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Graph Neural Network Enabled Fluid Antenna Systems: A Two-Stage Approach

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arXiv 2025年

作者： He, Changpeng Lu, Yang Chen, Wei Ai, Bo Wong, Kai-Kit Niyato, Dusit State Key Laboratory of Advanced Rail Autonomous Operation China School of Computer Science and Technology Beijing Jiaotong University Beijing100044 China School of Electronics and Information Engineering Beijing Jiaotong University Beijing100044 China Department of Electronic and Electrical Engineering University College London LondonWC1E 6BT United Kingdom Yonsei Frontier Laboratory The School of Integrated Technology Yonsei University Seoul03722 Korea Republic of College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

An emerging fluid antenna system (FAS) brings a new dimension, i.e., the antenna positions, to deal with the deep fading, but simultaneously introduces challenges related to the transmit design. This paper proposes an "unsupervised learning to optimize" paradigm to optimize the FAS. Particularly, we formulate the sum-rate and energy efficiency (EE) maximization problems for a multiple-user multiple-input single-output (MU-MISO) FAS and solved by a two-stage graph neural network (GNN) where the first stage and the second stage are for the inference of antenna positions and beamforming vectors, respectively. The outputs of the two stages are jointly input into a unsupervised loss function to train the two-stage GNN. The numerical results demonstrates that the advantages of the FAS for performance improvement and the two-stage GNN for real-time and scalable optimization. Besides, the two stages can function separately. © 2025, CC BY.

关键词： Graph neural networks

Dynamically controlling terahertz wavefronts with cascaded metasurfaces

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Advanced Photonics 2021年第3期3卷 93-102页

作者： Xiaodong Cai Rong Tang Haoyang Zhou Qiushi Li Shaojie Ma Dongyi Wang Tong Liu Xiaohui Ling Wei Tan Qiong He Shiyi Xiao Lei Zhou Shanghai University Key Laboratory of Specialty Fiber Optics and Optical Access NetworksJoint International Research Laboratory of Specialty Fiber Optics and Advanced CommunicationShanghai Institute for Advanced Communication and Data ScienceShanghaiChina Fudan University State Key Laboratory of Surface PhysicsKey Laboratory of Micro and Nano Photonic Structures(Ministry of Education)and Physics DepartmentShanghaiChina Hengyang Normal University College of Physics and Electronic EngineeringHengyangChina CAEP Microsystem and Terahertz Research CenterChengduChina CAEP Institute of Electronic EngineeringMianyangChina Fudan University Academy for Engineering and TechnologyShanghaiChina Collaborative Innovation Centre of Advanced Microstructures NanjingChina

Dynamically controlling terahertz(THz)wavefronts in a designable fashion is highly desired in ***,available methods working at microwave frequencies do not work well in the THz regime due to lacking suitable tunable elements with submicrometer ***,instead of locally controlling individual meta-atoms in a THz metasurface,we show that rotating different layers(each exhibiting a particular phase profile)in a cascaded metadevice at different speeds can dynamically change the effective Jonesmatrix property of the whole device,thus enabling extraordinary manipulations on the wavefront and polarization characteristics of a THz beam impinging on the *** illustrating our strategy based on model calculations,we experimentally demonstrate two proof-of-concept metadevices,each consisting of two carefully designed all-silicon transmissive metasurfaces exhibiting different phase *** two metasurfaces inside the fabricated devices at different speeds,we experimentally demonstrate that the first metadevice can efficiently redirect a normally incident THz beam to scan over a wide solid-angle range,while the second one can dynamically manipulate both the wavefront and polarization of a THz *** results pave the way to achieving dynamic control of THz beams,which is useful in many applications,such as THz radar,and bio-and chemical sensing and imaging.

