电离层总电子含量(total electron content,TEC)是无线电波传播和航天活动中的关键参数,建立高精度的电离层TEC预测模型具有重要意义。本文利用国际GNSS服务(International GNSS Service,IGS)欧洲定轨中心(Center for Orbit Determinati...电离层总电子含量(total electron content,TEC)是无线电波传播和航天活动中的关键参数,建立高精度的电离层TEC预测模型具有重要意义。本文利用国际GNSS服务(International GNSS Service,IGS)欧洲定轨中心(Center for Orbit Determination in Europe,CODE)提供的TEC数据,提出了一种结合时空Transformer(spatio-temporal transformer,STT)与时间卷积网络(temporal convolutional network,TCN)并引入时空注意力机制的组合预测模型TCN-STT,来对TEC进行预测。本研究基于中国及周边地区2000年至2023年共8766天的TEC数据,采用滑动窗口方法构建了8764个样本。所有样本依据Kp地磁指数(Kp<4,4≤Kp<7,Kp≥7)分为三类并进行随机抽样,确保在训练集、验证集和测试集中不同地磁活动强度的样本分布相对均匀,并最终按照8∶1∶1的比例进行划分。实验结果表明:在地磁平静期(Kp<4),样本的均方根误差(root mean square error,RMSE)均值为2.62 TECU,平均相对精度均值为90.48%;在地磁活跃期(4≤Kp<7),样本的RMSE均值增至3.94 TECU,平均相对精度均值下降至87.74%;而在地磁强扰期(Kp≥7),样本的RMSE均值进一步达到8.95 TECU,平均相对精度均值降低至81.28%。总体来看,模型在测试集全部样本上的RMSE均值为2.68 TECU,平均相对精度为90.36%。此外,模型在测试集全部样本上的预测值与真实值的相关系数为0.9866,决定系数(R2)为0.9734,充分表明模型具有优秀且稳定的预测性能。展开更多
In this paper,we established a class of parallel algorithm for solving low-rank tensor completion problem.The main idea is that N singular value decompositions are implemented in N different processors for each slice ...In this paper,we established a class of parallel algorithm for solving low-rank tensor completion problem.The main idea is that N singular value decompositions are implemented in N different processors for each slice matrix under unfold operator,and then the fold operator is used to form the next iteration tensor such that the computing time can be decreased.In theory,we analyze the global convergence of the algorithm.In numerical experiment,the simulation data and real image inpainting are carried out.Experiment results show the parallel algorithm outperform its original algorithm in CPU times under the same precision.展开更多
Aiming at the problem that infrared small target detection faces low contrast between the background and the target and insufficient noise suppression ability under the complex cloud background,an infrared small targe...Aiming at the problem that infrared small target detection faces low contrast between the background and the target and insufficient noise suppression ability under the complex cloud background,an infrared small target detection method based on the tensor nuclear norm and direction residual weighting was proposed.Based on converting the infrared image into an infrared patch tensor model,from the perspective of the low-rank nature of the background tensor,and taking advantage of the difference in contrast between the background and the target in different directions,we designed a double-neighborhood local contrast based on direction residual weighting method(DNLCDRW)combined with the partial sum of tensor nuclear norm(PSTNN)to achieve effective background suppression and recovery of infrared small targets.Experiments show that the algorithm is effective in suppressing the background and improving the detection ability of the target.展开更多
针对存在外部干扰以及模型不确定性的侧滑转弯(Side to Turn,STT)飞行器姿态跟踪控制问题,提出了一种反步自适应滑模控制方法。首先建立飞行器姿态动力学模型,并对其进行线性化处理,得到面向控制的三通道解耦的姿态数学模型;然后将控制...针对存在外部干扰以及模型不确定性的侧滑转弯(Side to Turn,STT)飞行器姿态跟踪控制问题,提出了一种反步自适应滑模控制方法。首先建立飞行器姿态动力学模型,并对其进行线性化处理,得到面向控制的三通道解耦的姿态数学模型;然后将控制器划分为外环过载控制器以及内环角速度控制器,并基于滑模控制理论分别设计双环控制器,同时设计自适应律对扰动进行补偿,使姿态控制系统对扰动及偏差具有良好的适应性和鲁棒性;最后利用Lyapunov稳定性定理证明了所设计控制器的稳定性。数值仿真结果表明,全飞行过程中过载跟踪偏差小于0.1,角速度跟踪偏差小于0.5(°)/s,气动舵偏未出现满偏及抖振情况,数值仿真结果验证了所设计控制器的有效性和可行性。展开更多
The monitoring signals of bearings from single-source sensor often contain limited information for characterizing various working condition,which may lead to instability and uncertainty of the class-imbalanced intelli...The monitoring signals of bearings from single-source sensor often contain limited information for characterizing various working condition,which may lead to instability and uncertainty of the class-imbalanced intelligent fault diagnosis.On the other hand,the vectorization of multi-source sensor signals may not only generate high-dimensional vectors,leading to increasing computational complexity and overfitting problems,but also lose the structural information and the coupling information.This paper proposes a new method for class-imbalanced fault diagnosis of bearing using support tensor machine(STM)driven by heterogeneous data fusion.The collected sound and vibration signals of bearings are successively decomposed into multiple frequency band components to extract various time-domain and frequency-domain statistical parameters.A third-order hetero-geneous feature tensor is designed based on multisensors,frequency band components,and statistical parameters.STM-based intelligent model is constructed to preserve the structural information of the third-order heterogeneous feature tensor for bearing fault diagnosis.A series of comparative experiments verify the advantages of the proposed method.展开更多
