This article briefly reviews the topic of complex network synchronization,with its graph-theoretic criterion,showing that the homogeneous and symmetrical network structures are essential for optimal synchronization.Fu...This article briefly reviews the topic of complex network synchronization,with its graph-theoretic criterion,showing that the homogeneous and symmetrical network structures are essential for optimal synchronization.Furthermore,it briefly reviews the notion of higher-order network topologies and shows their promising potential in application to evaluating the optimality of network synchronizability.展开更多
Information spreading has been investigated for many years,but the mechanism of why the information explosively catches on overnight is still under debate.This explosive spreading phenomenon was usually considered dri...Information spreading has been investigated for many years,but the mechanism of why the information explosively catches on overnight is still under debate.This explosive spreading phenomenon was usually considered driven separately by social reinforcement or higher-order interactions.However,due to the limitations of empirical data and theoretical analysis,how the higher-order network structure affects the explosive information spreading under the role of social reinforcement has not been fully explored.In this work,we propose an information-spreading model by considering the social reinforcement in real and synthetic higher-order networks,describable as hypergraphs.Depending on the average group size(hyperedge cardinality)and node membership(hyperdegree),we observe two different spreading behaviors:(i)The spreading progress is not sensitive to social reinforcement,resulting in the information localized in a small part of nodes;(ii)a strong social reinforcement will promote the large-scale spread of information and induce an explosive transition.Moreover,a large average group size and membership would be beneficial to the appearance of the explosive transition.Further,we display that the heterogeneity of the node membership and group size distributions benefit the information spreading.Finally,we extend the group-based approximate master equations to verify the simulation results.Our findings may help us to comprehend the rapidly information-spreading phenomenon in modern society.展开更多
As an important scheme of future global mobile satellite communication systems to provide multimedia service, a Double-Layer Satellite Network （DLSN） with MEO satellites and LEO satellites is proposed. The Inter-Orb...As an important scheme of future global mobile satellite communication systems to provide multimedia service, a Double-Layer Satellite Network （DLSN） with MEO satellites and LEO satellites is proposed. The Inter-Orbit-Links （IOLs） between layers is an essential factor, which affects the performances of the DLSN systems. Considering certain constellation parameters, the geometric characteristics of IOLs are described and the connectivity of MEO satellites and LEO satellites in the DLSN is analyzed. By computer simulation, the results show that IOLs should be selectively established according to certain parameters rather than the simple in-sight principle.展开更多
Nowadays,carbon frameworks derived from natural biomaterials have attracted extensive attention for electromagnetic interference(EMI)shielding due to their renewability and affordability.However,it is critical and cha...Nowadays,carbon frameworks derived from natural biomaterials have attracted extensive attention for electromagnetic interference(EMI)shielding due to their renewability and affordability.However,it is critical and challenging to achieve effective regulation of shielding effectiveness(SE)as well as weaken the strong EM reflection of highly conductive biomass-based carbon materials.Herein,commercial cotton pads with oriented structure were selected as carbonaceous precursor to fabricate aligned carbon networks by pyrolysis,and the EMI SE of the samples with increased temperature of 800-1000℃ can be accurately controlled in the effective range of~21.7-29.1,~27.7-37.1 and~32.7-43.3 d B with high reflection coefficient of>0.8 by changing the cross-angle between the electric-field direction of incident EM waves and the fiber-orientation direction due to the occurrence of opposite internal electric field.Moreover,the further construction of Salisbury absorber-liked double-layer structure could result in an ultralow reflection coefficient of only~0.06 but enhanced SE variation range up to~38.7-49.3 d B during the adjustment of cross-angle,possibly due to the destructive interference of EM waves in the double-layer carbon networks.This work would provide a simple and effective way for constructing high-performance biomass carbon materials with adjustable EMI shielding and ultra-low reflectivity.展开更多
In the current information society, the dissemination mechanisms and evolution laws of individual or collective opinions and their behaviors are the research hot topics in the field of opinion dynamics. First, in this...In the current information society, the dissemination mechanisms and evolution laws of individual or collective opinions and their behaviors are the research hot topics in the field of opinion dynamics. First, in this paper, a two-layer network consisting of an individual-opinion layer and a collective-opinion layer is constructed, and a dissemination model of opinions incorporating higher-order interactions(i.e. OIHOI dissemination model) is proposed. Furthermore, the dynamic equations of opinion dissemination for both individuals and groups are presented. Using Lyapunov's first method,two equilibrium points, including the negative consensus point and positive consensus point, and the dynamic equations obtained for opinion dissemination, are analyzed theoretically. In addition, for individual opinions and collective opinions,some conditions for reaching negative consensus and positive consensus as well as the theoretical expression for the dissemination threshold are put forward. Numerical simulations are carried to verify the feasibility and effectiveness of the proposed theoretical results, as well as the influence of the intra-structure, inter-connections, and higher-order interactions on the dissemination and evolution of individual opinions. The main results are as follows.(i) When the intra-structure of the collective-opinion layer meets certain characteristics, then a negative or positive consensus is easier to reach for individuals.(ii) Both negative consensus and positive consensus perform best in mixed type of inter-connections in the two-layer network.(iii) Higher-order interactions can quickly eliminate differences in individual opinions, thereby enabling individuals to reach consensus faster.展开更多
