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arXiv 2023年

作者： Xu, Lei Yi, Xinlei Wen, Guanghui Shi, Yang Johansson, Karl H. Yang, Tao The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China The Lab for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States The Department of Systems Science School of Mathematics Southeast University Nanjing210096 China The Department of Mechanical Engineering University of Victoria VictoriaBCV8W 2Y2 Canada The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden

In this paper, we study the distributed nonconvex optimization problem, aiming to minimize the average value of the local nonconvex cost functions using local information exchange. To reduce the communication overhead, we introduce three general classes of compressors, i.e., compressors with bounded relative compression error, compressors with globally bounded absolute compression error, and compressors with locally bounded absolute compression error. By integrating them, respectively, with the distributed gradient tracking algorithm, we then propose three corresponding compressed distributed nonconvex optimization algorithms. Motivated by the state-of-the-art BEER algorithm proposed in [1], which is an efficient compressed algorithm integrating gradient tracking with biased and contractive compressors, our first proposed algorithm extends this algorithm to accommodate both biased and non-contractive compressors. For each algorithm, we design a novel Lyapunov function to demonstrate its sublinear convergence to a stationary point if the local cost functions are smooth. Furthermore, when the global cost function satisfies the Polyak–Lojasiewicz (P–L) condition, we show that our proposed algorithms linearly converge to a global optimal point. It is worth noting that, for compressors with bounded relative compression error and globally bounded absolute compression error, our proposed algorithms’ parameters do not require prior knowledge of the P–L constant. The theoretical results are illustrated by numerical examples, demonstrating the effectiveness of the proposed algorithms in significantly reducing the communication burden while maintaining the convergence performance. Moreover, simulation results show that the proposed algorithms outperform the state-of-the-art compressed distributed nonconvex optimization algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Cost functions

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Technology Trends for Massive MIMO towards 6G

arXiv

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arXiv 2023年

作者： Huo, Yiming Lin, Xingqin Di, Boya Zhang, Hongliang Hernando, Francisco Javier Lorca Tan, Ahmet Serdar Mumtaz, Shahid Demir, Özlem Tuğfe Chen-Hu, Kun The Department of Electrical and Computer Engineering University of Victoria VictoriaBCV8P 5C2 Canada NVIDIA Santa ClaraCA95050 United States The School of Electronics Peking University Beijing100871 China Inter-Digital Communications Inc. London United Kingdom The Instituto de Telecomunicações Aveiro Portugal The Department of Computer Science KTH Royal Institute of Technology Stockholm Sweden The Department of Signal Theory and Communications Universidad Carlos III de Madrid Leganés28911 Spain

At the dawn of the next-generation wireless systems and networks, massive multiple-input multiple-output (MIMO) has been envisioned as one of the enabling technologies. With the continued success of being applied in the 5G and beyond, the massive MIMO technology has demonstrated its advantageousness, integrability, and extendibility. Moreover, several evolutionary features and revolutionizing trends for massive MIMO have gradually emerged in recent years, which are expected to reshape the future 6G wireless systems and networks. Specifically, the functions and performance of future massive MIMO systems will be enabled and enhanced via combining other innovative technologies, architectures, and strategies such as intelligent omnisurfaces (IOSs)/intelligent reflecting surfaces (IRSs), artificial intelligence (AI), THz communications, cell free architecture. Also, more diverse vertical applications based on massive MIMO will emerge and prosper, such as wireless localization and sensing, vehicular communications, non-terrestrial communications, remote sensing, inter-planetary communications. Copyright © 2023, The Authors. All rights reserved.

关键词： Remote sensing

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Enhanced Microgrid Reliability Through Software-Defined Networking and Extended Horizon Predictive Control

SSRN

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SSRN 2024年

作者： Guzmán, Ricardo Pérez Rivera, Marco Araya, Baldomero Gómez, Juan S. Salgueiro, Yamisleydi Restrepo, Carlos Wheeler, Patrick You, Minglei Sumner, Mark Department of Computer Science Faculty of Engineering Universidad de Talca Maule Curicó3341717 Chile Research Institute Faculty of Engineering University of Nottingham NottinghamNG7 2GT United Kingdom Laboratorio de Conversión de Energías y Electrónica de Potencia LCEEP Dirección de Investigación Universidad de Talca Maule Curicó3341717 Chile Department of Electrical Engineering Faculty of Engineering Universidad de Talca Maule Curicó3341717 Chile Department of Engineering Sciences The Energy Transformation Center Universidad Andrés Bello Santiago-Chile Santiago de Chile8370035 Chile Department of Industrial Engineering Faculty of Engineering Universidad de Talca Maule Curicó3341717 Chile

