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arXiv 2025年

作者： Luo, Ziyuan Rocha, Anderson Shi, Boxin Guo, Qing Li, Haoliang Wan, Renjie Department of Computer Science Hong Kong Baptist University Hong Kong Institute of Computing University of Campinas Brazil State Key Laboratory of Multimedia Information Processing and National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing100871 China A*STAR Singapore Department of Electrical Engineering City University of Hong Kong Hong Kong

Neural Radiance Fields (NeRF) have been gaining attention as a significant form of 3D content representation. With the proliferation of NeRF-based creations, the need for copyright protection has emerged as a critical issue. Although some approaches have been proposed to embed digital watermarks into NeRF, they often neglect essential model-level considerations and incur substantial time overheads, resulting in reduced imperceptibility and robustness, along with user inconvenience. In this paper, we extend the previous criteria for image watermarking to the model level and propose NeRF Signature, a novel watermarking method for NeRF. We employ a Codebook-aided Signature Embedding (CSE) that does not alter the model structure, thereby maintaining imperceptibility and enhancing robustness at the model level. Furthermore, after optimization, any desired signatures can be embedded through the CSE, and no fine-tuning is required when NeRF owners want to use new binary signatures. Then, we introduce a joint pose-patch encryption watermarking strategy to hide signatures into patches rendered from a specific viewpoint for higher robustness. In addition, we explore a Complexity-Aware Key Selection (CAKS) scheme to embed signatures in high visual complexity patches to enhance imperceptibility. The experimental results demonstrate that our method outperforms other baseline methods in terms of imperceptibility and robustness. The source code is available at: https://***/luo-ziyuan/NeRF_Signature. Copyright © 2025, The Authors. All rights reserved.

关键词： Image watermarking

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Space-Air-Ground Integrated Network (SAGIN) for 6G: Requirements, Architecture and Challenges

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China Communications 2022年第2期19卷 90-108页

作者： Huanxi Cui Jun Zhang Yuhui Geng Zhenyu Xiao Tao Sun Ning Zhang Jiajia Liu Qihui Wu Xianbin Cao School of Electronic and Information Engineering Beijing Key Laboratory for Network-Based Cooperative Air application ManagementBeihang UniversityBeijing 100191China Advanced Research Institute of Multidisciplinary Science Beijing Institute of TechnologyBeijing 100081China China Mobile Research Institute Beijing 100053China Department of Electrical and Computer Engineering University of WindsorONCanadaN9B 3P4 National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology the School of CybersecurityNorthwestern Polytechnical UniversityXi’anShaanxi710072China College of Electronics and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210007China

As the fifth-generation(5G)mobile communication network may not meet the requirements of emerging technologies and applications,including ubiquitous coverage,industrial internet of things(IIoT),ubiquitous artificial intelligence(AI),digital twins(DT),etc.,this paper aims to explore a novel space-air-ground integrated network(SAGIN)architecture to support these new requirements for the sixth-generation(6G)mobile communication network in a flexible,low-latency and efficient ***,we first review the evolution of the mobile communication network,followed by the application and technology requirements of *** the current 5G non-terrestrial network(NTN)architecture in supporting the new requirements is deeply *** that,we proposes a new flexible,low-latency and flat SAGIN architecture,and presents corresponding use ***,the future research directions are discussed.

关键词： 6G AI DT SAGIN NTN Network architecture

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AircraftVerse: a large-scale multimodal dataset of aerial vehicle designs 23

AircraftVerse: a large-scale multimodal dataset of aerial ve...

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Proceedings of the 37th International Conference on Neural Information Processing systems

作者： Adam D. Cobb Anirban Roy Daniel Elenius F. Michael Heim Brian Swenson Sydney Whittington James D. Walker Theodore Bapty Joseph Hite Karthik Ramani Christopher McComb Susmit Jha Computer Science Laboratory SRI International Mechanical Engineering Division Southwest Research Institute Intelligent Systems Division Southwest Research Institute Institute of Software Integrated Systems Vanderbilt University School of Mechanical Engineering Purdue University Department of Mechanical Engineering Carnegie Mellon University