The synchronizability of multiplex undirected regular networks has been intensively studied based on the study of the synchronizability of single-layer networks. However, most real networks are characterized by some d...The synchronizability of multiplex undirected regular networks has been intensively studied based on the study of the synchronizability of single-layer networks. However, most real networks are characterized by some degree of directionality. So multiplex directed networks can better explain the synchronizability phenomenon. Here, based on the theory of master stability function (MSF), we study the eigenvalue spectrum and synchronizability of double-layer inter-layer directed ring networks (Networks-A) and double-layer intra-layer directed ring networks (Networks-B). The eigenvalue spectrum of the supra-Laplacian matrix of the networks is rigorously derived, and the influence of the networks structure parameters on the network’s synchronizability is analyzed. The correctness of the theory is further verified by numerical simulation analysis. Finally, the synchronizability of four kinds of double-layer ring networks with different coupling modes, namely, Networks-A, Networks-B, Networks-C (double-layer undirected ring networks), and Networks-D (double-layer undirected inter-layer random-added-edge ring networks), is compared and the results can provide guidance for constructing the optimal synchronization network.展开更多
Previous studies presented the phase diagram induced by the disorder existing separately either in the higher-order topological states or in the topological trivial states, respectively. However, the influence of diso...Previous studies presented the phase diagram induced by the disorder existing separately either in the higher-order topological states or in the topological trivial states, respectively. However, the influence of disorder on the system with the coexistence of the higher-order topological states and other traditional topological states has not been investigated. In this paper, we investigate the disorder induced phase transition in the magnetic higher-order topological insulator. By using the convolutional neural network and non-commutative geometry methods, two independent phase diagrams are calculated.With the comparison between these two diagrams, a topological transition from the normal insulator to the Chern insulator is confirmed. Furthermore, the network based on eigenstate wavefunction studies also presents a transition between the higher-order topological insulator and the Chern insulator.展开更多
The present study investigates the quest for a fully distributed Nash equilibrium(NE) in networked non-cooperative games, with particular emphasis on actuator limitations. Existing distributed NE seeking approaches of...The present study investigates the quest for a fully distributed Nash equilibrium(NE) in networked non-cooperative games, with particular emphasis on actuator limitations. Existing distributed NE seeking approaches often overlook practical input constraints or rely on centralized information. To address these issues, a novel edge-based double-layer adaptive control framework is proposed. Specifically, adaptive scaling parameters are embedded into the edge weights of the communication graph, enabling a fully distributed scheme that avoids dependence on centralized or global knowledge. Every participant modifies its strategy by exclusively utilizing local information and communicating with its neighbors to iteratively approach the NE. By incorporating damping terms into the design of the adaptive parameters, the proposed approach effectively suppresses unbounded parameter growth and consequently guarantees the boundedness of the adaptive gains. In addition, to account for actuator saturation, the proposed distributed NE seeking approach incorporates a saturation function, which ensures that control inputs do not exceed allowable ranges. A rigorous Lyapunov-based analysis guarantees the convergence and boundedness of all system variables. Finally, the presentation of simulation results aims to validate the efficacy and theoretical soundness of the proposed approach.展开更多
The Laplacian eigenvalue spectrum of a complex network contains a great deal of information about the network topology and dynamics,particularly affecting the network synchronization process and performance.This artic...The Laplacian eigenvalue spectrum of a complex network contains a great deal of information about the network topology and dynamics,particularly affecting the network synchronization process and performance.This article briefly reviews the recent progress in the studies of network synchronizability,regarding its spectral criteria and topological optimization,and explores the role of higher-order topologies in measuring the optimal synchronizability of large-scale complex networks.展开更多
Due to the complex and changeable environment under water,the performance of traditional DOA estimation algorithms based on mathematical model,such as MUSIC,ESPRIT,etc.,degrades greatly or even some mistakes can be ma...Due to the complex and changeable environment under water,the performance of traditional DOA estimation algorithms based on mathematical model,such as MUSIC,ESPRIT,etc.,degrades greatly or even some mistakes can be made because of the mismatch between algorithm model and actual environment model.In addition,the neural network has the ability of generalization and mapping,it can consider the noise,transmission channel inconsistency and other factors of the objective environment.Therefore,this paper utilizes Back Propagation(BP)neural network as the basic framework of underwater DOA estimation.Furthermore,in order to improve the performance of DOA estimation of BP neural network,the following three improvements are proposed.(1)Aiming at the problem that the weight and threshold of traditional BP neural network converge slowly and easily fall into the local optimal value in the iterative process,PSO-BP-NN based on optimized particle swarm optimization(PSO)algorithm is proposed.(2)The Higher-order cumulant of the received signal is utilized to establish the training model.(3)A BP neural network training method for arbitrary number of sources is proposed.Finally,the effectiveness of the proposed algorithm is proved by comparing with the state-of-the-art algorithms and MUSIC algorithm.展开更多
