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IEEE Conference on Global Communications (GLOBECOM)

作者： Nuocheng Yang Sihua Wang Mingzhe Chen Christopher G. Brinton Changchuan Yin Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

In this paper, we consider the design of an energy efficient collaborative federated learning (CFL) methodology where devices exchange their local FL parameters with a subset of their neighbors without reliance on a parameter server. In the considered model, mobile devices implement the designed CFL to train their local FL models using their own datasets over a realistic wireless network. Due to the limited wireless resources and user movements, each device may not be able to transmit its FL parameters with all neighboring devices. Therefore, each device must select a subset of devices to share its FL parameters and optimize the transmit power. This problem is formulated as an optimization problem, whose goal is to minimize CFL training energy consumption while satisfying the delay and CFL training loss requirements. To solve this problem, a two-stage solution is proposed. At the first stage, a graph neural network (GNN) based algorithm is proposed, which enables each device to individually determine the subset of devices to transmit FL parameters using its neighboring devices' location and connection information. Compared to standard iterative algorithms that need to iteratively optimize device connections and transmit power, the proposed GNN based method can directly obtain the optimal device connections without iterative optimization. Given the optimal device connections, at the second stage, each device can directly obtain the optimal transmit power. Simulation results show that the proposed algorithm can decrease energy consumption by up to 46% compared to the algorithm where each device will directly connect to its first and second nearest neighbors.

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Phase Optimization and Relay Selection for Joint Relay and IRS-Assisted Communication

arXiv

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arXiv 2024年

作者： Uyoata, Uyoata E. Akinsolu, Mobayode O. Obayiuwana, Enoruwa Sangodoyin, Abimbola Adeogun, Ramoni Wireless Communication Network Section Aalborg University Aalborg Denmark Faculty of Arts Computing and Engineering Wrexham University WrexhamLL11 2AW United Kingdom Department of Electronic and Electrical Engineering Obafemi Awolowo University Ife Nigeria School of Computer Science University of Lincoln LincolnLN6 7TS United Kingdom

The use of Intelligent Reflecting Surfaces (IRSs) is considered a potential enabling technology for enhancing the spectral and energy efficiency of beyond 5G communication systems. In this paper, a joint relay and intelligent reflecting surface (IRS)-assisted communication is considered to investigate the gains of optimizing both the phase angles and selection of relays. The combination of successive refinement and reinforcement learning is proposed. Successive refinement algorithm is used for phase optimization and reinforcement learning is used for relay selection. Experimental results indicate that the proposed approach offers improved achievable rate performance and scales better with number of relays compared to considered benchmark approaches. © 2024, CC BY.

关键词： Reinforcement learning

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Master Stability Functions in Complex networks

arXiv

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arXiv 2024年

作者： Acharyya, Suman Pradhan, Priodyuti Meena, Chandrakala Complex Network Dynamics Lab Department of Mathematics Bar-Ilan University Ramat Gan5290002 Israel Networks.ai Lab Department of Computer Science & Engineering Indian Institute of Information Technology Karnataka Raichur584135 India Department of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun248007 India School of Physics Indian Institute of Science Education and Research Kerala Thiruvananthapuram695551 India

Synchronization is an emergent and fundamental phenomenon in nature and engineered systems. Understanding the stability of a synchronized phenomenon is crucial for ensuring functionality in various complex systems. The stability of the synchronization phenomenon is extensively studied using the Master Stability Function (MSF). This powerful and elegant tool plays a pivotal role in determining the stability of synchronization states, providing deep insights into synchronization in coupled systems. However, a major challenge lies in determining the MSF for complex dynamical networks driven by nonlinear interaction mechanisms. These mechanisms introduce additional complexity due to the intricate connectivity of the interacting elements and the complex dynamics governed by nonlinear processes, diverse parameters, and the higher dimensionality of the system. Although MSF analysis has been used for 25 years to study the stability of synchronization states, a systematic investigation of MSF across various networked systems remains missing from the literature. In this article, we present a simplified and unified MSF analysis for diverse undirected and directed networked systems. We begin with the analytical MSF framework for pairwise-coupled identical systems with diffusive and natural coupling schemes and extend our analysis to directed networks and multilayer networks, considering both intra-layer and inter-layer interactions. Furthermore, we revisit the MSF framework to incorporate higher-order interactions alongside pairwise interactions. To enhance understanding, we also provide a numerical analysis of synchronization in coupled Rössler systems under pairwise diffusive coupling and propose algorithms for determining the MSF, identifying stability regimes, and classifying MSF functions. Overall, the primary goal of this review is to present a systematic study of MSF in coupled dynamical networks in a clear and structured manner, making this powerful tool more accessible

