检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Lin, Hui Li, Nan Yao, Pengjuan Dong, Kexin Guo, Yuhan Hong, Danfeng Zhang, Ying Wen, Congcong China Academy of Electronics and Information Technology Beijing100846 China National Satellite Meteorological Center Innovation Center for FengYun Meteorological Satellite China Meteorological Administration Beijing100081 China State Key Laboratory of Hydroscience and Engineering Department of Hydraulic Engineering Tsinghua University Beijing100084 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Department of Electrical and Computer Engineering New York University Abu Dhabi Abu Dhabi United Arab Emirates

Object detection is a fundamental task in computer vision that involves accurately locating and classifying objects within images or video frames. In remote sensing, this task is particularly challenging due to the high resolution, multi-scale features, and diverse ground object characteristics inherent in satellite and UAV imagery. These challenges necessitate more advanced approaches for effective object detection in such environments. While deep learning methods have achieved remarkable success in remote sensing object detection, they typically rely on large amounts of labeled data. Acquiring sufficient labeled data, particularly for novel or rare objects, is both challenging and time-consuming in remote sensing scenarios, limiting the generalization capabilities of existing models. To address these challenges, few-shot learning (FSL) has emerged as a promising approach, aiming to enable models to learn new classes from limited labeled examples. Building on this concept, few-shot object detection (FSOD) specifically targets object detection challenges in data-limited conditions. However, the generalization capability of FSOD models, particularly in remote sensing, is often constrained by the complex and diverse characteristics of the objects present in such environments. In this paper, we propose the Generalization-Enhanced Few-Shot Object Detection (GE-FSOD) model to improve the generalization capability in remote sensing FSOD tasks. Our model introduces three key innovations: the Cross-Level Fusion Pyramid Attention Network (CFPAN) for enhanced multi-scale feature representation, the Multi-Stage Refinement Region Proposal Network (MRRPN) for more accurate region proposals, and the Generalized Classification Loss (GCL) for improved classification performance in few-shot scenarios. GE-FSOD demonstrates superior robustness and accuracy in remote sensing FSOD tasks through these enhancements. Extensive experiments on the DIOR and NWPU VHR-10 datasets show that our

关键词： Labeled data

来源：评论

学校读者我要写书评

暂无评论

Image retrieval based on multi-concept detector and semantic correlation

引用

Science China(Information Sciences) 2015年第12期58卷 104-118页

作者： XU HaiJiao HUANG ChangQin PAN Peng ZHAO GanSen XU ChunYan LU YanSheng CHEN Deng WU JiYi School of Computer Science and Technology Huazhong University of Science and Technology Research Center for Information Services and Software Technology South China Normal University E-Service Research Center Zhejiang University Electrical and Computer Engineering National University of Singapore

With the rapid development of future network, there has been an explosive growth in multimedia data such as web images. Hence, an efficient image retrieval engine is necessary. Previous studies concentrate on the single concept image retrieval, which has limited practical usability. In practice, users always employ an Internet image retrieval system with multi-concept queries, but, the related existing approaches are often ineffective because the only combination of single-concept query techniques is adopted. At present semantic concept based multi-concept image retrieval is becoming an urgent issue to be solved. In this paper, a novel Multi-Concept image Retrieval Model(MCRM) based on the multi-concept detector is proposed, which takes a multi-concept as a whole and directly learns each multi-concept from the rearranged multi-concept training set. After the corresponding retrieval algorithm is presented, and the log-likelihood function of predictions is maximized by the gradient descent approach. Besides, semantic correlations among single-concepts and multiconcepts are employed to improve the retrieval performance, in which the semantic correlation probability is estimated with three correlation measures, and the visual evidence is expressed by Bayes theorem, estimated by Support Vector Machine(SVM). Experimental results on Corel and IAPR data sets show that the approach outperforms the state-of-the-arts. Furthermore, the model is beneficial for multi-concept retrieval and difficult retrieval with few relevant images.