Neuromorphic computing,inspired by the human brain,uses memristor devices for complex tasks.Recent studies show that self-organizing random nanowires can implement neuromorphic information processing,enabling data ana...Neuromorphic computing,inspired by the human brain,uses memristor devices for complex tasks.Recent studies show that self-organizing random nanowires can implement neuromorphic information processing,enabling data analysis.This paper presents a model based on these nanowire networks,with an improved conductance variation profile.We suggest using these networks for temporal information processing via a reservoir computing scheme and propose an efficient data encoding method using voltage pulses.The nanowire network layer generates dynamic behaviors for pulse voltages,allowing time series prediction analysis.Our experiment uses a double stochastic nanowire network architecture for processing multiple input signals,outperforming traditional reservoir computing in terms of fewer nodes,enriched dynamics and improved prediction accuracy.Experimental results confirm the high accuracy of this architecture on multiple real-time series datasets,making neuromorphic nanowire networks promising for physical implementation of reservoir computing.展开更多
Consensus of higher-order multi-agent systems based on event-triggered control and signed network is studied in this paper,where signed multi-agent systems control protocol takes hostile relations into account between...Consensus of higher-order multi-agent systems based on event-triggered control and signed network is studied in this paper,where signed multi-agent systems control protocol takes hostile relations into account between different agents.Event-triggered protocol for the second-order signed multi-agent systems is proposed.Similar results are obtained for high-order signed multiagent systems.In addition,for the two control strategies proposed in this paper,it is proved that Zeno behaviours do not exist.Finally,simulation examples are presented to verify the correctness of the results.展开更多
This paper explores the synchronization of stochastic simplicial complexes with noise,modeled by stochastic differential equations of It?type.It establishes the relationship between synchronization and individual dyna...This paper explores the synchronization of stochastic simplicial complexes with noise,modeled by stochastic differential equations of It?type.It establishes the relationship between synchronization and individual dynamics,higher-order structures,coupling strengths,and noise.In particular,this study delves into the role of multi-body interactions,particularly focusing on the influence of higher-order simplicial structures on the overall synchronization behavior.Furthermore,the effects of noise on synchronizability in the stochastic simplicial complex are thoroughly examined.The obtained results indicate that the effects of noise on the synchronizability vary with the manner in which noise propagates.The presence of noise can regulate the synchronization pattern of the simplicial complex,transforming the unstable state into a stable state,and vice versa.These findings offer valuable insights and a theoretical foundation for improving the performance of real-world networks,such as communication networks,biological systems,and social networks,where noise is often inevitable.展开更多
Currently,the link prediction algorithms primarily focus on studying the interaction between nodes based on chain structure and star structure,which predominantly rely on low-order structural information and do not ex...Currently,the link prediction algorithms primarily focus on studying the interaction between nodes based on chain structure and star structure,which predominantly rely on low-order structural information and do not explore the multivariate interactions between nodes from the perspective of higher-order structural information present in the network.The cycle structure is a higher-order structure that lies between the star and clique structures,where all nodes within the same cycle can interact with each other,even in the absence of direct edges.If a node is encompassed by multiple cycles,it indicates that the node interacts and associates with a greater number of nodes in the network,and it means the node is more important in the network to some extent.Furthermore,if two nodes are included in multiple cycles,it signifies the two nodes are more likely to be connected.Therefore,firstly,a multi-information fusion node importance algorithm based on the cycle structure information is proposed,which integrates both high-order and low-order structural information.Secondly,the obtained integrated structure information and node feature information is regarded as the input features,a two-channel graph neural network model is designed to learn the cycle structure information.Then,the cycle structure information is utilised for the task of link prediction,and a graph neural link predictor with multi-information interactions based on the cycle structure is developed.Finally,extensive experimental validation and analysis show that the node ranking result of the proposed node importance index is more consistent with the actual situation,the proposed graph neural network model can effectively learn the cycle structure information,and using higher-order structural information—cycle information proves to significantly enhance the overall link prediction performance.展开更多
