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knowledge-driven prototype refinement for few-shot fine-grained recognition

knowledge-Based Systems 2025年 318卷

作者： Chen, Jiale Xu, Feng Lyu, Xin Zeng, Tao Li, Xin Chen, Shangjing College of Computer Science and Software Engineering Hohai University Nanjing210024 China School of Computer Engineering Jiangsu Ocean University Lianyungang222005 China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing211106 China

Advancements in deep learning have made image classification rival human performance with sufficient data and supervision. However, in domains with limited visual samples and high labeling costs, enabling AI systems to learn efficiently from few examples is challenging. This challenge is compounded in fine-grained categories, where subtle differences and scarce samples hinder robust representation extraction. To address this, we propose the knowledge-Driven Prototype Refinement (KDPR) framework, which enhances few-shot fine-grained recognition by integrating prior knowledge from text. KDPR simulates human focus on discriminative foreground regions to extract refined views, forming a dual-branch learning framework alongside original images. It also constructs an unsupervised adjacency graph among visual instances and uses graph neural networks to improve category representation robustness. Additionally, a knowledge transfer-based image recognizer integrates prior text embeddings with global semantics directly into visual recognition, providing extra semantic guidance. To optimize knowledge-to-vision mapping, an auxiliary spatial prototype calibration aligns prototype representations across multiple spaces. Extensive experiments on three fine-grained datasets and two popular backbones demonstrate the effectiveness and state-of-the-art performance of our approach, especially in 1-shot learning. The source code is available at: https://***/HHU-JialeChen/KDPRNet. © 2025

关键词： Zero-shot learning

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LESA: Learnable LLM Layer Scaling-Up

arXiv

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arXiv 2025年

作者： Yang, Yifei Cao, Zouying Ma, Xinbei Yao, Yao Qin, Libo Chen, Zhi Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China School of Computer Science and Engineering Central South University China ByteDance China

Training Large Language Models (LLMs) from scratch requires immense computational resources, making it prohibitively expensive. Model scaling-up offers a promising solution by leveraging the parameters of smaller models to create larger ones. However, existing depth scaling-up methods rely on empirical heuristic rules for layer duplication, which result in poorer initialization and slower convergence during continual pre-training. We propose LESA, a novel learnable method for depth scaling-up. By concatenating parameters from each layer and applying Singular Value Decomposition, we uncover latent patterns between layers, suggesting that inter-layer parameters can be learned. LESA uses a neural network to predict the parameters inserted between adjacent layers, enabling better initialization and faster training. Experiments show that LESA outperforms existing baselines, achieving superior performance with less than half the computational cost during continual pre-training. Extensive analyses demonstrate its effectiveness across different model sizes and tasks.1 Copyright © 2025, The Authors. All rights reserved.

关键词： Multilayer neural networks

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Two-Stage Stochastic Planning of Offshore Wind-Based Microgrids Integrating Hydrogen Production and Storage

Two-Stage Stochastic Planning of Offshore Wind-Based Microgr...

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International Conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Zhang, Haoran Liu, Haoran Ma, Cheng Zhang, Aichao Lei, Shunbo Bai, Jianming School of Science and Engineering The Chinese University of Hong Kong Guangdong Shenzhen518172 China Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network School of Science and Engineering The Chinese University of Hong Kong Guangdong Shenzhen518172 China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China Huadian Heavy Industries Co. Ltd Beijing100071 China

ISBN: (纸本)9789819618514

Microgrids that integrate high-penetration renewable energy sources (RESs), seawater desalination, energy storage, and hydrogen production are promising ways to reduce RESs curtailment and improve the system operational economics. This work investigates the holistic planning and operation of the hybrid hydrogen-electrical microgrids. To mitigate the uncertainty associated with RESs power outputs, we propose a scenario-based two-stage stochastic programming (SP) where the first stage and the second stage aim to minimize the total investment costs and the operational costs, respectively. The proposed SP optimizes the capacities of microgrids facilities while considering investment and operational costs (e.g., daily capital depreciation, RESs curtailment costs, and electricity purchasing costs). An improved scenario reduction method which incorporates Cholesky decomposition and k-means clustering algorithm is employed to solve this problem. Case studies are conducted on a microgrid test system. The results show that the proposed two-stage SP demonstrates enhanced utilization of RESs and reducing total investment and operational costs. The safe and stable operation of the hydrogen-electrical microgrids is also satisfied. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Windmill

