Replicating the chaotic characteristics inherent in nonlinear dynamical systems via machine learning(ML)is a key challenge in this rapidly advancing interdisciplinary field.In this work,we explore the potential of var...Replicating the chaotic characteristics inherent in nonlinear dynamical systems via machine learning(ML)is a key challenge in this rapidly advancing interdisciplinary field.In this work,we explore the potential of variational quantum circuits(VQC)for learning the stochastic properties of classical nonlinear dynamical systems.Specifically,we focus on the one-and two-dimensional logistic maps,which,while simple,remain under-explored in the context of learning dynamical characteristics.Our findings reveal that,even for such simple dynamical systems,accurately replicating longterm characteristics is hindered by a pronounced sensitivity to overfitting.While increasing the parameter complexity of the ML model typically enhances short-term prediction accuracy,it also leads to a degradation in the model’s ability to replicate long-term characteristics,primarily due to the detrimental effects of overfitting on generalization power.By comparing the VQC with two widely recognized classical ML techniques,which are long short-term memory(LSTM)networks for timeseries processing and reservoir computing,we demonstrate that VQC outperforms these methods in terms of replicating long-term characteristics.Our results suggest that for the ML of dynamics,it is demanded to develop more compact and efficient models(such as VQC)rather than more complicated and large-scale ones.展开更多
In this manuscript,we consider a non-autonomous dynamical system.Using the Carathéodory structure,we define a BS dimension on an arbitrary subset and obtain a Bowen’s equation that illustrates the relation of th...In this manuscript,we consider a non-autonomous dynamical system.Using the Carathéodory structure,we define a BS dimension on an arbitrary subset and obtain a Bowen’s equation that illustrates the relation of the BS dimension to the Pesin-Pitskel topological pressure given by Nazarian[24].Moreover,we establish a variational principle and an inverse variational principle for the BS dimension of non-autonomous dynamical systems.Finally,we also get an analogue of Billingsley’s theorem for the BS dimension of non-autonomous dynamical systems.展开更多
In this paper,an advanced satellite navigation filter design,referred to as the Variational Bayesian Maximum Correntropy Extended Kalman Filter(VBMCEKF),is introduced to enhance robustness and adaptability in scenario...In this paper,an advanced satellite navigation filter design,referred to as the Variational Bayesian Maximum Correntropy Extended Kalman Filter(VBMCEKF),is introduced to enhance robustness and adaptability in scenarios with non-Gaussian noise and heavy-tailed outliers.The proposed design modifies the extended Kalman filter(EKF)for the global navigation satellite system(GNSS),integrating the maximum correntropy criterion(MCC)and the variational Bayesian(VB)method.This adaptive algorithm effectively reduces non-line-of-sight(NLOS)reception contamination and improves estimation accuracy,particularly in time-varying GNSS measurements.Experimental results show that the proposed method significantly outperforms conventional approaches in estimation accuracy under heavy-tailed outliers and non-Gaussian noise.By combining MCC with VB approximation for real-time noise covariance estimation using fixed-point iteration,the VBMCEKF achieves superior filtering performance in challenging GNSS conditions.The method’s adaptability and precision make it ideal for improving satellite navigation performance in stochastic environments.展开更多
The purpose of this article is to introduce a new method with a self-adaptive stepsize for approximating a common solution of monotone inclusion problems and variational inequality problems in reflexive Banach spaces....The purpose of this article is to introduce a new method with a self-adaptive stepsize for approximating a common solution of monotone inclusion problems and variational inequality problems in reflexive Banach spaces.The strong convergence result for our method is established under some standard assumptions without any requirement of the knowledge of the Lipschitz constant of the mapping.Several numerical experiments are provided to verify the advantages and efficiency of proposed algorithms.展开更多
This study investigates the mercury(Hg)isotope geochemistry of pyrite from the Aketashi volcanic hosted massive sulfide(VMS)deposit in the West Kunlun Orogenic Belt,China,aiming to elucidate the sources of ore-forming...This study investigates the mercury(Hg)isotope geochemistry of pyrite from the Aketashi volcanic hosted massive sulfide(VMS)deposit in the West Kunlun Orogenic Belt,China,aiming to elucidate the sources of ore-forming fluids and the behavior of Hg isotopes during mineralization.The Aketashi deposit is located within a Late Paleozoic back-arc basin,which is characterized by extensive Carboniferous magmatism and hydrothermal activity.Analysis of 19 pyrite ore samples revealed Hg isotope composition with δ^(202)Hg values ranging from−2.48‰to 0.87‰ and Δ^(199)Hg values from−0.02‰ to 0.40‰.The negative δ^(202)Hg values are indicative of isotopic fractionation resulting from hydrothermal processes,whereas the positive Δ^(199)Hg values imply a mixed fluid source comprising both magmatic and seawater-derived components.This study highlights the unique advantage of Hg isotopes in tracing multi-source fluid interactions(e.g.,magmatic vs.seawater-derived)in VMS systems,providing a novel methodology for global metallogenic studies.Our findings not only resolve the longstanding debate on fluid origins at Aketashi but also establish a framework for applying Hg isotopes to other VMS deposits worldwide.展开更多
