Attacks on the cyber space is getting exponential in recent times.Illegal penetrations and breaches are real threats to the individuals and organizations.Conventional security systems are good enough to detect the kno...Attacks on the cyber space is getting exponential in recent times.Illegal penetrations and breaches are real threats to the individuals and organizations.Conventional security systems are good enough to detect the known threats but when it comes to Advanced Persistent Threats(APTs)they fails.These APTs are targeted,more sophisticated and very persistent and incorporates lot of evasive techniques to bypass the existing defenses.Hence,there is a need for an effective defense system that can achieve a complete reliance of security.To address the above-mentioned issues,this paper proposes a novel honeypot system that tracks the anonymous behavior of the APT threats.The key idea of honeypot leverages the concepts of graph theory to detect such targeted attacks.The proposed honey-pot is self-realizing,strategic assisted which withholds the APTs actionable tech-niques and observes the behavior for analysis and modelling.The proposed graph theory based self learning honeypot using the resultsγ(C(n,1)),γc(C(n,1)),γsc(C(n,1))outperforms traditional techniques by detecting APTs behavioral with detection rate of 96%.展开更多
Contrastive self‐supervised representation learning on attributed graph networks with Graph Neural Networks has attracted considerable research interest recently.However,there are still two challenges.First,most of t...Contrastive self‐supervised representation learning on attributed graph networks with Graph Neural Networks has attracted considerable research interest recently.However,there are still two challenges.First,most of the real‐word system are multiple relations,where entities are linked by different types of relations,and each relation is a view of the graph network.Second,the rich multi‐scale information(structure‐level and feature‐level)of the graph network can be seen as self‐supervised signals,which are not fully exploited.A novel contrastive self‐supervised representation learning framework on attributed multiplex graph networks with multi‐scale(named CoLM^(2)S)information is presented in this study.It mainly contains two components:intra‐relation contrast learning and interrelation contrastive learning.Specifically,the contrastive self‐supervised representation learning framework on attributed single‐layer graph networks with multi‐scale information(CoLMS)framework with the graph convolutional network as encoder to capture the intra‐relation information with multi‐scale structure‐level and feature‐level selfsupervised signals is introduced first.The structure‐level information includes the edge structure and sub‐graph structure,and the feature‐level information represents the output of different graph convolutional layer.Second,according to the consensus assumption among inter‐relations,the CoLM^(2)S framework is proposed to jointly learn various graph relations in attributed multiplex graph network to achieve global consensus node embedding.The proposed method can fully distil the graph information.Extensive experiments on unsupervised node clustering and graph visualisation tasks demonstrate the effectiveness of our methods,and it outperforms existing competitive baselines.展开更多
Controlling electromagnetic(EM)waves at will is fundamentally important for diverse applications,ranging from optical microcavities,super-resolution imaging,to quantum information processing.Decades ago,the forays int...Controlling electromagnetic(EM)waves at will is fundamentally important for diverse applications,ranging from optical microcavities,super-resolution imaging,to quantum information processing.Decades ago,the forays into metamaterials and transformation optics have ignited unprecedented interest to create an invisibility cloak—a closed space with any object inside invisible.However,all features of the scattering waves become stochastic and uncontrollable when EM waves interact with an open and disordered environment,making an open invisible space almost impossible.Counterintuitively,here we for the first time present an open,cluttered,and dynamic but invisible space,wherein any freely-moving object maintains invisible.To adapt to the disordered environment,we randomly organize a swarm of reconfigurable metasurfaces,and master them by MetaSeeker,a population-based reinforcement learning(RL).MetaSeeker constructs a narcissistic internal world to mirror the stochastic physical world,capable of autonomous preferment,evolution,and adaptation.In the perception-decision-execution experiment,multiple RL agents automatically interact with the ever-changing environments and integrate a post-hoc explainability to visualize the decision-making process.The hidden objects,such as vehicle cluster and experimenter,can freely scale,race,and track in the invisible space,with the environmental similarity of 99.5%.Our results constitute a monumental stride to reshape the evolutionary landscape of metasurfaces from individual to swarm intelligence and usher in the remote management of entire EM space.展开更多
Volumetric imaging is increasingly in demand for its precision in statistically visualizing and analyzing the intricacies of biological phenomena.To visualize the intricate details of these minute structures and facil...Volumetric imaging is increasingly in demand for its precision in statistically visualizing and analyzing the intricacies of biological phenomena.To visualize the intricate details of these minute structures and facilitate the analysis in biomedical research,high-signal-to-noise ratio(SNR)images are indispensable.However,the inevitable noise presents a significant barrier to imaging qualities.Here,we propose SelfMirror,a self-supervised deep-learning denoising method for volumetric image reconstruction.SelfMirror is developed based on the insight that the variation of biological structure is continuous and smooth;when the sampling interval in volumetric imaging is sufficiently small,the similarity of neighboring slices in terms of the spatial structure becomes apparent.Such similarity can be used to train our proposed network to revive the signals and suppress the noise accurately.The denoising performance of SelfMirror exhibits remarkable robustness and fidelity even in extremely low-SNR conditions.We demonstrate the broad applicability of SelfMirror on multiple imaging modalities,including two-photon microscopy,confocal microscopy,expansion microscopy,computed tomography,and 3D electron microscopy.This versatility extends from single neuron cells to tissues and organs,highlighting SelfMirror's potential for integration into diverse imaging and analysis pipelines.展开更多