关键词： terahertz beam-steering dielectric metasurfaces linear gradient phase

A Task Processing Efficiency Improvement Scheme Based on Cloud-Edge Architecture in Dense Computing Environments

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SSRN

SSRN 2022年

作者： Feng, Jiahui Qi, Jingze Liu, Yuanning Liu, Zhen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Graduate School of Engineering Nagasaki Institute of Applied Science 536 Aba-machi Nagasaki851-0193 Japan

As cloud-edge architecture combines low latency and high-performance computing, it has a wider range of application scenarios. Considering that, how to reasonably allocate tasks to the edge cloud or the central cloud and how to select appropriate virtual machines (VMs) for tasks are two key aspects affecting the efficiency of cloud-edge architecture. For the former, the ARDT algorithm is proposed, which dynamically adjusts the optimal threshold by predicting the number of real-time edge-end devices. For the latter, we design the DBMR algorithm based on the Max-Fit algorithm, in which we propose a structure of blocking to reduce the time complexity of the original algorithm from O(n2) to O(n(n-m)). Experimental results show that the ARDT-DBMR algorithm optimizes the average processing time and task failure rate by 30%-40% compared with the baseline algorithms and other advanced algorithms and significantly improves the problem of abnormal fluctuations in VM utilization in dense computing environments. © 2022, The Authors. All rights reserved.

关键词： Virtual machine

Joint Size and Placement Optimization for IRS-Aided Communications with Active and Passive Elements

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arXiv 2025年

作者： Peng, Qiaoyan Wu, Qingqing Chen, Wen Huang, Chaoying Zheng, Beixiong Ma, Shaodan Jian, Mengnan Chen, Yijian Yang, Jun The State Key Laboratory of Internet of Things for Smart City University of Macau 999078 China The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China The School of Microelectronics South China University of Technology Guangzhou511442 China The Shenzhen Research Institute of Big Data Shenzhen518172 China ZTE Corporation Shenzhen518057 China

Different types of intelligent reflecting surfaces (IRS) are exploited for assisting wireless communications. The joint use of passive IRS (PIRS) and active IRS (AIRS) emerges as a promising solution owing to their complementary advantages. They can be integrated into a single hybrid active-passive IRS (HIRS) or deployed in a distributed manner, which poses challenges in determining the IRS elements allocation and placement for rate maximization. In this paper, we investigate the capacity of an IRS-aided wireless communication system with both active and passive elements. Specifically, we consider three deployment schemes: 1) base station (BS)→HIRS→user (BHU);2) BS→AIRS→PIRS→user (BAPU);3) BS→PIRS→AIRS→user (BPAU). Under the line-of-sight channel model, we formulate a rate maximization problem via a joint optimization of the IRS elements allocation and placement. We first derive the optimized number of active and passive elements for BHU, BAPU, and BPAU schemes, respectively. Then, low-complexity HIRS/AIRS placement strategies are provided. To obtain more insights, we characterize the system capacity scaling orders for the three schemes with respect to the large total number of IRS elements, amplification power budget, and BS transmit power. Finally, simulation results are presented to validate our theoretical findings and show the performance difference among the BHU, BAPU, and BPAU schemes with the proposed joint design under various system setups. Copyright © 2025, The Authors. All rights reserved.

关键词： Budget control

M3oE: Multi-Domain Multi-Task Mixture-of-Experts Recommendation Framework

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arXiv 2024年

作者： Zhang, Zijian Cai, Qingpeng Liu, Ziru Hu, Lantao Liu, Shuchang Zhao, Xiangyu Liu, Qidong Jiang, Peng Yu, Jiaao Zhang, Chunxu Zhao, Hongwei Gai, Kun Jilin University City University of Hong Kong Jilin China Kuaishou Technology Beijing China City University of Hong Kong Hong Kong Xi’an Jiaotong University City University of Hong Kong Xi’an China Jilin University Jilin China Beijing China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China