This paper proposed a moment tensor regression prediction technology based on ResNet for microseismic events.Taking the great advantages of deep networks in classification and regression tasks,it can realize the great...This paper proposed a moment tensor regression prediction technology based on ResNet for microseismic events.Taking the great advantages of deep networks in classification and regression tasks,it can realize the great potential of fast and accurate inversion of microseismic moment tensors after the network trained.This ResNet-based moment tensor prediction technology,whose input is raw recordings,does not require the extraction of data features in advance.First,we tested the network using synthetic data and performed a quantitative assessment of the errors.The results demonstrate that the network exhibits high accuracy and efficiency during the prediction phase.Next,we tested the network using real microseismic data and compared the results with those from traditional inversion methods.The error in the results was relatively small compared to traditional methods.However,the network operates more efficiently without requiring manual intervention,making it highly valuable for near-real-time monitoring applications.展开更多
Absorption compensation is a process involving the exponential amplification of reflection amplitudes.This process amplifies the seismic signal and noise,thereby substantially reducing the signal-tonoise ratio of seis...Absorption compensation is a process involving the exponential amplification of reflection amplitudes.This process amplifies the seismic signal and noise,thereby substantially reducing the signal-tonoise ratio of seismic data.Therefore,this paper proposes a multichannel inversion absorption compensation method based on structure tensor regularization.First,the structure tensor is utilized to extract the spatial inclination of seismic signals,and the spatial prediction filter is designed along the inclination direction.The spatial prediction filter is then introduced into the regularization condition of multichannel inversion absorption compensation,and the absorption compensation is realized under the framework of multichannel inversion theory.The spatial predictability of seismic signals is also introduced into the objective function of absorption compensation inversion.Thus,the inversion system can effectively suppress the noise amplification effect during absorption compensation and improve the recovery accuracy of high-frequency signals.Synthetic and field data tests are conducted to demonstrate the accuracy and effectiveness of the proposed method.展开更多
Constraint satisfaction problems(CSPs)are a class of problems that are ubiquitous in science and engineering.They feature a collection of constraints specified over subsets of variables.A CSP can be solved either dire...Constraint satisfaction problems(CSPs)are a class of problems that are ubiquitous in science and engineering.They feature a collection of constraints specified over subsets of variables.A CSP can be solved either directly or by reducing it to other problems.This paper introduces the Julia ecosystem for solving and analyzing CSPs with a focus on the programming practices.We introduce some important CSPs and show how these problems are reduced to each other.We also show how to transform CSPs into tensor networks,how to optimize the tensor network contraction orders,and how to extract the solution space properties by contracting the tensor networks with generic element types.Examples are given,which include computing the entropy constant,analyzing the overlap gap property,and the reduction between CSPs.展开更多
When plants respond to drought stress,dynamic cellular changes occur,accompanied by alterations in gene expression,which often act through trans-regulation.However,the detection of trans-acting genetic variants and ne...When plants respond to drought stress,dynamic cellular changes occur,accompanied by alterations in gene expression,which often act through trans-regulation.However,the detection of trans-acting genetic variants and networks of genes is challenged by the large number of genes and markers.Using a tensor decomposition method,we identify trans-acting expression quantitative trait loci(trans-eQTLs)linked to gene modules,rather than individual genes,which were associated with maize drought response.Module-to-trait association analysis demonstrates that half of the modules are relevant to drought-related traits.Genome-wide association studies of the expression patterns of each module identify 286 trans-eQTLs linked to drought-responsive modules,the majority of which cannot be detected based on individual gene expression.Notably,the trans-eQTLs located in the regions selected during maize improvement tend towards relatively strong selection.We further prioritize the genes that affect the transcriptional regulation of multiple genes in trans,as exemplified by two transcription factor genes.Our analyses highlight that multidimensional reduction could facilitate the identification of trans-acting variations in gene expression in response to dynamic environments and serve as a promising technique for high-order data processing in future crop breeding.展开更多