The dynamic nature of power systems combined with the need for low-latency and loss-tolerant communications, presents significant challenges to maintaining system reliability and resiliency. This paper proposes a novel integration of Finite Control Set Model-based Predictive Control (FCS-MPC) with an extended prediction horizon and Software Defined Networked (SDN) to address the resiliency problem and voltage/frequency deviations associated with traditional hierarchical microgrid. The communication framework integrates SDN as a set of microservices distributed across local controllers and improved system reliability under communication *** secondary control considers the variability of communication latency and packet loss to adjust the shared reference based on the spatial and temporal correlation. The microgrid is subjected to four test scenarios to analyze the impact of communications on distributed generation, plug-and-play capacity and load variations. The control framework outperforms than classical alternatives to power sharing, recovery time and coordination between distributed generators. This method addresses the problem of low dynamic response of control strategies during disturbances, allowing the implementation of new, reliable and resilient hierarchical microgrids. © 2024, The Authors. All rights reserved.

关键词： Predictive control systems

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Optimizing Wireless Sensor Network Node Placement Using Bacterial Foraging Optimization 25th

Optimizing Wireless Sensor Network Node Placement Using Bact...

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25th International Conference on Internet Computing and IoT, ICOMP 2024, and 22nd International Conference on Embedded Systems, Cyber-physical Systems, and Applications, ESCS 2024, held as part of the World Congress in computer science, computer engineering and Applied Computing, CSCE 2024

作者： Priyadarshi, Rahul Chinnapurapu, Naga Raghuram Rawat, Piyush Yang, Tiansheng Rathore, Rajkumar Singh Bhubaneswar India Department of Electronics and Communication Engineering Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh Vijayawada520007 India School of Computer Science UPES Uttarakhand Dehradun248007 India University of South Wales Llantwit Road PontypriddCF37 B1DL United Kingdom Cardiff School of Technologies Cardiff Metropolitan University Llandaff Campus Western Avenue CardiffCF5 2QS United Kingdom

ISBN: (纸本)9783031859229

This paper investigates the application of Bacterial Foraging Optimization (BFO) for optimizing node placement in Wireless Sensor Networks (WSNs). Efficient node placement is crucial for enhancing network coverage, connectivity, and energy efficiency. BFO, inspired by bacterial foraging behavior, is particularly suited for this task due to its ability to adaptively explore and exploit search spaces. The study compares BFO against traditional optimization methods like Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), highlighting its superior performance in achieving optimal node configurations. Experimental results demonstrate significant improvements in coverage, connectivity, and energy consumption metrics, validating BFO as an effective tool for optimizing WSN deployments. This research contributes insights into leveraging BFO for enhancing WSN performance and identifies avenues for further exploration in dynamic and large-scale deployment scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Sensor nodes

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Emerging Two-Dimensional Conductivity at the Interface between Mott and Band Insulators

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Physical Review Letters 2024年第21期132卷 216201-216201页

作者： I. V. Maznichenko S. Ostanin D. Maryenko V. K. Dugaev E. Ya. Sherman P. Buczek I. Mertig M. Kawasaki A. Ernst Institute of Physics Martin Luther University Halle-Wittenberg D-06099 Halle Germany Department of Engineering and Computer Sciences Hamburg University of Applied Sciences Berliner Tor 7 D-20099 Hamburg Germany RIKEN Center for Emergent Matter Science (CEMS) Wako 351-0198 Japan Department of Physics and Medical Engineering Rzeszów University of Technology 35-959 Rzeszów Poland Department of Physical Chemistry and the EHU Quantum Center University of the Basque Country UPV/EHU Bilbao 48080 Spain Ikerbasque Basque Foundation for Science Bilbao Spain Department of Applied Physics and Quantum-Phase Electronics Center (QPEC) The University of Tokyo Tokyo 113-8656 Japan Institute for Theoretical Physics Johannes Kepler University A-4040 Linz Austria Max Planck Institute for Microstructure Physics Weinberg 2 D-06120 Halle Germany

