The authors consider the issue of hypothesis testing in varying-coefficient regression models with high-dimensional data.Utilizing kernel smoothing techniques,the authors propose a locally concerned U-statistic method...The authors consider the issue of hypothesis testing in varying-coefficient regression models with high-dimensional data.Utilizing kernel smoothing techniques,the authors propose a locally concerned U-statistic method to assess the overall significance of the coefficients.The authors establish that the proposed test is asymptotically normal under both the null hypothesis and local alternatives.Based on the locally concerned U-statistic,the authors further develop a globally concerned U-statistic to test whether the coefficient function is zero.A stochastic perturbation method is employed to approximate the distribution of the globally concerned test statistic.Monte Carlo simulations demonstrate the validity of the proposed test in finite samples.展开更多
Owing to their global search capabilities and gradient-free operation,metaheuristic algorithms are widely applied to a wide range of optimization problems.However,their computational demands become prohibitive when ta...Owing to their global search capabilities and gradient-free operation,metaheuristic algorithms are widely applied to a wide range of optimization problems.However,their computational demands become prohibitive when tackling high-dimensional optimization challenges.To effectively address these challenges,this study introduces cooperative metaheuristics integrating dynamic dimension reduction(DR).Building upon particle swarm optimization(PSO)and differential evolution(DE),the proposed cooperative methods C-PSO and C-DE are developed.In the proposed methods,the modified principal components analysis(PCA)is utilized to reduce the dimension of design variables,thereby decreasing computational costs.The dynamic DR strategy implements periodic execution of modified PCA after a fixed number of iterations,resulting in the important dimensions being dynamically identified.Compared with the static one,the dynamic DR strategy can achieve precise identification of important dimensions,thereby enabling accelerated convergence toward optimal solutions.Furthermore,the influence of cumulative contribution rate thresholds on optimization problems with different dimensions is investigated.Metaheuristic algorithms(PSO,DE)and cooperative metaheuristics(C-PSO,C-DE)are examined by 15 benchmark functions and two engineering design problems(speed reducer and composite pressure vessel).Comparative results demonstrate that the cooperative methods achieve significantly superior performance compared to standard methods in both solution accuracy and computational efficiency.Compared to standard metaheuristic algorithms,cooperative metaheuristics achieve a reduction in computational cost of at least 40%.The cooperative metaheuristics can be effectively used to tackle both high-dimensional unconstrained and constrained optimization problems.展开更多
Quantile regression(QR)has become an important tool to measure dependence of response variable's quantiles on a number of predictors for heterogeneous data,especially heavy-tailed data and outliers.However,it is q...Quantile regression(QR)has become an important tool to measure dependence of response variable's quantiles on a number of predictors for heterogeneous data,especially heavy-tailed data and outliers.However,it is quite challenging to make statistical inference on distributed high-dimensional QR with missing data due to the distributed nature,sparsity and missingness of data and nondifferentiable quantile loss function.To overcome the challenge,this paper develops a communicationefficient method to select variables and estimate parameters by utilizing a smooth function to approximate the non-differentiable quantile loss function and incorporating the idea of the inverse probability weighting and the penalty function.The proposed approach has three merits.First,it is both computationally and communicationally efficient because only the first-and second-order information of the approximate objective function are communicated at each iteration.Second,the proposed estimators possess the oracle property after a limited number of iterations without constraint on the number of machines.Third,the proposed method simultaneously selects variables and estimates parameters within a distributed framework,ensuring robustness to the specified response probability or propensity score function of the missing data mechanism.Simulation studies and a real example are used to illustrate the effectiveness of the proposed methodologies.展开更多
It is known that monotone recurrence relations can induce a class of twist homeomorphisms on the high-dimensional cylinder,which is an extension of the class of monotone twist maps on the annulus or two-dimensional cy...It is known that monotone recurrence relations can induce a class of twist homeomorphisms on the high-dimensional cylinder,which is an extension of the class of monotone twist maps on the annulus or two-dimensional cylinder.By constructing a bounded solution of the monotone recurrence relation,the main conclusion in this paper is acquired:The induced homeomorphism has Birkhoff orbits provided there is a compact forward-invariant set.Therefore,it generalizes Angenent's results in low-dimensional cases.展开更多
