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Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale AI Models

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IEEE Transactions on Vehicular Technology 2025年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Chen, Mingzhe Quek, Tony Q. S. Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL United States Singapore University of Technology and Design Dept. of Information Systems Technology and Design 487372 Singapore

Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models pose significant challenges for real-time communication scenarios. To address this, this paper proposes utilizing knowledge distillation (KD) techniques to extract and condense knowledge from LAI models, effectively reducing model complexity and computation latency. Nevertheless, the inherent complexity of LAI models leads to prolonged inference times during distillation, while their lack of channel awareness compromises the distillation performance. These limitations make standard KD methods unsuitable for task-oriented semantic communication scenarios. To address these issues, we propose a fast distillation method featuring a pre-stored compression mechanism that eliminates the need for repetitive inference, significantly improving efficiency. Furthermore, a channel adaptive module is incorporated to dynamically adjust the transmitted semantic information based on varying channel conditions, enhancing communication reliability and adaptability. In addition, an information bottleneck-based loss function is derived to guide the fast distillation process. Simulation results verify that the proposed scheme outperform baselines in term of task accuracy, model size, computation latency, and training data requirements. © 1967-2012 IEEE.

关键词： NP-hard

Double-Free-Layer Magnetic Tunnel Junctions for Probabilistic Bits

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Physical Review Applied 2021年第4期15卷 044049-044049页

作者： Kerem Y. Camsari Mustafa Mert Torunbalci William A. Borders Hideo Ohno Shunsuke Fukami Department of Electrical and Computer Engineering University of California Santa Barbara California 93106 USA Birck Nanotechnology Center Purdue University West Lafayette Indiana 47907 USA Laboratory for Nanoelectronics and Spintronics Research Institute of Electrical Communication Tohoku University Japan Center for Spintronics Research Network Tohoku University Japan Center for Science and Innovation in Spintronics Tohoku University Japan WPI Advanced Institute for Materials Research Tohoku University Japan Center for Innovative Integrated Electronic Systems Tohoku University Japan

Naturally random devices that exploit ambient thermal noise have recently attracted attention as hardware primitives for accelerating probabilistic computing applications. One such approach is to use a low barrier nanomagnet as the free layer of a magnetic tunnel junction (MTJ), the magnetic fluctuations of which are converted to resistance fluctuations in the presence of a stable fixed layer. Here, we propose and theoretically analyze a MTJ with no fixed layers but two free layers that are circularly shaped disk magnets. We use an experimentally benchmarked model that accounts for finite-temperature magnetization dynamics, bias-dependent charge, and spin-polarized currents as well as the dipolar coupling between the free layers. We obtain analytical results for statistical averages of fluctuations that are in good agreement with the numerical model. We find that the free layers with low diameters fluctuate to randomize the resistance of the MTJ in an approximately bias-independent manner. We show how such MTJs can be used to build a binary stochastic neuron (or a p-bit) in hardware. Unlike earlier stochastic MTJs that need to operate at a specific bias point to produce random fluctuations, the proposed design can be random for a wide range of bias values, independent of spin-transfer-torque pinning. Moreover, in the absence of a carefully optimized stabled fixed layer, the symmetric double-free-layer stack can be manufactured using present-day magnetoresistive random-access memory (MRAM) technology by minimal changes to the fabrication process. Such devices can be used as hardware accelerators in energy-efficient computing schemes that require a large throughput of tunably random bits.

