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Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition

植物表型组学（英文） 2024年第4期6卷 828-840页

作者： Jianwu Lin Xin Zhang Yongbin Qin Shengxian Yang Xingtian Wen Tomislav Cernava Quirico Migheli Xiaoyulong Chen Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China School of Biological Sciences Faculty of Environmental and Life SciencesUniversity of SouthamptonSouthampton S017 1BJUK Dipartimento di Agraria and NRD-Nucleo di Ricerca sulla Desertificazione Università degli Studi di SassariSassariItaly Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Life Sciences Guizhou UniversityGuiyang 550025China

Accurate identification of plant diseases is important for ensuring the safety of agricultural *** neural networks(CNNs)and visual transformers(VTs)can extract effective representations of images and have been widely used for the intelligent recognition of plant disease ***,CNNs have excellent local perception with poor global perception,and VTs have excellent global perception with poor local *** makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition *** this paper,we propose a local and global feature-aware dual-branch network,named LGNet,for the identification of plant *** specifically,we first design a dual-branch structure based on CNNs and VTs to extract the local and global ***,an adaptive feature fusion(AFF)module is designed to fuse the local and global features,thus driving the model to dynamically perceive the weights of different ***,we design a hierarchical mixed-scale unit-guided feature fusion(HMUFF)module to mine the key information in the features at different levels and fuse the differentiated information among them,thereby enhancing the model's multiscale perception ***,extensive experiments were conducted on the Al Challenger 2018 dataset and the self-collected corn disease(SCD)*** experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the Al Challenger 2018 dataset and the SCD dataset,with accuracies of 88.74％and 99.08％,respectively.

关键词： global plant disease dual-branch feature-aware local networks recognition

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SOUND ADVERSARIAL AUDIO-VISUAL NAVIGATION 10

SOUND ADVERSARIAL AUDIO-VISUAL NAVIGATION

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10th International Conference on Learning Representations, ICLR 2022

作者： Yu, Yinfeng Huang, Wenbing Sun, Fuchun Chen, Changan Wang, Yikai Liu, Xiaohong State Key Lab on Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University China Tsinghua University China College of Information Science and Engineering Xinjiang University China UT Austin United States JD Explore Academy ***

Audio-visual navigation task requires an agent to find a sound source in a realistic, unmapped 3D environment by utilizing egocentric audio-visual observations. Existing audio-visual navigation works assume a clean environment that solely contains the target sound, which, however, would not be suitable in most real-world applications due to the unexpected sound noise or intentional interference. In this work, we design an acoustically complex environment in which, besides the target sound, there exists a sound attacker playing a zero-sum game with the agent. More specifically, the attacker can move and change the volume and category of the sound to make the agent suffer from finding the sounding object, while the agent tries to dodge the attack and navigate to the goal under the intervention. Under certain constraints to the attacker, we can improve the robustness of the agent towards unexpected sound attacks in audio-visual navigation. For better convergence, we develop a joint training mechanism by employing the property of a centralized critic with decentralized actors. Experiments on two real-world 3D scan datasets (Replica and Matterport3D) verify the effectiveness and the robustness of the agent trained under our designed environment when transferred to the clean environment or the one containing sound attackers with random policy. Project: https://***/SAAVN. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Navigation

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Cloud-magnetic resonance imaging system:In the era of 6G and artificial intelligence

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Magnetic Resonance Letters 2025年第1期5卷 52-63页

作者： Yirong Zhou Yanhuang Wu Yuhan Su Jing Li Jianyu Cai Yongfu You Jianjun Zhou Di Guo Xiaobo Qu Department of Electronic Science Intelligent Medical Imaging R&D CenterFujian Provincial Key Laboratory of Plasma and Magnetic ResonanceNational Institute for Data Science in Health and MedicineXiamen UniversityXiamen361104China Department of Electronic Science Key Laboratory of Digital Fujian on IoT CommunicationXiamen UniversityXiamen361104China Shanghai Electric Group Co. LtdShanghai200002China China Telecom Group Quanzhou362018China China Mobile Group Xiamen361009China Department of Radiology Zhongshan Hospital(Xiamen)Fudan UniversityXiamen Municipal Clinical Research Center for Medical ImagingFujian Province Key Clinical Specialty Construction Project(Medical Imaging Department)Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial IntelligenceXiamen361006China School of Computer and Information Engineering Xiamen University of TechnologyXiamen361024China

