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eLight 2023年第1期3卷 156-168页

作者： Chenhao Wan Andy Chong Qiwen Zhan School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China School of Optical and Electronic Information and Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074HubeiChina Department of Physics Pusan National UniversityGeumjeong-GuBusan 46241South Korea Zhangjiang Laboratory 100 Haike RoadShanghai 201204China Shanghai Key Lab of Modern Optical System University of Shanghai for Science and TechnologyShanghai 200093China

Spatiotemporal vortices of light,featuring transverse orbital angular momentum(OAM)and energy circulation in the spatiotemporal domain,have received increasing attention *** experimental realization of the controllable generation of spatiotemporal vortices triggers a series of research in this *** review article covers the latest developments of spatiotemporal vortices of light ranging from theoretical physics,experimental generation schemes,and characterization methods,to applications and future *** new degree of freedom in photonic OAM endowed by spatiotemporal vortices paves the way to the discovery of novel physical mechanisms and photonic applications in light science.

关键词： Spatiotemporal optical vortex Orbital angular momentum Spin-orbit interaction Linear momentum density Toroidal vortex Topological charge

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U-Shaped Transformer-Based 360-Degree No Reference Image Quality Assessment 21

U-Shaped Transformer-Based 360-Degree No Reference Image Qua...

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21st IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2022

作者： Wei, Xuekai Zhou, Mingliang Kwong, Sam State Key Laboratory of Internet of Things for Smart City University of Macau Department of Electrical and Computer Engineering Macao999078 China School of Computer Science Chongqing University Chongqing400044 China City University of Hong Kong Department of Computer Science Kowloon999077 Hong Kong

ISBN: (纸本)9781665490849

Thanks to creative rendering and display techniques, 360-degree images can provide a more immersive and interactive experience for streaming users. However, such features make the perceptual characteristics of 360-degree images more complex than those of fixed-view images, making it impossible to achieve a comprehensive and accurate image quality assessment (IQA) task using a simple stack of pre-processing, post-processing, compression, and rendering tasks. In order to thoroughly learn global and local features in 360-degree images, reduce the complexity of multichannel neural network models and simplify the training process, this paper proposes a user-Aware joint architecture and an efficient converter dedicated to 360-degree no-reference (NR) IQA. The input of the proposed method is a 360-degree cubic mapping projection (CMP) image. In addition, the proposed 360-degree NRIQA method includes a non-overlapping self-Attentive selection module based on a dominant map and a feature extraction module based on a U-shaped transformer (U-former) to address perceptual region significance and projection distortion. The transformer-based architecture and the weighted averaging technique are jointly used to predict local perceptual quality. Experimental results obtained on widely used databases show that the proposed model outperforms other state-of-The-Art methods in the case of NR 360-degree image quality assessment. In addition, cross-database evaluation and ablation studies demonstrate the intrinsic robustness and generalization of the proposed model. © 2022 IEEE.

关键词： Feature extraction

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Rockburst prediction using artificial intelligence techniques:A review

Rock Mechanics Bulletin

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Rock Mechanics Bulletin 2024年第3期3卷 1-13页

作者： Yu Zhang Kongyi Fang Manchao He Dongqiao Liu Junchao Wang Zhengjia Guo School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and ArchitectureBeijing100044China State Key Laboratory for Geomechanics and Deep Underground Engineering China University of Mining and TechnologyBeijing100083China Thomas Lord Department of Computer Science University of Southern CaliforniaLos Angeles90007CAUSA

Rockburst is a phenomenon where sudden,catastrophic failure of the rock mass occurs in underground deep regions or areas with high tectonic stress during the excavation *** disasters endanger the safety of people's lives and property,national energy security,and social interests,so it is very important to accurately predict *** rockburst prediction has not been able to find an effective prediction method,and the study of the rockburst mechanism is facing a *** the development of artificial intelligence(AI)techniques in recent years,more and more experts and scholars have begun to introduce AI techniques into the study of the rockburst *** previous research,several scholars have attempted to summarize the application of AI techniques in rockburst ***,these studies either are not specifically focused on reviews of the application of AI techniques in rockburst prediction,or they do not provide a comprehensive *** on the advantages of extensive interdisciplinary research and a deep understanding of AI techniques,this paper conducts a comprehensive review of rockburst prediction methods leveraging AI ***,pertinent definitions of rockburst and its associated hazards are ***,the applications of both traditional prediction methods and those rooted in AI techniques for rockburst prediction are summarized,with emphasis placed on the respective advantages and disadvantages of each ***,the strengths and weaknesses of prediction methods leveraging AI are summarized,alongside forecasting future research trends to address existing challenges,while simultaneously proposing directions for improvement to advance the field and meet emerging demands effectively.

