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2022 International Symposium on Antennas and Propagation (ISAP)

作者： Guopeng Peng Chunheng Liu Jiahui Wu Yuxiang Zhou Xing Wang Jinyong Hou Institute of System Engineering Beijing China National Key Laboratory of Science and Technology on Antennas and Microwaves Xidian University Xi’an Shaanxi China Key Laboratory of Electronic Information Countermeasure and Simulation Technology Ministry of Education Xidian University Xi’an Shaanxi China

ISBN: (数字)9781665479622

ISBN: (纸本)9781665479639

The attributed scattering center extraction is aimed to solve a high-dimensional, and highly nonlinear problem, which generates high complexity and memory requirements. To alleviate this difficulty, this paper proposes an attributed scattering center extraction method with improved orthogonal matching pursuit (OMP). Local optimization orthogonal matching pursuit (LOOMP) and alternative optimization are utilized to estimate parameters and reduce the dictionary dimension. Experimental results show that the proposed method outperforms the existing frequency-domain method in terms of runtime and memory requirements.

关键词： Dictionaries Runtime Frequency-domain analysis Memory management Matching pursuit algorithms Scattering Complexity theory

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Two-photon imaging of rabbit mammary tissue with 3D reconstruction

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Optik 2024年 309卷

作者： Liu, Meng Hou, Guozhong Li, Yuan Deng, Yanyan Zhang, Yuan Wang, Xiaofeng Xu, Chang Song, Kaiyuan Liu, Xinlong Xia, Yuanqin Zhang, Yong Hebei University of Technology Hebei Key Laboratory of Advanced Laser Technology and Equipment School of Science Tianjin300401 China Research Center for Humanoid Sensing Research Institute of Intelligent Sensing Zhejiang Lab Hangzhou311100 China Cangzhou Normal University School of Physics and Information Engineering Cangzhou061001 China Technical Institute of Physics and Chemistry Chinese Academy of Sciences Beijing100190 China

A broadband two-photon fluorescence (TPF) imaging system, based on a femtosecond oscillator, has been constructed. This system enables TPF imaging of rabbit mammary gland tissue samples by detecting TPF signals generated from the tissue upon excitation by broadband femtosecond pulses. Specifically, TPF imaging utilizes a detection window of 565–615 nm to capture TPF signals from the fluorescent adenine dinucleotide (FAD) present in lactating cells within breast tissue. Moreover, the 3D reconstruction algorithm has been optimized to visualize the 3D structure of the rabbit mammary gland acinar *** optimized three-dimensional reconstruction algorithm can reduce ambiguous signal regions, making the reconstructed three-dimensional structure more reliable and accurate. This is of significant importance for studying the microscopic structure and cell distribution of breast tissue, contributing to a deeper understanding of the tissue's physiological status. © 2024 Elsevier GmbH

关键词： Tissue

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StyleGene: Crossover and Mutation of Region-level Facial Genes for Kinship Face Synthesis

StyleGene: Crossover and Mutation of Region-level Facial Gen...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Hao Li Xianxu Hou Zepeng Huang Linlin Shen Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of AI and Advanced Computing Xi'an Jiaotong-Liverpool University Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University

High-fidelity kinship face synthesis has many potential applications, such as kinship verification, missing child identification, and social media analysis. However, it is challenging to synthesize high-quality descendant faces with genetic relations due to the lack of large-scale, high-quality annotated kinship data. This paper proposes RFG (Region-level Facial Gene) extraction framework to address this issue. We propose to use IGE (Image-based Gene Encoder), LGE (Latent-based Gene Encoder) and Gene Decoder to learn the RFGs of a given face image, and the relationships between RFGs and the latent space of Style-GAN2. As cycle-like losses are designed to measure the $\mathcal{L}_{2}$ distances between the output of Gene Decoder and image encoder, and that between the output of LGE and IGE, only face images are required to train our framework, i.e. no paired kinship face data is required. Based upon the proposed RFGs, a crossover and mutation module is further designed to inherit the facial parts of parents. A Gene Pool has also been used to introduce the variations into the mutation of RFGs. The diversity of the faces of descendants can thus be significantly increased. Qualitative, quantitative, and subjective experiments on FIW, TSKinFace, and FF-Databases clearly show that the quality and diversity of kinship faces generated by our approach are much better than the existing state-of-the-art methods.