Magnetic resonance imaging(MRI)plays an important role in medical diagnosis,generating petabytes of image data annually in large *** voluminous data stream requires a significant amount of network bandwidth and extensive storage ***,local data processing demands substantial manpower and hardware *** isolation across different healthcare institutions hinders crossinstitutional collaboration in clinics and *** this work,we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing,6G bandwidth,edge computing,federated learning,and blockchain *** system is called Cloud-MRI,aiming at solving the problems of MRI data storage security,transmission speed,artificial intelligence(AI)algorithm maintenance,hardware upgrading,and collaborative *** workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw Data(ISMRMRD)***,the data are uploaded to the cloud or edge nodes for fast image reconstruction,neural network training,and automatic ***,the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other *** Cloud-MRI system will save the raw imaging data,reduce the risk of data loss,facilitate inter-institutional medical collaboration,and finally improve diagnostic accuracy and work efficiency.

关键词： Magnetic resonance imaging Cloud computing 6G bandwidth Artificial intelligence Edge computing Federated learning Blockchain

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Various topological phases and their abnormal effects of topological acoustic metamaterials

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Interdisciplinary Materials 2023年第2期2卷 179-230页

作者： Yan-Feng Chen Ze-Guo Chen Hao Ge Cheng He Xin Li Ming-Hui Lu Xiao-Chen Sun Si-Yuan Yu Xiujuan Zhang National Laboratory of Solid-State Microstructures&Department of Materials Science and Engineering Nanjing UniversityNanjingChina Collaborative Innovation Center of Advanced Microstructures Nanjing UniversityNanjingChina Jiangsu Key Laboratory of Artificial Functional Materials Nanjing UniversityNanjingChina School of Materials Science and Intelligent Engineering Nanjing UniversitySuzhouChina School of Mechanical Engineering Nanjing University of Science and TechnologyNanjingChina

The last 20 years have witnessed growing impacts of the topological concept on the branches of physics,including materials,electronics,photonics,and *** describes objects with some global invariant property under continuous deformation,which in mathematics could date back to the 17th century and mature in the 20th *** physics,it successfully underpinned the physics of the Quantum Hall effect in *** date,topology has been extensively applied to describe topological phases in acoustic *** artificial structures,acoustic metamaterials could be well theoretically analyzed,on-demand designed,and easily fabricated by modern techniques,such as three-dimensional *** new theoretical topological models were first discovered in acoustic metamaterials analogous to electronic counterparts,associated with novel effects for acoustics closer to *** this review,we focused on the concept of topology and its realization in airborne acoustic crystals,solid elastic phononic crystals,and surface acoustic wave *** also introduced emerging concepts of non-Hermitian,higher-order,and Floquet topological insulators in *** has been shown that the topology theory has such a powerful generality that among the disciplines from electron to photon and phonon,from electronic to photonics and acoustics,from acoustic topological theory to acoustic devices,could interact and be analogous to fertilize fantastic new ideas and prototype devices,which might find applications in acoustic engineering and noisevibration control engineering in the near future.

关键词： acoustic materials robust propagation topological acoustic metamaterials topological phases

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First wetland mapping at 10-m spatial resolution in South America using multi-source and multi-feature remote sensing data

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science China Earth sciences 2024年第10期67卷 3252-3269页

作者： Weiwei SUN Gang YANG Yuling HUANG Dehua MAO Ke HUANG Lin ZHU Xiangchao MENG Tian FENG Chao CHEN Yong GE Department of Geography and Spatial Information Techniques Ningbo UniversityNingbo 315211China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences&Natural Resources ResearchChinese Academy of SciencesBeijing 100101China Natural Resources and Planning Bureau Shanghai Pudong New DistrictShanghai 200100China Key Laboratory of Wetland Ecology and Environment Northeast Institute of Geography and AgroecologyChinese Academy of SciencesChangchun 130102China Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China School of Geography Science and Geomatics Engineering Suzhou University of Science and TechnologySuzhou 215009China

