The rapid expansion of offshore wind energy necessitates robust and cost-effective electrical collector system(ECS)designs that prioritize lifetime operational reliability.Traditional optimization approaches often sim...The rapid expansion of offshore wind energy necessitates robust and cost-effective electrical collector system(ECS)designs that prioritize lifetime operational reliability.Traditional optimization approaches often simplify reliability considerations or fail to holistically integrate them with economic and technical constraints.This paper introduces a novel,two-stage optimization framework for offshore wind farm(OWF)ECS planning that systematically incorporates reliability.The first stage employs Mixed-Integer Linear Programming(MILP)to determine an optimal radial network topology,considering linearized reliability approximations and geographical constraints.The second stage enhances this design by strategically placing tie-lines using a Mixed-Integer Quadratically Constrained Program(MIQCP).This stage leverages a dynamic-aware adaptation of Multi-Source Multi-Terminal Network Reliability(MSMT-NR)assessment,with its inherent nonlinear equations successfully transformed into a solvable MIQCP form for loopy networks.A benchmark case study demonstrates the framework’s efficacy,illustrating how increasing the emphasis on reliability leads to more distributed and interconnected network topologies,effectively balancing investment costs against enhanced system resilience.展开更多
The main aim of this paper is to compare the stability, in terms of systemic risk, of conventional and Islamic banking systems. To this aim, we propose correlation network models for stock market returns based on grap...The main aim of this paper is to compare the stability, in terms of systemic risk, of conventional and Islamic banking systems. To this aim, we propose correlation network models for stock market returns based on graphical Gaussian distributions, which allows us to capture the contagion effects that move along countries. We also consider Bayesian graphical models, to account for model uncertainty in the measurement of financial systems interconnectedness. Our proposed model is applied to the Middle East and North Africa (MENA) region banking sector, characterized by the presence of both conventional and Islamic banks, for the period from 2007 to the beginning of 2014. Our empirical findings show that there are differences in the systemic risk and stability of the two banking systems during crisis times. In addition, the differences are subject to country specific effects that are amplified during crisis period.展开更多
Wave velocities in haloanhydrites are difficult to determine and significantly depend on the mineralogy. We used petrophysical parameters to study the wave velocity in haloanhydrites in the Amur Darya Basin and constr...Wave velocities in haloanhydrites are difficult to determine and significantly depend on the mineralogy. We used petrophysical parameters to study the wave velocity in haloanhydrites in the Amur Darya Basin and constructed a template of the relation between haloanhydrite mineralogy （anhydrite, salt, mudstone, and pore water） and wave velocities. We used the relation between the P-wave rnoduli ratio and porosity as constraint and constructed a graphical model （petrophysical template） for the relation between wave velocity, mineral content and porosity. We tested the graphical model using rock core and well logging data.展开更多
This paper introduces a model-free reinforcement learning technique that is used to solve a class of dynamic games known as dynamic graphical games. The graphical game results from to make all the agents synchronize t...This paper introduces a model-free reinforcement learning technique that is used to solve a class of dynamic games known as dynamic graphical games. The graphical game results from to make all the agents synchronize to the state of a command multi-agent dynamical systems, where pinning control is used generator or a leader agent. Novel coupled Bellman equations and Hamiltonian functions are developed for the dynamic graphical games. The Hamiltonian mechanics are used to derive the necessary conditions for optimality. The solution for the dynamic graphical game is given in terms of the solution to a set of coupled Hamilton-Jacobi-Bellman equations developed herein. Nash equilibrium solution for the graphical game is given in terms of the solution to the underlying coupled Hamilton-Jacobi-Bellman equations. An online model-free policy iteration algorithm is developed to learn the Nash solution for the dynamic graphical game. This algorithm does not require any knowledge of the agents＇ dynamics. A proof of convergence for this multi-agent learning algorithm is given under mild assumption about the inter-connectivity properties of the graph. A gradient descent technique with critic network structures is used to implement the policy iteration algorithm to solve the graphical game online in real-time.展开更多