Data collected in fields such as cybersecurity and biomedicine often encounter high dimensionality and class imbalance.To address the problem of low classification accuracy for minority class samples arising from nume...Data collected in fields such as cybersecurity and biomedicine often encounter high dimensionality and class imbalance.To address the problem of low classification accuracy for minority class samples arising from numerous irrelevant and redundant features in high-dimensional imbalanced data,we proposed a novel feature selection method named AMF-SGSK based on adaptive multi-filter and subspace-based gaining sharing knowledge.Firstly,the balanced dataset was obtained by random under-sampling.Secondly,combining the feature importance score with the AUC score for each filter method,we proposed a concept called feature hardness to judge the importance of feature,which could adaptively select the essential features.Finally,the optimal feature subset was obtained by gaining sharing knowledge in multiple subspaces.This approach effectively achieved dimensionality reduction for high-dimensional imbalanced data.The experiment results on 30 benchmark imbalanced datasets showed that AMF-SGSK performed better than other eight commonly used algorithms including BGWO and IG-SSO in terms of F1-score,AUC,and G-mean.The mean values of F1-score,AUC,and Gmean for AMF-SGSK are 0.950,0.967,and 0.965,respectively,achieving the highest among all algorithms.And the mean value of Gmean is higher than those of IG-PSO,ReliefF-GWO,and BGOA by 3.72%,11.12%,and 20.06%,respectively.Furthermore,the selected feature ratio is below 0.01 across the selected ten datasets,further demonstrating the proposed method’s overall superiority over competing approaches.AMF-SGSK could adaptively remove irrelevant and redundant features and effectively improve the classification accuracy of high-dimensional imbalanced data,providing scientific and technological references for practical applications.展开更多
Objective Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health.Analysis of these mixture exposures presents several key challenges for environmental epidemio...Objective Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health.Analysis of these mixture exposures presents several key challenges for environmental epidemiology and risk assessment,including high dimensionality,correlated exposure,and subtle individual effects.Methods We proposed a novel statistical approach,the generalized functional linear model(GFLM),to analyze the health effects of exposure mixtures.GFLM treats the effect of mixture exposures as a smooth function by reordering exposures based on specific mechanisms and capturing internal correlations to provide a meaningful estimation and interpretation.The robustness and efficiency was evaluated under various scenarios through extensive simulation studies.Results We applied the GFLM to two datasets from the National Health and Nutrition Examination Survey(NHANES).In the first application,we examined the effects of 37 nutrients on BMI(2011–2016 cycles).The GFLM identified a significant mixture effect,with fiber and fat emerging as the nutrients with the greatest negative and positive effects on BMI,respectively.For the second application,we investigated the association between four pre-and perfluoroalkyl substances(PFAS)and gout risk(2007–2018 cycles).Unlike traditional methods,the GFLM indicated no significant association,demonstrating its robustness to multicollinearity.Conclusion GFLM framework is a powerful tool for mixture exposure analysis,offering improved handling of correlated exposures and interpretable results.It demonstrates robust performance across various scenarios and real-world applications,advancing our understanding of complex environmental exposures and their health impacts on environmental epidemiology and toxicology.展开更多
The decoherence of high-dimensional orbital angular momentum(OAM)entanglement in the weak scintillation regime has been investigated.In this study,we simulate atmospheric turbulence by utilizing a multiple-phase scree...The decoherence of high-dimensional orbital angular momentum(OAM)entanglement in the weak scintillation regime has been investigated.In this study,we simulate atmospheric turbulence by utilizing a multiple-phase screen imprinted with anisotropic non-Kolmogorov turbulence.The entanglement negativity and fidelity are introduced to quantify the entanglement of a high-dimensional OAM state.The numerical evaluation results indicate that entanglement negativity and fidelity last longer for a high-dimensional OAM state when the azimuthal mode has a lower value.Additionally,the evolution of higher-dimensional OAM entanglement is significantly influenced by OAM beam parameters and turbulence parameters.Compared to isotropic atmospheric turbulence,anisotropic turbulence has a lesser influence on highdimensional OAM entanglement.展开更多
Guaranteed cost consensus analysis and design problems for high-dimensional multi-agent systems with time varying delays are investigated. The idea of guaranteed cost con trol is introduced into consensus problems for...Guaranteed cost consensus analysis and design problems for high-dimensional multi-agent systems with time varying delays are investigated. The idea of guaranteed cost con trol is introduced into consensus problems for high-dimensiona multi-agent systems with time-varying delays, where a cos function is defined based on state errors among neighboring agents and control inputs of all the agents. By the state space decomposition approach and the linear matrix inequality(LMI)sufficient conditions for guaranteed cost consensus and consensu alization are given. Moreover, a guaranteed cost upper bound o the cost function is determined. It should be mentioned that these LMI criteria are dependent on the change rate of time delays and the maximum time delay, the guaranteed cost upper bound is only dependent on the maximum time delay but independen of the Laplacian matrix. Finally, numerical simulations are given to demonstrate theoretical results.展开更多
High-dimensional and sparse(HiDS)matrices commonly arise in various industrial applications,e.g.,recommender systems(RSs),social networks,and wireless sensor networks.Since they contain rich information,how to accurat...High-dimensional and sparse(HiDS)matrices commonly arise in various industrial applications,e.g.,recommender systems(RSs),social networks,and wireless sensor networks.Since they contain rich information,how to accurately represent them is of great significance.A latent factor(LF)model is one of the most popular and successful ways to address this issue.Current LF models mostly adopt L2-norm-oriented Loss to represent an HiDS matrix,i.e.,they sum the errors between observed data and predicted ones with L2-norm.Yet L2-norm is sensitive to outlier data.Unfortunately,outlier data usually exist in such matrices.For example,an HiDS matrix from RSs commonly contains many outlier ratings due to some heedless/malicious users.To address this issue,this work proposes a smooth L1-norm-oriented latent factor(SL-LF)model.Its main idea is to adopt smooth L1-norm rather than L2-norm to form its Loss,making it have both strong robustness and high accuracy in predicting the missing data of an HiDS matrix.Experimental results on eight HiDS matrices generated by industrial applications verify that the proposed SL-LF model not only is robust to the outlier data but also has significantly higher prediction accuracy than state-of-the-art models when they are used to predict the missing data of HiDS matrices.展开更多