Intriguingly, conducting perovskite interfaces between ordinary band insulators are widely explored, whereas similar interfaces with Mott insulators are still not quite understood. Here, we address the (001), (110), and (111) interfaces between the LaTiO3 Mott, and large band gap KTaO3 insulators. Based on first-principles calculations, we reveal a mechanism of interfacial conductivity, which is distinct from a formerly studied one applicable to interfaces between polar wideband insulators. Here, the key factor causing conductivity is the matching of oxygen octahedra tilting in KTaO3 and LaTiO3 which, due to a small gap in the LaTiO3 results in its sensitivity to the crystal structure, yields metallization of its overlayer and following charge transfer from Ti to Ta. Our findings, also applicable to other Mott insulators interfaces, shed light on the emergence of conductivity observed in LaTiO3/KTaO3 (110) where the “polar” arguments are not applicable and on the emergence of superconductivity in these structures.

关键词： Electronic structure Jahn-Teller effect Spin-orbit coupling Stress Structural properties Superconductors Antiferromagnets Magnetic insulators Mott insulators Solid-solid interfaces Thin films Two-dimensional electron gas Two-dimensional electron system Band structure methods Density functional theory

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Multimodal contrastive learning of urban space representations from POI data

arXiv

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arXiv 2024年

作者： Wang, Xinglei Cheng, Tao Law, Stephen Zeng, Zichao Yin, Lu Liu, Junyuan SpaceTimeLab Department of Civil Environmental and Geomatic Engineering University College London LondonWC1E 6BT United Kingdom Department of Geography University College London LondonWC1E 6BT United Kingdom School of Computer Science and Electronic Engineering University of Surrey SurreyGU2 7XH United Kingdom 3DIMPact Department of Civil Environmental and Geomatic Engineering University College London LondonWC1E 6BT United Kingdom

Existing methods for learning urban space representations from Point-of-Interest (POI) data face several limitations, including issues with geographical delineation, inadequate spatial information modelling, underutilisation of POI semantic attributes, and computational inefficiencies. To address these issues, we propose CaLLiPer (Contrastive Language-Location Pre-training), a novel representation learning model that directly embeds continuous urban spaces into vector representations that can capture the spatial and semantic distribution of urban environment. This model leverages a multimodal contrastive learning objective, aligning location embeddings with textual POI descriptions, thereby bypassing the need for complex training corpus construction and negative sampling. We validate CaLLiPer’s effectiveness by applying it to learning urban space representations in London, UK, where it demonstrates 5 - 15% improvement in predictive performance for land use classification and socioeconomic distribution mapping tasks compared to state-of-the-art methods. Visualisations of the learned representations further illustrate our model’s advantages in capturing spatial variations in urban semantics with high accuracy and fine resolution. Additionally, CaLLiPer achieves reduced training time, showcasing its efficiency and scalability. This work provides a promising pathway for scalable, semantically rich urban space representation learning that can support the development of geospatial foundation models. The implementation code is available at https://***/xlwang233/CaLLiPer. © 2024, CC BY-NC-SA.

关键词： Contrastive Learning

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Parallel Scale-wise Attention Network for Effective Scene Text Recognition

arXiv

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arXiv 2021年

作者： Sajid, Usman Chow, Michael Zhang, Jin Kim, Taejoon Wang, Guanghui Department of Electrical Engineering and Computer Science University of Kansas LawrenceKS66045 United States Sony Interactive Entertainment Global R and D Department of Computer Science Ryerson University TorontoONM5B 2K3 Canada

The paper proposes a new text recognition network for scene-text images. Many state-of-the-art methods employ the attention mechanism either in the text encoder or decoder for the text alignment. Although the encoder-based attention yields promising results, these schemes inherit noticeable limitations. They perform the feature extraction (FE) and visual attention (VA) sequentially, which bounds the attention mechanism to rely only on the FE final single-scale output. Moreover, the utilization of the attention process is limited by only applying it directly to the single scale feature-maps. To address these issues, we propose a new multi-scale and encoder-based attention network for text recognition that performs the multi-scale FE and VA in parallel. The multi-scale channels also undergo regular fusion with each other to develop the coordinated knowledge together. Quantitative evaluation and robustness analysis on the standard benchmarks demonstrate that the proposed network outperforms the state-of-the-art in most cases. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal encoding