We present AircraftVerse, a publicly available aerial vehicle design dataset. Aircraft design encompasses different physics domains and, hence, multiple modalities of representation. The evaluation of these cyber-physical system (CPS) designs requires the use of scientific analytical and simulation models ranging from computer- aided design tools for structural and manufacturing analysis, computational fluid dynamics tools for drag and lift computation, battery models for energy estimation, and simulation models for flight control and dynamics. AircraftVerse contains 27,714 diverse air vehicle designs - the largest corpus of engineering designs with this level of complexity. Each design comprises the following artifacts: a symbolic design tree describing topology, propulsion subsystem, battery subsystem, and other design details; a STandard for the Exchange of Product (STEP) model data; a 3D CAD design using a stereolithography (STL) file format; a 3D point cloud for the shape of the design; and evaluation results from high fidelity state-of-the-art physics models that characterize performance metrics such as maximum flight distance and hover-time. We also present baseline surrogate models that use different modalities of design representation to predict design performance metrics, which we provide as part of our dataset release. Finally, we discuss the potential impact of this dataset on the use of learning in aircraft design and, more generally, in CPS. AircraftVerse is accompanied by a data card, and it is released under Creative Commons Attribution-ShareAlike (CC BY-SA) license. The dataset is hosted at https://***/record/6525446 baseline models and code at https://***/SRI-CSL/AircraftVerse and the dataset description at https://***/.

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Broadband and repetition rate tunable frequency comb based on electro-optic time lens with AlGaAsOI waveguide

Broadband and repetition rate tunable frequency comb based o...

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49th European Conference on Optical Communications (ECOC 2023)

作者： C. Ma C. Wang Y. Pi L. Wang Z. He J. E. Bowers L. Chang Z. Cao S. Yu Research Center of Circuits and Systems Peng Cheng Laboratory Shenzhen 518055 Guangdong People's Republic of China Department of Electrical and Computer Engineering University of California Santa Barbara CA 93106 USA State Key Laboratory of Advanced Optical Communications System and Networks Peking University Beijing 100871 People's Republic of China

We present a tunable repetition rate frequency comb realized by cascaded electro-optical modulators with a high nonlinear AlGaAs-on-isolator waveguide. 18-26.5 GHz comb spacing tunability with broadband spectra was achieved. All the devices can be integrated onto a single chip, which makes this work useful for many applications.

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Distributionally Robust Mining for Proof-of-Work Blockchain under Resource Uncertainties

Distributionally Robust Mining for Proof-of-Work Blockchain ...

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IEEE Conference on Wireless Communications and Networking

作者： Xunqiang Lan Xiao Tang Ruonan Zhang Bin Li Daosen Zhai Wensheng Lin Zhu Han School of Electronics and Information Northwestern Polytechnical University Xi'an China National Mobile Communications Research Laboratory Southeast University Nanjing China National Key Laboratory of Wireless Communications Chengdu China Department of Electrical and Computer Engineering University of Houston Houston TX USA Department of Computer Science and Engineering Kyung Hee University Seoul South Korea

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

In blockchain systems characterized by computation competition, allocating computation resources is of paramount significance for the economic benefits of nodes. Besides, uncer-tainties of computation resources also affect the node's profits. In this paper, we address the computation resource allocation issue within a proof-of-work (PoW) blockchain system without exact information on the available resources, which impedes the direct investigation of the maximum mining profit. Correspondingly, we establish the chance-constrained threshold for maximum achievable profit through the blockchain in an uncertain environment and maximize this threshold under a given outage probability. Particularly, the uncertain computation resource is modeled only with its first and second statistics, which lack the exact distribution information. In this respect, we propose the distributionally robust approach to tackle the chance-constrained resource allocation strategy, which guarantees the intended profit threshold regardless of the actual distribution. We show that the considered problem admits a conditional value-at-risk (CVaR) approximation reformulation, which can be handled by alternately optimizing the resource allocation strategy and the profit threshold. Simulation results demonstrate that the proposed design is robust against the uncertainty distribution, and effectively guarantees the profits of miners.