关键词： Rockburst Rockburst prediction Artificial intelligence techniques

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Incentives in private collaborative machine learning 23

Incentives in private collaborative machine learning

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

作者： Rachael Hwee Ling Sim Yehong Zhang Trong Nghia Hoang Xinyi Xu Bryan Kian Hsiang Low Patrick Jaillet Department of Computer Science National University of Singapore Republic of Singapore Peng Cheng Laboratory People's Republic of China School of Electrical Engineering and Computer Science Washington State University Dept. of Electrical Engineering and Computer Science MIT

Collaborative machine learning involves training models on data from multiple parties but must incentivize their participation. Existing data valuation methods fairly value and reward each party based on shared data or model parameters but neglect the privacy risks involved. To address this, we introduce differential privacy (DP) as an incentive. Each party can select its required DP guarantee and perturb its sufficient statistic (SS) accordingly. The mediator values the perturbed SS by the Bayesian surprise it elicits about the model parameters. As our valuation function enforces a privacy-valuation trade-off, parties are deterred from selecting excessive DP guarantees that reduce the utility of the grand coalition's model. Finally, the mediator rewards each party with different posterior samples of the model parameters. Such rewards still satisfy existing incentives like fairness but additionally preserve DP and a high similarity to the grand coalition's posterior. We empirically demonstrate the effectiveness and practicality of our approach on synthetic and real-world datasets.

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Intra-class patch swap for self-distillation

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Neurocomputing 2025年 645卷

作者： Choi, Hongjun Jeon, Eun Som Shukla, Ankita Turaga, Pavan Lawrence Livermore National Laboratory LivermoreCA94550 United States Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of Department of Computer Science and Engineering University of Nevada RenoNV89557 United States Geometric Media Lab School of Arts Media and Engineering and School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85281 United States

Knowledge distillation (KD) is a valuable technique for compressing large deep learning models into smaller, edge-suitable networks. However, conventional KD frameworks rely on pre-trained high-capacity teacher networks, which introduce significant challenges such as increased memory/storage requirements, additional training costs, and ambiguity in selecting an appropriate teacher for a given student model. Although a teacher-free distillation (self-distillation) has emerged as a promising alternative, many existing approaches still rely on architectural modifications or complex training procedures, which limit their generality and efficiency. To address these limitations, we propose a novel framework based on teacher-free distillation that operates using a single student network without any auxiliary components, architectural modifications, or additional learnable parameters. Our approach is built on a simple yet highly effective augmentation, called intra-class patch swap augmentation. This augmentation simulates a teacher–student dynamic within a single model by generating pairs of intra-class samples with varying confidence levels, and then applying instance-to-instance distillation to align their predictive distributions. Our method is conceptually simple, model-agnostic, and easy to implement, requiring only a single augmentation function. Extensive experiments across image classification, semantic segmentation, and object detection show that our method consistently outperforms both existing self-distillation baselines and conventional teacher-based KD approaches. These results suggest that the success of self-distillation could hinge on the design of the augmentation itself. Our codes are available at https://***/hchoi71/Intra-class-Patch-Swap. © 2025 Elsevier B.V.

关键词： Semantic Segmentation

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Dual Encoders for Diffusion-based Image Inpainting

Dual Encoders for Diffusion-based Image Inpainting

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dezhi Zheng Kaijun Deng Jinbao Wang Linlin Shen Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current diffusion-based inpainting models struggle to preserve unmasked regions or generate highly coherent content. Additionally, it is hard for them to generate meaningful content for 3D inpainting. To tackle these challenges, we design a plug-and-play branch that runs through the entire generation process to enhance existing models. Specifically, we utilize dual encoders - a Convolutional Neural Network (CNN) encoder and the pre-trained Variational AutoEncoder (VAE) encoder, to encode masked images. The latent code and the feature map from the dual encoders are fed to diffusion models simultaneously. In addition, we apply Zero-padded initialization to solve the problem of mode collapse caused by this branch. Experiments on BrushBench and EditBench demonstrate that models with our plug-and-play branch can improve the coherence of inpainting, and our model achieves new state-of-the-art results.

关键词： Visualization Three-dimensional displays Codes Autoencoders Coherence Signal processing Diffusion models Decoding Convolutional neural networks Speech processing

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Frequency Comb of Spectrally Pure Biphotons Using Time-Varying Cavities

Frequency Comb of Spectrally Pure Biphotons Using Time-Varyi...

