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YOLOv9-AAG: Distinguishing Birds and Drones in Infrared and Visible Light Scenarios

IEEE Access 2025年 13卷 76609-76619页

作者： Suo, Peichun Zhu, Jincan Zhou, Qingyang Kou, Weili Wang, Xiuyan Suo, Wen Yunnan Medical Health Vocational College Information Center Kunming650033 China Southwest Forestry University College of Big Data and Intelligent Engineering Kunming650224 China The Key Laboratory of State Forestry and Grass land Administration on Forestry Ecological Big Data Kunming650224 China Yunnan College of Business Management Kunming650106 China

The ability to distinguish between birds and drones is essential for applications in wildlife preservation, aviation security, and defense operations. Reliable identification not only reduces the risk of bird strikes and monitors potential drone threats but also fosters the development of a secure and intelligent ecological environment. However, achieving high precision in recognition under complex and dynamic conditions remains a significant hurdle. To overcome this, we present an advanced detection framework, YOLOv9-AKConv-AFF-GSConv(YOLOv9-AAG), which substantially improves the accuracy of distinguishing birds from drones, even in varied poses, and enhances generalization capabilities in both infrared and visible light scenarios. The framework incorporates several key advancements. First, the GSConv is embedded into the head of the RepNSCPELAN4-AKConv module, enabling efficient extraction of edge and contour information without introducing additional computational cost. Second, the inclusion of the Attentional Feature Fusion (AFF) mechanism within AKConv amplifies the representation of shape features, particularly under challenging environments and multimodal scenarios. Lastly, the optimized AKConv module is effectively integrated with the original RepNSCPELAN4 model, facilitating more accurate and robust feature extraction for small targets, including birds and drones. Experimental evaluations underscore the effectiveness of the proposed framework. On an optical dataset containing multiple bird and drone targets, YOLOv9-AAG achieved recognition accuracy rates of 78.2%, while on an infrared dataset, it reached 86.1%. These results reflect improvements of 7.4% and 6.0%, respectively, over the baseline YOLOv9 model. Additionally, the F1 scores increased by 5.9% and 2.8%. On the publicly available CUB-200-2011 dataset and a combined dataset of visible and infrared images, the model attained mean Average Precision (mAP) scores of 81.9% and 81.6%, respectively, outper

关键词： Birds

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StoryLLaVA: Enhancing Visual Storytelling with Multi-Modal Large Language Models 31

StoryLLaVA: Enhancing Visual Storytelling with Multi-Modal L...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Yang, Li Xiao, Zhiding Huang, Wenxin Zhong, Xian Hubei Key Laboratory of Big Data Intelligent Analysis and Application School of Computer Science and Information Engineering Hubei University China Hubei Key Laboratory of Transportation Internet of Things School of Computer Science and Artificial Intelligence Wuhan University of Technology China

ISBN: (纸本)9798891761964

The rapid development of multi-modal large language models (MLLMs) has positioned visual storytelling as a crucial area in content creation. However, existing models often struggle to maintain temporal, spatial, and narrative coherence across image sequences and frequently lack the depth and engagement of human-authored stories. To address these challenges, we propose Story with Large Language and Vision Assistant (StoryLLaVA), a novel framework for enhancing visual storytelling. Our approach introduces a Topic-Driven Narrative Optimizer (TDNO) that improves both the training data and MLLM models by integrating image descriptions, topic generation, and GPT-4-based refinements. Furthermore, we employ a preference-based ranked story sampling method that aligns model outputs with human storytelling preferences through positive-negative pairing. These two phases of the framework differ in their training methods: the former uses supervised fine-tuning, while the latter incorporates reinforcement learning with positive and negative sample pairs. Experimental results demonstrate that StoryLLaVA outperforms current models in visual relevance, coherence, and fluency, with LLM-based evaluations confirming the generation of richer and more engaging narratives. The enhanced dataset and model are available at https://***/XxxZzD/StoryLLaVA. © 2025 Association for Computational Linguistics.