Background As information technology has advanced and been popularized,open pit mining has rapidly developed toward integration and digitization.The three-dimensional reconstruction technology has been successfully ap...Background As information technology has advanced and been popularized,open pit mining has rapidly developed toward integration and digitization.The three-dimensional reconstruction technology has been successfully applied to geological reconstruction and modeling of surface scenes in open pit mines.However,an integrated modeling method for surface and underground mine sites has not been reported.Methods In this study,we propose an integrated modeling method for open pit mines that fuses a real scene on the surface with an underground geological model.Based on oblique photography,a real-scene model was established on the surface.Based on the surface-stitching method proposed,the upper and lower surfaces and sides of the model were constructed in stages to construct a complete underground three-dimensional geological model,and the aboveground and underground models were registered together to build an integrated open pit mine model.Results The oblique photography method used reconstructed a surface model of an open pit mine using a real scene.The surface-stitching algorithm proposed was compared with the ball-pivoting and Poisson algorithms,and the integrity of the reconstructed model was markedly superior to that of the other two reconstruction methods.In addition,the surface-stitching algorithm was applied to the reconstruction of different formation models and showed good stability and reconstruction efficiency.Finally,the aboveground and underground models were accurately fitted after registration to form an integrated model.Conclusions The proposed method can efficiently establish an integrated open pit model.Based on the integrated model,an open pit auxiliary planning system was designed and realized.It supports the functions of mining planning and output calculation,assists users in mining planning and operation management,and improves production efficiency and management levels.展开更多
Using Bayesian networks to model promising solutions from the current population of the evolutionary algorithms can ensure efficiency and intelligence search for the optimum. However, to construct a Bayesian network t...Using Bayesian networks to model promising solutions from the current population of the evolutionary algorithms can ensure efficiency and intelligence search for the optimum. However, to construct a Bayesian network that fits a given dataset is a NP-hard problem, and it also needs consuming mass computational resources. This paper develops a methodology for constructing a graphical model based on Bayesian Dirichlet metric. Our approach is derived from a set of propositions and theorems by researching the local metric relationship of networks matching dataset. This paper presents the algorithm to construct a tree model from a set of potential solutions using above approach. This method is important not only for evolutionary algorithms based on graphical models, but also for machine learning and data mining. The experimental results show that the exact theoretical results and the approximations match very well.展开更多
We present a family of graphical representations for the O(N)spin model,where N≥1 represents the spin dimension,and N=1,2,3 corresponds to the Ising,XY and Heisenberg models,respectively.With an integer parameter 0≤...We present a family of graphical representations for the O(N)spin model,where N≥1 represents the spin dimension,and N=1,2,3 corresponds to the Ising,XY and Heisenberg models,respectively.With an integer parameter 0≤ℓ≤N/2,each configuration is the coupling of ℓ copies of subgraphs consisting of directed flows and N−2ℓ copies of subgraphs constructed by undirected loops,which we call the XY and Ising subgraphs,respectively.On each lattice site,the XY subgraphs satisfy the Kirchhoff flow-conservation law and the Ising subgraphs obey the Eulerian bond condition.Then,we formulate worm-type algorithms and simulate the O(N)model on the simple-cubic lattice for N from 2 to 6 at all possibleℓ.It is observed that the worm algorithm has much higher efficiency than the Metropolis method,and,for a given N,the efficiency is an increasing function ofℓ.Besides Monte Carlo simulations,we expect that these graphical representations would provide a convenient basis for the study of the O(N)spin model by other state-of-the-art methods like the tensor network renormalization.展开更多
In the technique of video multi-target tracking,the common particle filter can not deal well with uncertain relations among multiple targets.To solve this problem,many researchers use data association method to reduce...In the technique of video multi-target tracking,the common particle filter can not deal well with uncertain relations among multiple targets.To solve this problem,many researchers use data association method to reduce the multi-target uncertainty.However,the traditional data association method is difficult to track accurately when the target is occluded.To remove the occlusion in the video,combined with the theory of data association,this paper adopts the probabilistic graphical model for multi-target modeling and analysis of the targets relationship in the particle filter framework.Ex-perimental results show that the proposed algorithm can solve the occlusion problem better compared with the traditional algorithm.展开更多
