Medical procedures are inherently invasive and carry the risk of inducing pain to the mind and body.Recently,efforts have been made to alleviate the discomfort associated with invasive medical procedures through the u...Medical procedures are inherently invasive and carry the risk of inducing pain to the mind and body.Recently,efforts have been made to alleviate the discomfort associated with invasive medical procedures through the use of virtual reality(VR)technology.VR has been demonstrated to be an effective treatment for pain associated with medical procedures,as well as for chronic pain conditions for which no effective treatment has been established.The precise mechanism by which the diversion from reality facilitated by VR contributes to the diminution of pain and anxiety has yet to be elucidated.However,the provision of positive images through VR-based visual stimulation may enhance the functionality of brain networks.The salience network is diminished,while the default mode network is enhanced.Additionally,the medial prefrontal cortex may establish a stronger connection with the default mode network,which could result in a reduction of pain and anxiety.Further research into the potential of VR technology to alleviate pain could lead to a reduction in the number of individuals who overdose on painkillers and contribute to positive change in the medical field.展开更多
为了解决大型工程项目中文件的传输时间与成本问题,提出一个基于文件工作流的工程项目文件管理优化方法。首先,构建了工程项目文件管理环境和具有逻辑顺序的文件工作流模型,分析了文件的传输和缓存。在此基础上,将文件管理优化问题建模...为了解决大型工程项目中文件的传输时间与成本问题,提出一个基于文件工作流的工程项目文件管理优化方法。首先,构建了工程项目文件管理环境和具有逻辑顺序的文件工作流模型,分析了文件的传输和缓存。在此基础上,将文件管理优化问题建模为马尔可夫过程,通过设计状态空间、动作空间及奖励函数等实现文件工作流的任务完成时间与缓存成本的联合优化。其次,采用对抗式双重深度Q网络(dueling double deep Q network,D3QN)来降低训练时间,提高训练效率。仿真结果验证了提出方案在不同参数配置下文件传输的有效性,并且在任务体量增大时仍能保持较好的优化能力。展开更多
Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable challenges.The sharp increase in us...Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable challenges.The sharp increase in user demand for latency-sensitive tasks has inevitably led to offloading bottlenecks and insufficient computational capacity on individual satellite edge servers,making it necessary to implement effective task offloading scheduling to enhance user experience.In this paper,we propose a priority-based task scheduling strategy based on a Software-Defined Network(SDN)framework for satellite-terrestrial integrated networks,which clarifies the execution order of tasks based on their priority.Subsequently,we apply a Dueling-Double Deep Q-Network(DDQN)algorithm enhanced with prioritized experience replay to derive a computation offloading strategy,improving the experience replay mechanism within the Dueling-DDQN framework.Next,we utilize the Deep Deterministic Policy Gradient(DDPG)algorithm to determine the optimal resource allocation strategy to reduce the processing latency of sub-tasks.Simulation results demonstrate that the proposed d3-DDPG algorithm outperforms other approaches,effectively reducing task processing latency and thus improving user experience and system efficiency.展开更多
Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This st...Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This study proposes a novel end-to-end disparity estimation model to address these challenges.Our approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting interferences.This study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and efficiency.The model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video data.Experimental results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing parameters.Moreover,the model exhibited faster convergence during training,contributing to overall performance enhancement.This study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.展开更多
Security and safety remain paramount concerns for both governments and individuals worldwide.In today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable...Security and safety remain paramount concerns for both governments and individuals worldwide.In today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable to society.Consequently,there is a pressing need for swift identification of potential threats to preemptively alert law enforcement and security forces,thereby preventing potential attacks or violent incidents.Recent advancements in big data analytics and deep learning have significantly enhanced the capabilities of computer vision in object detection,particularly in identifying firearms.This paper introduces a novel automatic firearm detection surveillance system,utilizing a one-stage detection approach named MARIE(Mechanism for Realtime Identification of Firearms).MARIE incorporates the Single Shot Multibox Detector(SSD)model,which has been specifically optimized to balance the speed-accuracy trade-off critical in firearm detection applications.The SSD model was further refined by integrating MobileNetV2 and InceptionV2 architectures for superior feature extraction capabilities.The experimental results demonstrate that this modified SSD configuration provides highly satisfactory performance,surpassing existing methods trained on the same dataset in terms of the critical speedaccuracy trade-off.Through these innovations,MARIE sets a new standard in surveillance technology,offering a robust solution to enhance public safety effectively.展开更多