Multi-domain recommendation and multi-task recommendation have demonstrated their effectiveness in leveraging common information from different domains and objectives for comprehensive user modeling. Nonetheless, the practical recommendation usually faces multiple domains and tasks simultaneously, which cannot be well-addressed by current methods. To this end, we introduce M3oE, an adaptive Multi-domain Multi-task Mixture-of-Experts recommendation framework. M3oE integrates multi-domain information, maps knowledge across domains and tasks, and optimizes multiple objectives. We leverage three mixture-of-experts modules to learn common, domain-aspect, and task-aspect user preferences respectively to address the complex dependencies among multiple domains and tasks in a disentangled manner. Additionally, we design a two-level fusion mechanism for precise control over feature extraction and fusion across diverse domains and tasks. The framework’s adaptability is further enhanced by applying AutoML technique, which allows dynamic structure optimization. To the best of the authors’ knowledge, our M3oE is the first effort to solve multi-domain multi-task recommendation self-adaptively. Extensive experiments on two benchmark datasets against diverse baselines demonstrate M3oE’s superior performance. The implementation code is available to ensure reproducibility1 Copyright © 2024, The Authors. All rights reserved.

关键词： Recommender systems

Optimal Feature Combination Using SVM Algorithms for Brain-computer Interface

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Optimal Feature Combination Using SVM Algorithms for Brain-c...

2020 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2020

作者： Long, Jun Yu, Shulei Network Resource Management and Trust Evaluation Key Laboratory of Hunan School of Computer Engineering Central South University China National Engineering Laboratory for Medical Big Data Application Central South University Changsha China

ISBN: (纸本)9781728170046

This paper aims to establish a four-class classification brain-computer interface model with the optimal combination of features. By exploring the influence of feature vector combinations in different time windows on brain-computer interface (BCI) classification, the optimal 2-feature combination could be get. Four types of movement paradigms were studied based on the concentration of oxygenated hemoglobin (HbO) in the brain motor region. A total of 10 volunteers completed a set of movement patterns which included hand griping and arm lifting of two upper limbs. After extracting signal mean(SM) and signal slope(SS) eigenvectors, the 'one-versus-one' SVM algorithm was used for classification. The highest average classification accuracy rate reached 80.48%, and most of the participants got the highest classification accuracy in the combination time window of SM feature (05s) and SS feature (57s). In the case of the same time window of SM feature, the classification accuracy increased gradually with the selection of 35s, 46s, 57s respectively in the time window of SS feature. © 2020 IEEE.

关键词： Brain computer interface

The Self-organized Criticality Behaviors of Two New Parameters in SGR J1935+2154

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arXiv 2024年

作者： Xiao, Shuo Zhang, Shuang-Nan Xiong, Shao-Lin Wang, Ping Li, Xiu-Juan Dong, Ai-Jun Zhi, Qi-Jun Li, Di School of Physics and Electronic Science Guizhou Normal University Guiyang550001 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550001 China Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing100049 China School of Cyber Science and Engineering Qufu Normal University Qufu273165 China CAS Key Laboratory of FAST NAOC Chinese Academy of Sciences Beijing100101 China

The minimum variation timescale (MVT) and spectral lag of hundreds of X-ray bursts (XRBs) from soft gamma-ray repeater (SGR) J1935+2154 were analyzed in detail for the first time in our recent work, which are important probes for studying the physical mechanism and radiation region. In this work, we investigate their differential and cumulative distributions carefully and find that they follow power-law models. Besides, the distributions of fluctuations in both parameters follow the Tsallis q-Gaussian distributions and the q values are consistent for different scale intervals. Therefore, these results indicate that both parameters are scale-invariant, which provides new parameters for the study of self-organized criticality systems. Interestingly, we find that the q values for MVT and spectral lag are similar with duration and fluence, respectively. © 2024, CC BY.