Graph neural networks(GNN)have shown strong performance in node classification tasks,yet most existing models rely on uniform or shared weight aggregation,lacking flexibility in modeling the varying strength of relati...Graph neural networks(GNN)have shown strong performance in node classification tasks,yet most existing models rely on uniform or shared weight aggregation,lacking flexibility in modeling the varying strength of relationships among nodes.This paper proposes a novel graph coupling convolutional model that introduces an adaptive weighting mechanism to assign distinct importance to neighboring nodes based on their similarity to the central node.Unlike traditional methods,the proposed coupling strategy enhances the interpretability of node interactions while maintaining competitive classification performance.The model operates in the spatial domain,utilizing adjacency list structures for efficient convolution and addressing the limitations of weight sharing through a coupling-based similarity computation.Extensive experiments are conducted on five graph-structured datasets,including Cora,Citeseer,PubMed,Reddit,and BlogCatalog,as well as a custom topology dataset constructed from the Open University Learning Analytics Dataset(OULAD)educational platform.Results demonstrate that the proposed model achieves good classification accuracy,while significantly reducing training time through direct second-order neighbor fusion and data preprocessing.Moreover,analysis of neighborhood order reveals that considering third-order neighbors offers limited accuracy gains but introduces considerable computational overhead,confirming the efficiency of first-and second-order convolution in practical applications.Overall,the proposed graph coupling model offers a lightweight,interpretable,and effective framework for multi-label node classification in complex networks.展开更多
Simulating the traditional painting art by computer graphics is a challenging and attractive subject. Basing on the experience in the ink wash drawing, in this paper, we expound the artistic characters of ink wash p...Simulating the traditional painting art by computer graphics is a challenging and attractive subject. Basing on the experience in the ink wash drawing, in this paper, we expound the artistic characters of ink wash painting and particularly analyze the characteristics of the materials used in the ink wash drawing and the relationships between them. A simulation model is presented and some typical visual effects of the ink wash painting are realized.展开更多
General purpose graphic processing unit （GPU） calculation technology is gradually widely used in various fields. Its mode of single instruction, multiple threads is capable of seismic numerical simulation which has ...General purpose graphic processing unit （GPU） calculation technology is gradually widely used in various fields. Its mode of single instruction, multiple threads is capable of seismic numerical simulation which has a huge quantity of data and calculation steps. In this study, we introduce a GPU-based parallel calculation method of a precise integration method （PIM） for seismic forward modeling. Compared with CPU single-core calculation, GPU parallel calculating perfectly keeps the features of PIM, which has small bandwidth, high accuracy and capability of modeling complex substructures, and GPU calculation brings high computational efficiency, which means that high-performing GPU parallel calculation can make seismic forward modeling closer to real seismic records.展开更多
Tracer kinetic modeling in dynamic positron emission tomography （PET） has been widely used to investigate the characteristic distribution patterns or dysfunctions of neuroreceptors in brain diseases. Its practical g...Tracer kinetic modeling in dynamic positron emission tomography （PET） has been widely used to investigate the characteristic distribution patterns or dysfunctions of neuroreceptors in brain diseases. Its practical goal has progressed from regional data quantification to parametric mapping that produces images of kinetic-model parameters by fully exploiting the spatiotemporal information in dynamic PET data. Graphical analysis （GA） is a major parametric mapping technique that is independent on any compartmental model configuration, robust to noise, and computationally efficient. In this paper, we provide an overview of recent advances in the parametric mapping of neuroreceptor binding based on GA methods. The associated basic concepts in tracer kinetic modeling are presented, including commonly-used compartment models and major parameters of interest. Technical details of GA approaches for reversible and irreversible radioligands are described, considering both plasma input and reference tissue input models. Their statistical properties are discussed in view of parametric imaging.展开更多