关键词： Spintronics Superparamagnetism Magnetic tunnel junctions

Finding spin glass ground states through deep reinforcement learning

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arXiv 2021年

作者： Fan, Changjun Shen, Mutian Nussinov, Zohar Liu, Zhong Sun, Yizhou Liu, Yang-Yu College of Systems Engineering National University of Defense Technology Changsha410073 China Department of Physics Washington University in St. Louis Campus Box 1105 1 Brookings Drive St. LouisMO63130 United States Department of Computer Science University of California Los AngelesCA90024 United States Channing Division of Network Medicine Department of Medicine Brigham and Women's Hospital and Harvard Medical School BostonMA02115 United States

Spin glasses are disordered magnets with random interactions that are, generally, in conflict with each other. Finding the ground states of spin glasses is not only essential for the understanding of the nature of disordered magnetic and other physical systems, but also useful to solve a broad array of hard combinatorial optimization problems across multiple disciplines. Despite decades-long efforts, an algorithm with both high accuracy and high efficiency is still lacking. Here we introduce DIRAC - a deep reinforcement learning framework, which can be trained purely on small-scale spin glass instances and then applied to arbitrarily large ones. DIRAC displays better scalability than other methods and can be leveraged to enhance any thermal annealing method. Extensive calculations on 2D, 3D and 4D Edwards-Anderson spin glass instances demonstrate the superior performance of DIRAC over existing methods. As many hard combinatorial optimization problems have Ising spin glass formulations, our results suggest a promising tool in solving these hard problems. Moreover, the presented algorithm will help us better understand the nature of the low-temperature spin-glass phase, which is a fundamental challenge in statistical physics. © 2021, CC BY-NC-ND.

关键词： Spin glass

Finding shortest and nearly shortest path nodes in large substantially incomplete networks

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arXiv 2022年

作者： Kitsak, Maksim Ganin, Alexander Elmokashfi, Ahmed Cui, Hongzhu Eisenberg, Daniel A. Alderson, David L. Korkin, Dmitry Linkov, Igor Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Network Science Institute Northeastern University BostonMA02115 United States Department of Systems and Information Engineering University of Virginia CharlottesvilleVA22904 United States U.S. Army Engineer Research and Development Center Contractor Concord MA01742 United States Simula Metropolitan Center for Digital Engineering Oslo Norway Bioinformatics and Computational Biology Program Worcester Polytechnic Institute WorcesterMA01609 United States Institute for Genomic Medicine Columbia University Medical Center New YorkNY United States Operations Research Department Naval Postgraduate School MontereyCA93943 United States Data Science Program Worcester Polytechnic Institute WorcesterMA01609 United States Computer Science Department Worcester Polytechnic Institute WorcesterMA01609 United States U.S. Army Engineer Research and Development Center Environmental Laboratory Concord MA01742 United States

Dynamic processes on networks, be it information transfer in the Internet, contagious spreading in a social network, or neural signaling, take place along shortest or nearly shortest paths. Unfortunately, our maps of most large networks are substantially incomplete due to either the highly dynamic nature of networks, or high cost of network measurements, or both, rendering traditional path finding methods inefficient. We find that shortest paths in large real networks, such as the network of protein-protein interactions (PPI) and the Internet at the autonomous system (AS) level, are not random but are organized according to latent-geometric rules. If nodes of these networks are mapped to points in latent hyperbolic spaces, shortest paths in them align along geodesic curves connecting endpoint nodes. We find that this alignment is sufficiently strong to allow for the identification of shortest path nodes even in the case of substantially incomplete networks. We demonstrate the utility of latent-geometric path finding in problems of cellular pathway reconstruction and communication security. Copyright © 2022, The Authors. All rights reserved.

关键词： Proteins

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反向连边在大型分层网络中的影响（英文）

engineering 2024年第5期 240-249页

作者：曹浩森胡斌斌莫小雨陈都鑫高建喜袁烨陈关荣 Tamás Vicsek 管晓宏张海涛 MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Intelligent Manufacturing Equipment and Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology School of Mechanical and Aerospace Engineering Nanyang Technological University Jiangsu Key Laboratory of Networked Collective Intelligence School of Mathematics Southeast University Department of Computer Science Rensselaer Polytechnic Institute Department of Electronic Engineering City University of Hong Kong Department of Biological Physics E?tv?s University MOE Key Laboratory for Intelligent Networks and Network Security School of Automation Science and Engineering Xi'an Jiaotong University