关键词： Internet

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From model to implementation: a network algorithm programming language

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Science China(Information Sciences) 2020年第7期63卷 205-221页

作者： Jian WANG Jie AN Mingshuai CHEN Naijun ZHAN Lulin WANG Miaomiao ZHANG Ting GAN State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences School of Software Engineering Tongji University Network Integration Technology Research Department Huawei Technologies Co. Ltd. School of Computer Science Wuhan University

Software-defined networking(SDN) is a revolutionary technology that facilitates network management and enables programmatically efficient network configuration, thereby improving network performance and flexibility. However, as the application programming interfaces(APIs) of SDN are low-level or functionality-restricted, SDN programmers cannot easily keep pace with the ever-changing devices, topologies, and demands of SDN. By deriving motivation from industry practice, we define a novel network algorithm programming language(NAPL) that enhances the SDN framework with a rapid programming flow from topology-based network models to C++ implementations, thus bridging the gap between the limited capability of existing SDN APIs and the reality of practical network management. In contrast to several state-of-the-art languages, NAPL provides a range of critical high-level network programming features:(1) topology-based network modeling and visualization;(2) fast abstraction and expansion of network devices and constraints;(3) a declarative paradigm for the fast design of forwarding policies;(4) a built-in library for complex algorithm implementation;(5) full compatibility with C++ programming; and(6) userfriendly debugging support when compiling NAPL into highly readable C++ codes. The expressiveness and performance of NAPL are demonstrated in various industrial scenarios originating from practical network management.

关键词： software-defined networking(SDN) network algorithm programming language(NAPL) network abstraction

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Ferromagnetism triggered by band tripling in ruthenate Sr4Ru3O10

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Physical Review B 2024年第15期110卷 155134页

作者： N. Watanabe S. Souma K. Nakayama K. Yamauchi J. F. Ribeiro Y. Wang Miho Kitamura Koji Horiba Hiroshi Kumigashira T. Oguchi Z. Q. Mao Y. P. Chen T. Sato Department of Physics Graduate School of Science Advanced Institute for Materials Research (WPI-AIMR) Center for Science and Innovation in Spintronics (CSIS) Precursory Research for Embryonic Science and Technology (PRESTO) Center for Spintronics Research Network (CSRN) School of Electrical and Computer Engineering and Birck Nanotechnology Center Department of Physics Institute of Materials Structure Science (QST) Sendai 980-8579 Japan Institute of Multidisciplinary Research for Advanced Materials (IMRAM) Department of Physics and Astronomy and Purdue Quantum Science and Engineering Institute International Center for Synchrotron Radiation Innovation Smart (SRIS) Mathematical Science Center for Co-creative Society (MathCCS)

Itinerant ferromagnetism is understood in terms of a quasiparticle picture with renormalized many-body effects. While the ferromagnetic ground state is destabilized by thermal and quantum fluctuations leading to exotic states such as unconventional superconductivity, how the quasiparticles evolve across the ferromagnetic transition is a target of intensive debate. Here, we present a type of ferromagnetic transition that is accompanied by a drastic reconstruction of quasiparticle spectrum in a layered ferromagnetic ruthenate, Sr4Ru3O10. Angle-resolved photoemission spectroscopy uncovered that the three-dimensional coherent states below the ferromagnetic transition temperature (TC) turn into two-dimensional incoherent electronic states slightly above TC characterized by the disappearance of trilayer band splitting, ferromagnetic exchange splitting, and long-lived quasiparticles. Our findings suggest that the electronic coherence strongly modifies the fermiology and magnetic order, pointing to an intriguing coupling between quasiparticles and magnetic properties.