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CLEO: Fundamental science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Gaines, Jordan A. Myilswamy, Karthik V. Lukens, Joseph M. Weiner, Andrew M. School of Electrical and Computer Engineering Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Research Technology Office and Quantum Collaborative Arizona State University TempeAZ85287 United States Quantum Information Science Section Oak Ridge National Laboratory Oak RidgeTN37831 United States

We propose and analyze the use of linear, time-variant cavities to spectrally compress broadband frequency-correlated photon pairs into combs of spectrally pure biphotons. Our approach relies on rapid switching of inp... 详细信息

ISBN: (纸本)9781957171395

关键词： Broadband communication Couplings Electro-optical waveguides Photonics Switches Time-frequency analysis

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FreMIM: Fourier Transform Meets Masked Image Modeling for Medical Image Segmentation

arXiv

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arXiv 2023年

作者： Wang, Wenxuan Wang, Jing Chen, Chen Jiao, Jianbo Cai, Yuanxiu Song, Shanshan Li, Jiangyun School of Automation and Electrical Engineering University of Science and Technology Beijing China Center for Research in Computer Vision University of Central Florida United States School of Computer Science University of Birmingham United Kingdom

The research community has witnessed the powerful potential of self-supervised Masked Image Modeling (MIM), which enables the models capable of learning visual representation from unlabeled data. In this paper, to incorporate both the crucial global structural information and local details for dense prediction tasks, we alter the perspective to the frequency domain and present a new MIM-based framework named FreMIM for self-supervised pre-training to better accomplish medical image segmentation tasks. Based on the observations that the detailed structural information mainly lies in the high-frequency components and the high-level semantics are abundant in the low-frequency counterparts, we further incorporate multi-stage supervision to guide the representation learning during the pre-training phase. Extensive experiments on three benchmark datasets show the superior advantage of our FreMIM over previous state-of-the-art MIM methods. Compared with various baselines trained from scratch, our FreMIM could consistently bring considerable improvements to model performance. The code will be publicly available at https://***/Rubics-Xuan/FreMIM. Copyright © 2023, The Authors. All rights reserved.

关键词： Medical imaging

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Imperceptible and Sparse Adversarial Attacks via a Dual-Population-Based Constrained Evolutionary Algorithm

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第2期4卷 268-281页

作者： Tian, Ye Pan, Jingwen Yang, Shangshang Zhang, Xingyi He, Shuping Jin, Yaochu Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230088 China Anhui University School of Computer Science and Technology Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Hefei230601 China Anhui University Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Hefei230601 China Bielefeld University Faculty of Technology Bielefeld33619 Germany

The sparse adversarial attack has attracted increasing attention due to the merit of a low attack cost via changing a small number of pixels. However, the generated adversarial examples are easily detected in vision since the perturbation to each pixel is relatively large. To achieve imperceptible and sparse adversarial attacks, this article formulates a bi-objective constrained optimization problem, simultaneously minimizing the 0 and 2 distances to the original image, and proposes a dual-population-based constrained evolutionary algorithm to solve it. The proposed method solves the optimization problem by evolving two populations, where one population is responsible for finding feasible solutions (i.e., successful attacks) and the other one is to minimize both the 0 and 2 distances. Moreover, a population initialization strategy and two genetic operators are customized to accelerate the convergence speed. Experimental results indicate that the proposed method can achieve high success rates with low attack costs, and strikes a better balance between the 0 and 2 distances than state-of-the-art methods. © 2020 IEEE.

关键词： Evolutionary algorithms

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Large Scale Model Enabled Semantic Communications Based on Robust Knowledge Distillation

Large Scale Model Enabled Semantic Communications Based on R...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ding, Kuiyuan Liu, Fangfang Yang, Yang Chen, Mingzhe Guo, Caili Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering The Institute for Data Science and Computing Coral GablesFL33146 United States Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China

ISBN: (纸本)9798350351255

Large scale artificial intelligence (AI) models possess excellent capabilities in semantic representation and understanding, making them particularly well-suited for semantic encoding and decoding. However, the substantial scale of these AI models imposes unacceptable computational resources and communication delays. To address this issue, we propose a semantic communication scheme based on robust knowledge distillation (RKD-SC) for large scale model enabled semantic communications. In the considered system, a transmitter extracts the features of the source image for robust transmission and accurate image classification at the receiver. To effectively utilize the superior capability of large scale model while make the cost affordable, we first transfer knowledge from a large scale model to a smaller scale model to serve as the semantic encoder. Then, to enhance the robustness of the system against channel noise, we propose a channel-aware autoencoder (CAA) based on the Transformer architecture. Experimental results show that the encoder of proposed RKD-SC system can achieve over 93.3% of the performance of a large scale model while compressing 96.67% number of parameters. Code: https://***/echojayne/RKD-SC. © 2024 IEEE.

关键词： Channel coding

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