Hierarchical networks are frequently encountered in animal groups, gene networks, and artificial engineering systems such as multiple robots, unmanned vehicle systems, smart grids, wind farm networks,and so forth. The structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower-to higher-level nodes, such as lagging birds' howl in a flock or the opinions of lowerlevel individuals feeding back to higher-level ones in a social group. This study reveals that, for most large-scale real hierarchical networks, the majority of the reverse edges do not affect the synchronization process of the entire network; the synchronization process is influenced only by a small part of these reverse edges along specific paths. More surprisingly, a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%. The effect of such edges depends not on the network size but only on the average in-degree of the involved subnetwork. The overwhelming majority of active reverse edges turn out to have some kind of ‘‘bunchingo effect on the information flows of hierarchical networks, which slows down synchronization processes. This finding refines the current understanding of the role of reverse edges in many natural, social, and engineering hierarchical networks,which might be beneficial for precisely tuning the synchronization rhythms of these networks. Our study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.

关键词：

The Battery Capacity of Energy-storing Quantum systems

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arXiv 2023年

作者： Yang, Xue Yang, Yan-Han Alimuddin, Mir Salvia, Raffaele Fei, Shao-Ming Zhao, Li-Ming Nimmrichter, Stefan Luo, Ming-Xing The School of Information Science and Technology Southwest Jiaotong University Chengdu610031 China School of Computer and Network Security Chengdu University of Technology Chengdu610059 China Department of Physics of Complex Systems S. N. Bose National Center for Basic Sciences Kolkata700106 India Scuola Normale Superiore PisaI-56127 Italy School of Mathematical Sciences Capital Normal University Beijing100048 China Max-Planck-Institute for Mathematics in the Sciences Leipzig04103 Germany Naturwissenschaftlich-Technische Fakultät Universität Siegen Siegen57068 Germany CAS Center for Excellence in Quantum Information and Quantum Physics Hefei230026 China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China

The quantum battery capacity is introduced in this letter as a figure of merit that expresses the potential of a quantum system to store and supply energy. It is defined as the difference between the highest and the lowest energy that can be reached by means of the unitary evolution of the system. This function is closely connected to the ergotropy, but it does not depend on the temporary level of energy of the system. The capacity of a quantum battery can be directly linked with the entropy of the battery state, as well as with measures of coherence and entanglement. © 2023, CC BY.

关键词： Quantum optics

Noise bias compensation for reconstructed HDR images in two layer coding

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IEEJ Transactions on Electronics, Information and Systems

IEEJ Transactions on Electronics, Information and systems 2019年第12期139卷 1442-1451页

作者： Minewaki, Sayaka Hamzah, Fairoza Amira Binti Yoshida, Taichi Iwahashi, Masahiro Kiya, Hitoshi Department of Computer Science and Engineering National Institute of Technology Yuge College 1000 Kamijima-cho Ochi-gun Ehime794-2593 Japan Kyoto College of Graduate Studies for Informatics 7 Tanakamonzen-cho Sakyo-ku Kyoto606-8225 Japan Department of Computer and Network Engineering University of Electro-Communications 1-5-1 Chofugaoka Chofu Tokyo182-8585 Japan Department of Electrical Electronics and Information Engineering Nagaoka University of Technology 1603-1 Kamitomioka Nagaoka Niigata940-2188 Japan Department of Information and Communication Systems Faculty of System Design Tokyo Metropolitan University 6-6 Asahigaoka Hino Tokyo191-0065 Japan

We propose a two-layer coding method for HDR images with noise bias compensation (NBC). Although the mean of image noise is assumed to be zero originally, it becomes nonzero (that is, noise bias is present) after tone mapping, because of the nonlinearity of tone mapping. In the conventional two-layer coding, the reconstructed LDR image is decoded using a lossy encoder of a base layer that includes noise;the noise is included in an HDR image that is inverse tone mapped from a reconstructed LDR image with noise bias. We aim to improve the quality of reconstructed HDR images by compensating for noise bias. NBC classifies pixels in a noisy image into several subsets according to the pixel value after tone mapping, and compensates for the pixel value in each subset with a compensation value determined in advance. The compensation values are determined by using all pixel values in the LDR image that is input to the base layer, in addition to those in the HDR image that is inversely tone mapped from the reconstructed LDR image. We perform NBC following inverse tone mapping for the reconstructed LDR image. Experimental results show the effectiveness of the proposed method. © 2019 The Institute of Electrical Engineers of Japan.