Computing free energy is a fundamental problem in statistical physics.Recently,two distinct methods have been developed and have demonstrated remarkable success:the tensor-network-based contraction method and the neur...Computing free energy is a fundamental problem in statistical physics.Recently,two distinct methods have been developed and have demonstrated remarkable success:the tensor-network-based contraction method and the neural-network-based variational method.Tensor networks are accurate,but their application is often limited to low-dimensional systems due to the high computational complexity in high-dimensional systems.The neural network method applies to systems with general topology.However,as a variational method,it is not as accurate as tensor networks.In this work,we propose an integrated approach,tensor-network-based variational autoregressive networks(TNVAN),that leverages the strengths of both tensor networks and neural networks:combining the variational autoregressive neural network’s ability to compute an upper bound on free energy and perform unbiased sampling from the variational distribution with the tensor network’s power to accurately compute the partition function for small sub-systems,resulting in a robust method for precisely estimating free energy.To evaluate the proposed approach,we conducted numerical experiments on spin glass systems with various topologies,including two-dimensional lattices,fully connected graphs,and random graphs.Our numerical results demonstrate the superior accuracy of our method compared to existing approaches.In particular,it effectively handles systems with longrange interactions and leverages GPU efficiency without requiring singular value decomposition,indicating great potential in tackling statistical mechanics problems and simulating high-dimensional complex systems through both tensor networks and neural networks.展开更多
The quantum geometric tensor(QGT)is a fundamental quantity for characterizing the geometric properties of quantum states and plays an essential role in elucidating various physical phenomena.The traditional QGT,defned...The quantum geometric tensor(QGT)is a fundamental quantity for characterizing the geometric properties of quantum states and plays an essential role in elucidating various physical phenomena.The traditional QGT,defned only for pure states,has limited applicability in realistic scenarios where mixed states are common.To address this limitation,we generalize the defnition of the QGT to mixed states using the purifcation bundle and the covariant derivative.Notably,our proposed defnition reduces to the traditional QGT when mixed states approach pure states.In our framework,the real and imaginary parts of this generalized QGT correspond to the Bures metric and the mean gauge curvature,respectively,endowing it with a broad range of potential applications.Additionally,using our proposed mixed-state QGT,we derive the geodesic equation applicable to mixed states.This work establishes a unifed framework for the geometric analysis of both pure and mixed states,thereby deepening our understanding of the geometric properties of quantum states.展开更多
Dear Editor,This letter proposes the graph tensor alliance attention network(GT-A^(2)T)to represent a dynamic graph(DG)precisely.Its main idea includes 1)Establishing a unified spatio-temporal message propagation fram...Dear Editor,This letter proposes the graph tensor alliance attention network(GT-A^(2)T)to represent a dynamic graph(DG)precisely.Its main idea includes 1)Establishing a unified spatio-temporal message propagation framework on a DG via the tensor product for capturing the complex cohesive spatio-temporal interdependencies precisely and 2)Acquiring the alliance attention scores by node features and favorable high-order structural correlations.展开更多
The era of big data brings new challenges for information network systems(INS),simultaneously offering unprecedented opportunities for advancing intelligent intrusion detection systems.In this work,we propose a data-d...The era of big data brings new challenges for information network systems(INS),simultaneously offering unprecedented opportunities for advancing intelligent intrusion detection systems.In this work,we propose a data-driven intrusion detection system for Distributed Denial of Service(DDoS)attack detection.The system focuses on intrusion detection from a big data perceptive.As intelligent information processing methods,big data and artificial intelligence have been widely used in information systems.The INS system is an important information system in cyberspace.In advanced INS systems,the network architectures have become more complex.And the smart devices in INS systems collect a large scale of network data.How to improve the performance of a complex intrusion detection system with big data and artificial intelligence is a big challenge.To address the problem,we design a novel intrusion detection system(IDS)from a big data perspective.The IDS system uses tensors to represent large-scale and complex multi-source network data in a unified tensor.Then,a novel tensor decomposition(TD)method is developed to complete big data mining.The TD method seamlessly collaborates with the XGBoost(eXtreme Gradient Boosting)method to complete the intrusion detection.To verify the proposed IDS system,a series of experiments is conducted on two real network datasets.The results revealed that the proposed IDS system attained an impressive accuracy rate over 98%.Additionally,by altering the scale of the datasets,the proposed IDS system still maintains excellent detection performance,which demonstrates the proposed IDS system’s robustness.展开更多