Latent factor(LF)models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS)matrices which are commonly seen in various industrial applications.An LF model usually adopts iterativ...Latent factor(LF)models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS)matrices which are commonly seen in various industrial applications.An LF model usually adopts iterative optimizers,which may consume many iterations to achieve a local optima,resulting in considerable time cost.Hence,determining how to accelerate the training process for LF models has become a significant issue.To address this,this work proposes a randomized latent factor(RLF)model.It incorporates the principle of randomized learning techniques from neural networks into the LF analysis of HiDS matrices,thereby greatly alleviating computational burden.It also extends a standard learning process for randomized neural networks in context of LF analysis to make the resulting model represent an HiDS matrix correctly.Experimental results on three HiDS matrices from industrial applications demonstrate that compared with state-of-the-art LF models,RLF is able to achieve significantly higher computational efficiency and comparable prediction accuracy for missing data.I provides an important alternative approach to LF analysis of HiDS matrices,which is especially desired for industrial applications demanding highly efficient models.展开更多
The performance of conventional similarity measurement methods is affected seriously by the curse of dimensionality of high-dimensional data.The reason is that data difference between sparse and noisy dimensionalities...The performance of conventional similarity measurement methods is affected seriously by the curse of dimensionality of high-dimensional data.The reason is that data difference between sparse and noisy dimensionalities occupies a large proportion of the similarity,leading to the dissimilarities between any results.A similarity measurement method of high-dimensional data based on normalized net lattice subspace is proposed.The data range of each dimension is divided into several intervals,and the components in different dimensions are mapped onto the corresponding interval.Only the component in the same or adjacent interval is used to calculate the similarity.To validate this method,three data types are used,and seven common similarity measurement methods are compared.The experimental result indicates that the relative difference of the method is increasing with the dimensionality and is approximately two or three orders of magnitude higher than the conventional method.In addition,the similarity range of this method in different dimensions is [0,1],which is fit for similarity analysis after dimensionality reduction.展开更多
Image matching technology is theoretically significant and practically promising in the field of autonomous navigation.Addressing shortcomings of existing image matching navigation technologies,the concept of high-dim...Image matching technology is theoretically significant and practically promising in the field of autonomous navigation.Addressing shortcomings of existing image matching navigation technologies,the concept of high-dimensional combined feature is presented based on sequence image matching navigation.To balance between the distribution of high-dimensional combined features and the shortcomings of the only use of geometric relations,we propose a method based on Delaunay triangulation to improve the feature,and add the regional characteristics of the features together with their geometric characteristics.Finally,k-nearest neighbor(KNN)algorithm is adopted to optimize searching process.Simulation results show that the matching can be realized at the rotation angle of-8°to 8°and the scale factor of 0.9 to 1.1,and when the image size is 160 pixel×160 pixel,the matching time is less than 0.5 s.Therefore,the proposed algorithm can substantially reduce computational complexity,improve the matching speed,and exhibit robustness to the rotation and scale changes.展开更多
This paper deals with the representation of the solutions of a polynomial system, and concentrates on the high-dimensional case. Based on the rational univari- ate representation of zero-dimensional polynomial systems...This paper deals with the representation of the solutions of a polynomial system, and concentrates on the high-dimensional case. Based on the rational univari- ate representation of zero-dimensional polynomial systems, we give a new description called rational representation for the solutions of a high-dimensional polynomial sys- tem and propose an algorithm for computing it. By this way all the solutions of any high-dimensional polynomial system can be represented by a set of so-called rational- representation sets.展开更多
As a crucial data preprocessing method in data mining,feature selection(FS)can be regarded as a bi-objective optimization problem that aims to maximize classification accuracy and minimize the number of selected featu...As a crucial data preprocessing method in data mining,feature selection(FS)can be regarded as a bi-objective optimization problem that aims to maximize classification accuracy and minimize the number of selected features.Evolutionary computing(EC)is promising for FS owing to its powerful search capability.However,in traditional EC-based methods,feature subsets are represented via a length-fixed individual encoding.It is ineffective for high-dimensional data,because it results in a huge search space and prohibitive training time.This work proposes a length-adaptive non-dominated sorting genetic algorithm(LA-NSGA)with a length-variable individual encoding and a length-adaptive evolution mechanism for bi-objective highdimensional FS.In LA-NSGA,an initialization method based on correlation and redundancy is devised to initialize individuals of diverse lengths,and a Pareto dominance-based length change operator is introduced to guide individuals to explore in promising search space adaptively.Moreover,a dominance-based local search method is employed for further improvement.The experimental results based on 12 high-dimensional gene datasets show that the Pareto front of feature subsets produced by LA-NSGA is superior to those of existing algorithms.展开更多