Satellite altimetry missions at high latitude have opened new avenues for understanding the changes occurring over the ice-covered region.By incorporating Arctic satellite remote sensing data-including sea surface tem...Satellite altimetry missions at high latitude have opened new avenues for understanding the changes occurring over the ice-covered region.By incorporating Arctic satellite remote sensing data-including sea surface temperature(SST),sea surface height anomaly(SSHA),and sea surface salinity(SSS).This study employs a variational method to reconstruct the three-dimensional thermohaline structure of the Arctic Ocean.Compared to the Regional Arctic Reanalysis(RARE),the reconstruction well captures both the horizontal and vertical temperature and salinity structures in the Arctic.It demonstrates superior skill over RARE,when compared with Argo profiles and Ice-Tethered Profiler(ITP)observations.The reconstruction is particularly effective in ice-covered regions,where it more accurately captures the transition from Pacific water to Atlantic water compared to RARE.These findings underscore the potential of applying Arctic satellite data to reconstruct vertical thermohaline structures in the Arctic,particularly in areas due to lack of the subsurface observation reanalysis data exhibit significant biases.As Arctic satellite observations continue to advance,the applications of this method are becoming increasingly promising,which is useful for monitoring the ice-covered region environment and can be applied to oceanographic research.展开更多
Sciences and Technologies Team(ESTE),Abstract We consider nonlinear parabolic problems in a variational framework.The leading part is a monotone operator whose growth is controlled by time-and space-dependent Musielak...Sciences and Technologies Team(ESTE),Abstract We consider nonlinear parabolic problems in a variational framework.The leading part is a monotone operator whose growth is controlled by time-and space-dependent Musielak functions.On Musielak's controlling functions we impose regularity conditions which make it possible to extend certain classical results such as the density of smooth functions,a Poincar′e-type inequality,an integration-by-parts formula and a trace result.Bringing together these results,we adapt the classical theory of monotone operators and prove the well-posedness of the variational problem.展开更多
To solve the Poisson equation it is usually possible to discretize it into solving the corresponding linear system Ax=b.Variational quantum algorithms(VQAs)for the discretized Poisson equation have been studied before...To solve the Poisson equation it is usually possible to discretize it into solving the corresponding linear system Ax=b.Variational quantum algorithms(VQAs)for the discretized Poisson equation have been studied before.We present a VQA based on the banded Toeplitz systems for solving the Poisson equation with respect to the structural features of matrix A.In detail,we decompose the matrices A and A^(2)into a linear combination of the corresponding banded Toeplitz matrix and sparse matrices with only a few non-zero elements.For the one-dimensional Poisson equation with different boundary conditions and the d-dimensional Poisson equation with Dirichlet boundary conditions,the number of decomposition terms is less than that reported in[Phys.Rev.A 2023108,032418].Based on the decomposition of the matrix,we design quantum circuits that efficiently evaluate the cost function.Additionally,numerical simulation verifies the feasibility of the proposed algorithm.Finally,the VQAs for linear systems of equations and matrix-vector multiplications with the K-banded Toeplitz matrix T_(n)^(K)are given,where T_(n)^(K)∈R^(n×n)and K∈O(ploylogn).展开更多
Accurate medical image segmentation plays a crucial role in improving the precision of computer-aided diagnosis.However,complex boundary shapes,low contrast and blurred anatomical structures make fine-grained segmenta...Accurate medical image segmentation plays a crucial role in improving the precision of computer-aided diagnosis.However,complex boundary shapes,low contrast and blurred anatomical structures make fine-grained segmentation a challenging task.Variational Bayesian inference quantifies uncertainty through probability distributions and can construct robust probabilistic models for the boundaries of ambiguous organs and tissues.In this paper,we apply variational Bayesian inference to medical image segmentation and propose variational attention to model the uncertainty of low-contrast and blurry tissue and organ boundaries.This enhances the model's ability to perceive segmentation boundaries,improving robustness and segmentation accuracy.Variational attention first estimates the parameters of the probability distribution of latent representations based on input features.Then,it samples latent representations from the learnt distribution to generate attention weights that optimise the interaction between global features and ambiguous boundaries.We integrate variational attention into the U-Net model by replacing its skip connections,constructing a multi-scale variational attention segmentation model(V-UNet).Experiments on the ISBI 2012 and MoNuSeg 2018 datasets show that our method achieves Dice scores of 95.89%and 82.18%,respectively.Moreover,we integrate V-UNet into the Mask R-CNN framework by replacing the FPN feature extraction head and propose a two-stage segmentation method.Compared to the original Mask R-CNN,our method improves the Dice score by 0.81%,mAP by 8.06%and F1 score by 0.51%.展开更多