Considering dynamical disease spreading network consisting of moving individuals,a new double-layer network is constructed,one where the information dissemination process takes place and the other where the dynamics o...Considering dynamical disease spreading network consisting of moving individuals,a new double-layer network is constructed,one where the information dissemination process takes place and the other where the dynamics of disease spreading evolves.On the basis of Markov chains theory,a new model characterizing the coupled dynamics between information dissemination and disease spreading in populations of moving agents is established and corresponding state probability equations are formulated to describe the probability in each state of every node at each moment.Monte Carlo simulations are performed to characterize the interaction process between information and disease spreading and investigate factors that influence spreading dynamics.Simulation results show that the increasing of information transmission rate can reduce the scale of disease spreading in some degree.Shortening infection period and strengthening consciousness for self-protection by decreasing individual’s scope of activity both can effectively reduce the final refractory density for the disease but have less effect on the information dissemination.In addition,the increasing of vaccination rate or decreasing of long-range travel can also reduce the scale of disease spreading.展开更多
With COVID-19 continuing to rage around the world,there is a spread of epidemic-related information on social networking platforms.This phenomenon may inhibit or promote the scale of epidemic transmission.This study c...With COVID-19 continuing to rage around the world,there is a spread of epidemic-related information on social networking platforms.This phenomenon may inhibit or promote the scale of epidemic transmission.This study constructed a double-layer epidemic spreading–information dissemination network based on the movements of individuals across regions to analyze the dynamic evolution and coupling mechanism of information dissemination and epidemic transmission.We also proposed measures to control the spread of the epidemic by analyzing the factors affecting dynamic transmission.We constructed a state probability equation based on Markov chain theory and performed Monte Carlo simulations to demonstrate the interaction between information dissemination and epidemic transmission.The simulation results showed that the higher the information dissemination rate,the larger the scale of information dissemination and the smaller the scale of epidemic transmission.In addition,the higher the recovery rate of the epidemic or the lower the infection rate of the epidemic,the smaller the scale of information dissemination and the smaller the scale of epidemic transmission.Moreover,the greater the probability of individuals moving across regions,the larger the spread of the epidemic and information.Finally,the higher the probability of an individual taking preventive behavior,the smaller the spread of the epidemic and information.Therefore,it is possible to suppress epidemic spread by increasing the information dissemination rate,epidemic recovery rate,and probability of individuals taking preventive behavior,while also reducing the infection rate of the epidemic and appropriately implementing regional blockades.展开更多
This paper proposes an approach to detecting diseases in neem leaf that uses a Fuzzy-Higher Order Biologically Inspired Neuron Model(F-HOBINM)and adaptive neuro classifier(ANFIS).India exports USD 0.28-million worth o...This paper proposes an approach to detecting diseases in neem leaf that uses a Fuzzy-Higher Order Biologically Inspired Neuron Model(F-HOBINM)and adaptive neuro classifier(ANFIS).India exports USD 0.28-million worth of neem leaf to the UK,USA,UAE,and Europe in the form of dried leaves and powder,both of which help reduce diabetesrelated issues,cardiovascular problems,and eye disorders.Diagnosing neem leaf disease is difficult through visual interpretation,owing to similarity in their color and texture patterns.The most common diseases include bacterial blight,Colletotrichum and Alternaria leaf spot,blight,damping-off,powdery mildew,Pseudocercospora leaf spot,leaf web blight,and seedling wilt.However,traditional color and texture algorithms fail to identify leaf diseases due to irregular lumps and surfaces,and rough ridges,as the classification time involved takes as long as a week.The proposed F-HOBINM algorithm recognizes the leaf intensity through the leaky capacitor,and uses subjective intensity and physical stimulus to interpret the diagnosis.Further,the processed leaf images from the HOBINM algorithm are applied to the ANFIS classifier to identify neem leaf diseases.The experimental results show 92.18%accuracy from a database of 1,462 neem leaves.展开更多
We propose a novel hybrid algorithm of BP neural ytetwork and apply it to blind equalization.The algorithm combines the merits of Rosario algorithm and random optimization method. Its cost function has strict convex c...We propose a novel hybrid algorithm of BP neural ytetwork and apply it to blind equalization.The algorithm combines the merits of Rosario algorithm and random optimization method. Its cost function has strict convex character (after a threshold) and the algorithm converges much faster than the BPmethod[2], as an exalople, we evaluate its performance by using it into blind equalization. With the help ofHigher Order Culnulants (HOC), the blind equalization scheme converges much faster than the CMAalgorithm and superior to the Back-propagation,nethod[2] due to its ability of finding the optimal solutionwith relatively fewer iteration stops.展开更多
The orchestrated expression of thousands of genes gives rise to the complexity of the human brain.However,the structures governing these myriad gene-gene interactions remain unclear.By analyzing transcription data fro...The orchestrated expression of thousands of genes gives rise to the complexity of the human brain.However,the structures governing these myriad gene-gene interactions remain unclear.By analyzing transcription data from more than 2000 sites in six human brains,we found that pairwise interactions between genes,without considering any higher-order interactions,are sufficient to predict the transcriptional pattern of the genome for individual brain regions and the transcriptional profile of the entire brain consisting of more than 200 areas.These findings suggest a quadratic complexity of transcriptional patterns in the human brain,which is much simpler than expected.In addition,using a pairwise interaction model,we revealed that the strength of gene-gene interactions in the human brain gives rise to the nearly maximal number of transcriptional clusters,which may account for the functional and structural richness of the brain.展开更多
基金Hong Kong Research Grants Council under the GRF(9043664).