The quantum state transmission through the medium of high-dimensional many-particle system （boson or spinless fermion） is generally studied with a symmetry analysis. We discover that, if the spectrum of a Hamiltonia...The quantum state transmission through the medium of high-dimensional many-particle system （boson or spinless fermion） is generally studied with a symmetry analysis. We discover that, if the spectrum of a Hamiltonian matches the symmetry of a fermion or boson system in a certain fashion, a perfect quantum state transfer can be implemented without any operation on the medium with pre-engineered nearest neighbor （NN）. We also study a simple but realistic near half-filled tight-bindlng fermion system wlth uniform NN hopping integral. We show that an arbitrary many-particle state near the fermi surface can be perfectly transferred to its translational counterpart.展开更多
A new efficient two-party semi-quantum key agreement protocol is proposed with high-dimensional single-particle states.Different from the previous semi-quantum key agreement protocols based on the two-level quantum sy...A new efficient two-party semi-quantum key agreement protocol is proposed with high-dimensional single-particle states.Different from the previous semi-quantum key agreement protocols based on the two-level quantum system,the propounded protocol makes use of the advantage of the high-dimensional quantum system,which possesses higher efficiency and better robustness against eavesdropping.Besides,the protocol allows the classical participant to encode the secret key with qudit shifting operations without involving any quantum measurement abilities.The designed semi-quantum key agreement protocol could resist both participant attacks and outsider attacks.Meanwhile,the conjoint analysis of security and efficiency provides an appropriate choice for reference on the dimension of single-particle states and the number of decoy states.展开更多
Because all the known integrable models possess Schwarzian forms with Mobious transformation invariance,it may be one of the best ways to find new integrable models starting from some suitable Mobious transformation i...Because all the known integrable models possess Schwarzian forms with Mobious transformation invariance,it may be one of the best ways to find new integrable models starting from some suitable Mobious transformation invariant equations. In this paper, we study the Painlevé integrability of some special (3+1)-dimensional Schwarzian models.展开更多
Aimed at the issue that traditional clustering methods are not appropriate to high-dimensional data, a cuckoo search fuzzy-weighting algorithm for subspace clustering is presented on the basis of the exited soft subsp...Aimed at the issue that traditional clustering methods are not appropriate to high-dimensional data, a cuckoo search fuzzy-weighting algorithm for subspace clustering is presented on the basis of the exited soft subspace clustering algorithm. In the proposed algorithm, a novel objective function is firstly designed by considering the fuzzy weighting within-cluster compactness and the between-cluster separation, and loosening the constraints of dimension weight matrix. Then gradual membership and improved Cuckoo search, a global search strategy, are introduced to optimize the objective function and search subspace clusters, giving novel learning rules for clustering. At last, the performance of the proposed algorithm on the clustering analysis of various low and high dimensional datasets is experimentally compared with that of several competitive subspace clustering algorithms. Experimental studies demonstrate that the proposed algorithm can obtain better performance than most of the existing soft subspace clustering algorithms.展开更多
High-dimensional quantum resources provide the ability to encode several bits of information on a single photon,which can particularly increase the secret key rate rate of quantum key distribution(QKD)systems.Recently...High-dimensional quantum resources provide the ability to encode several bits of information on a single photon,which can particularly increase the secret key rate rate of quantum key distribution(QKD)systems.Recently,a practical four-dimensional QKD scheme based on time-bin quantum photonic state,only with two single-photon avalanche detectors as measurement setup,has been proven to have a superior performance than the qubit-based one.In this paper,we extend the results to our proposed eight-dimensional scheme.Then,we consider two main practical factors to improve its secret key bound.Concretely,we take the afterpulse effect into account and apply a finite-key analysis with the intensity fluctuations.Our secret bounds give consideration to both the intensity fluctuations and the afterpulse effect for the high-dimensional QKD systems.Numerical simulations show the bound of eight-dimensional QKD scheme is more robust to the intensity fluctuations but more sensitive to the afterpulse effect than the four-dimensional one.展开更多
基金supported by the National Social Science Foundation of China under Grant No.23&ZD126National Science Foundation of China under Grant No.12471256+1 种基金Natural Science Foundation of Shanxi Province under Grant No.202203021221219Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi under Grant No.2023L164。
文摘The authors consider the issue of hypothesis testing in varying-coefficient regression models with high-dimensional data.Utilizing kernel smoothing techniques,the authors propose a locally concerned U-statistic method to assess the overall significance of the coefficients.The authors establish that the proposed test is asymptotically normal under both the null hypothesis and local alternatives.Based on the locally concerned U-statistic,the authors further develop a globally concerned U-statistic to test whether the coefficient function is zero.A stochastic perturbation method is employed to approximate the distribution of the globally concerned test statistic.Monte Carlo simulations demonstrate the validity of the proposed test in finite samples.