The development of intelligent control techniques provides powerful means for the control of machine tools. In this paper, a intelligent control technique and an algorithm for precision control of CNC grinding of cera...The development of intelligent control techniques provides powerful means for the control of machine tools. In this paper, a intelligent control technique and an algorithm for precision control of CNC grinding of ceramic chips are introduced. In the process of ceramic chip CNC grinding, the dimension of the chips tends to get larger and the dimensional error to exceed the tolerance as the number of the chips increases which are machined on the same part program. There are many factors leading to the occurrence of the error and the law of error variation is very complicated. With the introduced intelligent self learning error compensation technique, the CNC system can improve the control strategy to compensate the error automatically. The simulational result is also given.展开更多
EarthCaching is a real success story in how Earth science can benefit by working with successful community programs that link self-learning,technology,and the thrill of outdoor adventure.With over 11,000 Earth science...EarthCaching is a real success story in how Earth science can benefit by working with successful community programs that link self-learning,technology,and the thrill of outdoor adventure.With over 11,000 Earth science sites worldwide visited by 1.3 million people,it is making a real impact toward bringing Earth science to the wider community.展开更多
A composite control strategy for the precalciner exit temperature in cement kiln is introduced based on a mathematical model. In this model, the raw meal flow, coal powder flow and wind flow are taken as three inpu...A composite control strategy for the precalciner exit temperature in cement kiln is introduced based on a mathematical model. In this model, the raw meal flow, coal powder flow and wind flow are taken as three input variables, the clinker fow and exit teperature of cement kiln are output variables, and other influencing factors are considered as disturbance. A composite control system is synthesied by integrating self learning PID, fuzzy and feedforward function into a combined controller, and the arithmetics for the self learning PID controller, fuzzy controller and feedforward controller are elaborated respectively. The control strategy has been realized by software in real practice at cement factory. Application results show that the composite control technology is superior to the general PID control in control effect, and is suitable to the industrial process control with slow parameter variation, nonlinearity and uncertainty.展开更多
文摘Attacks on the cyber space is getting exponential in recent times.Illegal penetrations and breaches are real threats to the individuals and organizations.Conventional security systems are good enough to detect the known threats but when it comes to Advanced Persistent Threats(APTs)they fails.These APTs are targeted,more sophisticated and very persistent and incorporates lot of evasive techniques to bypass the existing defenses.Hence,there is a need for an effective defense system that can achieve a complete reliance of security.To address the above-mentioned issues,this paper proposes a novel honeypot system that tracks the anonymous behavior of the APT threats.The key idea of honeypot leverages the concepts of graph theory to detect such targeted attacks.The proposed honey-pot is self-realizing,strategic assisted which withholds the APTs actionable tech-niques and observes the behavior for analysis and modelling.The proposed graph theory based self learning honeypot using the resultsγ(C(n,1)),γc(C(n,1)),γsc(C(n,1))outperforms traditional techniques by detecting APTs behavioral with detection rate of 96%.
基金support by the National Natural Science Foundation of China(NSFC)under grant number 61873274.
文摘Contrastive self‐supervised representation learning on attributed graph networks with Graph Neural Networks has attracted considerable research interest recently.However,there are still two challenges.First,most of the real‐word system are multiple relations,where entities are linked by different types of relations,and each relation is a view of the graph network.Second,the rich multi‐scale information(structure‐level and feature‐level)of the graph network can be seen as self‐supervised signals,which are not fully exploited.A novel contrastive self‐supervised representation learning framework on attributed multiplex graph networks with multi‐scale(named CoLM^(2)S)information is presented in this study.It mainly contains two components:intra‐relation contrast learning and interrelation contrastive learning.Specifically,the contrastive self‐supervised representation learning framework on attributed single‐layer graph networks with multi‐scale information(CoLMS)framework with the graph convolutional network as encoder to capture the intra‐relation information with multi‐scale structure‐level and feature‐level selfsupervised signals is introduced first.The structure‐level information includes the edge structure and sub‐graph structure,and the feature‐level information represents the output of different graph convolutional layer.Second,according to the consensus assumption among inter‐relations,the CoLM^(2)S framework is proposed to jointly learn various graph relations in attributed multiplex graph network to achieve global consensus node embedding.The proposed method can fully distil the graph information.Extensive experiments on unsupervised node clustering and graph visualisation tasks demonstrate the effectiveness of our methods,and it outperforms existing competitive baselines.
