The nestedness property has become an increasingly important means for devising efficient algorithms for network location problems.There have been attempts to explore the nestedness property of network location proble...The nestedness property has become an increasingly important means for devising efficient algorithms for network location problems.There have been attempts to explore the nestedness property of network location problems with some special cases of the convex ordered median objectives.However,there is little research on the nestedness property for those problems with the concave ordered median objectives.This paper constructs a tree network T and shows that the nestedness property cannot hold for the concave ordered median problem,which fills a gap in the research on the nestedness property.Finally,the authors pose an open problem on identifying the nestedness property for the continuous strategic ordered median problem.展开更多
Several data mining techniques such as Hidden Markov Model (HMM), artificial neural network, statistical techniques and expert systems are used to model network packets in the field of intrusion detection. In this pap...Several data mining techniques such as Hidden Markov Model (HMM), artificial neural network, statistical techniques and expert systems are used to model network packets in the field of intrusion detection. In this paper a novel intrusion detection mode based on understandable Neural Network Tree (NNTree) is pre-sented. NNTree is a modular neural network with the overall structure being a Decision Tree (DT), and each non-terminal node being an Expert Neural Network (ENN). One crucial advantage of using NNTrees is that they keep the non-symbolic model ENN’s capability of learning in changing environments. Another potential advantage of using NNTrees is that they are actually “gray boxes” as they can be interpreted easily if the num-ber of inputs for each ENN is limited. We showed through experiments that the trained NNTree achieved a simple ENN at each non-terminal node as well as a satisfying recognition rate of the network packets dataset. We also compared the performance with that of a three-layer backpropagation neural network. Experimental results indicated that the NNTree based intrusion detection model achieved better performance than the neural network based intrusion detection model.展开更多
Recently, a class of innovative notions on quantum network nonlocality(QNN), called full quantum network nonlocality(FQNN), have been proposed in Phys. Rev. Lett. 128 010403(2022). As the generalization of full networ...Recently, a class of innovative notions on quantum network nonlocality(QNN), called full quantum network nonlocality(FQNN), have been proposed in Phys. Rev. Lett. 128 010403(2022). As the generalization of full network nonlocality(FNN), l-level quantum network nonlocality(l-QNN) was defined in arxiv. 2306.15717 quant-ph(2024). FQNN is a NN that can be generated only from a network with all sources being non-classical. This is beyond the existing standard network nonlocality, which may be generated from a network with only a non-classical source. One of the challenging tasks is to establish corresponding Bell-like inequalities to demonstrate the FQNN or l-QNN. Up to now, the inequality criteria for FQNN and l-QNN have only been established for star and chain networks. In this paper, we devote ourselves to establishing Bell-like inequalities for networks with more complex structures. Note that star and chain networks are special kinds of tree-shaped networks. We first establish the Bell-like inequalities for verifying l-QNN in k-forked tree-shaped networks. Such results generalize the existing inequalities for star and chain networks. Furthermore, we find the Bell-like inequality criteria for l-QNN for general acyclic and cyclic networks. Finally, we discuss the demonstration of l-QNN in the well-known butterfly networks.展开更多
This study is aimed at the development of a statistical model for forecasting heavy rain in South Korea. For the 3-hour weather forecast system, the 10 km×10 km area-mean amount of rainfall at 6 stations （Seoul,...This study is aimed at the development of a statistical model for forecasting heavy rain in South Korea. For the 3-hour weather forecast system, the 10 km×10 km area-mean amount of rainfall at 6 stations （Seoul, Daejeon, Gangreung, （Jwangju, Busan, and Jeju） in South Korea are used. And the corresponding 45 synoptic factors generated by the numerical model are used as potential predictors. Four statistical forecast models （linear regression model, logistic regression model, neural network model and decision tree model） for the occurrence of heavy rain are based on the model output statistics （MOS） method. They are separately estimated by the same training data. The thresholds are considered to forecast the occurrence of heavy rain because the distribution of estimated values that are generated by each model is too skewed. The results of four models are compared via Heidke skill scores. As a result, the logistic regression model is recommended.展开更多
Generalized Farey tree network （GFTN） and generalized Farey organized pyramid network （CFOPN） model are proposed, and their topological characteristics are studied by both theoretical analysis and numerical simula...Generalized Farey tree network （GFTN） and generalized Farey organized pyramid network （CFOPN） model are proposed, and their topological characteristics are studied by both theoretical analysis and numerical simulations, which are in good accordance with each other. Then weighted GFTN is studied using cumulative distributions of its Farey number value, edge weight, and node strength. These results maybe helpful for future theoretical development of hybrid models.展开更多
Inferring network structures from available data has attracted much interest in network science;however,in many realistic networks,only some of the nodes are perceptible while others are hidden,making it a challenging...Inferring network structures from available data has attracted much interest in network science;however,in many realistic networks,only some of the nodes are perceptible while others are hidden,making it a challenging task.In this work,we develop a method for reconstructing the network with hidden nodes and links,taking account of fast-varying noise and time-delay interactions.By calculating the correlations of available data with different derivative orders for multiple pairs of accessible nodes,analyzing and integrating the relationships between different correlations,and defining diverse hidden-node-related reconstruction motifs,we can effectively identify the hidden nodes and hidden links in the network.展开更多
基金supported by the Macao Foundation under Grant No.0249National Natural Science Foundation of China under Grant No.70901050
文摘The nestedness property has become an increasingly important means for devising efficient algorithms for network location problems.There have been attempts to explore the nestedness property of network location problems with some special cases of the convex ordered median objectives.However,there is little research on the nestedness property for those problems with the concave ordered median objectives.This paper constructs a tree network T and shows that the nestedness property cannot hold for the concave ordered median problem,which fills a gap in the research on the nestedness property.Finally,the authors pose an open problem on identifying the nestedness property for the continuous strategic ordered median problem.