We propose a method that can achieve the Naxi-English bilingual word automatic alignment based on a log-linear model.This method defines the different Naxi-English structural feature functions,which are English-Naxi i...We propose a method that can achieve the Naxi-English bilingual word automatic alignment based on a log-linear model.This method defines the different Naxi-English structural feature functions,which are English-Naxi interval switching function and Naxi-English bilingual word position transformation function.With the manually labeled Naxi-English words alignment corpus,the parameters of the model are trained by using the minimum error,thus Naxi-English bilingual word alignment is achieved automatically.Experiments are conducted with IBM Model 3 as a benchmark,and the Naxi language constraints are introduced.The final experiment results show that the proposed alignment method achieves very good results:the introduction of the language characteristic function can effectively improve the accuracy of the Naxi-English Bilingual Word Alignment.展开更多
Sensors for fire alarms require a high level of predictive variables to ensure accurate detection, injury prevention, and loss prevention. Bayesian networks can aid in enhancing early fire detection capabilities and r...Sensors for fire alarms require a high level of predictive variables to ensure accurate detection, injury prevention, and loss prevention. Bayesian networks can aid in enhancing early fire detection capabilities and reducing the frequency of erroneous fire alerts, thereby enhancing the effectiveness of numerous safety monitoring systems. This research explores the development of optimized probabilistic graphic models for the discretization thresholds of alarm system predictor variables. The study presents a statistical model framework that increases the efficacy of fire detection by predicting the discretization thresholds of alarm system predictor variable fluctuations used to detect the onset of fire. The work applies the Bayesian networks and probabilistic visual models to reveal the specific characteristics required to cope with fire detection strategies and patterns. The adopted methodology utilizes a combination of prior knowledge and statistical data to draw conclusions from observations. Utilizing domain knowledge to compute conditional dependencies between network variables enabled predictions to be made through the application of specialized analytical and simulation techniques.展开更多
Gaussian graphical models(GGMs) are widely used as intuitive and efficient tools for data analysis in several application domains. To address the reproducibility issue of structure learning of a GGM, it is essential t...Gaussian graphical models(GGMs) are widely used as intuitive and efficient tools for data analysis in several application domains. To address the reproducibility issue of structure learning of a GGM, it is essential to control the false discovery rate(FDR) of the estimated edge set of the graph in terms of the graphical model. Hence, in recent years, the problem of GGM estimation with FDR control is receiving more and more attention. In this paper, we propose a new GGM estimation method by implementing multiple data splitting. Instead of using the node-by-node regressions to estimate each row of the precision matrix, we suggest directly estimating the entire precision matrix using the graphical Lasso in the multiple data splitting, and our calculation speed is p times faster than the previous. We show that the proposed method can asymptotically control FDR, and the proposed method has significant advantages in computational efficiency. Finally, we demonstrate the usefulness of the proposed method through a real data analysis.展开更多
Knowledge-based visual question answering(KB-VQA),requiring external world knowledge beyond the image for reasoning,is more challenging than traditional visual question answering.Recent works have demonstrated the eff...Knowledge-based visual question answering(KB-VQA),requiring external world knowledge beyond the image for reasoning,is more challenging than traditional visual question answering.Recent works have demonstrated the effectiveness of using a large(vision)language model as an implicit knowledge source to acquire the necessary information.However,the knowledge stored in large models(LMs)is often coarse-grained and inaccurate,causing questions requiring finer-grained information to be answered incorrectly.In this work,we propose a variational expectation-maximization(EM)framework that bootstraps the VQA performance of LMs with its own answer.In contrast to former VQA pipelines,we treat the outside knowledge as a latent variable.In the E-step,we approximate the posterior with two components:First,a rough answer,e.g.,a general description of the image,which is usually the strength of LMs,and second,a multi-modal neural retriever to retrieve question-specific knowledge from an external knowledge base.In the M-step,the training objective optimizes the ability of the original LMs to generate rough answers as well as refined answers based on the retrieved information.Extensive experiments show that our proposed framework,BootLM,has a strong retrieval ability and achieves state-of-the-art performance on knowledge-based VQA tasks.展开更多