文摘This article briefly reviews the topic of complex network synchronization,with its graph-theoretic criterion,showing that the homogeneous and symmetrical network structures are essential for optimal synchronization.Furthermore,it briefly reviews the notion of higher-order network topologies and shows their promising potential in application to evaluating the optimality of network synchronizability.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12305043 and 12165016)the Natural Science Foundation of Jiangsu Province(Grant No.BK20220511)+1 种基金the Project of Undergraduate Scientific Research(Grant No.22A684)the support from the Jiangsu Specially-Appointed Professor Program。
文摘Information spreading has been investigated for many years,but the mechanism of why the information explosively catches on overnight is still under debate.This explosive spreading phenomenon was usually considered driven separately by social reinforcement or higher-order interactions.However,due to the limitations of empirical data and theoretical analysis,how the higher-order network structure affects the explosive information spreading under the role of social reinforcement has not been fully explored.In this work,we propose an information-spreading model by considering the social reinforcement in real and synthetic higher-order networks,describable as hypergraphs.Depending on the average group size(hyperedge cardinality)and node membership(hyperdegree),we observe two different spreading behaviors:(i)The spreading progress is not sensitive to social reinforcement,resulting in the information localized in a small part of nodes;(ii)a strong social reinforcement will promote the large-scale spread of information and induce an explosive transition.Moreover,a large average group size and membership would be beneficial to the appearance of the explosive transition.Further,we display that the heterogeneity of the node membership and group size distributions benefit the information spreading.Finally,we extend the group-based approximate master equations to verify the simulation results.Our findings may help us to comprehend the rapidly information-spreading phenomenon in modern society.
基金National Natural Science Foundation of China(60532030)
文摘As an important scheme of future global mobile satellite communication systems to provide multimedia service, a Double-Layer Satellite Network （DLSN） with MEO satellites and LEO satellites is proposed. The Inter-Orbit-Links （IOLs） between layers is an essential factor, which affects the performances of the DLSN systems. Considering certain constellation parameters, the geometric characteristics of IOLs are described and the connectivity of MEO satellites and LEO satellites in the DLSN is analyzed. By computer simulation, the results show that IOLs should be selectively established according to certain parameters rather than the simple in-sight principle.
基金financial supports from Natural Science Foundation of Ningbo(202003N4026)S&T Innovation 2025 Major Special Programme of Ningbo(2018B10054)National Natural Science Foundation of China(62001065 and 51603218)。
文摘Nowadays,carbon frameworks derived from natural biomaterials have attracted extensive attention for electromagnetic interference(EMI)shielding due to their renewability and affordability.However,it is critical and challenging to achieve effective regulation of shielding effectiveness(SE)as well as weaken the strong EM reflection of highly conductive biomass-based carbon materials.Herein,commercial cotton pads with oriented structure were selected as carbonaceous precursor to fabricate aligned carbon networks by pyrolysis,and the EMI SE of the samples with increased temperature of 800-1000℃ can be accurately controlled in the effective range of~21.7-29.1,~27.7-37.1 and~32.7-43.3 d B with high reflection coefficient of>0.8 by changing the cross-angle between the electric-field direction of incident EM waves and the fiber-orientation direction due to the occurrence of opposite internal electric field.Moreover,the further construction of Salisbury absorber-liked double-layer structure could result in an ultralow reflection coefficient of only~0.06 but enhanced SE variation range up to~38.7-49.3 d B during the adjustment of cross-angle,possibly due to the destructive interference of EM waves in the double-layer carbon networks.This work would provide a simple and effective way for constructing high-performance biomass carbon materials with adjustable EMI shielding and ultra-low reflectivity.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.72031009 and 61473338)。
文摘In the current information society, the dissemination mechanisms and evolution laws of individual or collective opinions and their behaviors are the research hot topics in the field of opinion dynamics. First, in this paper, a two-layer network consisting of an individual-opinion layer and a collective-opinion layer is constructed, and a dissemination model of opinions incorporating higher-order interactions(i.e. OIHOI dissemination model) is proposed. Furthermore, the dynamic equations of opinion dissemination for both individuals and groups are presented. Using Lyapunov's first method,two equilibrium points, including the negative consensus point and positive consensus point, and the dynamic equations obtained for opinion dissemination, are analyzed theoretically. In addition, for individual opinions and collective opinions,some conditions for reaching negative consensus and positive consensus as well as the theoretical expression for the dissemination threshold are put forward. Numerical simulations are carried to verify the feasibility and effectiveness of the proposed theoretical results, as well as the influence of the intra-structure, inter-connections, and higher-order interactions on the dissemination and evolution of individual opinions. The main results are as follows.(i) When the intra-structure of the collective-opinion layer meets certain characteristics, then a negative or positive consensus is easier to reach for individuals.(ii) Both negative consensus and positive consensus perform best in mixed type of inter-connections in the two-layer network.(iii) Higher-order interactions can quickly eliminate differences in individual opinions, thereby enabling individuals to reach consensus faster.