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Rethinking-based Code Summarization with Chain of Comments 31

Rethinking-based Code Summarization with Chain of Comments

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31st International Conference on Computational Linguistics, COLING 2025

作者： Cao, Liuwen He, Hongkui Huang, Hailin Wang, Jiexin Cai, Yi School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China

ISBN: (纸本)9798891761964

Automatic code summarization aims to generate concise natural language descriptions (summary) for source code, which can free software developers from the heavy burden of manual commenting and software maintenance. Existing methods focus on learning a direct mapping from pure code to summaries, overlooking the significant heterogeneity gap between code and summary. Moreover, existing methods lack a human-like re-check process to evaluate whether the generated summaries match well with the code. To address these two limitations, we introduce RBCoSum, a novel framework that incorporates the generated Chain Of Comments (COC) as auxiliary intermediate information for the model to bridge the gap between code and summaries. Also, we propose a rethinking process where a learned ranker trained on our constructed ranking dataset scores the extent of matching between the generated summary and the code, selecting the highest-scoring summary to achieve a re-check process. We conduct extensive experiments to evaluate our approach and compare it with other automatic code summarization models as well as multiple code Large Language Models (LLMs). The experimental results show that RBCoSum is effective and outperforms baselines by a large margin. The human evaluation also proves the summaries generated with RBCoSum are more natural, informative, useful, and truthful. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Dual AAV Cluster-Assisted Maritime Physical-Layer Secure Communications via Collaborative Beamforming

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IEEE Internet of Things Journal 2025年第9期12卷 12589-12607页

作者： Huang, Jiawei Wang, Aimin Sun, Geng Li, Jiahui Wang, Jiacheng Du, Hongyang Niyato, Dusit Jilin University College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Nanyang Technological University College of Computing and Data Science Jurong West 639798 Singapore The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong

Autonomous aerial vehicles (AAVs) can be utilized as relay platforms to assist maritime wireless communications. However, complex channels and multipath effects at sea can adversely affect the quality of AAV transmitted signals. Collaborative beamforming (CB) can enhance the signal strength and range to assist the AAV relay for remote maritime communications. However, due to the open nature of AAV channels, security issue requires special consideration. This article proposes a dual AAV cluster-assisted system via CB to achieve physical-layer security in maritime wireless communications. Specifically, one AAV cluster forms a maritime AAV-enabled virtual antenna array (MUVAA) relay to forward data signals to the remote legitimate vessel, and the other AAV cluster forms an MUVAA jammer to send jamming signals to the remote eavesdropper. In this system, we formulate a secure and energy-efficient maritime communication multiobjective optimization problem (SEMCMOP) to maximize the signal-to-interference-plus-noise ratio (SINR) of the legitimate vessel, minimize the SINR of the eavesdropping vessel and minimize the total flight energy consumption of AAVs. Since the SEMCMOP is an NP-hard and large-scale optimization problem, we propose an improved swarm intelligence optimization algorithm with chaotic solution initialization and hybrid solution update strategies to solve the problem. Simulation results indicate that the proposed algorithm outperforms other comparison algorithms, and it can achieve more efficient signal transmission by using the CB-based method. © 2014 IEEE.

关键词： Multiobjective optimization

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Device-edge collaborative occluded face recognition method based on cross-domain feature fusion

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Digital Communications and Networks 2025年第2期11卷 482-492页

作者： Zhang, Puning Tan, Lei Yang, Zhigang Huang, Fengyi Sun, Lijun Peng, Haiying School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China Chongqing400065 China Chongqing Key Laboratory of Ubiquitous Sensing and Networking Chongqing400065 China Chongqing Innovation Center of Industrial Big-Data Co. Lt Chongqing400700 China

The lack of facial features caused by wearing masks degrades the performance of facial recognition systems. Traditional occluded face recognition methods cannot integrate the computational resources of the edge layer and the device layer. Besides, previous research fails to consider the facial characteristics including occluded and unoccluded parts. To solve the above problems, we put forward a device-edge collaborative occluded face recognition method based on cross-domain feature fusion. Specifically, the device-edge collaborative face recognition architecture gets the utmost out of maximizes device and edge resources for real-time occluded face recognition. Then, a cross-domain facial feature fusion method is presented which combines both the explicit domain and the implicit domain facial. Furthermore, a delay-optimized edge recognition task scheduling method is developed that comprehensively considers the task load, computational power, bandwidth, and delay tolerance constraints of the edge. This method can dynamically schedule face recognition tasks and minimize recognition delay while ensuring recognition accuracy. The experimental results show that the proposed method achieves an average gain of about 21% in recognition latency, while the accuracy of the face recognition task is basically the same compared to the baseline method. © 2024