关键词： Economics Uncertainty Simulation Computational modeling Proof of Work Robustness Blockchains

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Fast Adaptation for Deep Learning-based Wireless Communications

arXiv

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arXiv 2024年

作者： Wang, Ouya He, Hengtao Zhou, Shenglong Ding, Zhi Jin, Shi Letaief, Khaled B. Li, Geoffrey Ye The ITP Lab Department of Electrical and Electronic Engineering Imperial College London United Kingdom The School of Mathematics and Statistics Beijing Jiaotong University Beijing China The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The Department of Electrical and Computer Engineering University of California Davis United States The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China

The integration with artificial intelligence (AI) is recognized as one of the six usage scenarios in next-generation wireless communications. However, several critical challenges hinder the widespread application of deep learning (DL) techniques in wireless communications. In particular, existing DL-based wireless communications struggle to adapt to the rapidly changing wireless environments. In this paper, we discuss fast adaptation for DL-based wireless communications by using few-shot learning (FSL) techniques. We first identify the differences between fast adaptation in wireless communications and traditional AI tasks by highlighting two distinct FSL design requirements for wireless communications. To establish a wide perspective, we present a comprehensive review of the existing FSL techniques in wireless communications that satisfy these two design requirements. In particular, we emphasize the importance of applying domain knowledge in achieving fast adaptation. We specifically focus on multiuser multiple-input multiple-output (MU-MIMO) precoding as an examples to demonstrate the advantages of the FSL to achieve fast adaptation in wireless communications. Finally, we highlight several open research issues for achieving broadscope future deployment of fast adaptive DL in wireless communication applications. © 2024, CC BY.

关键词： Zero-shot learning

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Urban Digital Twins for Intelligent Road Inspection

Urban Digital Twins for Intelligent Road Inspection

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Fan, Rui Zhang, Yikang Guo, Sicen Li, Jiahang Feng, Yi Su, Shuai Zhang, Yanting Wang, Wenshuo Jiang, Yu Junaid Bocus, Mohammud Zhu, Xingyi Chen, Qijun Department of Control Science & Engineering Frontiers Science Center for Intelligent Autonomous Systems State Key Laboratory of Intelligent Autonomous Systems Shanghai201804 China Donghua University Department of Computer Science & Technology Shanghai201620 China McGill University Department of Civil Engineering MontrealH3A 0C3 Canada Cto Office at ClearMotion BillericaMA01821 United States University of Bristol Department of Electrical & Electronic Engineering BristolBS8 1UB United Kingdom Tongji University Key Laboratory of Road and Traffic Engineering Ministry of Education Shanghai200092 China

ISBN: (纸本)9781665480451

Urban digital twin (UDT) technologies offer new opportunities for intelligent road inspection (IRI). This paper first reviews the state-of-the-art algorithms used in the two key components of UDT-based IRI systems: (1) multi-temporal, multi-dimension, multi-score, and heterogeneous road data acquisition, and (2) road distress detection. This paper then summarizes the UDTIRI competition, organized in conjunction with IEEE Bigdata 2022. More details on our competition are available at ***/view/udtiri-workshop/bigdata-2022. © 2022 IEEE.

关键词： Data acquisition

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RoadFormer: Duplex Transformer for RGB-Normal Semantic Road Scene Parsing

arXiv

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arXiv 2023年

作者： Li, Jiahang Zhang, Yikang Yun, Peng Zhou, Guangliang Chen, Qijun Fan, Rui The College of Electronics & Information Engineering 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 The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

The recent advancements in deep convolutional neural networks have shown significant promise in the domain of road scene parsing. Nevertheless, the existing works focus primarily on freespace detection, with little attention given to hazardous road defects that could compromise both driving safety and comfort. In this article, we introduce RoadFormer, a novel Transformer-based data-fusion network developed for road scene parsing. RoadFormer utilizes a duplex encoder architecture to extract heterogeneous features from both RGB images and surface normal information. The encoded features are subsequently fed into a novel heterogeneous feature synergy block for effective feature fusion and recalibration. The pixel decoder then learns multi-scale long-range dependencies from the fused and recalibrated heterogeneous features, which are subsequently processed by a Transformer decoder to produce the final semantic prediction. Additionally, we release SYN-UDTIRI, the first large-scale road scene parsing dataset that contains over 10,407 RGB images, dense depth images, and the corresponding pixel-level annotations for both freespace and road defects of different shapes and sizes. Extensive experimental evaluations conducted on our SYN-UDTIRI dataset, as well as on three public datasets, including KITTI road, CityScapes, and ORFD, demonstrate that RoadFormer outperforms all other state-of-the-art networks for road scene parsing. Specifically, RoadFormer ranks first on the KITTI road benchmark. Our source code, created dataset, and demo video are publicly available at ***/RoadFormer. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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QuBEC: Boosting Equivalence Checking for Quantum circuits with QEC Embedding

