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Effects of spray adjuvants and operation modes on droplet deposition and elm aphid control using an unmanned aerial vehicle

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International Journal of Agricultural and Biological Engineering 2023年第2期16卷 1-9页

作者： Zechen Dou Zhihao Fang Xiaoqiang Han Muhammad Zeeshan Yapeng Liu Yubin Lan College of Agriculture/Key Laboratory of Oasis Agricultural Pest Management and Plant Protection Resources Utilization Shihezi UniversityShihezi 832003XinjiangChina College of Agricultural Engineering and Food Science Shandong University of TechnologyZibo 255049ShandongChina Shandong Provincial Engineering Technology Research Center for Agricultural Aviation Intelligent Equipment Zibo 255049ShandongChina College of Electronics Engineering(College of Artificial Intelligence) South China Agricultural UniversityGuangzhou 510642China

A conventional spraying mode and a fully autonomous fruit tree operation mode using a model DJ T30 unmanned aerial vehicle(UAV)were used to control aphids control on elm trees and to clarify the distribution of droplets in elm trees sprayed by a *** effects of six aviation spray adjuvants on elm canopy droplet deposition and aphid control were *** software was used to analyze and measure the droplet density and deposition of water sensitive paper in two modes;this was done to calculate the droplet uniformity,depositional penetration,and droplet penetration,and to verify the aphid control *** results showed that the droplet density increased by 79.7%-100.7% in the upper canopy and 0-394.1%in the lower canopy without adjuvants in the fully autonomous fruit tree operation *** upper canopy deposits increased by 65.7%-179.3%,and the lower canopy increased by 0-152.8%.When adjuvants were added,the droplet density in the upper canopy increased by 49.7-56.1%using Jiexiaofeng(JXF),and the lower canopy increased by 138.2%-177.8% using JXF,45.8%-141.3%using Beidatong(BDT),45.5%-92.9% using Gongbei(GB),0-93.5% using Maisi(MS),and 0-95.2%using Manniu(MN).The deposits of the upper canopy increased by 888.1-1154.2% using JXF,0-1298.3% using MN,0-343.9%using BDT,0-422.5% using GB,0-580.3% using *** lower canopy increased by 746.4%-1426.0%using JXF,226.2%-231.0% using BDT,435.8%-644.0% using GB,255.0%-322.4%using MS,and 249.3%-360.0%using *** JXF was added,the droplet uniformity,droplet penetration and depositional penetration were better than when using other *** effects of JXF,BDT and GB in controlling aphids was significantly better than other adjuvants(p<0.05).The following control effects were observed;94.1% with JXF,93.1% with BDT,and 93.3% with GB after 3 d of application,and 97.9% with JXF,95.6% with BDT,and 97.1% with GB after 7 d of *** the same time,the application of the fully autonomous fruit tree operation mode a

关键词： adjuvants UAV operation mode droplet deposition aphid

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TVG: A Training-free Transition Video Generation Method with Diffusion Models

arXiv

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arXiv 2024年

作者： Zhang, Rui Chen, Yaosen Liu, Yuegen Wang, Wei Wen, Xuming Wang, Hongxia Sobey Media Intelligence Laboratory Chengdu China School of Cyber Science and Engineering Sichuan University Chengdu China Key Laboratory of Data Protection and Intelligent Management Sichuan University Ministry of Education China University of Electronic Science and Technology of China Sichuan Chengdu611731 China

Transition videos play a crucial role in media production, enhancing the flow and coherence of visual narratives. Traditional methods like morphing often lack artistic appeal and require specialized skills, limiting their effectiveness. Recent advances in diffusion model-based video generation offer new possibilities for creating transitions but face challenges such as poor inter-frame relationship modeling and abrupt content changes. We propose a novel training-free Transition Video Generation (TVG) approach using video-level diffusion models that addresses these limitations without additional training. Our method leverages Gaussian Process Regression (GPR) to model latent representations, ensuring smooth and dynamic transitions between frames. Additionally, we introduce interpolation-based conditional controls and a Frequency-aware Bidirectional Fusion (FBiF) architecture to enhance temporal control and transition reliability. Evaluations of benchmark datasets and custom image pairs demonstrate the effectiveness of our approach in generating high-quality smooth transition videos. The code are provided in https://***/***. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Where is My Spot? Few-shot Image Generation via Latent Subspace Optimization