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Joule heating enables ultra-fast SiC ceramics joining in seconds

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Scripta Materialia 2025年 264卷

作者： Xu, Jie Zhou, Xiaobing Huang, Qing Zhejiang Key Laboratory of Data-Driven High-Safety Energy Materials and Applications Ningbo Key Laboratory of Special Energy Materials and Chemistry Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China University of Chinese Academy of Sciences Beijing 100049 China

Reliable ultra-fast pressure-less joining of Silicon Carbide (SiC) ceramic is a critical challenge for extreme environment applications. This study presents a novel approach for ultra-fast, pressure-less joining of SiC ceramics. Utilizing the resistance increase owing to the interface reaction at the interlayer, an ultra-fast Joule heating technique enabled robust SiC joints in just 23 s without external preheating. A 5-micron-thick aluminum foil was used as the initial joining layer, undergoing a rapid interface reaction to generate high-resistance compounds, such as Al4C3, Al4SiC4, and Al2OC during the flash heating process. This localized heating, which inhibited the energy dissipation in heating the entire joint assembly, facilitated the creation of strong SiC joints within seconds without pre-heating, achieving a shear strength up to 76 ± 12 MPa. The proposed joining method, utilizing the inherent resistance changes within the interlayer, demonstrates significant potential for joining other non-conductive ceramics effectively. © 2025 Acta Materialia Inc.

关键词： Ceramic materials

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Efficient Modeling Attack on Multiplexer PUFs via Kronecker Matrix Multiplication

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Wang, Hongfei Wan, Caixue Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China

The Physical Unclonable Function (PUF) is valued for its lightweight nature and unique functionality, making it a common choice for securing hardware products requiring authentication and key generation mechanisms. In response to the susceptibility of individual PUFs to modeling attacks, advanced PUF variants have been developed to improve security measures. One notable type in this regard is the multiplexer-based composition of arbiter PUFs, known as MPUF, which aims to meet high reliability and security standards simultaneously. Current research on attacking MPUF encounters challenges such as substantial demands for training CRPs and low success rates. In this work, we propose a novel numerical modeling attack strategy for MPUFs. Using Kronecker products from mathematical perspective, this method precisely describe the MPUF model without using complex network architectures, boosting attack accuracy and overall efficiency. Experiment comparison with state-of-the-art works demonstrates that our method achieves better performance in terms of attack accuracy, robustness, and efficiency. Our method is able to successfully attack a (512, 8)-MPUF in 32.71 minutes with 97.14% accuracy, outperforming all existing attack methods on MPUFs. More, we validate our method through experiments with hardware implementations on FPGAs. The advantages of our method also include the adaptability to be employed to attack other MPUF variations like cMPUF and rMPUF, and the capability to be integrated with an existing attack method for launching efficient attack on MPUFs leveraging reliability information. © 2025 IEEE.

关键词： modeling attack multiplexer-based PUF Physical unclonable function

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Towards Fair Federated Learning via Unbiased Feature Aggregation

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： He, Zeqing Wang, Zhibo Dong, Xiaowei Sun, Peng Ren, Ju Ren, Kui Zhejiang University State Key Laboratory of Blockchain and Data Security China Institute of Blockchain and Data Security Hangzhou310027 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Tsinghua University Department of Computer Science and Technology Beijing100084 China Zhongguancun Laboratory Beijing100094 China

