With the advances in scientific foundations and technological implementations,optical metrology has become versatile problem-solving backbones in manufacturing,fundamental research,and engineering applications,such as...With the advances in scientific foundations and technological implementations,optical metrology has become versatile problem-solving backbones in manufacturing,fundamental research,and engineering applications,such as quality control,nondestructive testing,experimental mechanics,and biomedicine.In recent years,deep learning,a subfield of machine learning,is emerging as a powerful tool to address problems by learning from data,largely driven by the availability of massive datasets,enhanced computational power,fast data storage,and novel training algorithms for the deep neural network.It is currently promoting increased interests and gaining extensive attention for its utilization in the field of optical metrology.Unlike the traditional,,physics-basedH approach,deep-learning-enabled optical metrology is a kind of,/data-drivenw approach,which has already provided numerous alternative solutions to many challenging problems in this field with better performances.In this review,we present an overview of the current status and the latest progress of deep-learning technologies in the field of optical metrology.We first briefly introduce both traditional image-processing algorithms in optical metrology and the basic concepts of deep learning,followed by a comprehensive review of its applications in various optical metrology tasks,such as fringe denoising,phase retrieval,phase unwrapping,subset correlation,and error compensation.The open challenges faced by the current deep-learning approach in optical metrology are then discussed.Finally,the directions for future research are outlined.展开更多
While originally designed for natural language processing tasks,the self-attention mechanism has recently taken various computer vision areas by storm.However,the 2D nature of images brings three challenges for applyi...While originally designed for natural language processing tasks,the self-attention mechanism has recently taken various computer vision areas by storm.However,the 2D nature of images brings three challenges for applying self-attention in computer vision:(1)treating images as 1D sequences neglects their 2D structures;(2)the quadratic complexity is too expensive for high-resolution images;(3)it only captures spatial adaptability but ignores channel adaptability.In this paper,we propose a novel linear attention named large kernel attention(LKA)to enable self-adaptive and long-range correlations in self-attention while avoiding its shortcomings.Furthermore,we present a neural network based on LKA,namely Visual Attention Network(VAN).While extremely simple,VAN achieves comparable results with similar size convolutional neural networks(CNNs)and vision transformers(ViTs)in various tasks,including image classification,object detection,semantic segmentation,panoptic segmentation,pose estimation,etc.For example,VAN-B6 achieves 87.8%accuracy on ImageNet benchmark,and sets new state-of-the-art performance(58.2%PQ)for panoptic segmentation.Besides,VAN-B2 surpasses Swin-T 4%mloU(50.1%vs.46.1%)for semantic segmentation on ADE20K benchmark,2.6%AP(48.8%vs.46.2%)for object detection on COCO dataset.It provides a novel method and a simple yet strong baseline for the community.The code is available at https://github.com/Visual-Attention-Network.展开更多
In wireless sensor network,virtual backbone is a cost effective broadcasting method.Connected dominating set formation is proposed to construct a virtual backbone.However,it is NP-Hard to find a minimum connected domi...In wireless sensor network,virtual backbone is a cost effective broadcasting method.Connected dominating set formation is proposed to construct a virtual backbone.However,it is NP-Hard to find a minimum connected dominating set in an arbitrary graph.In this paper,based on cross-entropy method,we present a novel backbone formulation algorithm(BFA-CE)in wireless sensor network.In BFA-CE,a maximal independent set is got at first and nodes in the independent set are required to get their action sets.Based on those action sets,a backbone is generated with the cross-entropy method.Simulation results show that our algorithm can effectively reduce the size of backbone network within a reasonable message overhead,and it has lower average node degree.This approach can be potentially used in designing efficient broadcasting strategy or working as a backup routing of wireless sensor network.展开更多
Optical transport networks are now the basic infrastructure of modern communications systems, including the SDH and WDM backbone network of local network operators, in the case of Cameroon. Given the colossal investme...Optical transport networks are now the basic infrastructure of modern communications systems, including the SDH and WDM backbone network of local network operators, in the case of Cameroon. Given the colossal investments required to deploy these networks, particularly related to the cost of equipment (optical fibers, transponders and multiplexers), the optimization of bandwidth and dynamic allocation of resources is essential to control operating costs and ensure continuity of service. Automatic switching technology for optical networks brings intelligence to the control plane to fully facilitate bandwidth utilization, traffic redirection, and automatic configuration of end-to-end services. This paper considers a local network operator’s WDM network without the implementation of the automatic switching technology, develops a network modeling software platform called Graphic Networks and using graph theory integrates a particularity of the automatic switching technology, which is the automatic rerouting of traffic in case of incident in the network. The incidents considered here are those links or route failures and node failures.展开更多