Where is My Spot? Few-shot Image Generation via Latent Subsp...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Chenxi Zheng Bangzhen Liu Huaidong Zhang Xuemiao Xu Shengfeng He South China University of Technology State Key Laboratory of Subtropical Building Science Ministry of Education Key Laboratory of Big Data and Intelligent Robot Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information Singapore Management University

Image generation relies on massive training data that can hardly produce diverse images of an unseen category according to a few examples. In this paper, we address this dilemma by projecting sparse few-shot samples into a continuous latent space that can potentially generate infinite unseen samples. The rationale behind is that we aim to locate a centroid latent position in a conditional StyleGAN, where the corresponding output image on that centroid can maximize the similarity with the given samples. Although the given samples are unseen for the conditional StyleGAN, we assume the neighboring latent subspace around the centroid belongs to the novel category, and therefore introduce two latent subspace optimization objectives. In the first one we use few-shot samples as positive anchors of the novel class, and adjust the StyleGAN to produce the corresponding results with the new class label condition. The second objective is to govern the generation process from the other way around, by altering the centroid and its surrounding latent subspace for a more precise generation of the novel class. These reciprocal optimization objectives inject a novel class into the StyleGAN latent subspace, and therefore new unseen samples can be easily produced by sampling images from it. Extensive experiments demonstrate superior few-shot generation performances compared with state-of-the-art methods, especially in terms of diversity and generation quality. Code is available at https://***/chansey0529/LSO.

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ReconBoost: Boosting Can Achieve Modality Reconcilement 41

ReconBoost: Boosting Can Achieve Modality Reconcilement

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41st International Conference on Machine Learning, ICML 2024

作者： Hua, Cong Xu, Qianqian Bao, Shilong Yang, Zhiyong Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores a novel multi-modal alternating learning paradigm pursuing a reconciliation between the exploitation of uni-modal features and the exploration of cross-modal interactions. This is motivated by the fact that current paradigms of multi-modal learning tend to explore multi-modal features simultaneously. The resulting gradient prohibits further exploitation of the features in the weak modality, leading to modality competition, where the dominant modality overpowers the learning process. To address this issue, we study the modality-alternating learning paradigm to achieve reconcilement. Specifically, we propose a new method called ReconBoost to update a fixed modality each time. Herein, the learning objective is dynamically adjusted with a reconcilement regularization against competition with the historical models. By choosing a KL-based reconcilement, we show that the proposed method resembles Friedman's Gradient-Boosting (GB) algorithm, where the updated learner can correct errors made by others and help enhance the overall performance. The major difference with the classic GB is that we only preserve the newest model for each modality to avoid overfitting caused by ensembling strong learners. Furthermore, we propose a memory consolidation scheme and a global rectification scheme to make this strategy more effective. Experiments over six multi-modal benchmarks speak to the efficacy of the method. We release the code at https://***/huacong/ReconBoost. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Beyond Textual Constraints: Learning Novel Diffusion Conditions with Fewer Examples

Beyond Textual Constraints: Learning Novel Diffusion Conditi...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yuyang Yu Bangzhen Liu Chenxi Zheng Xuemiao Xu Shengfeng He Huaidong Zhang South China University of Technology State Key Laboratory of Subtropical Building Science Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information Ministry of Education Key Laboratory of Big Data and Intelligent Robot Singapore Management University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In this paper, we delve into a novel aspect of learning novel diffusion conditions with datasets an order of magnitude smaller. The rationale behind our approach is the elimination of textual constraints during the few-shot learning process. To that end, we implement two optimization strategies. The first, prompt-free conditional learning, utilizes a prompt-free encoder derived from a pre-trained Stable Diffusion model. This strategy is designed to adapt new conditions to the diffusion process by minimizing the textual-visual cor-relation, thereby ensuring a more precise alignment between the generated content and the specified conditions. The second strategy entails condition-specific negative rectification, which addresses the inconsistencies typically brought about by Classifier-free guidance in few-shot training con-texts. Our extensive experiments across a variety of condition modalities demonstrate the effectiveness and efficiency of our framework, yielding results comparable to those obtained with datasets a thousand times larger. Our codes are available at https://***/Yuyan9Yu/BeyondTextConstraint.