基金funded by National Natural Science Foundation of China(Nos.12402142,11832013 and 11572134)Natural Science Foundation of Hubei Province(No.2024AFB235)+1 种基金Hubei Provincial Department of Education Science and Technology Research Project(No.Q20221714)the Opening Foundation of Hubei Key Laboratory of Digital Textile Equipment(Nos.DTL2023019 and DTL2022012).
文摘Owing to their global search capabilities and gradient-free operation,metaheuristic algorithms are widely applied to a wide range of optimization problems.However,their computational demands become prohibitive when tackling high-dimensional optimization challenges.To effectively address these challenges,this study introduces cooperative metaheuristics integrating dynamic dimension reduction(DR).Building upon particle swarm optimization(PSO)and differential evolution(DE),the proposed cooperative methods C-PSO and C-DE are developed.In the proposed methods,the modified principal components analysis(PCA)is utilized to reduce the dimension of design variables,thereby decreasing computational costs.The dynamic DR strategy implements periodic execution of modified PCA after a fixed number of iterations,resulting in the important dimensions being dynamically identified.Compared with the static one,the dynamic DR strategy can achieve precise identification of important dimensions,thereby enabling accelerated convergence toward optimal solutions.Furthermore,the influence of cumulative contribution rate thresholds on optimization problems with different dimensions is investigated.Metaheuristic algorithms(PSO,DE)and cooperative metaheuristics(C-PSO,C-DE)are examined by 15 benchmark functions and two engineering design problems(speed reducer and composite pressure vessel).Comparative results demonstrate that the cooperative methods achieve significantly superior performance compared to standard methods in both solution accuracy and computational efficiency.Compared to standard metaheuristic algorithms,cooperative metaheuristics achieve a reduction in computational cost of at least 40%.The cooperative metaheuristics can be effectively used to tackle both high-dimensional unconstrained and constrained optimization problems.
基金supported by the National Key R&D Program of China under Grant No.2022YFA1003701the Open Research Fund of Yunnan Key Laboratory of Statistical Modeling and Data Analysis,Yunnan University under Grant No.SMDAYB2023004。
文摘Quantile regression(QR)has become an important tool to measure dependence of response variable's quantiles on a number of predictors for heterogeneous data,especially heavy-tailed data and outliers.However,it is quite challenging to make statistical inference on distributed high-dimensional QR with missing data due to the distributed nature,sparsity and missingness of data and nondifferentiable quantile loss function.To overcome the challenge,this paper develops a communicationefficient method to select variables and estimate parameters by utilizing a smooth function to approximate the non-differentiable quantile loss function and incorporating the idea of the inverse probability weighting and the penalty function.The proposed approach has three merits.First,it is both computationally and communicationally efficient because only the first-and second-order information of the approximate objective function are communicated at each iteration.Second,the proposed estimators possess the oracle property after a limited number of iterations without constraint on the number of machines.Third,the proposed method simultaneously selects variables and estimates parameters within a distributed framework,ensuring robustness to the specified response probability or propensity score function of the missing data mechanism.Simulation studies and a real example are used to illustrate the effectiveness of the proposed methodologies.
基金Supported by the National Natural Science Foundation of China(12201446)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(22KJB110005)the Shuangchuang Program of Jiangsu Province(JSSCBS20220898)。
文摘It is known that monotone recurrence relations can induce a class of twist homeomorphisms on the high-dimensional cylinder,which is an extension of the class of monotone twist maps on the annulus or two-dimensional cylinder.By constructing a bounded solution of the monotone recurrence relation,the main conclusion in this paper is acquired:The induced homeomorphism has Birkhoff orbits provided there is a compact forward-invariant set.Therefore,it generalizes Angenent's results in low-dimensional cases.
基金supported by Fundamental Research Program of Shanxi Province(Nos.202203021211088,202403021212254,202403021221109)Graduate Research Innovation Project in Shanxi Province(No.2024KY616).