关键词： Gamma rays

Ontology Revision based on Pre-trained Language Models

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arXiv 2023年

作者： Ji, Qiu Zhang, Xiaoping Ye, Yuxin Qi, Guilin Li, Jiaye Li, Site Ren, Jianjie Lu, Songtao School of Modern Posts Institute of Modern Posts Nanjing University of Posts and Telecommunications Nanjing China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China National Data Center ofTraditional Chinese MedicineChina Academy of ChineseMedical Sciences Beijing100700 China College of Computer Science and Technology Jilin University Changchun130012 China School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China School of Mathematics Southeast University Nanjing China Chien-Shiung Wu College Southeast University Nanjing China

Ontology revision aims to seamlessly incorporate a new ontology into an existing ontology and plays a crucial role in tasks such as ontology evolution, ontology maintenance, and ontology alignment. Similar to repair single ontologies, resolving logical incoherence in the task of ontology revision is also important and meaningful, because incoherence is a main potential factor to cause inconsistency and reasoning with an inconsistent ontology will obtain meaningless answers. To deal with this problem, various ontology revision approaches have been proposed to define revision operators and design ranking strategies for axioms in an ontology. However, they rarely consider axiom semantics which provides important information to differentiate axioms. In addition, pre-trained models can be utilized to encode axiom semantics, and have been widely applied in many natural language processing tasks and ontology-related ones in recent years. Therefore, in this paper, we study how to apply pre-trained models to revise ontologies. We first define four scoring functions to rank axioms based on a pre-trained model by considering various information from an ontology. Based on the functions, an ontology revision algorithm is then proposed to deal with unsatisfiable concepts at once. To improve efficiency, an adapted revision algorithm is designed to deal with unsatisfiable concepts group by group. We conduct experiments over 19 ontology pairs and compare our algorithms and scoring functions with existing ones. According to the experiments, our algorithms could achieve promising performance. The adapted revision algorithm could improve the efficiency largely, and at most about 90% of the time could be saved for some ontology pairs. Some of our scoring functions like reliableOnt cos could help a revision algorithm obtain better results in many cases, especially for those challenging ontology pairs like OM8. We also provide discussion about the overall experimental results and guidelin

关键词： Ontology

Mining Repetitive Negative Sequential Patterns with Gap Constraints

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ACM Transactions on knowledge Discovery from data 2025年第4期19卷

作者： Li, Yan Wang, Zhulin Liu, Jing Guo, Lei Fournier-Viger, Philippe Wu, Youxi Wu, Xindong School of Economics and Management Hebei University of Technology Tianjin China School of Artificial Intelligence Hebei University of Technology Tianjin China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Hebei Key Laboratory of Big Data Computing Tianjin China Hefei University of Technology Hefei China

Sequential pattern mining (SPM) with gap constraints (or repetitive SPM or tandem repeat discovery in bioinformatics) can find frequent repetitive subsequences satisfying gap constraints, which are called positive sequential patterns with gap constraints (PSPGs). However, classical SPM with gap constraints cannot find the frequent missing items in the PSPGs. To tackle this issue, this article explores negative sequential patterns with gap constraints (NSPGs). We propose an efficient NSPG-Miner algorithm that can mine both frequent PSPGs and NSPGs simultaneously. To effectively reduce candidate patterns, we propose a pattern join strategy with negative patterns which can generate both positive and negative candidate patterns at the same time. To calculate the support (frequency of occurrence) of a pattern in each sequence, we explore a NegPair algorithm that employs a key-value pair array structure to deal with the gap constraints and the negative items simultaneously and can avoid redundant rescanning of the original sequence, thus improving the efficiency of the algorithm. To report the performance of NSPG-Miner, 11 competitive algorithms and 11 datasets are employed. The experimental results not only validate the effectiveness of the strategies adopted by NSPG-Miner but also verify that NSPG-Miner can discover more valuable information than the state-of-the-art algorithms. Copyright © 2025 held by the owner/author(s). Publication rights licensed to ACM.

关键词： sequential pattern mining gap constraint negative sequential pattern key-value pair