关键词： Training Computer vision Adaptation models Codes Text to image Diffusion processes Diffusion models

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Secure RFID-Assisted Authentication Protocol for Vehicular Cloud Computing Environment

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IEEE Transactions on intelligent Transportation Systems 2024年第9期25卷 12528-12537页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Tariq, Tayyaba Alenazi, Mohammed J. F. Das, Ashok Kumar University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Sichuan University Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Yunlin Douliu64002 Taiwan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

Vehicular network technology has made substantial advancements in recent years in the field of intelligent Transportation Systems. Vehicular Cloud Computing (VCC) has emerged as a novel paradigm with a substantial increase in data exchange within Vehicular ad-hoc networks (VANETs). VCC integrates cloud computing, vehicular networking, and Internet of Things (IoT) technologies. It enables Infrastructure-to-Vehicle (I2V), Vehicle-to-Vehicle (V2V), and Vehicle-to-Device (V2D) communication. VCC optimizes vehicle, cloud infrastructure, and IoT resources while addressing significant communication security and vehicle-user privacy challenges. To address these issues, we developed an RFID-based authentication protocol for VCC based on a Henon map using a hash function. In addition, we also incorporated a Physical Unclonable Function (PUF) to resist physical tampering attacks. We validate the protocol's security formally and informally. The formal security analysis is conducted through a widely used RoR model. We use the Scyther simulation tool to verify the proposed protocol's security against various attacks. Moreover, we compare the performance of our protocol with similar existing protocols across important performance parameters such as communication and computation overheads and security attributes. The proposed protocol yields substantial improvements, demonstrating a 40.74% reduction in computation overhead and a 13.03% decrease in communication overhead as compared to related protocols, delivering both enhanced performance and resource efficiency. The analysis demonstrates its capacity to support secure communication in the VCC environment and satisfy desirable security attributes. © 2024 IEEE.

关键词： Authentication

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MambaLCT: Boosting Tracking via Long-term Context State Space Model

arXiv

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arXiv 2024年

作者： Li, Xiaohai Zhong, Bineng Liang, Qihua Li, Guorong Mo, Zhiyi Song, Shuxiang Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China Guangxi Colleges Universities Key Laboratory of Intelligent Software Wuzhou University Wuzhou543002 China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences Beijing100101 China

Effectively constructing context information with long-term dependencies from video sequences is crucial for object tracking. However, the context length constructed by existing work is limited, only considering object information from adjacent frames or video clips, leading to insufficient utilization of contextual information. To address this issue, we propose MambaLCT, which constructs and utilizes target variation cues from the first frame to the current frame for robust tracking. First, a novel unidirectional Context Mamba module is designed to scan frame features along the temporal dimension, gathering target change cues throughout the entire sequence. Specifically, target-related information in frame features is compressed into a hidden state space through selective scanning mechanism. The target information across the entire video is continuously aggregated into target variation cues. Next, we inject the target change cues into the attention mechanism, providing temporal information for modeling the relationship between the template and search frames. The advantage of MambaLCT is its ability to continuously extend the length of the context, capturing complete target change cues, which enhances the stability and robustness of the tracker. Extensive experiments show that long-term context information enhances the model’s ability to perceive targets in complex scenarios. MambaLCT achieves new SOTA performance on six benchmarks while maintaining real-time running speeds. Code and models are available at https://***/GXNU-ZhongLab/MambaLCT. Copyright © 2024, The Authors. All rights reserved.

关键词： HTTP

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CIRI: Curricular Inactivation for Residue-aware One-shot Video Inpainting

CIRI: Curricular Inactivation for Residue-aware One-shot Vid...

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International Conference on Computer Vision (ICCV)

作者： Weiying Zheng Cheng Xu Xuemiao Xu Wenxi Liu Shengfeng He South China University of Technology State Key Laboratory of Subtropical Building Science Ministry of Education Key Laboratory of Big Data and Intelligent Robot Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information Fuzhou University Singapore Management University

Video inpainting aims at filling in missing regions of a video. However, when dealing with dynamic scenes with camera or object movements, annotating the inpainting target becomes laborious and impractical. In this paper, we resolve the one-shot video inpainting problem in which only one annotated first frame is provided. A naive solution is to propagate the initial target to the other frames with techniques like object tracking. In this context, the main obstacles are the unreliable propagation and the partially inpainted artifacts due to the inaccurate mask. For the former problem, we propose curricular inactivation to replace the hard masking mechanism for indicating the in-painting target, which is robust to erroneous predictions in long-term video inpainting. For the latter, we explore the properties of inpainting residue and present an online residue removal method in an iterative detect-and-refine manner. Extensive experiments on several real-world datasets demonstrate the quantitative and qualitative superiorities of our proposed method in one-shot video inpainting. More importantly, our method is extremely flexible that can be integrated with arbitrary traditional inpainting models, activating them to perform the reliable one-shot video inpainting task. Video demonstrations can be found in our supplement, and our code can be found at https://***/Arise-zwy/CIRI.