文摘Data collected in fields such as cybersecurity and biomedicine often encounter high dimensionality and class imbalance.To address the problem of low classification accuracy for minority class samples arising from numerous irrelevant and redundant features in high-dimensional imbalanced data,we proposed a novel feature selection method named AMF-SGSK based on adaptive multi-filter and subspace-based gaining sharing knowledge.Firstly,the balanced dataset was obtained by random under-sampling.Secondly,combining the feature importance score with the AUC score for each filter method,we proposed a concept called feature hardness to judge the importance of feature,which could adaptively select the essential features.Finally,the optimal feature subset was obtained by gaining sharing knowledge in multiple subspaces.This approach effectively achieved dimensionality reduction for high-dimensional imbalanced data.The experiment results on 30 benchmark imbalanced datasets showed that AMF-SGSK performed better than other eight commonly used algorithms including BGWO and IG-SSO in terms of F1-score,AUC,and G-mean.The mean values of F1-score,AUC,and Gmean for AMF-SGSK are 0.950,0.967,and 0.965,respectively,achieving the highest among all algorithms.And the mean value of Gmean is higher than those of IG-PSO,ReliefF-GWO,and BGOA by 3.72%,11.12%,and 20.06%,respectively.Furthermore,the selected feature ratio is below 0.01 across the selected ten datasets,further demonstrating the proposed method’s overall superiority over competing approaches.AMF-SGSK could adaptively remove irrelevant and redundant features and effectively improve the classification accuracy of high-dimensional imbalanced data,providing scientific and technological references for practical applications.
基金supported in part by the Young Scientists Fund of the National Natural Science Foundation of China(Grant Nos.82304253)(and 82273709)the Foundation for Young Talents in Higher Education of Guangdong Province(Grant No.2022KQNCX021)the PhD Starting Project of Guangdong Medical University(Grant No.GDMUB2022054).
文摘Objective Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health.Analysis of these mixture exposures presents several key challenges for environmental epidemiology and risk assessment,including high dimensionality,correlated exposure,and subtle individual effects.Methods We proposed a novel statistical approach,the generalized functional linear model(GFLM),to analyze the health effects of exposure mixtures.GFLM treats the effect of mixture exposures as a smooth function by reordering exposures based on specific mechanisms and capturing internal correlations to provide a meaningful estimation and interpretation.The robustness and efficiency was evaluated under various scenarios through extensive simulation studies.Results We applied the GFLM to two datasets from the National Health and Nutrition Examination Survey(NHANES).In the first application,we examined the effects of 37 nutrients on BMI(2011–2016 cycles).The GFLM identified a significant mixture effect,with fiber and fat emerging as the nutrients with the greatest negative and positive effects on BMI,respectively.For the second application,we investigated the association between four pre-and perfluoroalkyl substances(PFAS)and gout risk(2007–2018 cycles).Unlike traditional methods,the GFLM indicated no significant association,demonstrating its robustness to multicollinearity.Conclusion GFLM framework is a powerful tool for mixture exposure analysis,offering improved handling of correlated exposures and interpretable results.It demonstrates robust performance across various scenarios and real-world applications,advancing our understanding of complex environmental exposures and their health impacts on environmental epidemiology and toxicology.
基金supported by the Project of the Hubei Provincial Department of Science and Technology(Grant Nos.2022CFB957,2022CFB475)the National Natural Science Foundation of China(Grant No.11847118)。
文摘The decoherence of high-dimensional orbital angular momentum(OAM)entanglement in the weak scintillation regime has been investigated.In this study,we simulate atmospheric turbulence by utilizing a multiple-phase screen imprinted with anisotropic non-Kolmogorov turbulence.The entanglement negativity and fidelity are introduced to quantify the entanglement of a high-dimensional OAM state.The numerical evaluation results indicate that entanglement negativity and fidelity last longer for a high-dimensional OAM state when the azimuthal mode has a lower value.Additionally,the evolution of higher-dimensional OAM entanglement is significantly influenced by OAM beam parameters and turbulence parameters.Compared to isotropic atmospheric turbulence,anisotropic turbulence has a lesser influence on highdimensional OAM entanglement.
基金supported by Shaanxi Province Natural Science Foundation of Research Projects(2016JM6014)the Innovation Foundation of High-Tech Institute of Xi’an(2015ZZDJJ03)the Youth Foundation of HighTech Institute of Xi’an(2016QNJJ004)
文摘Guaranteed cost consensus analysis and design problems for high-dimensional multi-agent systems with time varying delays are investigated. The idea of guaranteed cost con trol is introduced into consensus problems for high-dimensiona multi-agent systems with time-varying delays, where a cos function is defined based on state errors among neighboring agents and control inputs of all the agents. By the state space decomposition approach and the linear matrix inequality(LMI)sufficient conditions for guaranteed cost consensus and consensu alization are given. Moreover, a guaranteed cost upper bound o the cost function is determined. It should be mentioned that these LMI criteria are dependent on the change rate of time delays and the maximum time delay, the guaranteed cost upper bound is only dependent on the maximum time delay but independen of the Laplacian matrix. Finally, numerical simulations are given to demonstrate theoretical results.