基金supported by the Science and Technology Project of China South Power Grid Co.,Ltd.,Grant Nos.036000KK52222044,GDKJXM20222430。
文摘The rapid expansion of offshore wind energy necessitates robust and cost-effective electrical collector system(ECS)designs that prioritize lifetime operational reliability.Traditional optimization approaches often simplify reliability considerations or fail to holistically integrate them with economic and technical constraints.This paper introduces a novel,two-stage optimization framework for offshore wind farm(OWF)ECS planning that systematically incorporates reliability.The first stage employs Mixed-Integer Linear Programming(MILP)to determine an optimal radial network topology,considering linearized reliability approximations and geographical constraints.The second stage enhances this design by strategically placing tie-lines using a Mixed-Integer Quadratically Constrained Program(MIQCP).This stage leverages a dynamic-aware adaptation of Multi-Source Multi-Terminal Network Reliability(MSMT-NR)assessment,with its inherent nonlinear equations successfully transformed into a solvable MIQCP form for loopy networks.A benchmark case study demonstrates the framework’s efficacy,illustrating how increasing the emphasis on reliability leads to more distributed and interconnected network topologies,effectively balancing investment costs against enhanced system resilience.
文摘The main aim of this paper is to compare the stability, in terms of systemic risk, of conventional and Islamic banking systems. To this aim, we propose correlation network models for stock market returns based on graphical Gaussian distributions, which allows us to capture the contagion effects that move along countries. We also consider Bayesian graphical models, to account for model uncertainty in the measurement of financial systems interconnectedness. Our proposed model is applied to the Middle East and North Africa (MENA) region banking sector, characterized by the presence of both conventional and Islamic banks, for the period from 2007 to the beginning of 2014. Our empirical findings show that there are differences in the systemic risk and stability of the two banking systems during crisis times. In addition, the differences are subject to country specific effects that are amplified during crisis period.
基金supported by the National Major Scientific and Technological Special Project(No.2011ZX05029-003)the project of the Research Institute of Petroleum Exploration&Development(No.2012Y-058)
文摘Wave velocities in haloanhydrites are difficult to determine and significantly depend on the mineralogy. We used petrophysical parameters to study the wave velocity in haloanhydrites in the Amur Darya Basin and constructed a template of the relation between haloanhydrite mineralogy （anhydrite, salt, mudstone, and pore water） and wave velocities. We used the relation between the P-wave rnoduli ratio and porosity as constraint and constructed a graphical model （petrophysical template） for the relation between wave velocity, mineral content and porosity. We tested the graphical model using rock core and well logging data.
基金supported by the Deanship of Scientific Research at King Fahd University of Petroleum & Minerals Project(No.JF141002)the National Science Foundation(No.ECCS-1405173)+3 种基金the Office of Naval Research(Nos.N000141310562,N000141410718)the U.S. Army Research Office(No.W911NF-11-D-0001)the National Natural Science Foundation of China(No.61120106011)the Project 111 from the Ministry of Education of China(No.B08015)
文摘This paper introduces a model-free reinforcement learning technique that is used to solve a class of dynamic games known as dynamic graphical games. The graphical game results from to make all the agents synchronize to the state of a command multi-agent dynamical systems, where pinning control is used generator or a leader agent. Novel coupled Bellman equations and Hamiltonian functions are developed for the dynamic graphical games. The Hamiltonian mechanics are used to derive the necessary conditions for optimality. The solution for the dynamic graphical game is given in terms of the solution to a set of coupled Hamilton-Jacobi-Bellman equations developed herein. Nash equilibrium solution for the graphical game is given in terms of the solution to the underlying coupled Hamilton-Jacobi-Bellman equations. An online model-free policy iteration algorithm is developed to learn the Nash solution for the dynamic graphical game. This algorithm does not require any knowledge of the agents＇ dynamics. A proof of convergence for this multi-agent learning algorithm is given under mild assumption about the inter-connectivity properties of the graph. A gradient descent technique with critic network structures is used to implement the policy iteration algorithm to solve the graphical game online in real-time.