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Multi-source dynamic time series data processing method based on comprehensive weighted fusion

Multi-source dynamic time series data processing method base...

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12th International Conference on Quality, Reliability, Risk, Maintenance, and Safety engineering (QR2MSE 2022)

作者： H. Geng F. Yuan B. Suo Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing People's Republic of China School of Mathematics Southwest Jiaotong University Chengdu People's Republic of China School of Information Engineering Southwest University of Science and Technology Mianyang People's Republic of China

Aiming at the fusion problem of multi-source heterogeneous dynamic data sources, based on the subjective and objective comprehensive weighting idea, an efficient multi-source dynamic data fusion algorithm is proposed. First, the outlier detection method based on linear regression model is used to preprocess the multi-source dynamic time series data. Then, determine the weight of the dynamic data. The prior weight of the data source is determined by the AHP method, cyclic scoring method, etc., and the posterior weight is determined according to the similarity between the time series data. Based on the Euclidean distance, a similarity measure between different data sources and a similarity measure between different dynamic data samples are constructed respectively. The former is used for the fusion of dynamic data standard deviation expectations, and the latter is used for the fusion of mean expectations. Finally, the comprehensive weight of each dynamic time series of each data source is obtained, and the comprehensive weighted fusion method is used to obtain the expected value and standard deviation of the dynamic response. The analysis of the calculation example shows that the proposed algorithm has higher computational efficiency than the method that treats dynamic data as several discrete static data.

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A Factorization Strategy for Tensor Robust PCA 5th

A Factorization Strategy for Tensor Robust PCA

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5th Asian Conference on Pattern Recognition, ACPR 2019

作者： Wang, Andong Jin, Zhong Yang, Jingyu School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Key Laboratory of Intelligent Perception and System for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9783030414030

Many kinds of real-world data, e.g., color images, videos, etc., are represented by tensors and may often be corrupted by outliers. Tensor robust principal component analysis (TRPCA) servers as a tensorial modification of the fundamental principal component analysis (PCA) which performs well in the presence of outliers. The recently proposed TRPCA model [12] based on tubal nuclear norm (TNN) has attracted much attention due to its superiority in many applications. However, TNN is computationally expensive, limiting the application of TRPCA for large tensors. To address this issue, we first propose a new TRPCA model by adopting a factorization strategy within the framework of tensor singular value decomposition (t-SVD). An algorithm based on the non-convex augmented Lagrangian method (ALM) is developed with convergence guarantee. Effectiveness and efficiency of the proposed algorithm is demonstrated through extensive experiments on both synthetic and real datasets. © Springer Nature Switzerland AG 2020.

关键词： Singular value decomposition

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A Communication Optimal Transport Approach to the Computation of Rate Distortion Functions

A Communication Optimal Transport Approach to the Computatio...

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IEEE (first ITW conference of the year) information Theory Workshop (ITW)

作者： Shitong Wu Wenhao Ye Hao Wu Huihui Wu Wenyi Zhang Bo Bai Department of Mathematical Sciences Tsinghua University Beijing China Theory Lab Central Research Institute 2012 Labs Huawei Tech. Co. Ltd Hong Kong SAR China Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei China

In this paper, we propose a new framework named Communication Optimal Transport (CommOT) for computing the rate distortion (RD) function. This work is motivated by observing the fact that the transition law and the relative entropy in communication theory can be viewed as the transport plan and the regularized objective function in the optimal transport (OT) model. However, unlike in classical OT problems, the RD function only possesses one-side marginal distribution. Hence, to maintain the OT structure, we introduce slackness variables to fulfill the other-side marginal distribution and then propose a general framework (CommOT) for the RD function. The CommOT model is solved via the alternating optimization technique and the well-known Sinkhorn algorithm. In particular, the expected distortion threshold can be converted into finding the unique root of a one-dimensional monotonic function with only a few steps. Numerical experiments show that our proposed framework (CommOT) for solving the RD function with given distortion threshold is efficient and accurate.