BACKGROUND The use of network pharmacology and blood metabolomics to study the patho-genesis of violent aggression in patients with schizophrenia and the related drug mechanisms of action provides new directions for r...BACKGROUND The use of network pharmacology and blood metabolomics to study the patho-genesis of violent aggression in patients with schizophrenia and the related drug mechanisms of action provides new directions for reducing the risk of violent aggression and optimizing treatment plans.AIM To explore the metabolic regulatory mechanism of olanzapine in treating patients with schizophrenia with a moderate to high risk of violent aggression.METHODS Metabolomic technology was used to screen differentially abundant metabolites in patients with schizophrenia with a moderate to high risk of violent aggression before and after olanzapine treatment,and the related metabolic pathways were identified.Network pharmacology was used to establish protein-protein interaction networks of the core targets of olanzapine.Gene Ontology functional analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were subsequently performed.RESULTS Compared with the healthy group,the patients with schizophrenia group presented significant changes in the levels of 24 metabolites related to the disruption of 9 metabolic pathways,among which the key pathways were the alanine,aspartate and glutamate metabolism and arginine biosynthesis pathways.After treatment with olanzapine,the levels of 10 differentially abundant metabolites were significantly reversed in patients with schizophrenia.Olanzapine effectively regulated six metabolic pathways,among which the key pathways were alanine,aspartate and glutamate metabolism and arginine biosynthesis pathways.Ten core targets of olanzapine were involved in several key pathways.CONCLUSION The metabolic pathways of alanine,aspartate,and glutamate metabolism and arginine biosynthesis are the key pathways involved in olanzapine treatment for aggressive schizophrenia.展开更多
Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently...Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.展开更多
Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay di...Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay differential equations(DDEs)and delay integrodifferential equations(DIDEs)with constant delays,primarily due to their low regularity at delayinduced breaking points.In this paper,a DNN method that combines multi-task learning(MTL)which is proposed to solve both the forward and inverse problems of DIDEs.The core idea of this approach is to divide the original equation into multiple tasks based on the delay,using auxiliary outputs to represent the integral terms,followed by the use of MTL to seamlessly incorporate the properties at the breaking points into the loss function.Furthermore,given the increased training dificulty associated with multiple tasks and outputs,we employ a sequential training scheme to reduce training complexity and provide reference solutions for subsequent tasks.This approach significantly enhances the approximation accuracy of solving DIDEs with DNNs,as demonstrated by comparisons with traditional DNN methods.We validate the effectiveness of this method through several numerical experiments,test various parameter sharing structures in MTL and compare the testing results of these structures.Finally,this method is implemented to solve the inverse problem of nonlinear DIDE and the results show that the unknown parameters of DIDE can be discovered with sparse or noisy data.展开更多
Artificial intelligence(AI)has taken breathtaking leaps forward in recent years,evolving into a strategic technology for pioneering the future.The growing demand for computing power—especially in demanding inference ...Artificial intelligence(AI)has taken breathtaking leaps forward in recent years,evolving into a strategic technology for pioneering the future.The growing demand for computing power—especially in demanding inference tasks,exemplified by generative AI models such as ChatGPT—poses challenges for conventional electronic computing systems.Advances in photonics technology have ignited interest in investigating photonic computing as a promising AI computing modality.Through the profound fusion of AI and photonics technologies,intelligent photonics is developing as an emerging interdisciplinary field with significant potential to revolutionize practical applications.Deep