基金Supported by Liaoning Province Science and Technology Research Project(2021JH1/10400011)National Natural Science Foundation of China(61971118).
文摘Background As information technology has advanced and been popularized,open pit mining has rapidly developed toward integration and digitization.The three-dimensional reconstruction technology has been successfully applied to geological reconstruction and modeling of surface scenes in open pit mines.However,an integrated modeling method for surface and underground mine sites has not been reported.Methods In this study,we propose an integrated modeling method for open pit mines that fuses a real scene on the surface with an underground geological model.Based on oblique photography,a real-scene model was established on the surface.Based on the surface-stitching method proposed,the upper and lower surfaces and sides of the model were constructed in stages to construct a complete underground three-dimensional geological model,and the aboveground and underground models were registered together to build an integrated open pit mine model.Results The oblique photography method used reconstructed a surface model of an open pit mine using a real scene.The surface-stitching algorithm proposed was compared with the ball-pivoting and Poisson algorithms,and the integrity of the reconstructed model was markedly superior to that of the other two reconstruction methods.In addition,the surface-stitching algorithm was applied to the reconstruction of different formation models and showed good stability and reconstruction efficiency.Finally,the aboveground and underground models were accurately fitted after registration to form an integrated model.Conclusions The proposed method can efficiently establish an integrated open pit model.Based on the integrated model,an open pit auxiliary planning system was designed and realized.It supports the functions of mining planning and output calculation,assists users in mining planning and operation management,and improves production efficiency and management levels.
基金This work was supported by the National Natural Science Foundation of China(No.60574075) and by Natural Science Foundation of ShaanxiProvince(No.2005A07).
文摘Using Bayesian networks to model promising solutions from the current population of the evolutionary algorithms can ensure efficiency and intelligence search for the optimum. However, to construct a Bayesian network that fits a given dataset is a NP-hard problem, and it also needs consuming mass computational resources. This paper develops a methodology for constructing a graphical model based on Bayesian Dirichlet metric. Our approach is derived from a set of propositions and theorems by researching the local metric relationship of networks matching dataset. This paper presents the algorithm to construct a tree model from a set of potential solutions using above approach. This method is important not only for evolutionary algorithms based on graphical models, but also for machine learning and data mining. The experimental results show that the exact theoretical results and the approximations match very well.
基金supported by the National Natural Science Foundation of China(under Grant No.12275263)the Innovation Program for Quantum Science and Technology(under Grant No.2021ZD0301900)the Natural Science Foundation of Fujian Province of China:2023J02032.
文摘We present a family of graphical representations for the O(N)spin model,where N≥1 represents the spin dimension,and N=1,2,3 corresponds to the Ising,XY and Heisenberg models,respectively.With an integer parameter 0≤ℓ≤N/2,each configuration is the coupling of ℓ copies of subgraphs consisting of directed flows and N−2ℓ copies of subgraphs constructed by undirected loops,which we call the XY and Ising subgraphs,respectively.On each lattice site,the XY subgraphs satisfy the Kirchhoff flow-conservation law and the Ising subgraphs obey the Eulerian bond condition.Then,we formulate worm-type algorithms and simulate the O(N)model on the simple-cubic lattice for N from 2 to 6 at all possibleℓ.It is observed that the worm algorithm has much higher efficiency than the Metropolis method,and,for a given N,the efficiency is an increasing function ofℓ.Besides Monte Carlo simulations,we expect that these graphical representations would provide a convenient basis for the study of the O(N)spin model by other state-of-the-art methods like the tensor network renormalization.
基金Supported by the National High Technology Research and Development Program of China(No.2007AA11Z227)the Natural Science Foundation of Jiangsu Province of China(No.BK2009352)the Fundamental Research Funds for the Central Universities of China(No.2010B16414)
文摘In the technique of video multi-target tracking,the common particle filter can not deal well with uncertain relations among multiple targets.To solve this problem,many researchers use data association method to reduce the multi-target uncertainty.However,the traditional data association method is difficult to track accurately when the target is occluded.To remove the occlusion in the video,combined with the theory of data association,this paper adopts the probabilistic graphical model for multi-target modeling and analysis of the targets relationship in the particle filter framework.Ex-perimental results show that the proposed algorithm can solve the occlusion problem better compared with the traditional algorithm.