Federated learning (FL) is a distributed machine learning framework that enables multiple clients to collaboratively train models without raw data exchange. Prior studies on FL mainly focus on optimizing learning performance, enhancing privacy preservation, and improving attack resilience. However, little work studies how to mitigate the unfairness of federated trained models while unfair models would make discriminatory decisions toward certain groups or populations (e.g., favoring males over females), leading to serious ethical concerns. Thus, it is crucial to mitigate model unfairness in FL, yet challenging as this requires centralized access to each data point's fairness-sensitive information (e.g., race, gender), which is prohibited in FL. In this work, we propose a novel fair FL framework FedUFA, where the server can aggregate clients' learned knowledge in an unbiased manner, to obtain fair and high-usability federated trained models. Specifically, to unearth the bias in clients' local data and account for potentially heterogeneous local models, we propose a knowledge distillation-based FL scheme, where clients' knowledge of learned features on a public dataset is amalgamated to the server for aggregation. We train an unbiased feature mapper at the server to remove fairness-sensitive latent features and extract fair representations from clients' submitted raw features. In particular, we design an adversarial training method to train the mapper, which involves a predictor aiming to maximize the prediction accuracy on the FL task and a discriminator intending to help identify fairness-sensitive features. Extensive experiments on real-world datasets demonstrate the effectiveness of FedUFA. © 2025 IEEE. All rights reserved,

关键词： Federated learning

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Privacy Law Enforcement Under Centralized Governance: A Qualitative Analysis of Four Years’ Special Privacy Rectification Campaigns

arXiv

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arXiv 2025年

作者： Jing, Tao Li, Yao Ye, Jingzhou Wang, Jie Wang, Xueqiang School of Cyber Science and Engineering Huazhong University of Science and Technology China JinYinHu Laboratory China University of Central Florida United States Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China

In recent years, major privacy laws like the GDPR have brought about positive changes. However, challenges remain in enforcing the laws, particularly due to under-resourced regulators facing a large number of potential privacy-violating software applications (apps) and the high costs of investigating them. Since 2019, China has launched a series of privacy enforcement campaigns known as Special Privacy Rectification Campaigns (SPRCs) to address widespread privacy violations in its mobile application (app) ecosystem. Unlike the enforcement of the GDPR, SPRCs are characterized by large-scale privacy reviews and strict sanctions, under the strong control of central authorities. In SPRCs, central government authorities issue administrative orders to mobilize various resources for market-wide privacy reviews of mobile apps. They enforce strict sanctions by requiring privacy-violating apps to rectify issues within a short timeframe or face removal from app stores. While there are a few reports on SPRCs, the effectiveness and potential problems of this campaign-style privacy enforcement approach remain unclear to the community. In this study, we conducted 18 semi-structured interviews with app-related engineers involved in SPRCs to better understand the campaign-style privacy enforcement. Based on the interviews, we reported our findings on a variety of aspects of SPRCs, such as the processes that app engineers regularly follow to achieve privacy compliance in SPRCs, the challenges they encounter, the solutions they adopt to address these challenges, and the impacts of SPRCs, etc. We found that app engineers face a series of challenges in achieving privacy compliance in their apps. For example, they receive inconsistent app privacy review reports from multiple app stores and have difficulties confirming the issues flagged by these reports;they also lack institutional support for studying privacy laws, self-validating privacy compliance of their apps, communicating effectiv

关键词： Differential privacy

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FM-FCN:A Neural Network with Filtering Modules for Accurate Vital Signs Extraction

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Research 2025年第1期2024卷 92-106页

作者： Fangfang Zhu Qichao Niu Xiang Li Qi Zhao Honghong Su Jianwei Shuai Department of Physics and Fujian Provincial Key Laboratory for Soft Functional Materials ResearchXiamen UniversityXiamen 361005China National Institute for Data Science in Health and Medicine and State Key Laboratory of Cellular Stress BiologyInnovation Center for Cell Signaling NetworkXiamen UniversityXiamen 361005China Vitalsilicon Technology Co.Ltd. JiaxingZhejiang 314006China School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan 114051China Yangtze Delta Region Institute of Tsinghua University ZhejiangJiaxing 314006China Wenzhou Institute University of Chinese Academy of SciencesWenzhou 325001China Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine Vision and Brain Health)Wenzhou 325001China

