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FDSR-INT: A Flexible On-Demand In-Band Telemetry Approach for Aerial Computing Networks

IEEE Internet of Things Journal 2025年

作者： Xu, Jinghan Xu, Xiaolong Zhao, Juan Gao, Honghao Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing The School of Computer Science Nanjing210023 China Jinling Institute of Technology School of Computer Science Nanjing211169 China Shanghai University School of Computer Engineering and Science Shanghai200444 China Gachon University College of Future Industry Gyeonggi-Do 461-701 Korea Republic of

In-band network telemetry (INT) is a new network measurement technique that provides real-time, fine-grained packet-level network measurements. However, standard INT lacks the flexibility to perform configurable on-demand network measurements. In this work, we propose a flexible on-demand network measurement mechanism (FDSR-INT) based on a dual bitmap by combining the programmability of segment routing based on IPv6 (SRv6) and the telemetry efficiency of the INT to achieve customizable network measurements. By designing the dual bitmap, the telemetry information of personalized probe nodes is supported, and the telemetry efficiency is improved. SRv6 is employed to direct measurement probe packets, ensuring coverage of a specified set of network targets. Its inherent programmability enables INT to conduct customized per-hop network measurements. The designed flexible INT field structure, which appends traceability information along with telemetry information, reduces the information traceability overhead in the control plane and improves the network compatibility of the mechanism. We solve the optimal probing path by solving a traveler problem in an auxiliary graph and design a greedy pathcutting algorithm to maximize the number of nodes for single packet probing while satisfying the packet length constraints to improve the success rate of the probing task. Finally, we implemented FDSR-INT using P4 and verified its performance experimentally in the constructed *** integrated simulation dynamic environment. FDSR-INT saves 30% of the data plane bandwidth and 54% of the data plane bandwidth on *** interfaces compared with SONM-SR-INT, etc. Furthermore, it has low control plane processing overhead and probe path transmission overhead. © 2014 IEEE.

关键词： Bandwidth

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Single-shot common-path encoded coherent diffraction imaging with OAM multiplexing

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Photonics Research 2025年第6期13卷 1620-1630页

作者： Mingli Sun Yingming Xu Yuanyuan Liu Chiye Li Bijun Xu Xiaogang Wang Junhui Shi Qiwen Zhan School of Science Zhejiang University of Science and Technology Hangzhou 310023 China Research Center for Novel Computational Sensing and Intelligent Processing Zhejiang Lab Hangzhou 311100 China e-mail: yingmingxu@*** School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai 200093 China e-mail: junhuishi@*** e-mail: qwzhan@***

Single-shot multi-frame phase imaging plays an important role in detecting continuous extreme physical phenomena, particularly suitable for measuring the density of media with non-repeatable changes and uncertainties. However, traditional single-pattern multiplexed imaging faces challenges in retrieving amplitude and phase information of multiple frames without sacrificing spatial resolution and phase accuracy. In this study, we demonstrate single-shot common-path encoded coherent diffraction imaging with orbital angular momentum (OAM) multiplexing. It employs a sequence of vortex illumination fields, combined with encoding wavefront modulation and a vortex multiplexing phase retrieval algorithm, to achieve the retrieval of complex amplitudes from dynamic samples in single shots. Our experimental validation demonstrated the capability to achieve 9-frame high-resolution phase imaging of the dynamic sample in a single diffraction pattern. The spatial resolution and phase accuracy improve to 9.84 μm and 4.7% with this lensless multiplexed imaging system, which is comparable to single-mode imaging. This technology provides a multiplexed dimension with orbital angular momentum and holds potential in the study of transient continuous phenomena.

关键词： Imaging systems Phase imaging Phase modulation Spatial frequency Spatial light modulators Spatial resolution

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Benchmarking Graph Representations and Graph Neural Networks for Multivariate Time Series Classification

arXiv

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arXiv 2025年

作者： Yang, Wennuo Wu, Shiling Zhou, Yuzhi Luo, Cheng He, Xilin Xie, Weicheng Shen, Linlin Song, Siyang Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China Guangdong Provincial Key Laboratory of Intelligent Information Processing China HBUG Lab University of Exeter United Kingdom