关键词： multi-concept image retrieval semantic correlation probability estimation concept learning visual evidence

来源：评论

学校读者我要写书评

暂无评论

JGC-IAGCL: Fusing joint graph convolution and intent-aware graph contrastive learning for explainable recommendation

引用

Information Fusion 2025年 123卷

作者： Yang, Zhi Lin, Chuan Qin, Yongbin Huang, Ruizhang Chen, Yanping Qin, Jiwei State Key Laboratory of Public Big Data Guizhou University Guiyang Guizhou550025 China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and Technology Guizhou University Guiyang Guizhou550025 China College of Information Science and Engineering Xinjiang University Xinjiang Uygur Autonomous Region Urumqi830046 China

Graph contrastive learning (GCL) enhances recommendation accuracy by leveraging self-supervised features to refine node representations from large-scale unlabeled data. Traditional GCL-based recommendation models typically construct contrastive views via graph augmentation (e.g., stochastic node/edge dropout) or embedding-space perturbation, aiming to maximize representation consistency. However, these methods struggle to effectively model and interpret user preferences and consumption intents, limiting explainability and recommendation performance. To address these challenges, we propose JGC-IAGCL (Joint Graph Convolution and Intent-Aware Graph Contrastive Learning), an explainable recommendation framework. JGC-IAGCL integrates joint graph convolution to capture implicit user preferences and employs intent-aware graph contrastive learning to extract explicit user intents from user–item interactions. By fusing these features, our method generates evenly distributed, intent-propensity-aware user/item representations. Theoretical analysis shows that JGC-IAGCL mitigates popularity bias while enhancing the exposure of long-tail items. Extensive experiments on four highly sparse public datasets validate its effectiveness, demonstrating superior recommendation accuracy and improved interpretability. © 2025

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

MIFNet: Learning Modality-Invariant Features for Generalizable Multimodal Image Matching

arXiv

引用

arXiv 2025年

作者： Liu, Yepeng Sun, Zhichao Yu, Baosheng Zhao, Yitian Du, Bo Xu, Yongchao Cheng, Jun National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430070 China Lee Kong Chian School of Medicine Nanyang Technological University 308232 Singapore Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Zhejiang Ningbo315211 China 1 Fusionpolis Way #21-01 Connexis South Tower Singapore138632 Singapore

Many keypoint detection and description methods have been proposed for image matching or registration. While these methods demonstrate promising performance for single-modality image matching, they often struggle with multimodal data because the descriptors trained on single-modality data tend to lack robustness against the non-linear variations present in multimodal data. Extending such methods to multimodal image matching often requires well-aligned multimodal data to learn modality-invariant descriptors. However, acquiring such data is often costly and impractical in many real-world scenarios. To address this challenge, we propose a modality-invariant feature learning network (MIFNet) to compute modality-invariant features for keypoint descriptions in multimodal image matching using only single-modality training data. Specifically, we propose a novel latent feature aggregation module and a cumulative hybrid aggregation module to enhance the base keypoint descriptors trained on single-modality data by leveraging pretrained features from Stable Diffusion models. We validate our method with recent keypoint detection and description methods in three multimodal retinal image datasets (CF-FA, CF-OCT, EMA-OCTA) and two remote sensing datasets (Optical-SAR and Optical-NIR). Extensive experiments demonstrate that the proposed MIFNet is able to learn modality-invariant feature for multimodal image matching without accessing the targeted modality and has good zero-shot generalization ability. The source code will be made publicly available. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Scalable Laser Manufacturing of High-Aspect-Ratio Superhydrophobic and Ferromagnetic Microcilia Arrays for Aqueous Droplet Transportation