Since the concept of quantum information masking was proposed by Modi et al(2018 Phys.Rev.Lett.120,230501),many interesting and significant results have been reported,both theoretically and experimentally.However,desi...Since the concept of quantum information masking was proposed by Modi et al(2018 Phys.Rev.Lett.120,230501),many interesting and significant results have been reported,both theoretically and experimentally.However,designing a quantum information masker is not an easy task,especially for larger systems.In this paper,we propose a variational quantum algorithm to resolve this problem.Specifically,our algorithm is a hybrid quantum-classical model,where the quantum device with adjustable parameters tries to mask quantum information and the classical device evaluates the performance of the quantum device and optimizes its parameters.After optimization,the quantum device behaves as an optimal masker.The loss value during optimization can be used to characterize the performance of the masker.In particular,if the loss value converges to zero,we obtain a perfect masker that completely masks the quantum information generated by the quantum information source,otherwise,the perfect masker does not exist and the subsystems always contain the original information.Nevertheless,these resulting maskers are still optimal.Quantum parallelism is utilized to reduce quantum state preparations and measurements.Our study paves the way for wide application of quantum information masking,and some of the techniques used in this study may have potential applications in quantum information processing.展开更多
This work elaborates an innovative mesh denoising approach that combines feature recovery and denoising in an alternating manner.It proposes a feature-driven variational model and introduces an iterative scheme that a...This work elaborates an innovative mesh denoising approach that combines feature recovery and denoising in an alternating manner.It proposes a feature-driven variational model and introduces an iterative scheme that alternates between feature recovery and the denoising process.The main idea is to estimate feature candidates,filter noisy face normals in the smooth(non-feature)domain,and utilize erosion and dilation operators on the feature candidates.By imposing connectivity constraints on normal vectors with large amplitude variations,the proposed scheme effectively removes noise and progressively recovers both sharp and small-scale features during the iterative process.To validate its effectiveness,this work conducts extensive numerical experiments on both simulated and real-scanned data.The results demonstrate significant improvements in noise reduction and feature preservation compared to existing methods.展开更多
The aerial deployment method enables Unmanned Aerial Vehicles(UAVs)to be directly positioned at the required altitude for their mission.This method typically employs folding technology to improve loading efficiency,wi...The aerial deployment method enables Unmanned Aerial Vehicles(UAVs)to be directly positioned at the required altitude for their mission.This method typically employs folding technology to improve loading efficiency,with applications such as the gravity-only aerial deployment of high-aspect-ratio solar-powered UAVs,and aerial takeoff of fixed-wing drones in Mars research.However,the significant morphological changes during deployment are accompanied by strong nonlinear dynamic aerodynamic forces,which result in multiple degrees of freedom and an unstable character.This hinders the description and analysis of unknown dynamic behaviors,further leading to difficulties in the design of deployment strategies and flight control.To address this issue,this paper proposes an analysis method for dynamic behaviors during aerial deployment based on the Variational Autoencoder(VAE).Focusing on the gravity-only deployment problem of highaspect-ratio foldable-wing UAVs,the method encodes the multi-degree-of-freedom unstable motion signals into a low-dimensional feature space through a data-driven approach.By clustering in the feature space,this paper identifies and studies several dynamic behaviors during aerial deployment.The research presented in this paper offers a new method and perspective for feature extraction and analysis of complex and difficult-to-describe extreme flight dynamics,guiding the research on aerial deployment drones design and control strategies.展开更多
Engineering tests can yield inaccurate data due to instrument errors,human factors,and environmental interference,introducing uncertainty in numerical model updating.This study employs the probability-box(p-box)method...Engineering tests can yield inaccurate data due to instrument errors,human factors,and environmental interference,introducing uncertainty in numerical model updating.This study employs the probability-box(p-box)method for representing observational uncertainty and develops a two-step approximate Bayesian computation(ABC)framework using time-series data.Within the ABC framework,Euclidean and Bhattacharyya distances are employed as uncertainty quantification metrics to delineate approximate likelihood functions in the initial and subsequent steps,respectively.A novel variational Bayesian Monte Carlo method is introduced to efficiently apply the ABC framework amidst observational uncertainty,resulting in rapid convergence and accurate parameter estimation with minimal iterations.The efficacy of the proposed updating strategy is validated by its application to a shear frame model excited by seismic wave and an aviation pump force sensor for thermal output analysis.The results affirm the efficiency,robustness,and practical applicability of the proposed method.展开更多