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Cross-Domain Few-Shot Specific Emitter Identification via Contrastive Self-Supervised Learning

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IEEE Communications Letters 2025年

作者： Zhang, Jingbo Liu, Qingshuo An, Shaoqian He, Ying Dalian Maritime University Information Science and Technology College National Engineering Research Center of Maritime Navigation System Dalian116026 China Dalian Maritime University Information Science and Technology College Liaoning Dalian116026 China

Specific Emitter Identification (SEI) is an emerging technology that leverages radio frequency fingerprint (RFF) to identify wireless devices. While deep learning methods have shown significant potential for automatic RFF feature extraction and identification, their reliance on large labeled datasets limits their practical applicability. This letter proposes a contrastive self-supervised learning framework integrating von Neumann entropy and exponential moving average (CSEE). With the support of CSEE, an RFF extractor can be pre-trained using unlabeled auxiliary datasets from the source domain. CSEE can directly control the eigenvalue distribution in the final representation’s autocorrelation matrix, thereby optimizing representation properties such as rank and isotropy while reducing information redundancy, thus preserving critical signal-domain features. In the target domain, only a few samples are needed to fine-tune the model to achieve good recognition accuracy. We conducted cross-domain SEI experiments using ADS-B as the source domain with LoRa and AIS as two separate target domains. The results show that with just 30 fine-tuned samples, the CSEE-pretrained RFF extractor achieved nearly 90% identification accuracy, surpassing other self-supervised learning methods. These findings demonstrate the strong generalization capability of CSEE across different target domains. © 1997-2012 IEEE.

关键词： Self-supervised learning

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CARZero: Cross-Attention Alignment for Radiology Zero-Shot Classification

CARZero: Cross-Attention Alignment for Radiology Zero-Shot C...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Haoran Lai Qingsong Yao Zihang Jiang Rongsheng Wang Zhiyang He Xiaodong Tao S. Kevin Zhou School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Hefei Anhui P.R.China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou Jiangsu P.R.China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China Medical Business Department iFlytek Co.Ltd Hefei China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

The advancement of Zero-Shot Learning in the medi-cal domain has been driven forward by using pretrained models on large-scale image-text pairs, focusing on image-text alignment. However, existing methods primarily rely on cosine similarity for alignment, which may not fully capture the complex relationship between medical images and reports. To address this gap, we introduce a novel approach called Cross-Attention Alignment for Radiology Zero-Shot Classification (CARZero). Our approach innovatively leverages cross-attention mechanisms to process image and report features, creating a Similarity Representation that more accurately reflects the intricate relationships in medical semantics. This representation is then linearly projected to form an image-text similarity matrix for cross-modality alignment. Additionally, recognizing the pivotal role of prompt selection in zero-shot learning, CARZero in-corporates a Large Language Model-based prompt alignment strategy. This strategy standardizes diverse diagnostic expressions into a unified format for both training and inference phases, overcoming the challenges of manual prompt design. Our approach is simple yet effective, demonstrating state-of-the-art performance in zero-shot classification on five official chest radiograph diagnostic test sets, including remarkable results on datasets with long-tail distributions of rare diseases. This achievement is attributed to our new image-text alignment strategy, which effectively addresses the complex relationship between medical images and reports. Code and models are available at https://***/laihaoran/CARZero.