关键词： Electronic structure Fermi surface Ferromagnetism Quasiparticles & collective excitations van Hove singularity Ruddlesden-Popper compounds Angle-resolved photoemission spectroscopy Density functional theory First-principles calculations

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TSdetector: Temporal-Spatial Self-correction Collaborative Learning for Colonoscopy Video Detection

arXiv

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arXiv 2024年

作者： Wang, Kai-Ni Wang, Haolin Zhou, Guang-Quan Wang, Yangang Yang, Ling Chen, Yang Li, Shuo School of Biological Science and Medical Engineering Southeast University Nanjing China Jiangsu Key Laboratory of Biomaterials and Devices Southeast University Nanjing China Laboratory of Image Science and Technology Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China TUGE Healthcare Nanjing China Institute of Medical Technology Peking University Health Science Center China Department of Computer and Data Science Department of Biomedical Engineering Case Western Reserve University United States

CNN-based object detection models that strike a balance between performance and speed have been gradually used in polyp detection tasks. Nevertheless, accurately locating polyps within complex colonoscopy video scenes remains challenging since existing methods ignore two key issues: intra-sequence distribution heterogeneity and precision-confidence discrepancy. To address these challenges, we propose a novel Temporal-Spatial self-correction detector (TSdetector), which first integrates temporal-level consistency learning and spatial-level reliability learning to detect objects continuously. Technically, we first propose a global temporal-aware convolution, assembling the preceding information to dynamically guide the current convolution kernel to focus on global features between sequences. In addition, we designed a hierarchical queue integration mechanism to combine multi-temporal features through a progressive accumulation manner, fully leveraging contextual consistency information together with retaining long-sequence-dependency features. Meanwhile, at the spatial level, we advance a position-aware clustering to explore the spatial relationships among candidate boxes for recalibrating prediction confidence adaptively, thus eliminating redundant bounding boxes efficiently. The experimental results on three publicly available polyp video dataset show that TSdetector achieves the highest polyp detection rate and outperforms other state-of-the-art methods. The code can be available at https://***/soleilssss/TSdetector. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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ACM-NDC study 2021-2022

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ACM Inroads 2022年第3期13卷 38-54页

作者： Zweben, Stuart Tims, Jodi L. Tucker, Cindy Timanovsky, Yan Department of Computer Science and Engineering The Ohio State University 2015 Neil Avenue ColumbusOH43210 United States Khoury College in the Global Network Valley The Practice-San Francisco Silicon Valley Northeastern University 360 Huntington Ave. BostonMA02115 United States 470 Cooper Dr LexingtonKY40506 United States Education and Professional Development Manager Association for Computing Machinery 1601 Broadway New YorkNY10019 United States

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Is the Performance of NOMA-aided Integrated Sensing and Multicast-Unicast Communications Improved by IRS?

Is the Performance of NOMA-aided Integrated Sensing and Mult...

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IEEE International Conference on Communications Workshops, ICC

作者： Yang Gou Yinghui Ye Guangyue Lu Lu Lv Rose Qingyang Hu Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts & Telecommunications China School of 'Telecommunications Engnieering Xidian University Xi'an China Department of Electrical and Computer Engineering Utah State University U.S.A.