基金Support by Sichuan Science and Technology Program[2023YFSY0026,2023YFH0004]Guangzhou Huashang University[2024HSZD01,HS2023JYSZH01].
文摘Graph neural networks(GNN)have shown strong performance in node classification tasks,yet most existing models rely on uniform or shared weight aggregation,lacking flexibility in modeling the varying strength of relationships among nodes.This paper proposes a novel graph coupling convolutional model that introduces an adaptive weighting mechanism to assign distinct importance to neighboring nodes based on their similarity to the central node.Unlike traditional methods,the proposed coupling strategy enhances the interpretability of node interactions while maintaining competitive classification performance.The model operates in the spatial domain,utilizing adjacency list structures for efficient convolution and addressing the limitations of weight sharing through a coupling-based similarity computation.Extensive experiments are conducted on five graph-structured datasets,including Cora,Citeseer,PubMed,Reddit,and BlogCatalog,as well as a custom topology dataset constructed from the Open University Learning Analytics Dataset(OULAD)educational platform.Results demonstrate that the proposed model achieves good classification accuracy,while significantly reducing training time through direct second-order neighbor fusion and data preprocessing.Moreover,analysis of neighborhood order reveals that considering third-order neighbors offers limited accuracy gains but introduces considerable computational overhead,confirming the efficiency of first-and second-order convolution in practical applications.Overall,the proposed graph coupling model offers a lightweight,interpretable,and effective framework for multi-label node classification in complex networks.
文摘Simulating the traditional painting art by computer graphics is a challenging and attractive subject. Basing on the experience in the ink wash drawing, in this paper, we expound the artistic characters of ink wash painting and particularly analyze the characteristics of the materials used in the ink wash drawing and the relationships between them. A simulation model is presented and some typical visual effects of the ink wash painting are realized.
基金supported by the National Natural Science Foundation of China (Nos 40974066 and 40821062)National Basic Research Program of China (No 2007CB209602)
文摘General purpose graphic processing unit （GPU） calculation technology is gradually widely used in various fields. Its mode of single instruction, multiple threads is capable of seismic numerical simulation which has a huge quantity of data and calculation steps. In this study, we introduce a GPU-based parallel calculation method of a precise integration method （PIM） for seismic forward modeling. Compared with CPU single-core calculation, GPU parallel calculating perfectly keeps the features of PIM, which has small bandwidth, high accuracy and capability of modeling complex substructures, and GPU calculation brings high computational efficiency, which means that high-performing GPU parallel calculation can make seismic forward modeling closer to real seismic records.
基金supported by a grant from the Korea Healthcare Technology R&D Project,Ministry of Health & Welfare,Republic of Korea(HI13C01630200)the Industrial Strategic Technology Development Program(10030030) funded by the Ministry of Trade, Industry & Energy,Korea
文摘Tracer kinetic modeling in dynamic positron emission tomography （PET） has been widely used to investigate the characteristic distribution patterns or dysfunctions of neuroreceptors in brain diseases. Its practical goal has progressed from regional data quantification to parametric mapping that produces images of kinetic-model parameters by fully exploiting the spatiotemporal information in dynamic PET data. Graphical analysis （GA） is a major parametric mapping technique that is independent on any compartmental model configuration, robust to noise, and computationally efficient. In this paper, we provide an overview of recent advances in the parametric mapping of neuroreceptor binding based on GA methods. The associated basic concepts in tracer kinetic modeling are presented, including commonly-used compartment models and major parameters of interest. Technical details of GA approaches for reversible and irreversible radioligands are described, considering both plasma input and reference tissue input models. Their statistical properties are discussed in view of parametric imaging.