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Inverse learning of black-box aggregator for robust Nash equilibrium

arXiv

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arXiv 2024年

作者： Chen, Guanpu Xu, Gehui He, Fengxiang Tao, Dacheng Parisini, Thomas Johansson, Karl Henrik Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden Digital Futures Stockholm100 44 Sweden Artificial Intelligence and its Applications Institute School of Informatics University of Edinburgh EdinburghEH8 9AB United Kingdom College of Computing & Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Engineering and Architecture University of Trieste Trieste34127 Italy

In this note, we investigate the robustness of Nash equilibria (NE) in multi-player aggregative games with coupling constraints. There are many algorithms for computing an NE of an aggregative game given a known aggregator. When the coupling parameters are affected by uncertainty, robust NE need to be computed. We consider a scenario where players' weight in the aggregator is unknown, making the aggregator kind of "a black box". We pursue a suitable learning approach to estimate the unknown aggregator by proposing an inverse variational inequality-based relationship. We then utilize the counterpart to reconstruct the game and obtain first-order conditions for robust NE in the worst case. Furthermore, we characterize the generalization property of the learning methodology via an upper bound on the violation probability. Simulation experiments show the effectiveness of the proposed inverse learning approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

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Lattice Codes for Lattice-Based PKE

arXiv

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arXiv 2022年

作者： Lyu, Shanxiang Liu, Ling Ling, Cong Lai, Junzuo Chen, Hao College of Cyber Security Jinan University Guangzhou510632 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom

Existing error correction mechanisms in lattice-based public key encryption (PKE) rely on either naive modulation or its concatenation with error correction codes (ECC). This paper shows that lattice coding, as a joint ECC and modulation technique, can substitute the naive modulation in existing lattice-based PKEs to enjoy better correction performance. We begin by modeling the FrodoPKE protocol as a noisy point-to-point communication system, where the communication channel is similar to the additive white Gaussian noise (AWGN) channel. To employ lattice codes for this special channel that hinges on hypercube shaping, we propose an efficient labeling function that converts between binary information bits and lattice codewords. The parameter sets of FrodoPKE are improved towards either higher security levels or smaller ciphertext sizes. For example, the proposed Frodo-1344-E8 has a 10-bit classical security gain over Frodo-1344. © 2022, CC BY.

关键词： White noise

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Efficient Privacy-Preserving ML for IoT: Cluster-Based Split Federated Learning Scheme for Non-IID Data

Efficient Privacy-Preserving ML for IoT: Cluster-Based Split...

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Cyber Security in Networking Conference (CSNet)

作者： Mohamad Arafeh Mohamad Wazzeh Hakima Ould-Slimane Chamseddine Talhi Azzam Mourad Hadi Otrok Department of Software and IT engineering Ecole de Technologie Superieure (ETS) Montreal Canada Department of Mathematics and Computer Science Universite de Quebec a Trois-Rivieres (UQTR) Canada Division of Science New York University Abu Dhabi UAE Department of CSM Cyber Security Systems and Applied AI Research Center Lebanese American University Beirut Lebanon Department of EECS Center of Cyber-Physical Systems (C2PS) Khalifa University Abu Dhabi UAE

This paper proposes a scheme addressing the challenges of integrating privacy-preserving distributed machine learning in the Internet of Things (IoT) context while improving the efficiency of the learning process and accelerating the convergence of the model. Specifically, it addresses the presence of non-independent and identically distributed (Non-IID) data and device heterogeneity, which occurs during parallel training and are common characteristics of IoT environments. In this context, SplitFed is a privacy-preserving technique that combines Federated Learning (FL) and Split Learning (SL) to enable fast and privacy-preserving model training on IoT devices. Through parallelism, FL allows fast and parallel model training. Furthermore, SL enables weak devices to train models through model splitting. However, SplitFed suffers from the increasing convergence time due to the FL parallelism introducing Non-IID and stragglers issues. In this paper, we aim to address the aforementioned problems by proposing a clustering parallelization scheme. As such, similar clients are combined based on their weight divergence and considered a single FL unit addressing the Non-IID problem. Moreover, to improve time efficiency and overcome the stragglers' presence, we offer a second clustering stage that separates clients by efficiency and uses FedAsync for model aggregation.

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