文摘The synchronizability of multiplex undirected regular networks has been intensively studied based on the study of the synchronizability of single-layer networks. However, most real networks are characterized by some degree of directionality. So multiplex directed networks can better explain the synchronizability phenomenon. Here, based on the theory of master stability function (MSF), we study the eigenvalue spectrum and synchronizability of double-layer inter-layer directed ring networks (Networks-A) and double-layer intra-layer directed ring networks (Networks-B). The eigenvalue spectrum of the supra-Laplacian matrix of the networks is rigorously derived, and the influence of the networks structure parameters on the network’s synchronizability is analyzed. The correctness of the theory is further verified by numerical simulation analysis. Finally, the synchronizability of four kinds of double-layer ring networks with different coupling modes, namely, Networks-A, Networks-B, Networks-C (double-layer undirected ring networks), and Networks-D (double-layer undirected inter-layer random-added-edge ring networks), is compared and the results can provide guidance for constructing the optimal synchronization network.
基金Project supported by the National Natural Science Foundation of China(Grant No.11822407)the Priority Academic Program Development of Jiangsu Higher Education Institutions,China
文摘Previous studies presented the phase diagram induced by the disorder existing separately either in the higher-order topological states or in the topological trivial states, respectively. However, the influence of disorder on the system with the coexistence of the higher-order topological states and other traditional topological states has not been investigated. In this paper, we investigate the disorder induced phase transition in the magnetic higher-order topological insulator. By using the convolutional neural network and non-commutative geometry methods, two independent phase diagrams are calculated.With the comparison between these two diagrams, a topological transition from the normal insulator to the Chern insulator is confirmed. Furthermore, the network based on eigenstate wavefunction studies also presents a transition between the higher-order topological insulator and the Chern insulator.
基金supported by the National Natural Science Foundation of China (Grant No.62173009)the National Key Research and Development Program of China (Grant No.2021ZD0112302)。
文摘The present study investigates the quest for a fully distributed Nash equilibrium(NE) in networked non-cooperative games, with particular emphasis on actuator limitations. Existing distributed NE seeking approaches often overlook practical input constraints or rely on centralized information. To address these issues, a novel edge-based double-layer adaptive control framework is proposed. Specifically, adaptive scaling parameters are embedded into the edge weights of the communication graph, enabling a fully distributed scheme that avoids dependence on centralized or global knowledge. Every participant modifies its strategy by exclusively utilizing local information and communicating with its neighbors to iteratively approach the NE. By incorporating damping terms into the design of the adaptive parameters, the proposed approach effectively suppresses unbounded parameter growth and consequently guarantees the boundedness of the adaptive gains. In addition, to account for actuator saturation, the proposed distributed NE seeking approach incorporates a saturation function, which ensures that control inputs do not exceed allowable ranges. A rigorous Lyapunov-based analysis guarantees the convergence and boundedness of all system variables. Finally, the presentation of simulation results aims to validate the efficacy and theoretical soundness of the proposed approach.
基金supported by the Hong Kong Research Grants Council under the GRF Grant City U11206320
文摘The Laplacian eigenvalue spectrum of a complex network contains a great deal of information about the network topology and dynamics,particularly affecting the network synchronization process and performance.This article briefly reviews the recent progress in the studies of network synchronizability,regarding its spectral criteria and topological optimization,and explores the role of higher-order topologies in measuring the optimal synchronizability of large-scale complex networks.
基金Strategic Priority Research Program of Chinese Academy of Sciences,Grant No.XDA28040000,XDA28120000Natural Science Foundation of Shandong Province,Grant No.ZR2021MF094+2 种基金Key R&D Plan of Shandong Province,Grant No.2020CXGC010804Central Leading Local Science and Technology Development Special Fund Project,Grant No.YDZX2021122Science&Technology Specific Projects in Agricultural High-tech Industrial Demonstration Area of the Yellow River Delta,Grant No.2022SZX11。
文摘Due to the complex and changeable environment under water,the performance of traditional DOA estimation algorithms based on mathematical model,such as MUSIC,ESPRIT,etc.,degrades greatly or even some mistakes can be made because of the mismatch between algorithm model and actual environment model.In addition,the neural network has the ability of generalization and mapping,it can consider the noise,transmission channel inconsistency and other factors of the objective environment.Therefore,this paper utilizes Back Propagation(BP)neural network as the basic framework of underwater DOA estimation.Furthermore,in order to improve the performance of DOA estimation of BP neural network,the following three improvements are proposed.(1)Aiming at the problem that the weight and threshold of traditional BP neural network converge slowly and easily fall into the local optimal value in the iterative process,PSO-BP-NN based on optimized particle swarm optimization(PSO)algorithm is proposed.(2)The Higher-order cumulant of the received signal is utilized to establish the training model.(3)A BP neural network training method for arbitrary number of sources is proposed.Finally,the effectiveness of the proposed algorithm is proved by comparing with the state-of-the-art algorithms and MUSIC algorithm.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. U20A20227,62076208, and 62076207)Chongqing Talent Plan “Contract System” Project (Grant No. CQYC20210302257)+3 种基金National Key Laboratory of Smart Vehicle Safety Technology Open Fund Project (Grant No. IVSTSKL-202309)the Chongqing Technology Innovation and Application Development Special Major Project (Grant No. CSTB2023TIAD-STX0020)College of Artificial Intelligence, Southwest UniversityState Key Laboratory of Intelligent Vehicle Safety Technology
文摘Neuromorphic computing,inspired by the human brain,uses memristor devices for complex tasks.Recent studies show that self-organizing random nanowires can implement neuromorphic information processing,enabling data analysis.This paper presents a model based on these nanowire networks,with an improved conductance variation profile.We suggest using these networks for temporal information processing via a reservoir computing scheme and propose an efficient data encoding method using voltage pulses.The nanowire network layer generates dynamic behaviors for pulse voltages,allowing time series prediction analysis.Our experiment uses a double stochastic nanowire network architecture for processing multiple input signals,outperforming traditional reservoir computing in terms of fewer nodes,enriched dynamics and improved prediction accuracy.Experimental results confirm the high accuracy of this architecture on multiple real-time series datasets,making neuromorphic nanowire networks promising for physical implementation of reservoir computing.