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Towards a Smaller Student: Capacity Dynamic Distillation for Efficient Image Retrieval

Towards a Smaller Student: Capacity Dynamic Distillation for...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yi Xie Huaidong Zhang Xuemiao Xu Jianqing Zhu Shengfeng He South China University of Technology State Key Laboratory of Subtropical Building Science Ministry of Education Key Laboratory of Big Data and Intelligent Robot Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information Huaqiao University Singapore Management University

Previous Knowledge Distillation based efficient image retrieval methods employ a lightweight network as the stu-dent model for fast inference. However, the lightweight stu-dent model lacks adequate representation capacity for effective knowledge imitation during the most critical early training period, causing final performance degeneration. To tackle this issue, we propose a Capacity Dynamic Distillation framework, which constructs a student model with editable representation capacity. Specifically, the employed student model is initially a heavy model to fruitfully learn distilled knowledge in the early training epochs, and the stu-dent model is gradually compressed during the training. To dynamically adjust the model capacity, our dynamic frame-work inserts a learnable convolutional layer within each residual block in the student model as the channel importance indicator. The indicator is optimized simultaneously by the image retrieval loss and the compression loss, and a retrieval- guided gradient resetting mechanism is proposed to release the gradient conflict. Extensive experiments show that our method has superior inference speed and accu-racy, e.g., on the VeRi-776 dataset, given the ResNet101 as a teacher, our method saves 67.13% model parameters and 65.67% FLOPs without sacrificing accuracy. Code is avail-able at https://***/SCY-X/Capacity_Dynamic_Distillation.

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Chinese visceral adiposity index outperforms other obesity indexes in association with increased overall cancer incidence: findings from prospective MJ cohort study

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British journal of cancer 2025年 2025 May 10页

作者： Mengying Wang Chi Pang Wen Junlong Pan Gege Sun David Ta-Wei Chu Huakang Tu Wenyuan Li Xifeng Wu Center of Clinical Big Data and Analytics of School of Public Health and the Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou Zhejiang China. Institute of Population Health Sciences National Health Research Institutes Zhunan Taiwan. Graduate Institute of Biomedical Sciences College of Medicine China Medical University Taichung Taiwan. MJ Health Management Center Taipei Taiwan. Center of Clinical Big Data and Analytics of School of Public Health and the Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou Zhejiang China. xifengw@***. Zhejiang Key Laboratory of Intelligent Preventive Medicine Hangzhou 310058 Zhejiang China. xifengw@***. National Institute for Data Science in Health and Medicine Zhejiang University Hangzhou 310058 Zhejiang China. xifengw@***. School of Medicine and Health Science George Washington University Washington DC USA. xifengw@***.

BACKGROUND:We assessed the associations of visceral adiposity indexes such as Chinese Visceral Adiposity Index (CVAI), Visceral Adiposity Index (VAI), Lipid Accumulation Product (LAP), waist circumference (WC), and waist-hip ratio (WHR) with overall and specific cancer incidence in a Chinese population. METHODS:332,297 individuals from the Taiwan MJ cohort (1996-2007) were included. We utilized multivariable Cox proportional hazards models to examine associations of baseline visceral adiposity indexes and cancer incidences. Sex-specific CVAI, VAI, and LAP were calculated, incorporating WC and triglycerides levels. CVAI and VAI also included body mass index and high-density lipoprotein, with CVAI further incorporating age. RESULTS:Higher CVAI was consistently associated with higher overall cancer incidence, with HRs of 1.45 (95% CI: 1.2-1.76) and 2.03 (95% CI: 1.52-2.72) for males and females, respectively, comparing the fifth quintile to the first. The HRs for WC were 1.27 (95% CI: 1.08-1.49) and 1.19 (95% CI: 1.01-1.40) for males and females, WHR was significantly associated with cancer risk in males (HR:1.28; 95% CI: 1.13-1.45), and LAP was significantly associated with cancer risk in females (HR: 1.25; 95% CI: 1.04-1.5). VAI was not associated with overall cancer incidence. DISCUSSION:CVAI is a superior clinical biomarker for predicting cancer incidence in the Chinese population compared to traditional visceral obesity indices.

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