electrochemical energy systems. © 2024 American Chemical Society.

关键词： Energy Evolution reactions Free energy Hydrogen Monolayers

Max-Min Beamformer for THz Wideband Communications

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Max-Min Beamformer for THz Wideband Communications

IEEE International Conference on Communications Workshops, ICC

作者： Boyu Ning Weidong Mei Lipeng Zhu Zhi Chen Rui Zhang National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China (UESTC) Chengdu China Department of Electrical and Computer Engineering National University of Singapore The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China

Analog beamforming is appealing for future terahertz (THz) communications since it can generate high-gain directional beams with low-cost phase shifters. However, the conventional analog beamforming based on array response vector to steer a narrow beam may suffer from severe performance loss in wideband systems due to the “beam squint” effect over frequency. To tackle this issue, we propose a new max-min beamformer design in this paper for THz wideband communication systems, which aims to maximize the minimum beam gain over the entire frequency band. To solve this challenging problem, an augmented Lagrange method (ALM) is proposed to obtain a high-quality solution with low complexity. Numerical results show that the proposed beam design can yield considerably better performance than the conventional narrowband beamformer, especially in the case of large angle of departure (AoD) from the base station (BS) to the user.

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A Dual-Objective Bandit-Based Opportunistic Band Selection Strategy for Hybrid-Band V2X Metaverse Content Update

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A Dual-Objective Bandit-Based Opportunistic Band Selection S...

IEEE Conference on Global Communications (GLOBECOM)

作者： Sherief Hashima Zubair Md Fadlullah Mostafa M. Fouda Kohei Hatano Eiji Takimoto Mohsen Guizani Computational Learning Theory Team RIKEN-AIP Fukuoka Japan Department of Computer Science Western University London ON Canada Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA Department of Informatics Kyushu University Fukuoka Japan Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) UAE

As vehicular communication networks embrace metaverse beyond 5G/6G systems, the rich content update via the least interfered subchannel of the optimal frequency band in a hybrid band vehicle to everything (V2X) setting emerges as a challenging optimization problem. We model this problem as a tradeoff between multi-band VR/AR devices attempting to perform metaverse scenes and environmental updates to metaverse roadside units (MRSUs) while minimizing energy consumption. Due to the computational hardness of this optimization, we formulate an opportunistic band selection problem using a multi-armed bandit (MAB) that provides a good quality solution in real-time without computationally burdening the already stretched augmented/virtual reality (AR/VR) units acting as transmitting nodes. The opportunistic use of scheduling rich content updates at traffic signals and stand-still scenarios maps well with the formulated bandit problem. We propose a Dual-Objective Minimax Optimal Stochastic Strategy (DOMOSS) as a natural solution to this problem. Through extensive computer-based simulations, we demonstrate the effectiveness of our proposal in contrast to baselines and comparable solutions. We also verify the quality of our solution and the convergence of the proposed strategy.

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Federated Multi-armed Bandits Under Byzantine Attacks

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arXiv 2022年

作者： Saday, Artun Demirel, Ilker Yıldırım, Yigit Tekin, Cem Department of Electrical and Electronics Engineering Bilkent University Ankara06800 Turkey Computer Science & Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States

Multi-armed bandits (MAB) is a sequential decision-making model in which the learner controls the trade-off between exploration and exploitation to maximize its cumulative reward. Federated multi-armed bandits (FMAB) is an emerging framework where a cohort of learners with heterogeneous local models play a MAB game and communicate their aggregated feedback to a server to learn a globally optimal arm. Two key hurdles in FMAB are communication-efficient learning and resilience to adversarial attacks. To address these issues, we study the FMAB problem in the presence of Byzantine clients who can send false model updates threatening the learning process. We analyze the sample complexity and the regret of β-optimal arm identification. We borrow tools from robust statistics and propose a median-of-means (MoM)-based online algorithm, Fed-MoM-UCB, to cope with Byzantine clients. In particular, we show that if the Byzantine clients constitute less than half of the cohort, the cumulative regret with respect to β-optimal arms is bounded over time with high probability, showcasing both communication efficiency and Byzantine resilience. We analyze the interplay between the algorithm parameters, a discernibility margin, regret, communication cost, and the arms’ suboptimality gaps. We demonstrate Fed-MoM-UCB’s effectiveness against the baselines in the presence of Byzantine attacks via experiments. Copyright © 2022, The Authors. All rights reserved.

关键词： Federated learning