In this study,we employed Bayesian inversion coupled with the summation-by-parts and simultaneousapproximation-term(SBP-SAT)forward simulation method to elucidate the mechanisms behind mininginduced seismic events cau...In this study,we employed Bayesian inversion coupled with the summation-by-parts and simultaneousapproximation-term(SBP-SAT)forward simulation method to elucidate the mechanisms behind mininginduced seismic events caused by fault slip and their potential effects on rockbursts.Through Bayesian inversion,it is determined that the sources near fault FQ14 have a significant shear component.Additionally,we analyzed the stress and displacement fields of high-energy events,along with the hypocenter distribution of aftershocks,which aided in identifying the slip direction of the critically stressed fault FQ14.We also performed forward modeling to capture the complex dynamics of fault slip under varying friction laws and shear fracture modes.The selection of specific friction laws for fault slip models was based on their ability to accurately replicate observed slip behavior under various external loading conditions,thereby enhancing the applicability of our findings.Our results suggest that the slip behavior of fault FQ14 can be effectively understood by comparing different scenarios.展开更多
To enhance the inference efficiency of convolutional neural network(CNN),tensor parallelism is employed to improve the parallelism within operators.However,existing methods are customized to specific networks and hard...To enhance the inference efficiency of convolutional neural network(CNN),tensor parallelism is employed to improve the parallelism within operators.However,existing methods are customized to specific networks and hardware,limiting their generalizability.This paper proposes an approach called resource-adaptive tensor decomposition(RATD)for CNN operators,which aims to achieve an optimal match between computational resources and parallel computing tasks.Firstly,CNN is represented with fine-grained tensors at the lower graph level,thereby decoupling tensors that can be computed in parallel within operators.Secondly,the convolution and pooling operators are fused,and the decoupled tensor blocks are scheduled in parallel.Finally,a cost model is constructed,based on runtime and resource utilization,to iteratively refine the process of tensor block decomposition and automatically determine the optimal tensor decomposition.Experimental results demonstrate that the proposed RATD improves the accuracy of the model by 11%.Compared with CUDA(compute unified device architecture)deep neural network library(cuDNN),RATD achieves an average speedup ratio of 1.21 times in inference time across various convolution kernels,along with a 12%increase in computational resource utilization.展开更多
The tensor force changes the nuclear shell structure and thus may result in underlying influence of the collectivity and decay properties of the nucleus.We carefully examined the impact of the monopole and multipole e...The tensor force changes the nuclear shell structure and thus may result in underlying influence of the collectivity and decay properties of the nucleus.We carefully examined the impact of the monopole and multipole effects originating from the tensor force on both the collectivity and the matrix element for the neutrinoless double-β(0νββ)decay,using the generatorcoordinate method with an effective interaction.To analyze the effect of the tensor force,we employed an effective Hamiltonian associated with the monopole-based universal interaction that explicitly consists of the central,tensor,and spin-orbit coupling terms.The interferences among the shell structure,quadrupole collectivity,nucleon occupancy,and 0νββmatrix elements were analyzed in detail.A better understanding of the tensor force would be of great importance in reducing the theoretical uncertainty in 0νββnuclear matrix element calculations.展开更多