Wetland degradation has been accelerating in recent years globally. Accurate information on the geographic distribution and categories of wetlands is essential for their conservation and management. Despite being the world′s fourth largest continent, South America has limited research on wetland mapping, and there is currently no available map that provides comprehensive information on wetland distribution and categories in the region. To address this issue, we used Sentinel-1, Sentinel-2 and SRTM data, developed a sample collection method and a wetland mapping method with a collection of multi-source features such as optical features, polarization features and shape features for South American wetlands. We produced a 10-m resolution wetland map based on the Google Earth Engine(GEE) platform. Our Level-1 wetland cover map accurately captured six wetland sub-categories with an overall accuracy of 96.24% and a kappa coefficient of 0.8649, while our Level-2 water cover map included five sub-categories with an overall accuracy of 97.23% and a kappa coefficient of0.9368. The results show that the total area of existing wetlands in South America is approximately 1,737,000 km~2, which is6.8% of the total land area. Among the ten wetland categories, shallow sea had the largest area(960,527.4 km~2), while aquaculture ponds had the smallest area 1513.6 km~2. Swamp had the second largest area(306,240.1 km~2). Brazil, Argentina,Venezuela, Bolivia, and Colombia were found to have the largest wetland areas, with Brazil and Colombia having the most diverse wetland categories. This product can serve as baseline data for subsequent monitoring, management, and conservation of South American wetlands.

关键词： Wetland mapping Google Earth Engine Sentinel imagery South America

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3D Building Generation in Minecraft via Large Language Models

3D Building Generation in Minecraft via Large Language Model...

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IEEE Symposium on Computational Intelligence and Games, CIG

作者： Shiying Hu Zengrong Huang Chengpeng Hu Jialin Liu Department of Computer Science and Engineering Guangdong Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350350678

ISBN: (纸本)9798350350685

Recently, procedural content generation has exhibited considerable advancements in the domain of 2D game level generation such as Super Mario Bros. and Sokoban through large language models (LLMs). To further validate the capabilities of LLMs, this paper explores how LLMs contribute to the generation of 3D buildings in a sandbox game, Minecraft. We propose a Text to Building in Minecraft (T2BM) model, which involves refining prompts, decoding interlayer representation and repairing. Facade, indoor scene and functional blocks like doors are supported in the generation. Experiments are conducted to evaluate the completeness and satisfaction of buildings generated via LLMs. It shows that LLMs hold significant potential for 3D building generation. Given appropriate prompts, LLMs can generate correct buildings in Minecraft with complete structures and incorporate specific building blocks such as windows and beds, meeting the specified requirements of human users.

关键词： Three-dimensional displays Procedural generation Large language models Buildings Refining Windows Decoding

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Joint Routing and Scheduling for In-Vehicle Networks: A Deterministic Perspective

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on intelligent Vehicles 2024年 1-15页

作者： Wang, Wenxun Wang, Wenyong Yu, Haisheng Wu, Bochun Zou, Sai Ni, Wei Zhang, Ji School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China International Institute of Next Generation Internet Macau University of Science and Technology Macau China State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China College of Big Data and Information Engineering Guizhou University Guiyang China Data61 Business Unit CSIRO Canberra ACT Australia PA Company SC Branch China Telecom Co. LTD Chendu China

Deterministic transmission refers to the guaranteed delivery of data to its destination within a specific time frame along an optimal route, making it an indispensable prerequisite for the operation of autonomous vehicles. The in-vehicle networks (IVNs) face significant challenges in the deterministic transmission of large volumes of perception and control data from and to various vehicle components. In this paper, the cooperative scheduling and routing problem among multi-area control units (ACUs) in IVN is discussed from the view of deterministic transmission, and a ResVmix method is proposed to find the optimal two-layer assignment with ACU cooperation. The multi-cycle queuing and forwarding (multi-CQF) mechanism is employed to schedule the perceived data according to different quality of service (QoS) requirements. The cooperation among multiple ACUs enables deterministic routing of data. The two-layer optimization problems on multi-CQF and ACU are formulated as a+ decentralized partially observable Markov decision processes (Dec-POMDP) and solved using the enhanced multi-agent deep reinforcement learning (MADRL) method. Simulation results demonstrate that ResVmix significantly enhances the deterministic transmission performance of IVN. Compared to traditional shortest path algorithms and state-of-the-art MADRL methods, ResVmix achieves a 22.4% increase in arrival rate and a 20.1% increase in confirmed arrivals, respectively. IEEE

关键词： Reinforcement learning

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Skeleton-based Action Recognition Using Two-stream Graph Convolutional Network with Pose Refinement 41

Skeleton-based Action Recognition Using Two-stream Graph Con...