关键词： Face recognition

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Contribution of Transmembrane Protein 68 to Triglyceride Synthesis and Lipid Droplet Formation Differs From Diacylglycerol Acyltransferase

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生物化学与生物物理进展 2025年第3期52卷 705-715页

作者： YU Qing FU Yang-Yang ZENG Fan-Si PANG Hui-Min HUANG Fei-Fei CHANG Ping-An Chongqing Key Laboratory of Big Data for Bio-Intelligence School of Life Health Information Science and EngineeringChongqing University of Posts and TelecommunicationsChongqing 400065China

Objective To characterize transmembrane protein 68(TMEM68)in an alternative triacylglycerol(TAG)biosynthesis pathway,and determine the interplay between TMEM68 and the canonical TAG synthesis enzyme acyl-CoA:diacylglycerol acyltransferase(DGAT).Methods Effects of exogenous fatty acid and monoacylglycerol on TAG synthesis and lipid droplet(LD)formation in TMEM68 overexpression and knockout cells treated with DGAT inhibitor or not were investigated by comparing LD morphology,Oil Red O staining,and measurement of TAG *** were stained with fluorescence dye and observed by confocal fluorescence *** levels were determined with an enzyme-based triglyceride assay *** of TMEM68 and DGAT1 was detected by co-expression and confocal fluorescence microscopy and their interaction was determined by ***-qPCR and immunoblotting assay were used to detect the expression of *** The synthesis of TAG catalyzed by TMEM68 was independent of DGAT *** exogenous fatty acids and monoacylglycerol promoted TAG synthesis mainly through DGAT in human neuroblastoma *** LDs formed by TMEM68 were different in morphology from those by *** addition,TMEM68 and DGAT1 colocalized in the same endoplasmic reticulum(ER)compartment but did not interact ***68 overexpression reduced the expression of DGAT1,the major DGAT enzyme involved in TAG synthesis,while TMEM68 knockout had little *** The TMEM68-mediated TAG synthesis pathway has distinct features from the canonical DGAT pathway,however,TMEM68 and DGAT may coregulate intracellular TAG levels.

关键词： triacylglycerol lipid droplet TMEM68 GAT

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Optical transmittance and pulse shape discrimination of polystyrene/poly(methyl methacrylate)-based plastic scintillators

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Nuclear Science and Techniques 2025年第1期36卷 94-102页

作者： Yi-Yao Liang Ying-Du Liu Pu-Sen Wang Jie Bao Xiao-Ping Ouyang School of Materials Science and Engineering Xiangtan UniversityXiangtan 411105China Nuclear Data Key Laboratory China Institute of Atomic EnergyBeijing 102413China

Polymethyl methacrylate(PMMA)is an optically transparent thermoplastic with favorable processing *** this study,a series of plastic scintillators are prepared via thermal polymerization,and the impact of PMMA content on their transparency and pulse shape discrimination(PSD)ability is *** fabricated samples,comprising a polystyrene(PS)-PMMA matrix,30.0 wt%2,5-diphenyloxazole(PPO),and 0.2 wt%9,10-diphenylanthracene(DPA),exhibit high transparency with transmissivity ranging from 70.0 to 90.0%(above 415.0 nm)and demonstrate excellent n/γdiscrimination *** increased with increasing PMMA content across the entire visible light ***,the PSD performance gradually deteriorated when the aromatic matrix was replaced with *** scintillator containing 20.0 wt%PMMA demonstrated the best stability concerning PSD properties and relative light yields.