Heterogeneous graphs generally refer to graphs with different types of nodes and edges.A common approach for extracting useful information from heterogeneous graphs is to use meta-graphs,which can be seen as a special...Heterogeneous graphs generally refer to graphs with different types of nodes and edges.A common approach for extracting useful information from heterogeneous graphs is to use meta-graphs,which can be seen as a special kind of directed acyclic graph with same node and edge types as the heterogeneous graph.However,how to design proper metagraphs is challenging.Recently,there have been many works on learning suitable metagraphs from a heterogeneous graph.Existing methods generally introduce continuous weights for edges that are independent of each other,which ignores the topological structures of meta-graphs and can be ineffective.To address this issue,the authors propose a new viewpoint from tensor on learning meta-graphs.Such a viewpoint not only helps interpret the limitation of existing works by CANDECOMP/PARAFAC(CP)decomposition,but also inspires us to propose a topology-aware tensor decomposition,called TENSUS,that reflects the structure of DAGs.The proposed topology-aware tensor decomposition is easy to use and simple to implement,and it can be taken as a plug-in part to upgrade many existing works,including node classification and recommendation on heterogeneous graphs.Experimental results on different tasks demonstrate that the proposed method can significantly improve the state-of-the-arts for all these tasks.展开更多
文摘电离层总电子含量(total electron content,TEC)是无线电波传播和航天活动中的关键参数,建立高精度的电离层TEC预测模型具有重要意义。本文利用国际GNSS服务(International GNSS Service,IGS)欧洲定轨中心(Center for Orbit Determination in Europe,CODE)提供的TEC数据,提出了一种结合时空Transformer(spatio-temporal transformer,STT)与时间卷积网络(temporal convolutional network,TCN)并引入时空注意力机制的组合预测模型TCN-STT,来对TEC进行预测。本研究基于中国及周边地区2000年至2023年共8766天的TEC数据,采用滑动窗口方法构建了8764个样本。所有样本依据Kp地磁指数(Kp<4,4≤Kp<7,Kp≥7)分为三类并进行随机抽样,确保在训练集、验证集和测试集中不同地磁活动强度的样本分布相对均匀,并最终按照8∶1∶1的比例进行划分。实验结果表明:在地磁平静期(Kp<4),样本的均方根误差(root mean square error,RMSE)均值为2.62 TECU,平均相对精度均值为90.48%;在地磁活跃期(4≤Kp<7),样本的RMSE均值增至3.94 TECU,平均相对精度均值下降至87.74%;而在地磁强扰期(Kp≥7),样本的RMSE均值进一步达到8.95 TECU,平均相对精度均值降低至81.28%。总体来看,模型在测试集全部样本上的RMSE均值为2.68 TECU,平均相对精度为90.36%。此外,模型在测试集全部样本上的预测值与真实值的相关系数为0.9866,决定系数(R2)为0.9734,充分表明模型具有优秀且稳定的预测性能。
基金Supported by National Nature Science Foundation(12371381)Nature Science Foundation of Shanxi(202403021222270)。
文摘In this paper,we established a class of parallel algorithm for solving low-rank tensor completion problem.The main idea is that N singular value decompositions are implemented in N different processors for each slice matrix under unfold operator,and then the fold operator is used to form the next iteration tensor such that the computing time can be decreased.In theory,we analyze the global convergence of the algorithm.In numerical experiment,the simulation data and real image inpainting are carried out.Experiment results show the parallel algorithm outperform its original algorithm in CPU times under the same precision.
基金Supported by the Key Laboratory Fund for Equipment Pre-Research(6142207210202)。
文摘Aiming at the problem that infrared small target detection faces low contrast between the background and the target and insufficient noise suppression ability under the complex cloud background,an infrared small target detection method based on the tensor nuclear norm and direction residual weighting was proposed.Based on converting the infrared image into an infrared patch tensor model,from the perspective of the low-rank nature of the background tensor,and taking advantage of the difference in contrast between the background and the target in different directions,we designed a double-neighborhood local contrast based on direction residual weighting method(DNLCDRW)combined with the partial sum of tensor nuclear norm(PSTNN)to achieve effective background suppression and recovery of infrared small targets.Experiments show that the algorithm is effective in suppressing the background and improving the detection ability of the target.
文摘针对存在外部干扰以及模型不确定性的侧滑转弯(Side to Turn,STT)飞行器姿态跟踪控制问题,提出了一种反步自适应滑模控制方法。首先建立飞行器姿态动力学模型,并对其进行线性化处理,得到面向控制的三通道解耦的姿态数学模型;然后将控制器划分为外环过载控制器以及内环角速度控制器,并基于滑模控制理论分别设计双环控制器,同时设计自适应律对扰动进行补偿,使姿态控制系统对扰动及偏差具有良好的适应性和鲁棒性;最后利用Lyapunov稳定性定理证明了所设计控制器的稳定性。数值仿真结果表明,全飞行过程中过载跟踪偏差小于0.1,角速度跟踪偏差小于0.5(°)/s,气动舵偏未出现满偏及抖振情况,数值仿真结果验证了所设计控制器的有效性和可行性。
基金supported by the National Natural Science Foundation of China(No.52275104)the Science and Technology Innovation Program of Hunan Province(No.2023RC3097).
文摘The monitoring signals of bearings from single-source sensor often contain limited information for characterizing various working condition,which may lead to instability and uncertainty of the class-imbalanced intelligent fault diagnosis.On the other hand,the vectorization of multi-source sensor signals may not only generate high-dimensional vectors,leading to increasing computational complexity and overfitting problems,but also lose the structural information and the coupling information.This paper proposes a new method for class-imbalanced fault diagnosis of bearing using support tensor machine(STM)driven by heterogeneous data fusion.The collected sound and vibration signals of bearings are successively decomposed into multiple frequency band components to extract various time-domain and frequency-domain statistical parameters.A third-order hetero-geneous feature tensor is designed based on multisensors,frequency band components,and statistical parameters.STM-based intelligent model is constructed to preserve the structural information of the third-order heterogeneous feature tensor for bearing fault diagnosis.A series of comparative experiments verify the advantages of the proposed method.