Multivariate Time Series Classification (MTSC) enables the analysis if complex temporal data, and thus serves as a cornerstone in various real-world applications, ranging from healthcare to finance. Since the relationship among variables in MTS usually contain crucial cues, a large number of graph-based MTSC approaches have been proposed, as the graph topology and edges can explicitly represent relationships among variables (channels), where not only various MTS graph representation learning strategies but also different Graph Neural Networks (GNNs) have been explored. Despite such progresses, there is no comprehensive study that fairly benchmarks and investigates the performances of existing widely-used graph representation learning strategies/GNN classifiers in the application of different MTSC tasks. In this paper, we present the first benchmark which systematically investigates the effectiveness of the widely-used three node feature definition strategies, four edge feature learning strategies and five GNN architecture, resulting in 60 different variants for graph-based MTSC. These variants are developed and evaluated with a standardized data pipeline and training/validation/testing strategy on 26 widely-used suspensor MTSC datasets. Our experiments highlight that node features significantly influence MTSC performance, while the visualization of edge features illustrates why adaptive edge learning outperforms other edge feature learning methods. The code of the proposed benchmark is publicly available at https://***/CVI-yangwn/*** Codes 68T10 © 2025, CC BY.

关键词： Graph neural networks

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Towards information Sharing Beetle Antennae Search Optimization 24th

Towards Information Sharing Beetle Antennae Search Optimizat...

引用

24th International Conference on Algorithms and Architectures for Parallel processing, ICA3PP 2024

作者： Liu, Xuan Wang, Chenyan Liu, Wenjian Zhang, Lefeng Liu, Xianggan Gao, Yutong Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE Minzu University of China Beijing China Hainan International College of Minzu University of China Li’an International Education Innovation pilot Zone Hainan China City University of Macau China Natural Language Processing and Knowledge Graph Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9789819615278

The study of bioinformatics-based evolutionary computation has long been of significant interest within the scientific community. Beetle antennae search algorithm is widely used because of its lightweight, however, it lacks information sharing due to individual iteration in the algorithm. In this paper, we propose an innovative pheromone-based beetle antennae search algorithm, which evolves from a single iterative individual in BAS algorithm to multiple parallel iterative individuals and incorporates the pheromone sharing mechanism found in ant colony optimization algorithm. Applying the pheromone-based beetle antennae search algorithm to the virtual machine placement problem in cloud computing, we find that pheromone sharing mechanism allows the PB-BAS algorithm to exhibit superior optimization capabilities and effectively avoids convergence to local optimal. To verify the performance of the algorithm, we select other alternative algorithms and conduct a large number of comparative experiments under different experimental setups, the experimental results show the effectiveness and efficiency of our algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ant colony optimization

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S2KAN-SLAM: Elastic Neural LiDAR SLAM with SDF Submaps and Kolmogorov-Arnold Networks

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Wang, Zhong Zhang, Lin Wang, Hesheng Shanghai Jiao Tong University State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China Tongji University School of Computer Science and Technology National Pilot Software Engineering School with Chinese Characteristics Shanghai201804 China

Traditional LiDAR SLAM approaches prioritize localization over mapping, yet high-precision dense maps are essential for numerous applications involving intelligent agents. Recent advancements have introduced methods leveraging neural fields to enhance mapping capabilities;however, these approaches still face several limitations. Firstly, concerning scene representation, they typically employ neural fields with high-dimensional features and multi-layer perceptron decoders utilizing non-continuous activation functions. This results in low learning efficiency and challenges in capturing high-frequency signals. Secondly, in terms of scene organization, these methods often treat the entire scene as a singular neural field, leading to inefficiencies, inflexibility, and difficulties in rectifying accumulated errors when mapping large-scale environments over extended periods. To tackle the first issue, we propose a lightweight continuous SDF regression approach by encoding the scene in single-valued embeddings and decoding SDF values from a Kolmogorov-Arnold Network. By minimizing discrepancies in measuring range, sampling distance, and decoded SDF values, we facilitate iterative frame-to-model tracking and bundle adjustment neural mapping. To mitigate the second challenge, we propose structuring the whole scene into multiple neural SDF submaps. By establishing node-node, node-submap, and loop closure constraints into a global pose graph, the system can create dense neural maps with global consistency across large-scale scenes. Experimental evaluations in both real-world and simulated settings indicate that our system achieves superior mapping completeness and accuracy, enhanced learning efficiency, reduced memory consumption, and greater flexibility compared to its counterparts. © 2025 IEEE.