arXiv

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arXiv 2023年

作者： Lu, Chao Choudhury, Navnil Banerjee, Utsav Saki, Abdullah Ash Basu, Kanad The Department of Electrical and Computer Engineering University of Texas at Dallas Richardson GA United States Department of Electronic Systems Engineering Indian Institute of Science Karnataka Bengaluru India IBM research New YorkNY United States

Quantum computing has proven to be capable of accelerating many algorithms by performing tasks that classical computers cannot. Currently, Noisy Intermediate Scale Quantum (NISQ) machines struggle from scalability and noise issues to render a commercial quantum computer. However, the physical and software improvements of a quantum computer can efficiently control quantum gate noise. As the complexity of quantum algorithms and implementation increases, software control of quantum circuits may lead to a more intricate design. Consequently, the verification of quantum circuits becomes crucial in ensuring the correctness of the compilation, along with other processes, including quantum error correction and assertions, that can increase the fidelity of quantum circuits. In this paper, we propose a Decision Diagram-based quantum equivalence checking approach, QuBEC, that requires less latency compared to existing techniques, while accounting for circuits with quantum error correction redundancy. Our proposed methodology reduces verification time on certain benchmark circuits by up to 271.49×, while the number of Decision Diagram nodes required is reduced by up to 798.31×, compared to state-of-the-art strategies. The proposed QuBEC framework can contribute to the advancement of quantum computing by enabling faster and more efficient verification of quantum circuits, paving the way for the development of larger and more complex quantum algorithms. © 2023, CC0.

关键词： Timing circuits

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Optimising Event-Driven Spiking Neural Network with Regularisation and Cutoff

arXiv

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arXiv 2023年

作者： Wu, Dengyu Jin, Gaojie Yu, Han Yi, Xinping Huang, Xiaowei Department of Computer Science University of Liverpool Liverpool United Kingdom National Mobile Communications Research Laboratory Southeast University Nanjing China State Key Laboratory of Computer Science Institute of Software CAS Beijing China Department of Electrical Engineering Chalmers University of Technology Gothenburg Sweden

Spiking neural network (SNN), as the next generation of artificial neural network (ANN), offer a closer mimicry of natural neural networks and hold promise for significant improvements in computational efficiency. However, the current SNN is trained to infer over a fixed duration, overlooking the potential of dynamic inference in SNN. In this paper, we strengthen the marriage between SNN and event-driven processing with a proposal to consider a cutoff in SNN, which can terminate SNN anytime during inference to achieve efficient inference. Two novel optimisation techniques are presented to achieve inference efficient SNN: a Top-K cutoff and a regularisation. The proposed regularisation influences the training process, optimising SNN for the cutoff, while the Top-K cutoff technique optimises the inference phase. We conduct an extensive set of experiments on multiple benchmark frame-based datasets, such as CIFAR10/100, Tiny-ImageNet, and event-based datasets, including CIFAR10-DVS, N-Caltech101 and DVS128 Gesture. The experimental results demonstrate the effectiveness of our techniques in both ANN-to-SNN conversion and direct training, enabling SNNs to require 1.76 to 2.76 × fewer timesteps for CIFAR-10, while achieving 1.64 to 1.95× fewer timesteps across all event-based datasets, with near-zero accuracy loss. These findings affirms the compatibility and potential benefits of our techniques in enhancing accuracy and reducing inference latency when integrated with existing methods. Code available: https://***/Dengyu-Wu/SNNCutoff © 2023, CC BY.

关键词： Computational efficiency

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