基金supported in part by the National Natural Science Foundation of China(61702475,61772493,61902370,62002337)in part by the Natural Science Foundation of Chongqing,China(cstc2019jcyj-msxmX0578,cstc2019jcyjjqX0013)+1 种基金in part by the Chinese Academy of Sciences“Light of West China”Program,in part by the Pioneer Hundred Talents Program of Chinese Academy of Sciencesby Technology Innovation and Application Development Project of Chongqing,China(cstc2019jscx-fxydX0027)。
文摘High-dimensional and sparse(HiDS)matrices commonly arise in various industrial applications,e.g.,recommender systems(RSs),social networks,and wireless sensor networks.Since they contain rich information,how to accurately represent them is of great significance.A latent factor(LF)model is one of the most popular and successful ways to address this issue.Current LF models mostly adopt L2-norm-oriented Loss to represent an HiDS matrix,i.e.,they sum the errors between observed data and predicted ones with L2-norm.Yet L2-norm is sensitive to outlier data.Unfortunately,outlier data usually exist in such matrices.For example,an HiDS matrix from RSs commonly contains many outlier ratings due to some heedless/malicious users.To address this issue,this work proposes a smooth L1-norm-oriented latent factor(SL-LF)model.Its main idea is to adopt smooth L1-norm rather than L2-norm to form its Loss,making it have both strong robustness and high accuracy in predicting the missing data of an HiDS matrix.Experimental results on eight HiDS matrices generated by industrial applications verify that the proposed SL-LF model not only is robust to the outlier data but also has significantly higher prediction accuracy than state-of-the-art models when they are used to predict the missing data of HiDS matrices.
基金supported in part by the National Natural Science Foundation of China (6177249391646114)+1 种基金Chongqing research program of technology innovation and application (cstc2017rgzn-zdyfX0020)in part by the Pioneer Hundred Talents Program of Chinese Academy of Sciences
文摘Latent factor(LF)models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS)matrices which are commonly seen in various industrial applications.An LF model usually adopts iterative optimizers,which may consume many iterations to achieve a local optima,resulting in considerable time cost.Hence,determining how to accelerate the training process for LF models has become a significant issue.To address this,this work proposes a randomized latent factor(RLF)model.It incorporates the principle of randomized learning techniques from neural networks into the LF analysis of HiDS matrices,thereby greatly alleviating computational burden.It also extends a standard learning process for randomized neural networks in context of LF analysis to make the resulting model represent an HiDS matrix correctly.Experimental results on three HiDS matrices from industrial applications demonstrate that compared with state-of-the-art LF models,RLF is able to achieve significantly higher computational efficiency and comparable prediction accuracy for missing data.I provides an important alternative approach to LF analysis of HiDS matrices,which is especially desired for industrial applications demanding highly efficient models.
基金Supported by the National Natural Science Foundation of China(No.61502475)the Importation and Development of High-Caliber Talents Project of the Beijing Municipal Institutions(No.CIT&TCD201504039)
文摘The performance of conventional similarity measurement methods is affected seriously by the curse of dimensionality of high-dimensional data.The reason is that data difference between sparse and noisy dimensionalities occupies a large proportion of the similarity,leading to the dissimilarities between any results.A similarity measurement method of high-dimensional data based on normalized net lattice subspace is proposed.The data range of each dimension is divided into several intervals,and the components in different dimensions are mapped onto the corresponding interval.Only the component in the same or adjacent interval is used to calculate the similarity.To validate this method,three data types are used,and seven common similarity measurement methods are compared.The experimental result indicates that the relative difference of the method is increasing with the dimensionality and is approximately two or three orders of magnitude higher than the conventional method.In addition,the similarity range of this method in different dimensions is [0,1],which is fit for similarity analysis after dimensionality reduction.
基金supported by the National Natural Science Foundations of China(Nos.51205193,51475221)
文摘Image matching technology is theoretically significant and practically promising in the field of autonomous navigation.Addressing shortcomings of existing image matching navigation technologies,the concept of high-dimensional combined feature is presented based on sequence image matching navigation.To balance between the distribution of high-dimensional combined features and the shortcomings of the only use of geometric relations,we propose a method based on Delaunay triangulation to improve the feature,and add the regional characteristics of the features together with their geometric characteristics.Finally,k-nearest neighbor(KNN)algorithm is adopted to optimize searching process.Simulation results show that the matching can be realized at the rotation angle of-8°to 8°and the scale factor of 0.9 to 1.1,and when the image size is 160 pixel×160 pixel,the matching time is less than 0.5 s.Therefore,the proposed algorithm can substantially reduce computational complexity,improve the matching speed,and exhibit robustness to the rotation and scale changes.
基金The National Grand Fundamental Research 973 Program (2004CB318000) of China
文摘This paper deals with the representation of the solutions of a polynomial system, and concentrates on the high-dimensional case. Based on the rational univari- ate representation of zero-dimensional polynomial systems, we give a new description called rational representation for the solutions of a high-dimensional polynomial sys- tem and propose an algorithm for computing it. By this way all the solutions of any high-dimensional polynomial system can be represented by a set of so-called rational- representation sets.