基金supported by the National Natural Science dation Foun-of China(Grant Number 42272204)Key Laboratory of Coal sources Re-Exploration and Comprehensive Utilization,Ministry of Natural Resources,Canada(SMDZ-KF2024-4)+1 种基金the Fundamental Research Funds for the Central Universities,China(Grant No.2024JCCXDC06)supported in part by open fund project of State Key Laboratory for Fine Exploration and Intelligent Development of Coal Research(SKLCRSM23KFA04)。
文摘This paper proposed a moment tensor regression prediction technology based on ResNet for microseismic events.Taking the great advantages of deep networks in classification and regression tasks,it can realize the great potential of fast and accurate inversion of microseismic moment tensors after the network trained.This ResNet-based moment tensor prediction technology,whose input is raw recordings,does not require the extraction of data features in advance.First,we tested the network using synthetic data and performed a quantitative assessment of the errors.The results demonstrate that the network exhibits high accuracy and efficiency during the prediction phase.Next,we tested the network using real microseismic data and compared the results with those from traditional inversion methods.The error in the results was relatively small compared to traditional methods.However,the network operates more efficiently without requiring manual intervention,making it highly valuable for near-real-time monitoring applications.
基金funded by the National Key R&D Program of China(Grant no.2018YFA0702504)the Sinopec research project(P22162).
文摘Absorption compensation is a process involving the exponential amplification of reflection amplitudes.This process amplifies the seismic signal and noise,thereby substantially reducing the signal-tonoise ratio of seismic data.Therefore,this paper proposes a multichannel inversion absorption compensation method based on structure tensor regularization.First,the structure tensor is utilized to extract the spatial inclination of seismic signals,and the spatial prediction filter is designed along the inclination direction.The spatial prediction filter is then introduced into the regularization condition of multichannel inversion absorption compensation,and the absorption compensation is realized under the framework of multichannel inversion theory.The spatial predictability of seismic signals is also introduced into the objective function of absorption compensation inversion.Thus,the inversion system can effectively suppress the noise amplification effect during absorption compensation and improve the recovery accuracy of high-frequency signals.Synthetic and field data tests are conducted to demonstrate the accuracy and effectiveness of the proposed method.
基金funded by the National Key R&D Program of China(Grant No.2024YFE0102500)the National Natural Science Foundation of China(Grant No.12404568)+1 种基金the Guangzhou Municipal Science and Technology Project(Grant No.2023A03J00904)the Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area,China and the Undergraduate Research Project from HKUST(Guangzhou).
文摘Constraint satisfaction problems(CSPs)are a class of problems that are ubiquitous in science and engineering.They feature a collection of constraints specified over subsets of variables.A CSP can be solved either directly or by reducing it to other problems.This paper introduces the Julia ecosystem for solving and analyzing CSPs with a focus on the programming practices.We introduce some important CSPs and show how these problems are reduced to each other.We also show how to transform CSPs into tensor networks,how to optimize the tensor network contraction orders,and how to extract the solution space properties by contracting the tensor networks with generic element types.Examples are given,which include computing the entropy constant,analyzing the overlap gap property,and the reduction between CSPs.
基金supported by the Biological Breeding-National Science and Technology Major Project(2023ZD04076)the Guangxi Key Research and Development Projects of China(GuikeAB21238004)the Agricultural Science and Technology Innovation Program.
文摘When plants respond to drought stress,dynamic cellular changes occur,accompanied by alterations in gene expression,which often act through trans-regulation.However,the detection of trans-acting genetic variants and networks of genes is challenged by the large number of genes and markers.Using a tensor decomposition method,we identify trans-acting expression quantitative trait loci(trans-eQTLs)linked to gene modules,rather than individual genes,which were associated with maize drought response.Module-to-trait association analysis demonstrates that half of the modules are relevant to drought-related traits.Genome-wide association studies of the expression patterns of each module identify 286 trans-eQTLs linked to drought-responsive modules,the majority of which cannot be detected based on individual gene expression.Notably,the trans-eQTLs located in the regions selected during maize improvement tend towards relatively strong selection.We further prioritize the genes that affect the transcriptional regulation of multiple genes in trans,as exemplified by two transcription factor genes.Our analyses highlight that multidimensional reduction could facilitate the identification of trans-acting variations in gene expression in response to dynamic environments and serve as a promising technique for high-order data processing in future crop breeding.