Ralstonia solanacearum, the causative agent of bacterial wilt, is a soil-borne pathogen that poses a widespread threat to plants in the Solanaceae family. To elucidate the mechanism by which limonene exerts its effect...Ralstonia solanacearum, the causative agent of bacterial wilt, is a soil-borne pathogen that poses a widespread threat to plants in the Solanaceae family. To elucidate the mechanism by which limonene exerts its effects on R. solanacearum, we first assessed the impact of limonene on the physiological indicators of the pathogen and subsequently analyzed its transcriptome and metabolome. Our findings indicate that limonene has a potent inhibitory effect on R. solanacearum, and it also suppresses the formation of the bacterial community biofilm. Limonene primarily regulates the terpene biosynthesis pathway in R. solanacearum, thereby potentially affecting signal transduction in the pathogen and disrupting its normal growth and development. These results significantly enhance our understanding of limonene’s response to the induction of bacterial wilt and provide a reference for further prevention and control of R. solanacearum.展开更多
Developing novel unfused building blocks with simple synthesis and low cost is essential to advance and enrich cost-effective poly-mer donors;however,it remains a challenge due to the lack of efficient molecular strat...Developing novel unfused building blocks with simple synthesis and low cost is essential to advance and enrich cost-effective poly-mer donors;however,it remains a challenge due to the lack of efficient molecular strategies.Herein,a class of low-cost and fully unfused polymer donors with precisely regulated backbone planarity via halogenation was designed and synthesized,namely PDTBTBz-2H,PDTBTBz-2F,and PDTBTBz-2Cl.These polymer donors possess a four-step synthesis route with over 80%yield from cheap raw chemicals comparable to existing low-cost polymer donors,such as PTQ10.Benefitting from the planar backbone via in-corporating the F…S non-covalent interactions,PDTBTBz-2F exhibits more robust J-type aggregation in solution and a long-ranged molecular stacking in film relative to PDTBTBz-2H and PDTBTBz-2Cl.Moreover,the systematical study of PDTBTBz-based organic so-lar cells(OSCs)reveals the close relationship between optimized molecular self-assembly and charge separation/transport regarding backbone halogenation when paired with the non-fullerene acceptor(Y6-BO-4F).As a result,the photovoltaic devices based on semicrystalline PDTBTBz-2F achieved a promising power conversion efficiency(PCE)of 12.37%.Our work highlighted the influence of backbone halogenation on the molecular self-assembly properties and a potential unfused backbone motif for further developing cost-effective OSCs.展开更多
基金National Natural Science Foundation of China(U21B2033,62075096,62005121)Leading Technology of Jiangsu Basic Research Plan(BK20192003)+3 种基金"333 Engineering"Research Projea of Jiangsu Province(BRA2016407)Jiangsu Provincial"One belt and one road"innovation cooperation project(BZ2020007)Fundamental Research Funds for the Central Universities(30921011208,30919011222,30920032101)Open Research Fund of Jiangsu Key Laboratory of Spearal Imaging&Intelligent Sense(JSGP202105).
文摘With the advances in scientific foundations and technological implementations,optical metrology has become versatile problem-solving backbones in manufacturing,fundamental research,and engineering applications,such as quality control,nondestructive testing,experimental mechanics,and biomedicine.In recent years,deep learning,a subfield of machine learning,is emerging as a powerful tool to address problems by learning from data,largely driven by the availability of massive datasets,enhanced computational power,fast data storage,and novel training algorithms for the deep neural network.It is currently promoting increased interests and gaining extensive attention for its utilization in the field of optical metrology.Unlike the traditional,,physics-basedH approach,deep-learning-enabled optical metrology is a kind of,/data-drivenw approach,which has already provided numerous alternative solutions to many challenging problems in this field with better performances.In this review,we present an overview of the current status and the latest progress of deep-learning technologies in the field of optical metrology.We first briefly introduce both traditional image-processing algorithms in optical metrology and the basic concepts of deep learning,followed by a comprehensive review of its applications in various optical metrology tasks,such as fringe denoising,phase retrieval,phase unwrapping,subset correlation,and error compensation.The open challenges faced by the current deep-learning approach in optical metrology are then discussed.Finally,the directions for future research are outlined.
基金supported by National Key R&D Program of China(Project No.2021ZD0112902)the National Natural Science Foundation of China(Project No.62220106003)Tsinghua-Tencent Joint Laboratory for Internet Innovation Technology.