引用

Small Methods 2025年

作者： Tan, Min Gong, Rongrong Chen, Yi Kim, Minseong Lu, Xupeng Liu, Huan Yang, Rongliang Xu, Yang Zhong, Haosong Huang, Yangyi Fan, Zhiyong Liu, Yang Li, Mitch Guijun Center on Smart Manufacturing Division of Integrative Systems and Design The Hong Kong University of Science and Technology Clear Water Bay Kowloon 999077 Hong Kong State Key Laboratory of Advanced Displays and Optoelectronics Technologies The Hong Kong University of Science and Technology Clear Water Bay Kowloon 999077 Hong Kong Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon 999077 Hong Kong Department of Applied Physics The Hong Kong Polytechnic University Hung Hom 999077 Hong Kong Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction The Hong Kong University of Science and Technology Clear Water Bay Kowloon 999077 Hong Kong

Biological cilia exhibit metachronal movements that enable the expulsion of substances such as mucus and bacterial cells. Inspired by biological cilia, significant progress has been made in recent years in the development of artificial cilia. In particular, magnetic actuation has emerged as a prominent strategy for real-time, remote-controlled manipulation, offering noninvasive and reversible operation without inducing irreversible damage. However, the fabrication of artificial microcilia is currently constrained by limitations in achieving high aspect ratios, cost-effectiveness, and scalable production. In this study, advanced laser manufacturing is used to drill porous silicon (Si) templates, successfully demolding microcilia with a high aspect ratio (exceeding 9). By integrating silicon dioxide (SiO2) nanoparticles, a superhydrophobic surface is achieved with a hierarchical micro-nano structure. The experiments demonstrated that these structured microcilia not only exhibit remarkable durability but also maintain long-term superhydrophobicity. Furthermore, by blending with magnetic iron (II, III) oxide (Fe3O4) nanoparticles, superhydrophobic magnetic microcilia arrays (SMMA) are developed, enabling droplet transportation on their surface controlled by an external magnetic field. These artificial microcilia have potential applications in biomedical devices, self-cleaning anti-fouling surfaces, and human sensing technologies. © 2025 Wiley-VCH GmbH.

关键词： Porous silicon

来源：评论

学校读者我要写书评

暂无评论

Sensing With Random Communication Signals

arXiv

引用

arXiv 2025年

作者： Lu, Shihang Liu, Fan Xiong, Yifeng Du, Zhen Cui, Yuanhao Li, Shuangyang Yuan, Weijie Yang, Jie Jin, Shi National Mobile Communications Research Laboratory Southeast University Nanjing210096 China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing210044 China Faculty of Electrical Engineering and Computer Science Technical University of Berlin Berlin10587 Germany Southern University of Science and Technology Shenzhen518055 China Frontiers Science Center for Mobile Information Communication and Security and also The Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Nanjing211189 China

Communication-centric Integrated Sensing and Communication (ISAC) has been recognized as a promising methodology to implement wireless sensing functionality over existing network architectures, due to its cost-effectiveness and backward compatibility to legacy cellular systems. However, the inherent randomness of the communication signal may incur huge fluctuations in sensing capabilities, leading to unfavorable detection and estimation performance. To address this issue, we elaborate on random ISAC signal processing methods in this article, aiming at improving the sensing performance without unduly deteriorating the communication functionality. Specifically, we commence by discussing the fundamentals of sensing with random communication signals, including the performance metrics and optimal ranging waveforms. Building on these concepts, we then present a general framework for random ISAC signal transmission, followed by an in-depth exploration of time-domain pulse shaping, frequency-domain constellation shaping, and spatial-domain precoding methods. We provide a comprehensive overview of each of these topics, including models, results, and design guidelines. Finally, we conclude this article by identifying several promising research directions for random ISAC signal transmission. © 2025, CC BY-SA.