Geochemical survey data are essential across Earth Science disciplines but are often affected by noise,which can obscure important geological signals and compromise subsequent prediction and interpretation.Quantifying...Geochemical survey data are essential across Earth Science disciplines but are often affected by noise,which can obscure important geological signals and compromise subsequent prediction and interpretation.Quantifying prediction uncertainty is hence crucial for robust geoscientific decision-making.This study proposes a novel deep learning framework,the Spatially Constrained Variational Autoencoder(SC-VAE),for denoising geochemical survey data with integrated uncertainty quantification.The SC-VAE incorporates spatial regularization,which enforces spatial coherence by modeling inter-sample relationships directly within the latent space.The performance of the SC-VAE was systematically evaluated against a standard Variational Autoencoder(VAE)using geochemical data from the gold polymetallic district in the northwestern part of Sichuan Province,China.Both models were optimized using Bayesian optimization,with objective functions specifically designed to maintain essential geostatistical characteristics.Evaluation metrics include variogram analysis,quantitative measures of spatial interpolation accuracy,visual assessment of denoised maps,and statistical analysis of data distributions,as well as decomposition of uncertainties.Results show that the SC-VAE achieves superior noise suppression and better preservation of spatial structure compared to the standard VAE,as demonstrated by a significant reduction in the variogram nugget effect and an increased partial sill.The SC-VAE produces denoised maps with clearer anomaly delineation and more regularized data distributions,effectively mitigating outliers and reducing kurtosis.Additionally,it delivers improved interpolation accuracy and spatially explicit uncertainty estimates,facilitating more reliable and interpretable assessments of prediction confidence.The SC-VAE framework thus provides a robust,geostatistically informed solution for enhancing the quality and interpretability of geochemical data,with broad applicability in mineral exploration,environmental geochemistry,and other Earth Science domains.展开更多
In the realm of video understanding,the demand for accurate and contextually rich video captioning has surged with the increasing volume and complexity of multimedia content.This research introduces an innovative solu...In the realm of video understanding,the demand for accurate and contextually rich video captioning has surged with the increasing volume and complexity of multimedia content.This research introduces an innovative solution for video captioning by integrating a Convolutional BiLSTM Convolutional Bidirectional Long Short-Term Memory(BiLSTM)constructed Variational Sequence-to-Sequence(CBVSS)approach.The proposed framework is adept at capturing intricate temporal dependencies within video sequences,enabling a more nuanced and contextually relevant description of dynamic scenes.However,optimizing its parameters for improved performance remains a crucial challenge.In response,in this research Golden Eagle Optimization(GEO)a metaheuristic optimization technique is used to fine-tune the Convolutional BiLSTM variational sequence-to-sequence model parameters.The application of GEO aims to enhancing the CBVSS ability to produce more exact and contextually rich video captions.The proposed attains an overall higher Recall of 59.75%and Precision of 63.78%for both datasets.Additionally,the proposed CBVSS method demonstrated superior performance across both datasets,achieving the highest METEOR(25.67)and CIDER(39.87)scores on the ActivityNet dataset,and further outperforming all compared models on the YouCook2 dataset with METEOR(28.67)and CIDER(43.02),highlighting its effectiveness in generating semantically rich and contextually accurate video captions.展开更多
We investigate dynamical quantum phase transitions(DQPTs)in Marko-vian open quantum systems using a variational quantum simulation(VQS)algorithm based on quantum state diffusion(QSD).This approach reformulates the Lin...We investigate dynamical quantum phase transitions(DQPTs)in Marko-vian open quantum systems using a variational quantum simulation(VQS)algorithm based on quantum state diffusion(QSD).This approach reformulates the Lindblad master equation as an ensemble of pure-state trajectories,enabling efficient simula-tion of dissipative quantum dynam-ics with effectively reduced quantum resources.Focusing on the one-di-mensional transverse-field Ising mod-el(TFIM),we simulate quench dynamics under both local and global Lindblad dissipation.The QSD-VQS algorithm accurately captures the nonanalytic cusps in the Loschmidt rate function,and reveals their modulation by dissipation strength and system size.Notably,DQPTs are gradually suppressed under strong local dissipation,while they persist under strong global dissipation due to collective environmental effects.Benchmarking against exact Lindblad solutions confirms the high accuracy and scalability of our method.展开更多
基金Project supported in part by Beijing Natural Science Foundation(Grant No.1232025)Peng Huanwu Visiting Pro-fessor Program,and Academy for Multidisciplinary Studies,Capital Normal University.