基金supported in part by the National Natural Science Foundation of China(62172065,62072060)。
文摘As a crucial data preprocessing method in data mining,feature selection(FS)can be regarded as a bi-objective optimization problem that aims to maximize classification accuracy and minimize the number of selected features.Evolutionary computing(EC)is promising for FS owing to its powerful search capability.However,in traditional EC-based methods,feature subsets are represented via a length-fixed individual encoding.It is ineffective for high-dimensional data,because it results in a huge search space and prohibitive training time.This work proposes a length-adaptive non-dominated sorting genetic algorithm(LA-NSGA)with a length-variable individual encoding and a length-adaptive evolution mechanism for bi-objective highdimensional FS.In LA-NSGA,an initialization method based on correlation and redundancy is devised to initialize individuals of diverse lengths,and a Pareto dominance-based length change operator is introduced to guide individuals to explore in promising search space adaptively.Moreover,a dominance-based local search method is employed for further improvement.The experimental results based on 12 high-dimensional gene datasets show that the Pareto front of feature subsets produced by LA-NSGA is superior to those of existing algorithms.
基金The project supported by National Natural Science Foundation of China under Grant Nos. 90203018, 10474104, and 10447133, and the Knowledge Innovation Program （KIP） of the Chinese Academy of Sciences, the National Fundamental Research Program of China under Grant No. 2001CB309310
文摘The quantum state transmission through the medium of high-dimensional many-particle system （boson or spinless fermion） is generally studied with a symmetry analysis. We discover that, if the spectrum of a Hamiltonian matches the symmetry of a fermion or boson system in a certain fashion, a perfect quantum state transfer can be implemented without any operation on the medium with pre-engineered nearest neighbor （NN）. We also study a simple but realistic near half-filled tight-bindlng fermion system wlth uniform NN hopping integral. We show that an arbitrary many-particle state near the fermi surface can be perfectly transferred to its translational counterpart.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61871205 and 61561033)the Major Academic Discipline and Technical Leader of Jiangxi Province,China(Grant No.20162BCB22011).
文摘A new efficient two-party semi-quantum key agreement protocol is proposed with high-dimensional single-particle states.Different from the previous semi-quantum key agreement protocols based on the two-level quantum system,the propounded protocol makes use of the advantage of the high-dimensional quantum system,which possesses higher efficiency and better robustness against eavesdropping.Besides,the protocol allows the classical participant to encode the secret key with qudit shifting operations without involving any quantum measurement abilities.The designed semi-quantum key agreement protocol could resist both participant attacks and outsider attacks.Meanwhile,the conjoint analysis of security and efficiency provides an appropriate choice for reference on the dimension of single-particle states and the number of decoy states.
文摘Because all the known integrable models possess Schwarzian forms with Mobious transformation invariance,it may be one of the best ways to find new integrable models starting from some suitable Mobious transformation invariant equations. In this paper, we study the Painlevé integrability of some special (3+1)-dimensional Schwarzian models.
基金supported in part by the National Natural Science Foundation of China (Nos. 61303074, 61309013)the Programs for Science, National Key Basic Research and Development Program ("973") of China (No. 2012CB315900)Technology Development of Henan province (Nos.12210231003, 13210231002)
文摘Aimed at the issue that traditional clustering methods are not appropriate to high-dimensional data, a cuckoo search fuzzy-weighting algorithm for subspace clustering is presented on the basis of the exited soft subspace clustering algorithm. In the proposed algorithm, a novel objective function is firstly designed by considering the fuzzy weighting within-cluster compactness and the between-cluster separation, and loosening the constraints of dimension weight matrix. Then gradual membership and improved Cuckoo search, a global search strategy, are introduced to optimize the objective function and search subspace clusters, giving novel learning rules for clustering. At last, the performance of the proposed algorithm on the clustering analysis of various low and high dimensional datasets is experimentally compared with that of several competitive subspace clustering algorithms. Experimental studies demonstrate that the proposed algorithm can obtain better performance than most of the existing soft subspace clustering algorithms.
基金Project supported by the National Key Research and Development Program of China(Grant No.2020YFA0309702)the National Natural Science Foundation of China(Grant Nos.62101597,61605248,61675235,and 61505261)+2 种基金the China Postdoctoral Science Foundation(Grant No.2021M691536)the Natural Science Foundation of Henan Province,China(Grant Nos.202300410534 and 202300410532)the Anhui Initiative Fund in Quantum Information Technologies。
文摘High-dimensional quantum resources provide the ability to encode several bits of information on a single photon,which can particularly increase the secret key rate rate of quantum key distribution(QKD)systems.Recently,a practical four-dimensional QKD scheme based on time-bin quantum photonic state,only with two single-photon avalanche detectors as measurement setup,has been proven to have a superior performance than the qubit-based one.In this paper,we extend the results to our proposed eight-dimensional scheme.Then,we consider two main practical factors to improve its secret key bound.Concretely,we take the afterpulse effect into account and apply a finite-key analysis with the intensity fluctuations.Our secret bounds give consideration to both the intensity fluctuations and the afterpulse effect for the high-dimensional QKD systems.Numerical simulations show the bound of eight-dimensional QKD scheme is more robust to the intensity fluctuations but more sensitive to the afterpulse effect than the four-dimensional one.