learning,as a subset of AI,presents efficient avenues for optimizing photonic design,developing intelligent optical systems,and performing optical data processing and analysis.Employing AI in photonics can empower applications such as smartphone cameras,biomedical microscopy,and virtual and augmented reality displays.Conversely,leveraging photonics-based devices and systems for the physical implementation of neural networks enables high speed and low energy consumption.Applying photonics technology in AI computing is expected to have a transformative impact on diverse fields,including optical communications,automatic driving,and astronomical observation.Here,recent advances in intelligent photonics are presented from the perspective of the synergy between deep learning and metaphotonics,holography,and quantum photonics.This review also spotlights relevant applications and offers insights into challenges and prospects.展开更多
This paper highlights the crucial role of Indonesia’s GNSS receiver network in advancing Equatorial Plasma Bubble(EPB)studies in Southeast and East Asia,as ionospheric irregularities within EPB can disrupt GNSS signa...This paper highlights the crucial role of Indonesia’s GNSS receiver network in advancing Equatorial Plasma Bubble(EPB)studies in Southeast and East Asia,as ionospheric irregularities within EPB can disrupt GNSS signals and degrade positioning accuracy.Managed by the Indonesian Geospatial Information Agency(BIG),the Indonesia Continuously Operating Reference Station(Ina-CORS)network comprises over 300 GNSS receivers spanning equatorial to southern low-latitude regions.Ina-CORS is uniquely situated to monitor EPB generation,zonal drift,and dissipation across Southeast Asia.We provide a practical tool for EPB research,by sharing two-dimensional rate of Total Electron Content(TEC)change index(ROTI)derived from this network.We generate ROTI maps with a 10-minute resolution,and samples from May 2024 are publicly available for further scientific research.Two preliminary findings from the ROTI maps of Ina-CORS are noteworthy.First,the Ina-CORS ROTI maps reveal that the irregularities within a broader EPB structure persist longer,increasing the potential for these irregularities to migrate farther eastward.Second,we demonstrate that combined ROTI maps from Ina-CORS and GNSS receivers in East Asia and Australia can be used to monitor the development of ionospheric irregularities in Southeast and East Asia.We have demonstrated the combined ROTI maps to capture the development of ionospheric irregularities in the Southeast/East Asian sector during the G5 Geomagnetic Storm on May 11,2024.We observed simultaneous ionospheric irregularities in Japan and Australia,respectively propagating northwestward and southwestward,before midnight,whereas Southeast Asia’s equatorial and low-latitude regions exhibited irregularities post-midnight.By sharing ROTI maps from Indonesia and integrating them with regional GNSS networks,researchers can conduct comprehensive EPB studies,enhancing the understanding of EPB behavior across Southeast and East Asia and contributing significantly to ionospheric research.展开更多
Photonic neural networks have garnered significant attention in recent years due to their ultra-high computational speed,broad bandwidth,and parallel processing capabilities.However,compared to conventional electronic...Photonic neural networks have garnered significant attention in recent years due to their ultra-high computational speed,broad bandwidth,and parallel processing capabilities.However,compared to conventional electronic nonlinear activa-tion function(NAF),progress on efficient and easily implementable optical nonlinear activation function(ONAF)was barely reported.To address this issue,we proposed a programmable,low-loss ONAF device based on a silicon micro-ring resonator capped with the Antimony selenide(Sb_(2)Se_(3))thin films,and with indium tin oxide(ITO)used as the microheater.Leveraging our self-developed phase-transformation kinetic and optical models,we successfully simulated the phase-transition behavior of Sb_(2)Se_(3)and three different ONAFs—ELU,ReLU,and radial basis function(RBF)were achieved according to discernible optical responses of proposed devices under different phase-change extents.Classification results from the Fashion MNIST dataset demonstrated that these ONAFs can be considered as appropriate substitutes for traditional NAF.This indicated the bright prospect of the proposed device for nonlinear activation function in future photonic neural networks.展开更多