关键词： Mapping

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SLC2-SLAM: Semantic-guided Loop Closure using Shared Latent Code for NeRF SLAM

arXiv

引用

arXiv 2025年

作者： Ming, Yuhang Ma, Di Dai, Weichen Yang, Han Fan, Rui Zhang, Guofeng Kong, Wanzeng School of Computer Science Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province Hangzhou Dianzi University Hangzhou310018 China College of Electronics & Information Engineering Shanghai Institute of Intelligent Science and Technology Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China State Key Lab of CAD&CG Zhejiang University Hangzhou310058 China

Targeting the notorious cumulative drift errors in NeRF SLAM, we propose a Semantic-guided Loop Closure using Shared Latent Code, dubbed SLC2-SLAM. We argue that latent codes stored in many NeRF SLAM systems are not fully exploited, as they are only used for better reconstruction. In this paper, we propose a simple yet effective way to detect potential loops using the same latent codes as local features. To further improve the loop detection performance, we use the semantic information, which are also decoded from the same latent codes to guide the aggregation of local features. Finally, with the potential loops detected, we close them with a graph optimization followed by bundle adjustment to refine both the estimated poses and the reconstructed scene. To evaluate the performance of our SLC2-SLAM, we conduct extensive experiments on Replica and ScanNet datasets. Our proposed semantic-guided loop closure significantly outperforms the pre-trained NetVLAD and ORB combined with Bag-of-Words, which are used in all the other NeRF SLAM with loop closure. As a result, our SLC2-SLAM also demonstrated better tracking and reconstruction performance, especially in larger scenes with more loops, like ScanNet. Copyright © 2025, The Authors. All rights reserved.

关键词： SLAM robotics

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Cloud-magnetic resonance imaging system:In the era of 6G and artificial intelligence

引用

Magnetic Resonance Letters 2025年第1期5卷 52-63页

作者： Yirong Zhou Yanhuang Wu Yuhan Su Jing Li Jianyu Cai Yongfu You Jianjun Zhou Di Guo Xiaobo Qu Department of Electronic Science Intelligent Medical Imaging R&D CenterFujian Provincial Key Laboratory of Plasma and Magnetic ResonanceNational Institute for Data Science in Health and MedicineXiamen UniversityXiamen361104China Department of Electronic Science Key Laboratory of Digital Fujian on IoT CommunicationXiamen UniversityXiamen361104China Shanghai Electric Group Co. LtdShanghai200002China China Telecom Group Quanzhou362018China China Mobile Group Xiamen361009China Department of Radiology Zhongshan Hospital(Xiamen)Fudan UniversityXiamen Municipal Clinical Research Center for Medical ImagingFujian Province Key Clinical Specialty Construction Project(Medical Imaging Department)Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial IntelligenceXiamen361006China School of Computer and Information Engineering Xiamen University of TechnologyXiamen361024China

Magnetic resonance imaging(MRI)plays an important role in medical diagnosis,generating petabytes of image data annually in large *** voluminous data stream requires a significant amount of network bandwidth and extensive storage ***,local data processing demands substantial manpower and hardware *** isolation across different healthcare institutions hinders crossinstitutional collaboration in clinics and *** this work,we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing,6G bandwidth,edge computing,federated learning,and blockchain *** system is called Cloud-MRI,aiming at solving the problems of MRI data storage security,transmission speed,artificial intelligence(AI)algorithm maintenance,hardware upgrading,and collaborative *** workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw Data(ISMRMRD)***,the data are uploaded to the cloud or edge nodes for fast image reconstruction,neural network training,and automatic ***,the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other *** Cloud-MRI system will save the raw imaging data,reduce the risk of data loss,facilitate inter-institutional medical collaboration,and finally improve diagnostic accuracy and work efficiency.

关键词： Magnetic resonance imaging Cloud computing 6G bandwidth Artificial intelligence Edge computing Federated learning Blockchain

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Unsupervised Learning of Global Object-Centric Representations for Compositional Scene Understanding

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IEEE Transactions on Visualization and computer Graphics 2025年 PP卷 PP页

作者： Chen, Tonglin Huang, Yinxuan Huang, Jinghao Li, Bin Xue, Xiangyang Fudan University Shanghai Key Lab of Intelligent Information Processing School of Computer Science Shanghai 200433 China

The ability to extract invariant visual features of objects from complex scenes and identify the same objects in different scenes is inborn for humans. To endow AI systems with such capability, we introduce a novel compositional scene understanding method known as Compositional Scene understanding via Global Object-centric representations (CSGO). CSGO achieves comprehensive scene understanding, including the discovery and identification of objects, by leveraging a set of learnable global object-centric representations in an unsupervised manner. CSGO comprises three components: 1) Local Object-Centric Learning, which is responsible for extracting localized and scene-specific object-centric representations to discover objects;2) Image Decoding, facilitating the reconstruction of object and scene images using the obtained object-centric representation as input;and 3) Global Object-Centric Learning, identifying the object across diverse scenes according to a set of learnable global object-centric representations, which indicates the scene-free intrinsic attributes (i.e., appearance and shape) of objects. Experimental results on three synthetic datasets and one real-world scene dataset demonstrate that CSGO has excellent object identification and attribute disentanglement abilities. Furthermore, the scene decomposition performance (indicating object discovery performance) of CSGO is superior to comparison methods. © 1995-2012 IEEE.