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第41届中国控制会议

作者： Biao Zheng Luefeng Chen Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the development of science and technology,graph convolutional network has made great progress in improving the accuracy of action ***,there still exists some deficiencies in current ***,the human skeleton point coordinates entering into the network are barely refined,which may cause large ***,the second-order information(the length and direction of bones),which can reflect action characteristics discriminatively,is rarely *** solve the above issues,a two stream graph convolutional network with pose refinement for skeleton based action recognition is ***,we use an adaptive block to to help improve the *** test our method on Kinetics dataset and the experiment show it can get better results than some recent methods,which plays a positive role in future research.

关键词： Graph convolutional network Pose refinement Skeleton based action recognition Adaptive block

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Constructing Lithium-Free Anode/Separator Interface via 3D Carbon Fabric Scaffold for Ultrasafe Lithium Metal Batteries

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Research 2024年第3期2023卷 359-368页

作者： Dongdong Li Shengchen Yang Zijian Zheng Wen-Yong Lai State Key Laboratory of Organic Electronics and Information Displays(SKLOEID) Institute of Advanced Materials(IAM)School of Chemistry and Life SciencesNanjing University of Posts and Telecommunications9 Wenyuan RoadNanjing 210023China Department of Materials Chemistry Huzhou University1 Xueshi RoadHuzhou 313000China Laboratory for Advanced Interfacial Materials and Devices School of Fashion and TextilesDepartment of Applied Biology and Chemical TechnologyResearch Institute for Intelligent Wearable Systems(RI-IWEAR)Research Institute for Smart Energy(RISE)The Hong Kong Polytechnic UniversityHong Kong SAR 999077China

Metallic lithium represents a promising anode candidate to be utilized in future high-energy lithium ***,the undesirable dendrite growth and fragile solid-electrolyte interphase(SEI)pose critical challenge for pursuing further practical *** contrast to traditional approaches of using inert/lithiophilicity coating,here,we demonstrate a reverse strategy of introducing a highly conductive and lithophobic carbon fabric(CF)scaffold on lithium foil to guide a favorable nucleation site of lithium far away from the anode/separator *** CF scaffold with high conductivity can couple with inner electric field for achieving a uniform distribution of the lithium-ion flux,while the lithophobic feature offers the condition to guide the preferred deposition of lithium onto the underlying lithium foil,which greatly reduces the risk of dendrite-induced short ***,the SEI immersed in the CF scaffold is well supported by CF fibers and therefore exhibits extremely high stability during charge–discharge *** a result,the lithium/CF anodes show>2,000-h stable cycling at 0.5 mA cm−*** metal batteries equipped with our lithium/CF anode deliver a high capacity retention of~99.99%per cycle,i.e.,retain~97.3%capacity after 200 *** unique interface-regulation strategy is versatile for various conductive scaffolds(e.g.,ultrathin and ultralight conductive fabrics),exhibiting high superiority for highly safe lithium metal batteries.

关键词： lithium equipped versatile

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Robustness-Eva-MRC: Assessing and Analyzing the Robustness of Neural Models in Extractive Machine Reading Comprehension

SSRN

引用

SSRN 2023年

作者： Fang, Jingliang Xu, Hua Wu, Zhijing Gao, Kai Che, Xiaoyin Hui, Haotian State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing China School of Information Science and Engineering Hebei University of Science and Technology China Siemens AG Beijing China

Deep neural networks, despite their remarkable success in various language understanding tasks, have been found vulnerable to adversarial attacks and subtle input perturbations, revealing a robustness shortfall. To explore this, this paper presents Robustness-Eva-MRC, an interactive platform designed to assess and analyze the robustness of pre-trained and large-scale language models in extractive machine reading comprehension (MRC) tasks. The platform integrates eight adversarial attack methods across character-, word-, and sentence-levels, and applies them to five MRC datasets, thereby fabricating challenging adversarial testing sets. Then it evaluates the MRC models on both original and adversarial sets, yielding insights into their robustness through performance gaps. Moreover, Robustness-Eva-MRC provides comprehensive visualizations and detailed case studies, enhancing the understanding of model robustness. A screencast video and additional material are available at https://***/distantJing/Robustness-Eva-MRC. © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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