Non-stoichiometric carbides have been proven to be effective electromagnetic wave(EMW)absorbing materials.In this study,phase and morphology of XZnC(X=Fe/Co/Cu)loaded on a three dimensional(3D)network structure melami...Non-stoichiometric carbides have been proven to be effective electromagnetic wave(EMW)absorbing materials.In this study,phase and morphology of XZnC(X=Fe/Co/Cu)loaded on a three dimensional(3D)network structure melamine sponge(MS)carbon composites were investigated through vacuum filtration followed by calcination.The FeZnC/CoZnC/CuZnC with carbon nanotubes(CNTs)were uniformly dispersed on the surface of melamine sponge carbon skeleton and Co-containing sample exhibits the highest CNTs concentration.The minimum reflection loss(RL_(min))of the CoZnC/MS composite(m_(composite):m_(paraffin)=1:1,m represents mass)reached-33.60 dB,and the effective absorption bandwidth(EAB)reached 9.60 GHz.The outstanding electromagnetic wave absorption(EMWA)properties of the CoZnC/MS composite can be attributed to its unique hollow structure,which leads to multiple reflections and scattering.The formed conductive network improves dielectric and conductive loss.The incorporation of Co enhances the magnetic loss capability and optimizes interfacial polarization and dipole polarization.By simultaneously improving dielectric and magnetic losses,ex-cellent impedance matching performance is achieved.The clarification of element replacement in XZnC/MS composites provides an effi-cient design perspective for high-performance non-stoichiometric carbide EMW absorbers.展开更多
Environmentalmonitoring systems based on remote sensing technology have a wider monitoringrange and longer timeliness, which makes them widely used in the detection andmanagement of pollution sources. However, haze we...Environmentalmonitoring systems based on remote sensing technology have a wider monitoringrange and longer timeliness, which makes them widely used in the detection andmanagement of pollution sources. However, haze weather conditions degrade image qualityand reduce the precision of environmental monitoring systems. To address this problem,this research proposes a remote sensing image dehazingmethod based on the atmosphericscattering model and a dark channel prior constrained network. The method consists ofa dehazing network, a dark channel information injection network (DCIIN), and a transmissionmap network. Within the dehazing network, the branch fusion module optimizesfeature weights to enhance the dehazing effect. By leveraging dark channel information,the DCIIN enables high-quality estimation of the atmospheric veil. To ensure the outputof the deep learning model aligns with physical laws, we reconstruct the haze image usingthe prediction results from the three networks. Subsequently, we apply the traditionalloss function and dark channel loss function between the reconstructed haze image and theoriginal haze image. This approach enhances interpretability and reliabilitywhile maintainingadherence to physical principles. Furthermore, the network is trained on a synthesizednon-homogeneous haze remote sensing dataset using dark channel information from cloudmaps. The experimental results show that the proposed network can achieve better imagedehazing on both synthetic and real remote sensing images with non-homogeneous hazedistribution. This research provides a new idea for solving the problem of decreased accuracyof environmental monitoring systems under haze weather conditions and has strongpracticability.展开更多
文摘现有的基于卷积神经网络(convolutional neural network,CNN)的环路滤波器倾向于将多个网络应用于不同的量化参数(quantization parameter,QP),消耗训练模型中的大量资源,并增加内存负担。针对这一问题,提出一种基于CNN的QP自适应环路滤波器。首先,设计一个轻量级分类网络,按照滤波难易程度将编码树单元(coding tree unit,CTU)划分为难、中、易3类;然后,构建3个融合了特征信息增强融合模块的基于CNN的滤波网络,以满足不同QP下的3类CTU滤波需求。将所提出的环路滤波器集成到多功能视频编码(versatile video coding,VVC)标准H.266/VVC的测试软件VTM 6.0中,替换原有的去块效应滤波器(deblocking filter,DBF)、样本自适应偏移(sample adaptive offset,SAO)滤波器和自适应环路滤波器。实验结果表明,该方法平均降低了3.14%的比特率差值(Bjøntegaard delta bit rate,BD-BR),与其他基于CNN的环路滤波器相比,显著提高了压缩效率,并减少了压缩伪影。
文摘Medical procedures are inherently invasive and carry the risk of inducing pain to the mind and body.Recently,efforts have been made to alleviate the discomfort associated with invasive medical procedures through the use of virtual reality(VR)technology.VR has been demonstrated to be an effective treatment for pain associated with medical procedures,as well as for chronic pain conditions for which no effective treatment has been established.The precise mechanism by which the diversion from reality facilitated by VR contributes to the diminution of pain and anxiety has yet to be elucidated.However,the provision of positive images through VR-based visual stimulation may enhance the functionality of brain networks.The salience network is diminished,while the default mode network is enhanced.Additionally,the medial prefrontal cortex may establish a stronger connection with the default mode network,which could result in a reduction of pain and anxiety.Further research into the potential of VR technology to alleviate pain could lead to a reduction in the number of individuals who overdose on painkillers and contribute to positive change in the medical field.