基金Supported in part by the National Natural Science Foundation of China (No.60272046, No.60102011), Na-tional High Technology Project of China (No.2002AA143010), Natural Science Foundation of Jiangsu Province (No.BK2001042), and the Foundation for Excellent Doctoral Dissertation of Southeast Univer-sity (No.YBJJ0412).
文摘Several data mining techniques such as Hidden Markov Model (HMM), artificial neural network, statistical techniques and expert systems are used to model network packets in the field of intrusion detection. In this paper a novel intrusion detection mode based on understandable Neural Network Tree (NNTree) is pre-sented. NNTree is a modular neural network with the overall structure being a Decision Tree (DT), and each non-terminal node being an Expert Neural Network (ENN). One crucial advantage of using NNTrees is that they keep the non-symbolic model ENN’s capability of learning in changing environments. Another potential advantage of using NNTrees is that they are actually “gray boxes” as they can be interpreted easily if the num-ber of inputs for each ENN is limited. We showed through experiments that the trained NNTree achieved a simple ENN at each non-terminal node as well as a satisfying recognition rate of the network packets dataset. We also compared the performance with that of a three-layer backpropagation neural network. Experimental results indicated that the NNTree based intrusion detection model achieved better performance than the neural network based intrusion detection model.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12271394 and 12071336)the Key Research and Development Program of Shanxi Province(Grant No.202102010101004)。
文摘Recently, a class of innovative notions on quantum network nonlocality(QNN), called full quantum network nonlocality(FQNN), have been proposed in Phys. Rev. Lett. 128 010403(2022). As the generalization of full network nonlocality(FNN), l-level quantum network nonlocality(l-QNN) was defined in arxiv. 2306.15717 quant-ph(2024). FQNN is a NN that can be generated only from a network with all sources being non-classical. This is beyond the existing standard network nonlocality, which may be generated from a network with only a non-classical source. One of the challenging tasks is to establish corresponding Bell-like inequalities to demonstrate the FQNN or l-QNN. Up to now, the inequality criteria for FQNN and l-QNN have only been established for star and chain networks. In this paper, we devote ourselves to establishing Bell-like inequalities for networks with more complex structures. Note that star and chain networks are special kinds of tree-shaped networks. We first establish the Bell-like inequalities for verifying l-QNN in k-forked tree-shaped networks. Such results generalize the existing inequalities for star and chain networks. Furthermore, we find the Bell-like inequality criteria for l-QNN for general acyclic and cyclic networks. Finally, we discuss the demonstration of l-QNN in the well-known butterfly networks.
文摘This study is aimed at the development of a statistical model for forecasting heavy rain in South Korea. For the 3-hour weather forecast system, the 10 km×10 km area-mean amount of rainfall at 6 stations （Seoul, Daejeon, Gangreung, （Jwangju, Busan, and Jeju） in South Korea are used. And the corresponding 45 synoptic factors generated by the numerical model are used as potential predictors. Four statistical forecast models （linear regression model, logistic regression model, neural network model and decision tree model） for the occurrence of heavy rain are based on the model output statistics （MOS） method. They are separately estimated by the same training data. The thresholds are considered to forecast the occurrence of heavy rain because the distribution of estimated values that are generated by each model is too skewed. The results of four models are compared via Heidke skill scores. As a result, the logistic regression model is recommended.
基金supported by the Nature Science Foundation of China under Grand Nos. 70431002, 60874087, 60773120, and 10647001the Nature Science Foundation of Beijing under Grand No. 4092040
文摘Generalized Farey tree network （GFTN） and generalized Farey organized pyramid network （CFOPN） model are proposed, and their topological characteristics are studied by both theoretical analysis and numerical simulations, which are in good accordance with each other. Then weighted GFTN is studied using cumulative distributions of its Farey number value, edge weight, and node strength. These results maybe helpful for future theoretical development of hybrid models.
基金supported by the National Natural Science Foundation of China(Grant No.11835003)supported by the National Natural Science Foundation of China(Grant Nos.12375033,12235007,and 11975131)+7 种基金the Natural Science Foundation of Zhejiang(Grant No.LY23A050002)the K.C.Wong Magna Fund at Ningbo Universitysupported by the National Natural Science Foundation of China(Grant No.T2122016)the National Science and Technology Innovation 2030 Major Program(Grant Nos.2021ZD0203700,and 2021ZD0203705)the Fundamental Research Funds for the Central Universities(Grant No.2022CDJKYJH034)supported by the National Institutes of Health(Grant Nos.R01 HL134709,R01 HL139829,R01 HL157116,and P01 HL164311)supported by the National Natural Science Foundation of China(Grant No.11905291)CAS Project for Young Scientists in Basic Research(Grant No.YSBR-041)。
文摘Inferring network structures from available data has attracted much interest in network science;however,in many realistic networks,only some of the nodes are perceptible while others are hidden,making it a challenging task.In this work,we develop a method for reconstructing the network with hidden nodes and links,taking account of fast-varying noise and time-delay interactions.By calculating the correlations of available data with different derivative orders for multiple pairs of accessible nodes,analyzing and integrating the relationships between different correlations,and defining diverse hidden-node-related reconstruction motifs,we can effectively identify the hidden nodes and hidden links in the network.