基金supported by the National Natural Science Foundation of China under grants[grant number 61573138].
文摘Consensus of higher-order multi-agent systems based on event-triggered control and signed network is studied in this paper,where signed multi-agent systems control protocol takes hostile relations into account between different agents.Event-triggered protocol for the second-order signed multi-agent systems is proposed.Similar results are obtained for high-order signed multiagent systems.In addition,for the two control strategies proposed in this paper,it is proved that Zeno behaviours do not exist.Finally,simulation examples are presented to verify the correctness of the results.
基金Project supported in part by the National Natural Science Foundation of China(Grant Nos.62473284,61973064,62203327)Hebei Natural Science Foundation(Grant No.F2022501024)。
文摘This paper explores the synchronization of stochastic simplicial complexes with noise,modeled by stochastic differential equations of It?type.It establishes the relationship between synchronization and individual dynamics,higher-order structures,coupling strengths,and noise.In particular,this study delves into the role of multi-body interactions,particularly focusing on the influence of higher-order simplicial structures on the overall synchronization behavior.Furthermore,the effects of noise on synchronizability in the stochastic simplicial complex are thoroughly examined.The obtained results indicate that the effects of noise on the synchronizability vary with the manner in which noise propagates.The presence of noise can regulate the synchronization pattern of the simplicial complex,transforming the unstable state into a stable state,and vice versa.These findings offer valuable insights and a theoretical foundation for improving the performance of real-world networks,such as communication networks,biological systems,and social networks,where noise is often inevitable.
基金National Key Research and Development Program of China,Grant/Award Number:2020YFC1523300Construction of Innovation Platform Program of Qinghai Province of China,Grant/Award Number:2022-ZJ-T02。
文摘Currently,the link prediction algorithms primarily focus on studying the interaction between nodes based on chain structure and star structure,which predominantly rely on low-order structural information and do not explore the multivariate interactions between nodes from the perspective of higher-order structural information present in the network.The cycle structure is a higher-order structure that lies between the star and clique structures,where all nodes within the same cycle can interact with each other,even in the absence of direct edges.If a node is encompassed by multiple cycles,it indicates that the node interacts and associates with a greater number of nodes in the network,and it means the node is more important in the network to some extent.Furthermore,if two nodes are included in multiple cycles,it signifies the two nodes are more likely to be connected.Therefore,firstly,a multi-information fusion node importance algorithm based on the cycle structure information is proposed,which integrates both high-order and low-order structural information.Secondly,the obtained integrated structure information and node feature information is regarded as the input features,a two-channel graph neural network model is designed to learn the cycle structure information.Then,the cycle structure information is utilised for the task of link prediction,and a graph neural link predictor with multi-information interactions based on the cycle structure is developed.Finally,extensive experimental validation and analysis show that the node ranking result of the proposed node importance index is more consistent with the actual situation,the proposed graph neural network model can effectively learn the cycle structure information,and using higher-order structural information—cycle information proves to significantly enhance the overall link prediction performance.
基金This research has been supported by the National Natural Science Foundation of China(Grant No.61672298 and 61373136)the National Social Science Foundation of China(Grant No.13BTQ046)+3 种基金the High-level Introduction of Talent Scientific Research Start-up Fund of Jiangsu Police Institute(Grant No.JSPI17GKZL403)the Scientific Research Program of Jiangsu Police Institute(Grant No.2017SJYZQ01)the Science and Technology Plan Projects of Jiangsu Province(Grant No.BE2017067)and the Research Foundation for Humanities and Social Sciences of Ministry of Education of China(Grant No.15YJAZH016).