文摘为了解决大型工程项目中文件的传输时间与成本问题,提出一个基于文件工作流的工程项目文件管理优化方法。首先,构建了工程项目文件管理环境和具有逻辑顺序的文件工作流模型,分析了文件的传输和缓存。在此基础上,将文件管理优化问题建模为马尔可夫过程,通过设计状态空间、动作空间及奖励函数等实现文件工作流的任务完成时间与缓存成本的联合优化。其次,采用对抗式双重深度Q网络(dueling double deep Q network,D3QN)来降低训练时间,提高训练效率。仿真结果验证了提出方案在不同参数配置下文件传输的有效性,并且在任务体量增大时仍能保持较好的优化能力。
文摘Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable challenges.The sharp increase in user demand for latency-sensitive tasks has inevitably led to offloading bottlenecks and insufficient computational capacity on individual satellite edge servers,making it necessary to implement effective task offloading scheduling to enhance user experience.In this paper,we propose a priority-based task scheduling strategy based on a Software-Defined Network(SDN)framework for satellite-terrestrial integrated networks,which clarifies the execution order of tasks based on their priority.Subsequently,we apply a Dueling-Double Deep Q-Network(DDQN)algorithm enhanced with prioritized experience replay to derive a computation offloading strategy,improving the experience replay mechanism within the Dueling-DDQN framework.Next,we utilize the Deep Deterministic Policy Gradient(DDPG)algorithm to determine the optimal resource allocation strategy to reduce the processing latency of sub-tasks.Simulation results demonstrate that the proposed d3-DDPG algorithm outperforms other approaches,effectively reducing task processing latency and thus improving user experience and system efficiency.
基金Supported by Sichuan Science and Technology Program(2023YFSY0026,2023YFH0004)Supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korean government(MSIT)(No.RS-2022-00155885,Artificial Intelligence Convergence Innovation Human Resources Development(Hanyang University ERICA)).
文摘Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This study proposes a novel end-to-end disparity estimation model to address these challenges.Our approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting interferences.This study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and efficiency.The model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video data.Experimental results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing parameters.Moreover,the model exhibited faster convergence during training,contributing to overall performance enhancement.This study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.
文摘Security and safety remain paramount concerns for both governments and individuals worldwide.In today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable to society.Consequently,there is a pressing need for swift identification of potential threats to preemptively alert law enforcement and security forces,thereby preventing potential attacks or violent incidents.Recent advancements in big data analytics and deep learning have significantly enhanced the capabilities of computer vision in object detection,particularly in identifying firearms.This paper introduces a novel automatic firearm detection surveillance system,utilizing a one-stage detection approach named MARIE(Mechanism for Realtime Identification of Firearms).MARIE incorporates the Single Shot Multibox Detector(SSD)model,which has been specifically optimized to balance the speed-accuracy trade-off critical in firearm detection applications.The SSD model was further refined by integrating MobileNetV2 and InceptionV2 architectures for superior feature extraction capabilities.The experimental results demonstrate that this modified SSD configuration provides highly satisfactory performance,surpassing existing methods trained on the same dataset in terms of the critical speedaccuracy trade-off.Through these innovations,MARIE sets a new standard in surveillance technology,offering a robust solution to enhance public safety effectively.