关键词： Compositional scene modeling disentangled learning global object-centric representation unsupervised learning

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Radiologist-in-the-Loop Self-Training for Generalizable CT Metal Artifact Reduction

引用

IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Ma, Chenglong Li, Zilong Li, Yuanlin Han, Jing Zhang, Junping Zhang, Yi Liu, Jiannan Shan, Hongming Fudan University Institute of Science and Technology for Brain-inspired Intelligence Moe Frontiers Center for Brain Science Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Shanghai200433 China Fudan University Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Shanghai200433 China Shanghai Ninth People's Hospital Department of Oral Maxillofacial Head and Neck Oncology China Shanghai Jiao Tong University School of Medicine College of Stomatology China Shanghai Key Laboratory of Stomatology Shanghai Research Institute of Stomatology National Center for Stomatology National Clinical Research Center for Oral Diseases Shanghai200011 China Sichuan University School of Cyber Science and Engineering Chengdu Sichuan610065 China

Metal artifacts in computed tomography (CT) images can significantly degrade image quality and impede accurate diagnosis. Supervised metal artifact reduction (MAR) methods, trained using simulated datasets, often struggle to perform well on real clinical CT images due to a substantial domain gap. Although state-of-the-art semi-supervised methods use pseudo ground-truths generated by a prior network to mitigate this issue, their reliance on a fixed prior limits both the quality and quantity of these pseudo ground-truths, introducing confirmation bias and reducing clinical applicability. To address these limitations, we propose a novel radiologist-in-the-loop self-training framework for MAR, termed RISE-MAR, which can integrate radiologists' feedback into the semi-supervised learning process, progressively improving the quality and quantity of pseudo ground-truths for enhanced generalization on real clinical CT images. For quality assurance, we introduce a clinical quality assessor model that emulates radiologist evaluations, effectively selecting high-quality pseudo ground-truths for semi-supervised training. For quantity assurance, our self-training framework iteratively generates additional high-quality pseudo groundtruths, expanding the clinical dataset and further improving model generalization. Extensive experimental results on multiple clinical datasets demonstrate the superior generalization performance of our RISE-MAR over state-of-the-art methods, advancing the development of MAR models for practical application. © 2025 IEEE.

关键词： computerized tomography

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Scaling Laws for Data-Efficient Visual Transfer Learning

arXiv

引用

arXiv 2025年

作者： Yang, Wenxuan Wei, Qingqu Ma, Chenxi Tan, Weimin Yan, Bo Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China

Current scaling laws for visual AI models focus predominantly on large-scale pretraining, leaving a critical gap in understanding how performance scales for data-constrained downstream tasks. To address this limitation, this paper establishes the first practical framework for data-efficient scaling laws in visual transfer learning, addressing two fundamental questions: 1) How do scaling behaviors shift when downstream tasks operate with limited data? 2) What governs the efficacy of knowledge distillation under such constraints? Through systematic analysis of vision tasks across data regimes (1K–1M samples), we propose the distillation boundary theory, revealing a critical turning point in distillation efficiency: 1) Distillation superiority: In data-scarce conditions, distilled models significantly outperform their non-distillation counterparts, efficiently leveraging inherited knowledge to compensate for limited training samples. 2) Pre-training dominance: As pretraining data increases beyond a critical threshold, non-distilled models gradually surpass distilled versions, suggesting diminishing returns from knowledge inheritance when sufficient task-specific data becomes available. Empirical validation across various model scales (2.5M to 38M parameters) and data volumes demonstrate these performance inflection points, with error difference curves transitioning from positive to negative values at critical data thresholds, confirming our theoretical predictions. This work redefines scaling laws for data-limited regimes, bridging the knowledge gap between large-scale pretraining and practical downstream adaptation, addressing a critical barrier to understanding vision model scaling behaviors and optimizing computational resource allocation. Copyright © 2025, The Authors. All rights reserved.

关键词： Resource allocation

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