关键词： Training Zero-shot learning Semantics Focusing Manuals Radiology Pattern recognition

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CHAINS:CHAIN-based fusion safety system framework for intelligent connected vehicle

Chain

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Chain 2024年第1期1卷 2-45页

作者： Shichun Yang Bin Sun Haoran Guang Rui Wang Bowen Zheng Weifeng Gong Yu Wang Yi Shi Zexiang Tong Lisheng Zhang Kaiyi Yang Xinhua Liu Yaoguang Cao Jun Li Yunpeng Wang School of Transportation Science and Engineering Beihang UniversityBeijing 100091China Innovation Center of New Energy Vehicle Digital Supervision Technology and Application for State Market Regulation Beijing 100091China Imperial College London Ringgold standard institution-Dyson School of Design Engineering London SW72AZUK Research Institute for Frontier Science Beihang UniversityBeijing 100091China State Key Lab of Intelligent Transportation System Beijing 100091China School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China

Intelligent connected vehicles,as the focus of the global automotive industry,are currently at a critical stage of large-scale ***,during the development process of vehicles from mechanical systems with limited functions to mobile intelligence with complex and multiple functions,the issues of functional safety,cybersecurity,and safety of the intended functionality are the main challenges of the industrialization of intelligent connected vehicles,including multiple safety risks such as hardware and software failures,insufficient performance in edge scenarios,cyber-attacks and data *** this paper,the safety and security issues of intelligent connected vehicles,the challenges posed by emerging technology applications,and related solutions are systematically reviewed and summarized.A fusion safety system framework with the safety cube as the core of protection and control is proposed innovatively based on a field-vehicle-human safety interactional model,realizing stereoscopic,deep,and comprehensive safety protection through end-cloud ***,an X-shaped fusion safety development process based on CHAIN is *** the empowerment of digital twin and AI technologies,it could approach interaction between physical entities and digital twin models and the automation of the development process,thereby satisfying the demands of fusion safety system design,intelligent development,rapid delivery,and continuous *** fusion safety system framework and X-shaped development process proposed in this paper can provide important insight into intelligent transportation vehicles and systems'safety and security design and development.

关键词： CHAIN fusion safety X-shaped development process safety brain

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FR²Seg: Continual Segmentation Across Multiple Sites via Fourier Style Replay and Adaptive Consistency Regularization 39

FR<sup>2</sup>Seg: Continual Segmentation Across Multiple Si...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xu, Cheng Zhang, Weiwen Zhang, Hongrui Xu, Xuemiao Zhang, Huaidong Zou, Jing Qin, Jing South China University of Technology China The Hong Kong Polytechnic University Hong Kong Guangdong Engineering Center for Large Model and GenAI Technology China State Key Laboratory of Subtropical Building Science China Ministry of Education Key Laboratory of Big Data and Intelligent Robot China Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information China

ISBN: (纸本)157735897X

In clinical imaging, medical segmentation networks typically require continually adapting to new data from multiple sites over time, as aggregating all data for learning at once can be impractical due to storage limitations and privacy concerns. However, existing methods basically overlook domain-specific characteristics and fall short of adequately capturing domain-invariant knowledge during continual learning, leading to undesired catastrophic forgetting of previous sites and inferior generalization to new sites. To tackle this issue, this paper introduces FR²Seg, to sufficiently exploit both domain-specific and domain-invariant knowledge for efficient continual learning with the aid of low-frequency cues. For the former aspect, we propose a Fourier style replay module to synthesize pseudo images with old-site styles for data augmentation during new-site training, effectively preventing catastrophic forgetting without sacrificing data privacy. For the latter, we present a Fourier adaptive consistency regularization to identify and constrain the optimization of domain-invariant parameters with explicit awareness of knowledge transferability across sites, ensuring excellent generalizability to new sites. Experimental results on two public datasets confirm our method's superiority over existing state-of-the-art continual learning methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Image segmentation

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