In this paper, we consider intelligent reflecting surface (IRS) in a non-orthogonal multiple access (NOMA)-aided Integrated Sensing and Multicast-Unicast Communication (ISMUC) system, where the multicast signal is used for sensing and communications while the unicast signal is used only for communications. Our goal is to depict whether the IRS improves the performance of NOMA-ISMUC system or not under the imperfect/perfect successive interference cancellation (SIC) scenario. Towards this end, we formulate a non-convex problem to maximize the unicast rate while ensuring the minimum target illumination power and multicast rate. To settle this problem, we employ the Dinkelbach method to transform this original problem into an equivalent one, which is then solved via alternating optimization algorithm and semidefinite relaxation (SDR) with Sequential Rank-One Constraint Relaxation (SROCR). Based on this, an iterative algorithm is devised to obtain a near-optimal solution. computer simulations verify the quick convergence of the devised iterative algorithm, and provide insightful results. Compared to NOMA-ISMUC without IRS, IRS-aided NOMA-ISMUC achieves a higher rate with perfect SIC but keeps the almost same rate in the case of imperfect SIC.

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SGSM: A FOUNDATION-MODEL-LIKE SEMI-GENERALIST SENSING MODEL

arXiv

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arXiv 2024年

作者： Yang, Tianjian Zhou, Hao Liu, Shuo Guo, Kaiwen Hou, Yiwen Du, Haohua Liu, Zhi Li, Xiang-Yang School of Computer Science and Technology University of Science and Technology China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology China Deqing Alpha Innovation Institute Zhejiang Huzhou China School of Cyber Science and Technology Beihang University China Department of Computer and Network Engineering The University of Electro-Communications Tokyo182-8585 Japan

The significance of intelligent sensing systems is growing in the realm of smart services. These systems extract relevant signal features and generate informative representations for particular tasks. However, building the feature extraction component for such systems requires extensive domain-specific expertise or data. The exceptionally rapid development of foundation models is likely to usher in newfound abilities in such intelligent sensing. We propose a new scheme for sensing model, which we refer to as semi-generalist sensing model (SGSM). SGSM is able to semiautomatically solve various tasks using relatively less task-specific labeled data compared to traditional systems. Built through the analysis of the common theoretical model, SGSM can depict different modalities, such as the acoustic and Wi-Fi signal. Experimental results on such two heterogeneous sensors illustrate that SGSM functions across a wide range of scenarios, thereby establishing its broad applicability. In some cases, SGSM even achieves better performance than sensor-specific specialized solutions. Wi-Fi evaluations indicate a 20% accuracy improvement when applying SGSM to an existing sensing model. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Uncertainty-aware Sampling for Long-tailed Semi-supervised Learning

arXiv

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arXiv 2024年

作者： Yang, Kuo Li, Duo Hu, Menghan Zhai, Guangtao Yang, Xiaokang Zhang, Xiao-Ping The Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronic Engineering East China Normal University Shanghai200241 China The Kargobot of DiDi Shanghai201210 China The Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai200240 China Tsinghua Berkeley Shenzhen Institute Shenzhen China The Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University ONM5B 2K3 Canada

For semi-supervised learning with imbalance classes, the long-tailed distribution of data will increase the model prediction bias toward dominant classes, undermining performance on less frequent classes. Existing methods also face challenges in ensuring the selection of sufficiently reliable pseudo-labels for model training and there is a lack of mechanisms to adjust the selection of more reliable pseudo-labels based on different training stages. To mitigate this issue, we introduce uncertainty into the modeling process for pseudo-label sampling, taking into account that the model performance on the tailed classes varies over different training stages. For example, at the early stage of model training, the limited predictive accuracy of model results in a higher rate of uncertain pseudo-labels. To counter this, we propose an Uncertainty-Aware Dynamic Threshold Selection (UDTS) approach. This approach allows the model to perceive the uncertainty of pseudo-labels at different training stages, thereby adaptively adjusting the selection thresholds for different classes. Compared to other methods such as the baseline method FixMatch, UDTS achieves an increase in accuracy of at least approximately 5.26%, 1.75%, 9.96%, and 1.28% on the natural scene image datasets CIFAR10-LT, CIFAR100-LT, STL-10-LT, and the medical image dataset TissueMNIST, respectively. The source code of UDTS is publicly available at: https://***/yangk/UDTS. Copyright © 2024, The Authors. All rights reserved.

关键词： Uncertainty analysis

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