基金Supported by Henan Provincial Science and Technology Research Project,No.242102310203.
文摘BACKGROUND The use of network pharmacology and blood metabolomics to study the patho-genesis of violent aggression in patients with schizophrenia and the related drug mechanisms of action provides new directions for reducing the risk of violent aggression and optimizing treatment plans.AIM To explore the metabolic regulatory mechanism of olanzapine in treating patients with schizophrenia with a moderate to high risk of violent aggression.METHODS Metabolomic technology was used to screen differentially abundant metabolites in patients with schizophrenia with a moderate to high risk of violent aggression before and after olanzapine treatment,and the related metabolic pathways were identified.Network pharmacology was used to establish protein-protein interaction networks of the core targets of olanzapine.Gene Ontology functional analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were subsequently performed.RESULTS Compared with the healthy group,the patients with schizophrenia group presented significant changes in the levels of 24 metabolites related to the disruption of 9 metabolic pathways,among which the key pathways were the alanine,aspartate and glutamate metabolism and arginine biosynthesis pathways.After treatment with olanzapine,the levels of 10 differentially abundant metabolites were significantly reversed in patients with schizophrenia.Olanzapine effectively regulated six metabolic pathways,among which the key pathways were alanine,aspartate and glutamate metabolism and arginine biosynthesis pathways.Ten core targets of olanzapine were involved in several key pathways.CONCLUSION The metabolic pathways of alanine,aspartate,and glutamate metabolism and arginine biosynthesis are the key pathways involved in olanzapine treatment for aggressive schizophrenia.
基金National Natural Science Foundation of China(11971211,12171388).
文摘Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.
文摘Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay differential equations(DDEs)and delay integrodifferential equations(DIDEs)with constant delays,primarily due to their low regularity at delayinduced breaking points.In this paper,a DNN method that combines multi-task learning(MTL)which is proposed to solve both the forward and inverse problems of DIDEs.The core idea of this approach is to divide the original equation into multiple tasks based on the delay,using auxiliary outputs to represent the integral terms,followed by the use of MTL to seamlessly incorporate the properties at the breaking points into the loss function.Furthermore,given the increased training dificulty associated with multiple tasks and outputs,we employ a sequential training scheme to reduce training complexity and provide reference solutions for subsequent tasks.This approach significantly enhances the approximation accuracy of solving DIDEs with DNNs,as demonstrated by comparisons with traditional DNN methods.We validate the effectiveness of this method through several numerical experiments,test various parameter sharing structures in MTL and compare the testing results of these structures.Finally,this method is implemented to solve the inverse problem of nonlinear DIDE and the results show that the unknown parameters of DIDE can be discovered with sparse or noisy data.
基金supported by the National Natural Science Foundation of China(62035003 and 62235009).
文摘Artificial intelligence(AI)has taken breathtaking leaps forward in recent years,evolving into a strategic technology for pioneering the future.The growing demand for computing power—especially in demanding inference tasks,exemplified by generative AI models such as ChatGPT—poses challenges for conventional electronic computing systems.Advances in photonics technology have ignited interest in investigating photonic computing as a promising AI computing modality.Through the profound fusion of AI and photonics technologies,intelligent photonics is developing as an emerging interdisciplinary field with significant potential to revolutionize practical applications.Deep learning,as a subset of AI,presents efficient avenues for optimizing photonic design,developing intelligent optical systems,and performing optical data processing and analysis.Employing AI in photonics can empower applications such as smartphone cameras,biomedical microscopy,and virtual and augmented reality displays.Conversely,leveraging photonics-based devices and systems for the physical implementation of neural networks enables high speed and low energy consumption.Applying photonics technology in AI computing is expected to have a transformative impact on diverse fields,including optical communications,automatic driving,and astronomical observation.Here,recent advances in intelligent photonics are presented from the perspective of the synergy between deep learning and metaphotonics,holography,and quantum photonics.This review also spotlights relevant applications and offers insights into challenges and prospects.