Neural networks excel at capturing local spatial patterns through convolutional modules,but they may struggle to identify and effectively utilize the morphological and amplitude periodic nature of physiological *** this work,we propose a novel network named filtering module fully convolutional network(FM-FCN),which fuses traditional filtering techniques with neural networks to amplify physiological signals and suppress ***,instead of using a fully connected layer,we use an FCN to preserve the time-dimensional correlation information of physiological signals,enabling multiple cycles of signals in the network and providing a basis for signal ***,we introduce the FM as a network module that adapts to eliminate unwanted interference,leveraging the structure of the *** approach builds a bridge between deep learning and signal processing ***,we evaluate the performance of FM-FCN using remote *** results demonstrate that FM-FCN outperforms the second-ranked method in terms of both blood volume pulse(BVP)signal and heart rate(HR)*** substantially improves the quality of BVP waveform reconstruction,with a decrease of 20.23%in mean absolute error(MAE)and an increase of 79.95%in signal-to-noise ratio(SNR).Regarding HR estimation accuracy,FM-FCN achieves a decrease of 35.85%in MAE,29.65%in error standard deviation,and 32.88%decrease in 95%limits of agreement width,meeting clinical standards for HR accuracy *** results highlight its potential in improving the accuracy and reliability of vital sign measurement through high-quality BVP signal *** codes and datasets are available online at https://***/zhaoqi106/FM-FCN.

关键词： physiological signalsin filtering module fully convolutional network fm fcn which vital signs extraction amplify physiological signals convolutional modulesbut neural networks filtering module capturing local spatial patterns

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Synchrony of gross primary productivity resistance and resilience in rapid response to spring excessive precipitation across northern China’s dryland ecosystems

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Environmental Research Letters 2025年第5期20卷

作者： Li, Xuejiao Zhang, Jutao Feng, Qi Liu, Wei Ao, Yong Zhu, Meng Yin, Xinwei Xue, Yuanyuan Su, Yingqing Yang, Xiaomei State Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands Northwest Institute of Eco-Environment and Resources Chinese Academy of Sciences Lanzhou730000 China National Cryosphere Desert Data Center Northwest Institute of Eco-Environment and Resources Chinese Academy of Sciences Lanzhou730000 China University of Chinese Academy of Sciences Beijing100049 China Shaanxi Key Laboratory of Land Reclamation Engineering Key Laboratory of Degraded and Unused Land Consolidation Engineering College of Land Engineering Chang’an University Xi’an710054 China

Spring precipitation directly impacts the growth and development of vegetation in dryland ecosystems, which in turn affects the biomass of the growing season and fundamentally maintains the ecosystem stability. However, the effects of recently frequent variabilities in spring precipitation on ecosystem productivity are still unclear. Here, we investigate the spatiotemporal patterns of precipitation and gross primary productivity (GPP) in the spring, as well as the impact of spring excessive precipitation on GPP over dryland ecosystems in northern China, based on remote sensing datasets of precipitation and GPP at eight-day intervals from 2000 to 2018. We also assess the sensitivity and stability of GPP to spring excessive precipitation. Spring excessive precipitation had a unidirectional driving effect on vegetation growth and an evident lag effect of an eight-day period across more than 87% of the drylands in northern China. This indicates that dryland ecosystems in northern China respond quickly to excessive precipitation in the spring. Vegetation sensitivity to excessive precipitation is the weakest at an aridity index of around 0.1. Drylands with high temperatures and large precipitation exhibit positive sensitivity, strong resistance, and high resilience of vegetation to excessive precipitation. There is a synchronous change between the resistance and resilience of GPP to excessive precipitation. Our results provide an innovative understanding of the interaction mechanism between precipitation variations and dryland ecosystems, offering theoretical support for ecological restoration, sustainable development, and carbon neutrality in dryland regions. © 2025 The Author(s). Published by IOP Publishing Ltd.

关键词： Biotic

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Dual Encoders for Diffusion-based Image Inpainting

Dual Encoders for Diffusion-based Image Inpainting

引用

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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