关键词： Signaling

来源：评论

学校读者我要写书评

暂无评论

AT-diff: An adversarial diffusion model for unrestricted adversarial examples generation

引用

Knowledge-Based Systems 2025年 320卷

作者： Yuan, Chengsheng Pang, Jingfa Fei, Jianwei Li, Xinting Xia, Zhihua School of Computer Science Nanjing University of Information Science and Technology Nanjing210044 China Engineering Research Center of Digital Forensics of Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China Faculty of Science and Technology University of Macau Macau519000 China School of Foreign Languages National University of Defense Technology Nanjing210039 China College of Cyber Security Jinan University Guangzhou510632 China

In recent years, deep learning has seen widespread application in many fields. However, the emergence of adversarial examples has revealed a critical vulnerability, making them susceptible to potential attacks. Although numerous adversarial attack methods have been proposed, they are often constrained by certain constraints. Most current adversarial attack methods are based on addition of perturbations, which are confined to clean examples and fail to generate diverse and natural adversarial examples. Moreover, owing to the additional perturbations, these methods exhibit limited robustness against adversarial defenses, such as image denoising. To tackle these challenges, this paper proposed a three-stage attack framework based on an adversarial diffusion model, named AT-Diff (Adversarial Transfer on Diffusion model). Firstly, to address the issue of inadequate robustness in traditional adversarial examples, we utilize a diffusion model to learn the adversarial example distribution of the target model and generate highly realistic adversarial examples from scratch without any additional perturbations, thereby significantly enhancing their performance under defense measures. Additionally, we design a Temporal Adaptive Loss (TAL) function, which dynamically adjusts the visual loss and the adversarial loss according to the current time step, ensuring that the generated adversarial examples gradually enhance their adversarial efficacy while maintaining high visual quality. Extensive experiments conducted on MNIST, Fashion-MNIST and CIFAR-10 datasets have shown that our method attains noteworthy results in imperceptibility, while also exhibiting stronger robustness. © 2025 Elsevier B.V.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Pre-Equalization Aided Grant-Free Massive Access in Massive MIMO System

arXiv

引用

arXiv 2025年

作者： Wang, Yueqing Mei, Yikun Gao, Zhen Wan, Ziwei Ning, Boyu Mi, De Muhaidat, Sami School of Information and Electronics Beijing Institute of Technology Beijing100081 China MIIT Key Laboratory of Complex-Field Intelligent Sensing Beijing Institute of Technology Beijing100081 China Jiaxing314019 China Advanced Technology Research Institute Beijing Institute of Technology Jinan250307 China National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu611731 China School of Computing and Digital Technology Birmingham City University BirminghamB55JU United Kingdom KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi United Arab Emirates

The spatial diversity and multiplexing advantages of massive multi-input-multi-output (mMIMO) can significantly improve the capacity of massive non-orthogonal multiple access (NOMA) in machine type communications. However, state-of-the-art grant-free massive NOMA schemes for mMIMO systems require accurate estimation of random access channels to perform activity detection and the following coherent data demodulation, which suffers from excessive pilot overhead and access latency. To address this, we propose a pre-equalization aided grant-free massive access scheme for mMIMO systems, where an iterative detection scheme is conceived. Specifically, the base station (BS) firstly activates one of its antennas (i.e., beacon antenna) to broadcast a beacon signal, which facilitates the user equipment (UEs) to perform downlink channel estimation and pre-equalize the uplink random access signal with respect to the channels associated with the beacon antenna. During the uplink transmission stage, the BS detects UEs’ activity and data by using the proposed iterative detection algorithm, which consists of three modules: coarse data detection (DD), data-aided channel estimation (CE), and fine DD. In the proposed algorithm, the joint activity and DD is firstly performed based on the signals received by the beacon antenna. Subsequently, the DD is further refined by iteratively performing data-aided CE module and fine DD module using signals received by all BS antennas. Our simulation results demonstrate that the proposed scheme outperforms state-of-the-art mMIMO-based grant-free massive NOMA schemes with the same access latency. Simulation codes are provided to reproduce the results in this article: https://***/owenwang517/tvt-2025. Copyright © 2025, The Authors. All rights reserved.