文摘Replicating the chaotic characteristics inherent in nonlinear dynamical systems via machine learning(ML)is a key challenge in this rapidly advancing interdisciplinary field.In this work,we explore the potential of variational quantum circuits(VQC)for learning the stochastic properties of classical nonlinear dynamical systems.Specifically,we focus on the one-and two-dimensional logistic maps,which,while simple,remain under-explored in the context of learning dynamical characteristics.Our findings reveal that,even for such simple dynamical systems,accurately replicating longterm characteristics is hindered by a pronounced sensitivity to overfitting.While increasing the parameter complexity of the ML model typically enhances short-term prediction accuracy,it also leads to a degradation in the model’s ability to replicate long-term characteristics,primarily due to the detrimental effects of overfitting on generalization power.By comparing the VQC with two widely recognized classical ML techniques,which are long short-term memory(LSTM)networks for timeseries processing and reservoir computing,we demonstrate that VQC outperforms these methods in terms of replicating long-term characteristics.Our results suggest that for the ML of dynamics,it is demanded to develop more compact and efficient models(such as VQC)rather than more complicated and large-scale ones.
基金supported by the NSFC(12461012)and the NSF of Chongqing(CSTB2024NSCQ-MSX1246).
文摘In this manuscript,we consider a non-autonomous dynamical system.Using the Carathéodory structure,we define a BS dimension on an arbitrary subset and obtain a Bowen’s equation that illustrates the relation of the BS dimension to the Pesin-Pitskel topological pressure given by Nazarian[24].Moreover,we establish a variational principle and an inverse variational principle for the BS dimension of non-autonomous dynamical systems.Finally,we also get an analogue of Billingsley’s theorem for the BS dimension of non-autonomous dynamical systems.
基金supported by the National Science and Technology Council,Taiwan under grants NSTC 111-2221-E-019-047 and NSTC 112-2221-E-019-030.
文摘In this paper,an advanced satellite navigation filter design,referred to as the Variational Bayesian Maximum Correntropy Extended Kalman Filter(VBMCEKF),is introduced to enhance robustness and adaptability in scenarios with non-Gaussian noise and heavy-tailed outliers.The proposed design modifies the extended Kalman filter(EKF)for the global navigation satellite system(GNSS),integrating the maximum correntropy criterion(MCC)and the variational Bayesian(VB)method.This adaptive algorithm effectively reduces non-line-of-sight(NLOS)reception contamination and improves estimation accuracy,particularly in time-varying GNSS measurements.Experimental results show that the proposed method significantly outperforms conventional approaches in estimation accuracy under heavy-tailed outliers and non-Gaussian noise.By combining MCC with VB approximation for real-time noise covariance estimation using fixed-point iteration,the VBMCEKF achieves superior filtering performance in challenging GNSS conditions.The method’s adaptability and precision make it ideal for improving satellite navigation performance in stochastic environments.
基金Supported by NSFC(No.12171062)the Natural Science Foundation of Chongqing(No.CSTB2022NSCQ-JQX0004)+1 种基金the Chongqing Talent Support Program(No.cstc2024ycjh-bgzxm0121)Science and Technology Project of Chongqing Education Committee(No.KJZD-M202300503)。
文摘The purpose of this article is to introduce a new method with a self-adaptive stepsize for approximating a common solution of monotone inclusion problems and variational inequality problems in reflexive Banach spaces.The strong convergence result for our method is established under some standard assumptions without any requirement of the knowledge of the Lipschitz constant of the mapping.Several numerical experiments are provided to verify the advantages and efficiency of proposed algorithms.
基金Innovative Research Group Project of the National Natural Science Foundation of China(NSFC Nos.U1603245,U1703242,U1812402).
文摘This study investigates the mercury(Hg)isotope geochemistry of pyrite from the Aketashi volcanic hosted massive sulfide(VMS)deposit in the West Kunlun Orogenic Belt,China,aiming to elucidate the sources of ore-forming fluids and the behavior of Hg isotopes during mineralization.The Aketashi deposit is located within a Late Paleozoic back-arc basin,which is characterized by extensive Carboniferous magmatism and hydrothermal activity.Analysis of 19 pyrite ore samples revealed Hg isotope composition with δ^(202)Hg values ranging from−2.48‰to 0.87‰ and Δ^(199)Hg values from−0.02‰ to 0.40‰.The negative δ^(202)Hg values are indicative of isotopic fractionation resulting from hydrothermal processes,whereas the positive Δ^(199)Hg values imply a mixed fluid source comprising both magmatic and seawater-derived components.This study highlights the unique advantage of Hg isotopes in tracing multi-source fluid interactions(e.g.,magmatic vs.seawater-derived)in VMS systems,providing a novel methodology for global metallogenic studies.Our findings not only resolve the longstanding debate on fluid origins at Aketashi but also establish a framework for applying Hg isotopes to other VMS deposits worldwide.