关键词： Image denoising

衰亡链揭示互利共生生态系统中安全与崩溃的中间阶段（英文）

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engineering 2024年第12期 89-98页

作者：王光炜刘雪明肖颖袁烨潘林强管晓宏高建喜张海涛 The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Digital Manufacturing Equipment and Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Guangdong HUST Industrial Technology Research Institute Huazhong University of Science and Technology Guangdong Provincial Engineering Technology Center of Autonomous Unmanned Vessels MOE Key Laboratory of Intelligent Networks and Network Security&State Key Laboratory of Manufacturing Systems Xi'an Jiaotong University Tsinghua National Laboratory of Information Science and Technology Department of Automation Tsinghua University Department of Computer Science Rensselaer Polytechnic Institute

Ecosystems are undergoing unprecedented persistent deterioration due to unsustainable anthropogenic human activities, such as overfishing and deforestation, and the effects of such damage on ecological stability are uncertain. Despite recent advances in experimental and theoretical studies on regime shifts and tipping points, theoretical tools for understanding the extinction chain, which is the sequence of species extinctions resulting from overexploitation, are still lacking, especially for large-scale nonlinear networked systems. In this study, we developed a mathematical tool to predict regime shifts and extinction chains in ecosystems under multiple exploitation situations and verified it in 26 real-world mutualistic networks of various sizes and densities. We discovered five phases during the exploitation process: safe,partial extinction, bistable, tristable, and collapse, which enabled the optimal design of restoration strategies for degraded or collapsed systems. We validated our approach using a 20-year dataset from an eelgrass restoration project. Counterintuitively, we also found a specific region in the diagram spanning exploitation rates and competition intensities, where exploiting more species helps increase *** computational tool provides insights into harvesting, fishing, exploitation, or deforestation plans while conserving or restoring the biodiversity of mutualistic ecosystems.

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Hardware-aware in situ Boltzmann machine learning using stochastic magnetic tunnel junctions

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arXiv 2021年

作者： Kaiser, Jan Borders, William A. Camsari, Kerem Y. Fukami, Shunsuke Ohno, Hideo Datta, Supriyo Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47906 United States Laboratory for Nanoelectronics and Spintronics Research Institute of Electrical Communication Tohoku University Sendai Japan Department of Electrical and Computer Engineering University of California Santa Barbara Santa BarbaraCA93106 United States Center for Innovative Integrated Electronic Systems Tohoku University Sendai Japan Center for Spintronics Research Network Tohoku University Sendai Japan Center for Science and Innovation in Spintronics Tohoku University Sendai Japan WPI-Advanced Institute for Materials Research Tohoku University Sendai Japan

One of the big challenges of current electronics is the design and implementation of hardware neural networks that perform fast and energy-efficient machine learning. Spintronics is a promising catalyst for this field with the capabilities of nanosecond operation and compatibility with existing microelectronics. Considering large-scale, viable neuromorphic systems however, variability of device properties is a serious concern. In this paper, we show an autonomously operating circuit that performs hardware-aware machine learning utilizing probabilistic neurons built with stochastic magnetic tunnel junctions. We show that in situ learning of weights and biases in a Boltzmann machine can counter device-to-device variations and learn the probability distribution of meaningful operations such as a full adder. This scalable autonomously operating learning circuit using spintronics-based neurons could be especially of interest for standalone artificial-intelligence devices capable of fast and efficient learning at the edge. Copyright © 2021, The Authors. All rights reserved.

关键词： Energy efficiency