关键词： Channel estimation

来源：评论

学校读者我要写书评

暂无评论

Establishing a Modeling System in 3-km Horizontal Resolution for Global Atmospheric Circulation triggered by Submarine Volcanic Eruptions with 400 Billion Smoothed Particle Hydrodynamics 23

Establishing a Modeling System in 3-km Horizontal Resolution...

引用

2023 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2023

作者： Huang, Shenghong Chen, Junshi Zhang, Ziyu Hao, Xiaoyu Gu, Jun An, Hong Zhao, Chun Hu, Yan Wang, Zhanming Chen, Longkui Luo, Yifan Yao, Jineng Zhang, Yi Zhao, Yang Wang, Zhihao Jia, Dongning Jin, Zhao Song, Changming Luo, Xisheng He, Xiaobin Chen, Dexun Hefei China Laoshan Laboratory Qingdao China National Research Center of Parallel Computer Engineering and Technology Beijing China

ISBN: (纸本)9798400701092

People are increasingly concerned about how tectonic processes affect climate and vice versa. We establish a cross-sphere modeling system for volcanic eruptions and atmosphere circulation on a new Sunway supercomputer with a spatial resolution from 10m locally to 3km globally, using an improved multimedium and multiphase smoothed particle hydrodynamics (SPH) combined with a fully coupled meteorology-chemistry global atmospheric modeling scheme. We achieve 400 billion particles and 80% parallel efficiency using 39,000,000 processor cores. The simulation captures the whole dynamic process of the Tonga eruption from shock waves, earthquakes, tsunamis, mushroom clouds to the following 6 - 7 days of transport and diffusion of ash and water vapor, and preliminarily obtains the influence effect of full coupling of volcano, earthquake, ocean and atmosphere. This work is of great significance for deeply understanding the interaction between tectonic processes and climate change, and establishing an early warning simulation system for similar global hazard events. © 2023 ACM.

关键词： Supercomputers

来源：评论

学校读者我要写书评

暂无评论

DMSACNN: Deep Multiscale Attentional Convolutional Neural Network for EEG-Based Motor Decoding

引用

IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Liu, Ke Xing, Xin Yang, Tao Yu, Zhuliang Xiao, Bin Wang, Guoyin Wu, Wei Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Intelligent Computing China Chongqing University of Posts and Telecommunications Key Laboratory of Cyberspace Big Data Intelligent Security of Ministry of Education China South China University of Technology College of Automation Science and Engineering Guangzhou510641 China Chongqing Normal University National Center for Applied Mathematics in Chongqing Chongqing401331 China Shanghai Jiao Tong University School of Medicine Songjiang Hospital Songjiang Research Institute Shanghai Key Laboratory of Emotions and Affective Disorders Shanghai201600 China

Objective: Accurate decoding of electroencephalogram (EEG) signals has become more significant for the brain-computer interface (BCI). Specifically, motor imagery and motor execution (MI/ME) tasks enable the control of external devices by decoding EEG signals during imagined or real movements. However, accurately decoding MI/ME signals remains a challenge due to the limited utilization of temporal information and ineffective feature selection methods. Methods: This paper introduces DMSACNN, an end-to-end deep multiscale attention convolutional neural network for MI/ME-EEG decoding. DMSACNN incorporates a deep multiscale temporal feature extraction module to capture temporal features at various levels. These features are then processed by a spatial convolutional module to extract spatial features. Finally, a local and global feature fusion attention module is utilized to combine local and global information and extract the most discriminative spatiotemporal features. Main results: DMSACNN achieves impressive accuracies of 78.20%, 96.34% and 70.90% for hold-out analysis on the BCI-IV-2a, High Gamma and OpenBMI datasets, respectively, outperforming most of the state-of-the-art methods. Conclusion and significance: These results highlight the potential of DMSACNN in robust BCI applications. Our proposed method provides a valuable solution to improve the accuracy of the MI/ME-EEG decoding, which can pave the way for more efficient and reliable BCI systems. The source code for DMSACNN is available at https://***/xingxin-99/***. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：