基金sponsored by the Key Research and Development Program of the Ministry of Science and Technology under Grant Nos.2022YFA1404704,2022YFA1405200,and 2022YFA1404902the National Natural Science Foundation of China(NNSFC)under Grant Nos.62422514,62471432,and 62101485+1 种基金the Key Research and Development Program of Zhejiang Province under Grant No.2022C01036the Fundamental Research Funds for the Central Universities.
文摘Controlling electromagnetic(EM)waves at will is fundamentally important for diverse applications,ranging from optical microcavities,super-resolution imaging,to quantum information processing.Decades ago,the forays into metamaterials and transformation optics have ignited unprecedented interest to create an invisibility cloak—a closed space with any object inside invisible.However,all features of the scattering waves become stochastic and uncontrollable when EM waves interact with an open and disordered environment,making an open invisible space almost impossible.Counterintuitively,here we for the first time present an open,cluttered,and dynamic but invisible space,wherein any freely-moving object maintains invisible.To adapt to the disordered environment,we randomly organize a swarm of reconfigurable metasurfaces,and master them by MetaSeeker,a population-based reinforcement learning(RL).MetaSeeker constructs a narcissistic internal world to mirror the stochastic physical world,capable of autonomous preferment,evolution,and adaptation.In the perception-decision-execution experiment,multiple RL agents automatically interact with the ever-changing environments and integrate a post-hoc explainability to visualize the decision-making process.The hidden objects,such as vehicle cluster and experimenter,can freely scale,race,and track in the invisible space,with the environmental similarity of 99.5%.Our results constitute a monumental stride to reshape the evolutionary landscape of metasurfaces from individual to swarm intelligence and usher in the remote management of entire EM space.
基金National Natural Science Foundation of China(62027812,62333012)。
文摘Volumetric imaging is increasingly in demand for its precision in statistically visualizing and analyzing the intricacies of biological phenomena.To visualize the intricate details of these minute structures and facilitate the analysis in biomedical research,high-signal-to-noise ratio(SNR)images are indispensable.However,the inevitable noise presents a significant barrier to imaging qualities.Here,we propose SelfMirror,a self-supervised deep-learning denoising method for volumetric image reconstruction.SelfMirror is developed based on the insight that the variation of biological structure is continuous and smooth;when the sampling interval in volumetric imaging is sufficiently small,the similarity of neighboring slices in terms of the spatial structure becomes apparent.Such similarity can be used to train our proposed network to revive the signals and suppress the noise accurately.The denoising performance of SelfMirror exhibits remarkable robustness and fidelity even in extremely low-SNR conditions.We demonstrate the broad applicability of SelfMirror on multiple imaging modalities,including two-photon microscopy,confocal microscopy,expansion microscopy,computed tomography,and 3D electron microscopy.This versatility extends from single neuron cells to tissues and organs,highlighting SelfMirror's potential for integration into diverse imaging and analysis pipelines.
文摘The development of intelligent control techniques provides powerful means for the control of machine tools. In this paper, a intelligent control technique and an algorithm for precision control of CNC grinding of ceramic chips are introduced. In the process of ceramic chip CNC grinding, the dimension of the chips tends to get larger and the dimensional error to exceed the tolerance as the number of the chips increases which are machined on the same part program. There are many factors leading to the occurrence of the error and the law of error variation is very complicated. With the introduced intelligent self learning error compensation technique, the CNC system can improve the control strategy to compensate the error automatically. The simulational result is also given.
文摘EarthCaching is a real success story in how Earth science can benefit by working with successful community programs that link self-learning,technology,and the thrill of outdoor adventure.With over 11,000 Earth science sites worldwide visited by 1.3 million people,it is making a real impact toward bringing Earth science to the wider community.
文摘A composite control strategy for the precalciner exit temperature in cement kiln is introduced based on a mathematical model. In this model, the raw meal flow, coal powder flow and wind flow are taken as three input variables, the clinker fow and exit teperature of cement kiln are output variables, and other influencing factors are considered as disturbance. A composite control system is synthesied by integrating self learning PID, fuzzy and feedforward function into a combined controller, and the arithmetics for the self learning PID controller, fuzzy controller and feedforward controller are elaborated respectively. The control strategy has been realized by software in real practice at cement factory. Application results show that the composite control technology is superior to the general PID control in control effect, and is suitable to the industrial process control with slow parameter variation, nonlinearity and uncertainty.