基金supported by Projects 12325501,12047503,and 12247104 of the National Natural Science Foundation of ChinaProject ZDRW-XX-2022-3-02 of the Chinese Academy of Sciencessupported by the Innovation Program for Quantum Science and Technology project 2021ZD0301900。
文摘Computing free energy is a fundamental problem in statistical physics.Recently,two distinct methods have been developed and have demonstrated remarkable success:the tensor-network-based contraction method and the neural-network-based variational method.Tensor networks are accurate,but their application is often limited to low-dimensional systems due to the high computational complexity in high-dimensional systems.The neural network method applies to systems with general topology.However,as a variational method,it is not as accurate as tensor networks.In this work,we propose an integrated approach,tensor-network-based variational autoregressive networks(TNVAN),that leverages the strengths of both tensor networks and neural networks:combining the variational autoregressive neural network’s ability to compute an upper bound on free energy and perform unbiased sampling from the variational distribution with the tensor network’s power to accurately compute the partition function for small sub-systems,resulting in a robust method for precisely estimating free energy.To evaluate the proposed approach,we conducted numerical experiments on spin glass systems with various topologies,including two-dimensional lattices,fully connected graphs,and random graphs.Our numerical results demonstrate the superior accuracy of our method compared to existing approaches.In particular,it effectively handles systems with longrange interactions and leverages GPU efficiency without requiring singular value decomposition,indicating great potential in tackling statistical mechanics problems and simulating high-dimensional complex systems through both tensor networks and neural networks.
基金supported by the National Natural Science Foundation of China(Grant Nos.12347104,U24A2017,12461160276,and 12175075)the National Key Research and Development Program of China(Grant No.2023YFC2205802)+1 种基金the Natural Science Foundation of Jiangsu Province(Grant Nos.BK20243060 and BK20233001)in part by the State Key Laboratory of Advanced Optical Communication Systems and Networks,China。
文摘The quantum geometric tensor(QGT)is a fundamental quantity for characterizing the geometric properties of quantum states and plays an essential role in elucidating various physical phenomena.The traditional QGT,defned only for pure states,has limited applicability in realistic scenarios where mixed states are common.To address this limitation,we generalize the defnition of the QGT to mixed states using the purifcation bundle and the covariant derivative.Notably,our proposed defnition reduces to the traditional QGT when mixed states approach pure states.In our framework,the real and imaginary parts of this generalized QGT correspond to the Bures metric and the mean gauge curvature,respectively,endowing it with a broad range of potential applications.Additionally,using our proposed mixed-state QGT,we derive the geodesic equation applicable to mixed states.This work establishes a unifed framework for the geometric analysis of both pure and mixed states,thereby deepening our understanding of the geometric properties of quantum states.
基金supported in part by the National Natural Science Foundation of China(62372385).
文摘Dear Editor,This letter proposes the graph tensor alliance attention network(GT-A^(2)T)to represent a dynamic graph(DG)precisely.Its main idea includes 1)Establishing a unified spatio-temporal message propagation framework on a DG via the tensor product for capturing the complex cohesive spatio-temporal interdependencies precisely and 2)Acquiring the alliance attention scores by node features and favorable high-order structural correlations.
基金supported in part by the National Nature Science Foundation of China under Project 62166047in part by the Yunnan International Joint Laboratory of Natural Rubber Intelligent Monitor and Digital Applications under Grant 202403AP140001in part by the Xingdian Talent Support Program under Grant YNWR-QNBJ-2019-270.
文摘The era of big data brings new challenges for information network systems(INS),simultaneously offering unprecedented opportunities for advancing intelligent intrusion detection systems.In this work,we propose a data-driven intrusion detection system for Distributed Denial of Service(DDoS)attack detection.The system focuses on intrusion detection from a big data perceptive.As intelligent information processing methods,big data and artificial intelligence have been widely used in information systems.The INS system is an important information system in cyberspace.In advanced INS systems,the network architectures have become more complex.And the smart devices in INS systems collect a large scale of network data.How to improve the performance of a complex intrusion detection system with big data and artificial intelligence is a big challenge.To address the problem,we design a novel intrusion detection system(IDS)from a big data perspective.The IDS system uses tensors to represent large-scale and complex multi-source network data in a unified tensor.Then,a novel tensor decomposition(TD)method is developed to complete big data mining.The TD method seamlessly collaborates with the XGBoost(eXtreme Gradient Boosting)method to complete the intrusion detection.To verify the proposed IDS system,a series of experiments is conducted on two real network datasets.The results revealed that the proposed IDS system attained an impressive accuracy rate over 98%.Additionally,by altering the scale of the datasets,the proposed IDS system still maintains excellent detection performance,which demonstrates the proposed IDS system’s robustness.