基金JSPS KAKENHI Grant Number16H06286 supports global GNSS ionospheric maps (TEC,ROTI,and detrended TEC maps) developed by the Institute for SpaceEarth Environmental Research (ISEE) of Nagoya Universitysupport of the 2024 JASSO Follow-up Research Fellowship Program for a 90-day visiting research at the Institute for Space-Earth Environmental Research (ISEE),Nagoya University+3 种基金the support received from Telkom University under the“Skema Penelitian Terapan Periode I Tahun Anggaran 2024”the Memorandum of Understanding for Research Collaboration on Regional Ionospheric Observation (No:092/SAM3/TE-DEK/2021)the National Institute of Information and Communications Technology (NICT) International Exchange Program 2024-2025(No.2024-007)support for a one-year visiting research at Hokkaido University
文摘This paper highlights the crucial role of Indonesia’s GNSS receiver network in advancing Equatorial Plasma Bubble(EPB)studies in Southeast and East Asia,as ionospheric irregularities within EPB can disrupt GNSS signals and degrade positioning accuracy.Managed by the Indonesian Geospatial Information Agency(BIG),the Indonesia Continuously Operating Reference Station(Ina-CORS)network comprises over 300 GNSS receivers spanning equatorial to southern low-latitude regions.Ina-CORS is uniquely situated to monitor EPB generation,zonal drift,and dissipation across Southeast Asia.We provide a practical tool for EPB research,by sharing two-dimensional rate of Total Electron Content(TEC)change index(ROTI)derived from this network.We generate ROTI maps with a 10-minute resolution,and samples from May 2024 are publicly available for further scientific research.Two preliminary findings from the ROTI maps of Ina-CORS are noteworthy.First,the Ina-CORS ROTI maps reveal that the irregularities within a broader EPB structure persist longer,increasing the potential for these irregularities to migrate farther eastward.Second,we demonstrate that combined ROTI maps from Ina-CORS and GNSS receivers in East Asia and Australia can be used to monitor the development of ionospheric irregularities in Southeast and East Asia.We have demonstrated the combined ROTI maps to capture the development of ionospheric irregularities in the Southeast/East Asian sector during the G5 Geomagnetic Storm on May 11,2024.We observed simultaneous ionospheric irregularities in Japan and Australia,respectively propagating northwestward and southwestward,before midnight,whereas Southeast Asia’s equatorial and low-latitude regions exhibited irregularities post-midnight.By sharing ROTI maps from Indonesia and integrating them with regional GNSS networks,researchers can conduct comprehensive EPB studies,enhancing the understanding of EPB behavior across Southeast and East Asia and contributing significantly to ionospheric research.
基金supported by the National Natural Science Foundation of China(Grant Nos.62104114,62404111)Natural Science Foundation of Jiangsu Province(Grant Nos.BK20240635,BZ2021031)+4 种基金Opening Project of Advanced Integrated Circuit Package and Testing Research Center of Jiangsu Province(Grant No.NTIKFJJ202303)Natural Science Foundation of the Jiangsu Higher Education Institutions of China(Grant No.24KJB510025)Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications(Grant Nos.NY223157,NY223156)Natural Science Foundation of Nanjing University of Posts and Telecommunications(Grant No.NY224140)Project funded by China Postdoctoral Science Foundation(Grant No.2023M732916).