基金The National Key R&D Program of China under contract No.2022YFE0106400the China Scholarship Council under contract No.202206710071+2 种基金the Postgraduate Research&Practice Innovation Program of Jiangsu Province under contract No.KYCX23_0657the Special Founds for Creative Research under contract No.2022C61540the Opening Project of the Key Laboratory of Marine Environmental Information Technology under contract No.521037412.
文摘Satellite altimetry missions at high latitude have opened new avenues for understanding the changes occurring over the ice-covered region.By incorporating Arctic satellite remote sensing data-including sea surface temperature(SST),sea surface height anomaly(SSHA),and sea surface salinity(SSS).This study employs a variational method to reconstruct the three-dimensional thermohaline structure of the Arctic Ocean.Compared to the Regional Arctic Reanalysis(RARE),the reconstruction well captures both the horizontal and vertical temperature and salinity structures in the Arctic.It demonstrates superior skill over RARE,when compared with Argo profiles and Ice-Tethered Profiler(ITP)observations.The reconstruction is particularly effective in ice-covered regions,where it more accurately captures the transition from Pacific water to Atlantic water compared to RARE.These findings underscore the potential of applying Arctic satellite data to reconstruct vertical thermohaline structures in the Arctic,particularly in areas due to lack of the subsurface observation reanalysis data exhibit significant biases.As Arctic satellite observations continue to advance,the applications of this method are becoming increasingly promising,which is useful for monitoring the ice-covered region environment and can be applied to oceanographic research.
文摘Sciences and Technologies Team(ESTE),Abstract We consider nonlinear parabolic problems in a variational framework.The leading part is a monotone operator whose growth is controlled by time-and space-dependent Musielak functions.On Musielak's controlling functions we impose regularity conditions which make it possible to extend certain classical results such as the density of smooth functions,a Poincar′e-type inequality,an integration-by-parts formula and a trace result.Bringing together these results,we adapt the classical theory of monotone operators and prove the well-posedness of the variational problem.
基金supported by the Shandong Provincial Natural Science Foundation for Quantum Science under Grant No.ZR2021LLZ002the Fundamental Research Funds for the Central Universities under Grant No.22CX03005A。
文摘To solve the Poisson equation it is usually possible to discretize it into solving the corresponding linear system Ax=b.Variational quantum algorithms(VQAs)for the discretized Poisson equation have been studied before.We present a VQA based on the banded Toeplitz systems for solving the Poisson equation with respect to the structural features of matrix A.In detail,we decompose the matrices A and A^(2)into a linear combination of the corresponding banded Toeplitz matrix and sparse matrices with only a few non-zero elements.For the one-dimensional Poisson equation with different boundary conditions and the d-dimensional Poisson equation with Dirichlet boundary conditions,the number of decomposition terms is less than that reported in[Phys.Rev.A 2023108,032418].Based on the decomposition of the matrix,we design quantum circuits that efficiently evaluate the cost function.Additionally,numerical simulation verifies the feasibility of the proposed algorithm.Finally,the VQAs for linear systems of equations and matrix-vector multiplications with the K-banded Toeplitz matrix T_(n)^(K)are given,where T_(n)^(K)∈R^(n×n)and K∈O(ploylogn).
基金supported by the China Chongqing Municipal Education Commission(Grant KJZDM202500505)China Chongqing Municipal Science and Technology Bureau(Grants CSTB2024TIADCYKJCXX0009,CSTB2024NSCQ-LZX0043)+1 种基金Chongqing University of Technology graduate education high-quality development project(Grants gzlsz202304,gzlkc202401,gzltd202502)Chongqing University of Technology-Chongqing LINGLUE Technology Co.Ltd.Electronic Information(artificial intelligence)graduate joint training base.
文摘Accurate medical image segmentation plays a crucial role in improving the precision of computer-aided diagnosis.However,complex boundary shapes,low contrast and blurred anatomical structures make fine-grained segmentation a challenging task.Variational Bayesian inference quantifies uncertainty through probability distributions and can construct robust probabilistic models for the boundaries of ambiguous organs and tissues.In this paper,we apply variational Bayesian inference to medical image segmentation and propose variational attention to model the uncertainty of low-contrast and blurry tissue and organ boundaries.This enhances the model's ability to perceive segmentation boundaries,improving robustness and segmentation accuracy.Variational attention first estimates the parameters of the probability distribution of latent representations based on input features.Then,it samples latent representations from the learnt distribution to generate attention weights that optimise the interaction between global features and ambiguous boundaries.We integrate variational attention into the U-Net model by replacing its skip connections,constructing a multi-scale variational attention segmentation model(V-UNet).Experiments on the ISBI 2012 and MoNuSeg 2018 datasets show that our method achieves Dice scores of 95.89%and 82.18%,respectively.Moreover,we integrate V-UNet into the Mask R-CNN framework by replacing the FPN feature extraction head and propose a two-stage segmentation method.Compared to the original Mask R-CNN,our method improves the Dice score by 0.81%,mAP by 8.06%and F1 score by 0.51%.