基金supported by the National Nature Science Foundation of China under Grants No.60863011,No.61175068,No.61100205,No.60873001the Fundamental Research Funds for the Central Universities under Grant No.2009RC0212+1 种基金the National Innovation Fund for Technology-based Firms under Grant No.11C26215305905the Open Fund of Software Engineering Key Laboratory of Yunnan Province under Grant No.2011SE14
文摘We propose a method that can achieve the Naxi-English bilingual word automatic alignment based on a log-linear model.This method defines the different Naxi-English structural feature functions,which are English-Naxi interval switching function and Naxi-English bilingual word position transformation function.With the manually labeled Naxi-English words alignment corpus,the parameters of the model are trained by using the minimum error,thus Naxi-English bilingual word alignment is achieved automatically.Experiments are conducted with IBM Model 3 as a benchmark,and the Naxi language constraints are introduced.The final experiment results show that the proposed alignment method achieves very good results:the introduction of the language characteristic function can effectively improve the accuracy of the Naxi-English Bilingual Word Alignment.
文摘Sensors for fire alarms require a high level of predictive variables to ensure accurate detection, injury prevention, and loss prevention. Bayesian networks can aid in enhancing early fire detection capabilities and reducing the frequency of erroneous fire alerts, thereby enhancing the effectiveness of numerous safety monitoring systems. This research explores the development of optimized probabilistic graphic models for the discretization thresholds of alarm system predictor variables. The study presents a statistical model framework that increases the efficacy of fire detection by predicting the discretization thresholds of alarm system predictor variable fluctuations used to detect the onset of fire. The work applies the Bayesian networks and probabilistic visual models to reveal the specific characteristics required to cope with fire detection strategies and patterns. The adopted methodology utilizes a combination of prior knowledge and statistical data to draw conclusions from observations. Utilizing domain knowledge to compute conditional dependencies between network variables enabled predictions to be made through the application of specialized analytical and simulation techniques.
基金partially supported by the National Natural Science Foundation of China(Grant No.12171079)the National Key R&D Program of China(Grant No.2020YFA0714102)+1 种基金partially supported by the National Natural Science Foundation of China(Grant No.12101116)the National Key Research and Development Program of China(Grant No.2022YFA1003701)。
文摘Gaussian graphical models(GGMs) are widely used as intuitive and efficient tools for data analysis in several application domains. To address the reproducibility issue of structure learning of a GGM, it is essential to control the false discovery rate(FDR) of the estimated edge set of the graph in terms of the graphical model. Hence, in recent years, the problem of GGM estimation with FDR control is receiving more and more attention. In this paper, we propose a new GGM estimation method by implementing multiple data splitting. Instead of using the node-by-node regressions to estimate each row of the precision matrix, we suggest directly estimating the entire precision matrix using the graphical Lasso in the multiple data splitting, and our calculation speed is p times faster than the previous. We show that the proposed method can asymptotically control FDR, and the proposed method has significant advantages in computational efficiency. Finally, we demonstrate the usefulness of the proposed method through a real data analysis.
文摘Knowledge-based visual question answering(KB-VQA),requiring external world knowledge beyond the image for reasoning,is more challenging than traditional visual question answering.Recent works have demonstrated the effectiveness of using a large(vision)language model as an implicit knowledge source to acquire the necessary information.However,the knowledge stored in large models(LMs)is often coarse-grained and inaccurate,causing questions requiring finer-grained information to be answered incorrectly.In this work,we propose a variational expectation-maximization(EM)framework that bootstraps the VQA performance of LMs with its own answer.In contrast to former VQA pipelines,we treat the outside knowledge as a latent variable.In the E-step,we approximate the posterior with two components:First,a rough answer,e.g.,a general description of the image,which is usually the strength of LMs,and second,a multi-modal neural retriever to retrieve question-specific knowledge from an external knowledge base.In the M-step,the training objective optimizes the ability of the original LMs to generate rough answers as well as refined answers based on the retrieved information.Extensive experiments show that our proposed framework,BootLM,has a strong retrieval ability and achieves state-of-the-art performance on knowledge-based VQA tasks.