文摘Considering dynamical disease spreading network consisting of moving individuals,a new double-layer network is constructed,one where the information dissemination process takes place and the other where the dynamics of disease spreading evolves.On the basis of Markov chains theory,a new model characterizing the coupled dynamics between information dissemination and disease spreading in populations of moving agents is established and corresponding state probability equations are formulated to describe the probability in each state of every node at each moment.Monte Carlo simulations are performed to characterize the interaction process between information and disease spreading and investigate factors that influence spreading dynamics.Simulation results show that the increasing of information transmission rate can reduce the scale of disease spreading in some degree.Shortening infection period and strengthening consciousness for self-protection by decreasing individual’s scope of activity both can effectively reduce the final refractory density for the disease but have less effect on the information dissemination.In addition,the increasing of vaccination rate or decreasing of long-range travel can also reduce the scale of disease spreading.
基金supported by National Natural Science Foundation of China(Grant No.71673256).
文摘With COVID-19 continuing to rage around the world,there is a spread of epidemic-related information on social networking platforms.This phenomenon may inhibit or promote the scale of epidemic transmission.This study constructed a double-layer epidemic spreading–information dissemination network based on the movements of individuals across regions to analyze the dynamic evolution and coupling mechanism of information dissemination and epidemic transmission.We also proposed measures to control the spread of the epidemic by analyzing the factors affecting dynamic transmission.We constructed a state probability equation based on Markov chain theory and performed Monte Carlo simulations to demonstrate the interaction between information dissemination and epidemic transmission.The simulation results showed that the higher the information dissemination rate,the larger the scale of information dissemination and the smaller the scale of epidemic transmission.In addition,the higher the recovery rate of the epidemic or the lower the infection rate of the epidemic,the smaller the scale of information dissemination and the smaller the scale of epidemic transmission.Moreover,the greater the probability of individuals moving across regions,the larger the spread of the epidemic and information.Finally,the higher the probability of an individual taking preventive behavior,the smaller the spread of the epidemic and information.Therefore,it is possible to suppress epidemic spread by increasing the information dissemination rate,epidemic recovery rate,and probability of individuals taking preventive behavior,while also reducing the infection rate of the epidemic and appropriately implementing regional blockades.
文摘This paper proposes an approach to detecting diseases in neem leaf that uses a Fuzzy-Higher Order Biologically Inspired Neuron Model(F-HOBINM)and adaptive neuro classifier(ANFIS).India exports USD 0.28-million worth of neem leaf to the UK,USA,UAE,and Europe in the form of dried leaves and powder,both of which help reduce diabetesrelated issues,cardiovascular problems,and eye disorders.Diagnosing neem leaf disease is difficult through visual interpretation,owing to similarity in their color and texture patterns.The most common diseases include bacterial blight,Colletotrichum and Alternaria leaf spot,blight,damping-off,powdery mildew,Pseudocercospora leaf spot,leaf web blight,and seedling wilt.However,traditional color and texture algorithms fail to identify leaf diseases due to irregular lumps and surfaces,and rough ridges,as the classification time involved takes as long as a week.The proposed F-HOBINM algorithm recognizes the leaf intensity through the leaky capacitor,and uses subjective intensity and physical stimulus to interpret the diagnosis.Further,the processed leaf images from the HOBINM algorithm are applied to the ANFIS classifier to identify neem leaf diseases.The experimental results show 92.18%accuracy from a database of 1,462 neem leaves.
文摘We propose a novel hybrid algorithm of BP neural ytetwork and apply it to blind equalization.The algorithm combines the merits of Rosario algorithm and random optimization method. Its cost function has strict convex character (after a threshold) and the algorithm converges much faster than the BPmethod[2], as an exalople, we evaluate its performance by using it into blind equalization. With the help ofHigher Order Culnulants (HOC), the blind equalization scheme converges much faster than the CMAalgorithm and superior to the Back-propagation,nethod[2] due to its ability of finding the optimal solutionwith relatively fewer iteration stops.
基金the National Key Research and Development Program of China(2017YFA0105203)the National Natural Science Foundation of China(81671855)+1 种基金Strategic Priority Research Program of the Chinese Academy of Sciences(XDB32040200)Beijing Academy of Artificial Intelligence,and Beijing Advanced Discipline Fund。
文摘The orchestrated expression of thousands of genes gives rise to the complexity of the human brain.However,the structures governing these myriad gene-gene interactions remain unclear.By analyzing transcription data from more than 2000 sites in six human brains,we found that pairwise interactions between genes,without considering any higher-order interactions,are sufficient to predict the transcriptional pattern of the genome for individual brain regions and the transcriptional profile of the entire brain consisting of more than 200 areas.These findings suggest a quadratic complexity of transcriptional patterns in the human brain,which is much simpler than expected.In addition,using a pairwise interaction model,we revealed that the strength of gene-gene interactions in the human brain gives rise to the nearly maximal number of transcriptional clusters,which may account for the functional and structural richness of the brain.