基金Supported by the National Natural Science Foundation of China(under Grant Nos.12105090 and 12074107)the Program of Outstanding Young and Middle-aged Scientific and Technological Innovation Team of Colleges and Universities in Hubei Province of China(under Grant No.T2020001)the Innovation Group Project of the Natural Science Foundation of Hubei Province of China(under Grant No.2022CFA012)。
文摘Since the concept of quantum information masking was proposed by Modi et al(2018 Phys.Rev.Lett.120,230501),many interesting and significant results have been reported,both theoretically and experimentally.However,designing a quantum information masker is not an easy task,especially for larger systems.In this paper,we propose a variational quantum algorithm to resolve this problem.Specifically,our algorithm is a hybrid quantum-classical model,where the quantum device with adjustable parameters tries to mask quantum information and the classical device evaluates the performance of the quantum device and optimizes its parameters.After optimization,the quantum device behaves as an optimal masker.The loss value during optimization can be used to characterize the performance of the masker.In particular,if the loss value converges to zero,we obtain a perfect masker that completely masks the quantum information generated by the quantum information source,otherwise,the perfect masker does not exist and the subsystems always contain the original information.Nevertheless,these resulting maskers are still optimal.Quantum parallelism is utilized to reduce quantum state preparations and measurements.Our study paves the way for wide application of quantum information masking,and some of the techniques used in this study may have potential applications in quantum information processing.
基金supported in part by the National Natural Science Foundation of China(62476219,62206220,12271140,12326609)the Young Talent Fund of Association for Science and Technology in Shaanxi,China(20230140)+1 种基金the Chunhui Program of Ministry of Education of China(HZKY20220537)the Fundamental Funds for the Central Universities(G2023KY0601).
文摘This work elaborates an innovative mesh denoising approach that combines feature recovery and denoising in an alternating manner.It proposes a feature-driven variational model and introduces an iterative scheme that alternates between feature recovery and the denoising process.The main idea is to estimate feature candidates,filter noisy face normals in the smooth(non-feature)domain,and utilize erosion and dilation operators on the feature candidates.By imposing connectivity constraints on normal vectors with large amplitude variations,the proposed scheme effectively removes noise and progressively recovers both sharp and small-scale features during the iterative process.To validate its effectiveness,this work conducts extensive numerical experiments on both simulated and real-scanned data.The results demonstrate significant improvements in noise reduction and feature preservation compared to existing methods.
基金co-supported by the Natural Science Basic Research Program of Shaanxi,China(No.2023-JC-QN-0043)the ND Basic Research Funds,China(No.G2022WD).
文摘The aerial deployment method enables Unmanned Aerial Vehicles(UAVs)to be directly positioned at the required altitude for their mission.This method typically employs folding technology to improve loading efficiency,with applications such as the gravity-only aerial deployment of high-aspect-ratio solar-powered UAVs,and aerial takeoff of fixed-wing drones in Mars research.However,the significant morphological changes during deployment are accompanied by strong nonlinear dynamic aerodynamic forces,which result in multiple degrees of freedom and an unstable character.This hinders the description and analysis of unknown dynamic behaviors,further leading to difficulties in the design of deployment strategies and flight control.To address this issue,this paper proposes an analysis method for dynamic behaviors during aerial deployment based on the Variational Autoencoder(VAE).Focusing on the gravity-only deployment problem of highaspect-ratio foldable-wing UAVs,the method encodes the multi-degree-of-freedom unstable motion signals into a low-dimensional feature space through a data-driven approach.By clustering in the feature space,this paper identifies and studies several dynamic behaviors during aerial deployment.The research presented in this paper offers a new method and perspective for feature extraction and analysis of complex and difficult-to-describe extreme flight dynamics,guiding the research on aerial deployment drones design and control strategies.
基金supported by the National Natural Science Foundation of China(Grant No.U23B20105).
文摘Engineering tests can yield inaccurate data due to instrument errors,human factors,and environmental interference,introducing uncertainty in numerical model updating.This study employs the probability-box(p-box)method for representing observational uncertainty and develops a two-step approximate Bayesian computation(ABC)framework using time-series data.Within the ABC framework,Euclidean and Bhattacharyya distances are employed as uncertainty quantification metrics to delineate approximate likelihood functions in the initial and subsequent steps,respectively.A novel variational Bayesian Monte Carlo method is introduced to efficiently apply the ABC framework amidst observational uncertainty,resulting in rapid convergence and accurate parameter estimation with minimal iterations.The efficacy of the proposed updating strategy is validated by its application to a shear frame model excited by seismic wave and an aviation pump force sensor for thermal output analysis.The results affirm the efficiency,robustness,and practical applicability of the proposed method.