文摘Photonic neural networks have garnered significant attention in recent years due to their ultra-high computational speed,broad bandwidth,and parallel processing capabilities.However,compared to conventional electronic nonlinear activa-tion function(NAF),progress on efficient and easily implementable optical nonlinear activation function(ONAF)was barely reported.To address this issue,we proposed a programmable,low-loss ONAF device based on a silicon micro-ring resonator capped with the Antimony selenide(Sb_(2)Se_(3))thin films,and with indium tin oxide(ITO)used as the microheater.Leveraging our self-developed phase-transformation kinetic and optical models,we successfully simulated the phase-transition behavior of Sb_(2)Se_(3)and three different ONAFs—ELU,ReLU,and radial basis function(RBF)were achieved according to discernible optical responses of proposed devices under different phase-change extents.Classification results from the Fashion MNIST dataset demonstrated that these ONAFs can be considered as appropriate substitutes for traditional NAF.This indicated the bright prospect of the proposed device for nonlinear activation function in future photonic neural networks.
基金supported by the National Natural Science Foundation of China(Nos.52101274,52377026 and 52472131)Taishan Scholars and Young Experts Program of Shandong Province,China(No.tsqn202103057)+4 种基金Natural Science Foundation of Shandong Province,China(Nos.ZR2020QE011 and ZR2022ME089)the Qingchuang Talents Induction Program of Shandong Higher Education Institution,China(Research and Innovation Team of Structural-Functional Polymer Composites)Youth Top Talent Foundation of Yantai University,China(No.2219008)Graduate Innovation Foundation of Yantai University,China(No.GIFYTU2240)College Student Innovation and Entrepreneurship Training Program Project,China(No.202311066088).
文摘Non-stoichiometric carbides have been proven to be effective electromagnetic wave(EMW)absorbing materials.In this study,phase and morphology of XZnC(X=Fe/Co/Cu)loaded on a three dimensional(3D)network structure melamine sponge(MS)carbon composites were investigated through vacuum filtration followed by calcination.The FeZnC/CoZnC/CuZnC with carbon nanotubes(CNTs)were uniformly dispersed on the surface of melamine sponge carbon skeleton and Co-containing sample exhibits the highest CNTs concentration.The minimum reflection loss(RL_(min))of the CoZnC/MS composite(m_(composite):m_(paraffin)=1:1,m represents mass)reached-33.60 dB,and the effective absorption bandwidth(EAB)reached 9.60 GHz.The outstanding electromagnetic wave absorption(EMWA)properties of the CoZnC/MS composite can be attributed to its unique hollow structure,which leads to multiple reflections and scattering.The formed conductive network improves dielectric and conductive loss.The incorporation of Co enhances the magnetic loss capability and optimizes interfacial polarization and dipole polarization.By simultaneously improving dielectric and magnetic losses,ex-cellent impedance matching performance is achieved.The clarification of element replacement in XZnC/MS composites provides an effi-cient design perspective for high-performance non-stoichiometric carbide EMW absorbers.
基金supported by the National Natural Science Foundation of China(No.51605054).
文摘Environmentalmonitoring systems based on remote sensing technology have a wider monitoringrange and longer timeliness, which makes them widely used in the detection andmanagement of pollution sources. However, haze weather conditions degrade image qualityand reduce the precision of environmental monitoring systems. To address this problem,this research proposes a remote sensing image dehazingmethod based on the atmosphericscattering model and a dark channel prior constrained network. The method consists ofa dehazing network, a dark channel information injection network (DCIIN), and a transmissionmap network. Within the dehazing network, the branch fusion module optimizesfeature weights to enhance the dehazing effect. By leveraging dark channel information,the DCIIN enables high-quality estimation of the atmospheric veil. To ensure the outputof the deep learning model aligns with physical laws, we reconstruct the haze image usingthe prediction results from the three networks. Subsequently, we apply the traditionalloss function and dark channel loss function between the reconstructed haze image and theoriginal haze image. This approach enhances interpretability and reliabilitywhile maintainingadherence to physical principles. Furthermore, the network is trained on a synthesizednon-homogeneous haze remote sensing dataset using dark channel information from cloudmaps. The experimental results show that the proposed network can achieve better imagedehazing on both synthetic and real remote sensing images with non-homogeneous hazedistribution. This research provides a new idea for solving the problem of decreased accuracyof environmental monitoring systems under haze weather conditions and has strongpracticability.