关键词： Neutron detection Plastic scintillation Pulse shape discrimination Polymethyl-methacrylate Polystyrene

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Histological Image Diagnosis of Breast Cancer Based on Multi-Attention Convolution Neural Network

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Journal of Shanghai Jiaotong university(Science) 2025年第1期30卷 91-106页

作者： XU Wangwang XU Liangfeng LIU Ninghui LU Na Key Laboratory of Knowledge Engineering with Big Data(Hefei University of Technology) Ministry of EducationHefei230601China School of Computer and Information Hefei University of TechnologyHefei230601China First Affiliated Hospital of Anhui Medical University Hefei230022China

Breast cancer is a serious and high morbidity disease in women,and it is the main cause of cancer death in ***,getting tested and diagnosed early can reduce the risk of *** present,there are clinical examinations,imaging screening and biopsies,among which histopathological examination is the gold ***,the process is complicated and time-consuming,and misdiagnosis may *** paper puts forward a classification framework based on deep learning,introducing multi-attention mechanism,selecting kernel convolution instead of ordinary convolution,and using different weights and combinations to pay attention to the accuracy index and growth rate of the *** addition,we also compared the learning rate *** function can fine-tune the learning rate to achieve good performance,using label softening to reduce the loss error caused by model error recognition in the label,and assigning different category weights in the loss function to balance the positive and negative *** used the BreakHis data set to automatically classify histological images into benign and malignant,four categories and eight *** results showed that the accuracy of binary classifications ranged from 98.23%to 98.83%,and that of multiple classifications ranged from 97.89%to 98.11%.

关键词： breast cancer deep learning attentional mechanism classification diagnosis

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Controlled-source electromagnetic noise attenuation via a deep convolutional neural network and high-quality sounding curve screening mechanism

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Geophysics 2025年第3期90卷 WA125-WA140页

作者： Liu, Yecheng Li, Diquan Li, Jin Zhang, Xian Central South University Monitoring Ministry of Education Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Changsha China Hunan Provincial Key Laboratory of Non-ferrous Resources and Geological Hazard Detection Changsha China Central South University School of Geoscience and Info-physics Changsha China Hunan Normal University College of Information Science and Engineering Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha China Hunan University of Finance and Economics School of Information Technology and Management Hunan Provincial Key Laboratory of Finance & Economics Big Data Science and Technology Changsha China

Strong noise is one of the biggest challenges in controlled-source electromagnetic (CSEM) exploration, which severely affects the quality of the recorded signal. We develop a novel and effective CSEM noise attenuation method that integrates a deep convolutional neural network (DCNN) with a high-quality sounding curve screening mechanism. This method first uses a DCNN to learn the large-scale noise characteristics of CSEM measured data and build a DCNN denoising model for attenuating the noise. Because some impulse noise remains after DCNN denoising, the high-quality sounding curve screening mechanism is innovatively applied to optimize the DCNN denoised data. The core of this method is the smoothness value (SV), which quantitatively evaluates the smoothness of the sounding curve (apparent resistivity curve). In effect, the mechanism segments the overall DCNN denoised data and calculates the corresponding sounding curve and its SV for each segment. The highest-quality sounding curve can be screened out by comparing the SVs of all curves as well as obtaining the highest-quality data segment present in the overall DCNN denoised data. Finally, our method is validated with synthetic and field data, demonstrating its feasibility and effectiveness in attenuating large-scale CSEM noise. The quality of CSEM data, which is heavily affected by noise, is significantly improved after processing by our method. © 2025 Society of Exploration Geophysicists. All rights reserved.

关键词： Deep neural networks

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A HIGH ORDER SCHEME FOR FRACTIONAL DIFFERENTIAL EQUATIONS WITH THE CAPUTO-HADAMARD DERIVATIVE

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Journal of Computational Mathematics 2025年第3期43卷 615-640页

作者： Xingyang Ye Junying Cao Chuanju Xu School of Science Jimei UniversityXiamen 361021China School of Data Science and Information Engineering Guizhou Minzu UniversityGuiyang 550025China School of Mathematical Sciences and Fujian Provincial Key Laboratory of Mathematical Modeling and High Performance Scientific Computing Xiamen UniversityXiamen 361005China

In this paper,we consider numerical solutions of the fractional diffusion equation with theαorder time fractional derivative defined in the Caputo-Hadamard sense.A high order time-stepping scheme is constructed,analyzed,and numerically *** contribution of the paper is twofold:1)regularity of the solution to the underlying equation is investigated,2)a rigorous stability and convergence analysis for the proposed scheme is performed,which shows that the proposed scheme is 3+αorder *** numerical examples are provided to verify the theoretical statement.

关键词： Caputo-Hadamard derivative Fractional differential equations High order scheme Stability and convergence analysis

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