基金supported by the National Natural Science Foundation of China(Nos.42530801,42425208)the Natural Science Foundation of Hubei Province(China)(No.2023AFA001)+1 种基金the MOST Special Fund from State Key Laboratory of Geological Processes and Mineral Resources,China University of Geosciences(No.MSFGPMR2025-401)the China Scholarship Council(No.202306410181)。
文摘Geochemical survey data are essential across Earth Science disciplines but are often affected by noise,which can obscure important geological signals and compromise subsequent prediction and interpretation.Quantifying prediction uncertainty is hence crucial for robust geoscientific decision-making.This study proposes a novel deep learning framework,the Spatially Constrained Variational Autoencoder(SC-VAE),for denoising geochemical survey data with integrated uncertainty quantification.The SC-VAE incorporates spatial regularization,which enforces spatial coherence by modeling inter-sample relationships directly within the latent space.The performance of the SC-VAE was systematically evaluated against a standard Variational Autoencoder(VAE)using geochemical data from the gold polymetallic district in the northwestern part of Sichuan Province,China.Both models were optimized using Bayesian optimization,with objective functions specifically designed to maintain essential geostatistical characteristics.Evaluation metrics include variogram analysis,quantitative measures of spatial interpolation accuracy,visual assessment of denoised maps,and statistical analysis of data distributions,as well as decomposition of uncertainties.Results show that the SC-VAE achieves superior noise suppression and better preservation of spatial structure compared to the standard VAE,as demonstrated by a significant reduction in the variogram nugget effect and an increased partial sill.The SC-VAE produces denoised maps with clearer anomaly delineation and more regularized data distributions,effectively mitigating outliers and reducing kurtosis.Additionally,it delivers improved interpolation accuracy and spatially explicit uncertainty estimates,facilitating more reliable and interpretable assessments of prediction confidence.The SC-VAE framework thus provides a robust,geostatistically informed solution for enhancing the quality and interpretability of geochemical data,with broad applicability in mineral exploration,environmental geochemistry,and other Earth Science domains.
文摘In the realm of video understanding,the demand for accurate and contextually rich video captioning has surged with the increasing volume and complexity of multimedia content.This research introduces an innovative solution for video captioning by integrating a Convolutional BiLSTM Convolutional Bidirectional Long Short-Term Memory(BiLSTM)constructed Variational Sequence-to-Sequence(CBVSS)approach.The proposed framework is adept at capturing intricate temporal dependencies within video sequences,enabling a more nuanced and contextually relevant description of dynamic scenes.However,optimizing its parameters for improved performance remains a crucial challenge.In response,in this research Golden Eagle Optimization(GEO)a metaheuristic optimization technique is used to fine-tune the Convolutional BiLSTM variational sequence-to-sequence model parameters.The application of GEO aims to enhancing the CBVSS ability to produce more exact and contextually rich video captions.The proposed attains an overall higher Recall of 59.75%and Precision of 63.78%for both datasets.Additionally,the proposed CBVSS method demonstrated superior performance across both datasets,achieving the highest METEOR(25.67)and CIDER(39.87)scores on the ActivityNet dataset,and further outperforming all compared models on the YouCook2 dataset with METEOR(28.67)and CIDER(43.02),highlighting its effectiveness in generating semantically rich and contextually accurate video captions.
基金supported by the National Natural Science Foundation of China(Nos.22273122,T2350009)the Guangdong Provincial Natural Science Foundation(No.2024A1515011504)computational resources and services provided by the national supercomputer center in Guangzhou.
文摘We investigate dynamical quantum phase transitions(DQPTs)in Marko-vian open quantum systems using a variational quantum simulation(VQS)algorithm based on quantum state diffusion(QSD).This approach reformulates the Lindblad master equation as an ensemble of pure-state trajectories,enabling efficient simula-tion of dissipative quantum dynam-ics with effectively reduced quantum resources.Focusing on the one-di-mensional transverse-field Ising mod-el(TFIM),we simulate quench dynamics under both local and global Lindblad dissipation.The QSD-VQS algorithm accurately captures the nonanalytic cusps in the Loschmidt rate function,and reveals their modulation by dissipation strength and system size.Notably,DQPTs are gradually suppressed under strong local dissipation,while they persist under strong global dissipation due to collective environmental effects.Benchmarking against exact Lindblad solutions confirms the high accuracy and scalability of our method.