基金the Graduate Innovation Program of China University of Mining and Technology,the Fundamental Research Funds for the Central Universities(Grant No.2023WLKXJ017)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX23_2776)the Shandong Energy Group(Grant No.SNKJ2022BJ03-R28)。
文摘In this study,we employed Bayesian inversion coupled with the summation-by-parts and simultaneousapproximation-term(SBP-SAT)forward simulation method to elucidate the mechanisms behind mininginduced seismic events caused by fault slip and their potential effects on rockbursts.Through Bayesian inversion,it is determined that the sources near fault FQ14 have a significant shear component.Additionally,we analyzed the stress and displacement fields of high-energy events,along with the hypocenter distribution of aftershocks,which aided in identifying the slip direction of the critically stressed fault FQ14.We also performed forward modeling to capture the complex dynamics of fault slip under varying friction laws and shear fracture modes.The selection of specific friction laws for fault slip models was based on their ability to accurately replicate observed slip behavior under various external loading conditions,thereby enhancing the applicability of our findings.Our results suggest that the slip behavior of fault FQ14 can be effectively understood by comparing different scenarios.
基金Supported by the National Science and Technology Major Project of China(No.2022ZD0119003)the National Natural Science Foundation of China(No.61834005).
文摘To enhance the inference efficiency of convolutional neural network(CNN),tensor parallelism is employed to improve the parallelism within operators.However,existing methods are customized to specific networks and hardware,limiting their generalizability.This paper proposes an approach called resource-adaptive tensor decomposition(RATD)for CNN operators,which aims to achieve an optimal match between computational resources and parallel computing tasks.Firstly,CNN is represented with fine-grained tensors at the lower graph level,thereby decoupling tensors that can be computed in parallel within operators.Secondly,the convolution and pooling operators are fused,and the decoupled tensor blocks are scheduled in parallel.Finally,a cost model is constructed,based on runtime and resource utilization,to iteratively refine the process of tensor block decomposition and automatically determine the optimal tensor decomposition.Experimental results demonstrate that the proposed RATD improves the accuracy of the model by 11%.Compared with CUDA(compute unified device architecture)deep neural network library(cuDNN),RATD achieves an average speedup ratio of 1.21 times in inference time across various convolution kernels,along with a 12%increase in computational resource utilization.
基金supported by the National Natural Science Foundation of China(No.12275369)the Fundamental Research Funds for the Central Universities,Sun Yat-sen University(No.22qntd3101)the Guangdong Major Project of Basic and Applied Basic Research(2021B0301030006)。
文摘The tensor force changes the nuclear shell structure and thus may result in underlying influence of the collectivity and decay properties of the nucleus.We carefully examined the impact of the monopole and multipole effects originating from the tensor force on both the collectivity and the matrix element for the neutrinoless double-β(0νββ)decay,using the generatorcoordinate method with an effective interaction.To analyze the effect of the tensor force,we employed an effective Hamiltonian associated with the monopole-based universal interaction that explicitly consists of the central,tensor,and spin-orbit coupling terms.The interferences among the shell structure,quadrupole collectivity,nucleon occupancy,and 0νββmatrix elements were analyzed in detail.A better understanding of the tensor force would be of great importance in reducing the theoretical uncertainty in 0νββnuclear matrix element calculations.
基金National Key Research and Development Program of China,Grant/Award Number:2023YFB2903904。
文摘Heterogeneous graphs generally refer to graphs with different types of nodes and edges.A common approach for extracting useful information from heterogeneous graphs is to use meta-graphs,which can be seen as a special kind of directed acyclic graph with same node and edge types as the heterogeneous graph.However,how to design proper metagraphs is challenging.Recently,there have been many works on learning suitable metagraphs from a heterogeneous graph.Existing methods generally introduce continuous weights for edges that are independent of each other,which ignores the topological structures of meta-graphs and can be ineffective.To address this issue,the authors propose a new viewpoint from tensor on learning meta-graphs.Such a viewpoint not only helps interpret the limitation of existing works by CANDECOMP/PARAFAC(CP)decomposition,but also inspires us to propose a topology-aware tensor decomposition,called TENSUS,that reflects the structure of DAGs.The proposed topology-aware tensor decomposition is easy to use and simple to implement,and it can be taken as a plug-in part to upgrade many existing works,including node classification and recommendation on heterogeneous graphs.Experimental results on different tasks demonstrate that the proposed method can significantly improve the state-of-the-arts for all these tasks.