文摘While originally designed for natural language processing tasks,the self-attention mechanism has recently taken various computer vision areas by storm.However,the 2D nature of images brings three challenges for applying self-attention in computer vision:(1)treating images as 1D sequences neglects their 2D structures;(2)the quadratic complexity is too expensive for high-resolution images;(3)it only captures spatial adaptability but ignores channel adaptability.In this paper,we propose a novel linear attention named large kernel attention(LKA)to enable self-adaptive and long-range correlations in self-attention while avoiding its shortcomings.Furthermore,we present a neural network based on LKA,namely Visual Attention Network(VAN).While extremely simple,VAN achieves comparable results with similar size convolutional neural networks(CNNs)and vision transformers(ViTs)in various tasks,including image classification,object detection,semantic segmentation,panoptic segmentation,pose estimation,etc.For example,VAN-B6 achieves 87.8%accuracy on ImageNet benchmark,and sets new state-of-the-art performance(58.2%PQ)for panoptic segmentation.Besides,VAN-B2 surpasses Swin-T 4%mloU(50.1%vs.46.1%)for semantic segmentation on ADE20K benchmark,2.6%AP(48.8%vs.46.2%)for object detection on COCO dataset.It provides a novel method and a simple yet strong baseline for the community.The code is available at https://github.com/Visual-Attention-Network.
基金supported partially by the science and technology project of CQ CSTC(No.cstc2012jjA40037)
文摘In wireless sensor network,virtual backbone is a cost effective broadcasting method.Connected dominating set formation is proposed to construct a virtual backbone.However,it is NP-Hard to find a minimum connected dominating set in an arbitrary graph.In this paper,based on cross-entropy method,we present a novel backbone formulation algorithm(BFA-CE)in wireless sensor network.In BFA-CE,a maximal independent set is got at first and nodes in the independent set are required to get their action sets.Based on those action sets,a backbone is generated with the cross-entropy method.Simulation results show that our algorithm can effectively reduce the size of backbone network within a reasonable message overhead,and it has lower average node degree.This approach can be potentially used in designing efficient broadcasting strategy or working as a backup routing of wireless sensor network.
文摘Optical transport networks are now the basic infrastructure of modern communications systems, including the SDH and WDM backbone network of local network operators, in the case of Cameroon. Given the colossal investments required to deploy these networks, particularly related to the cost of equipment (optical fibers, transponders and multiplexers), the optimization of bandwidth and dynamic allocation of resources is essential to control operating costs and ensure continuity of service. Automatic switching technology for optical networks brings intelligence to the control plane to fully facilitate bandwidth utilization, traffic redirection, and automatic configuration of end-to-end services. This paper considers a local network operator’s WDM network without the implementation of the automatic switching technology, develops a network modeling software platform called Graphic Networks and using graph theory integrates a particularity of the automatic switching technology, which is the automatic rerouting of traffic in case of incident in the network. The incidents considered here are those links or route failures and node failures.
文摘Ralstonia solanacearum, the causative agent of bacterial wilt, is a soil-borne pathogen that poses a widespread threat to plants in the Solanaceae family. To elucidate the mechanism by which limonene exerts its effects on R. solanacearum, we first assessed the impact of limonene on the physiological indicators of the pathogen and subsequently analyzed its transcriptome and metabolome. Our findings indicate that limonene has a potent inhibitory effect on R. solanacearum, and it also suppresses the formation of the bacterial community biofilm. Limonene primarily regulates the terpene biosynthesis pathway in R. solanacearum, thereby potentially affecting signal transduction in the pathogen and disrupting its normal growth and development. These results significantly enhance our understanding of limonene’s response to the induction of bacterial wilt and provide a reference for further prevention and control of R. solanacearum.
基金supported by the National Natural Science Foundation of China (52203241,21905225,22005121)the Science and Technology Program of Shaanxi Province (2022JM-229,2023-JC-QN-0448)+1 种基金Jiangsu Key Laboratory for Carbon-Based Functional Materials&Devices,Soochow University (KJS2208)H.Y.W.acknowledges the financial support from the National Research Foundation of Korea (2019R1A6A1A11044070,2020M3H4A3081814).
文摘Developing novel unfused building blocks with simple synthesis and low cost is essential to advance and enrich cost-effective poly-mer donors;however,it remains a challenge due to the lack of efficient molecular strategies.Herein,a class of low-cost and fully unfused polymer donors with precisely regulated backbone planarity via halogenation was designed and synthesized,namely PDTBTBz-2H,PDTBTBz-2F,and PDTBTBz-2Cl.These polymer donors possess a four-step synthesis route with over 80%yield from cheap raw chemicals comparable to existing low-cost polymer donors,such as PTQ10.Benefitting from the planar backbone via in-corporating the F…S non-covalent interactions,PDTBTBz-2F exhibits more robust J-type aggregation in solution and a long-ranged molecular stacking in film relative to PDTBTBz-2H and PDTBTBz-2Cl.Moreover,the systematical study of PDTBTBz-based organic so-lar cells(OSCs)reveals the close relationship between optimized molecular self-assembly and charge separation/transport regarding backbone halogenation when paired with the non-fullerene acceptor(Y6-BO-4F).As a result,the photovoltaic devices based on semicrystalline PDTBTBz-2F achieved a promising power conversion efficiency(PCE)of 12.37%.Our work highlighted the influence of backbone halogenation on the molecular self-assembly properties and a potential unfused backbone motif for further developing cost-effective OSCs.
