Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep...Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep learning to medium-range regional weather forecasting with limited data remains a significant challenge.In this work,three key solutions are proposed:(1)motivated by the need to improve model performance in data-scarce regional forecasting scenarios,the authors innovatively apply semantic segmentation models,to better capture spatiotemporal features and improve prediction accuracy;(2)recognizing the challenge of overfitting and the inability of traditional noise-based data augmentation methods to effectively enhance model robustness,a novel learnable Gaussian noise mechanism is introduced that allows the model to adaptively optimize perturbations for different locations,ensuring more effective learning;and(3)to address the issue of error accumulation in autoregressive prediction,as well as the challenge of learning difficulty and the lack of intermediate data utilization in one-shot prediction,the authors propose a cascade prediction approach that effectively resolves these problems while significantly improving model forecasting performance.The method achieves a competitive result in The East China Regional AI Medium Range Weather Forecasting Competition.Ablation experiments further validate the effectiveness of each component,highlighting their contributions to enhancing prediction performance.展开更多
针对现有SLAM算法在渲染真实感、内存占用和复杂场景适应性方面的不足,提出了一种基于3D Gaussians Splatting的密集SLAM算法——TIGO-SLAM(tensor illumination and Gaussian optimization for indoor SLAM)。该算法集成了基于神经网...针对现有SLAM算法在渲染真实感、内存占用和复杂场景适应性方面的不足,提出了一种基于3D Gaussians Splatting的密集SLAM算法——TIGO-SLAM(tensor illumination and Gaussian optimization for indoor SLAM)。该算法集成了基于神经网络的张量光照模型、改进的高斯遮罩算法以及网格化神经场的几何和颜色属性表示,具体创新包括:a)基于神经网络的张量光照模型,增强镜面反射与漫反射效果,从而提升了渲染真实感;b)通过冗余高斯剔除机制改进高斯遮罩算法,有效降低了内存消耗并提高了实时性;c)结合网格化神经场的几何与颜色属性表示,采用优化的码本存储方式,显著提高了渲染性能和场景重建精度。实验结果表明,TIGO-SLAM在室内场景渲染、内存优化和复杂场景适应性方面均有显著提升,特别是在动态室内环境中的渲染和重建效果表现突出,为SLAM技术在资源受限设备上的应用提供了新的可能。展开更多
Advances in software and hardware technologies have facilitated the production of quadrotor unmanned aerial vehicles(UAVs).Nowadays,people actively use quadrotor UAVs in essential missions such as search and rescue,co...Advances in software and hardware technologies have facilitated the production of quadrotor unmanned aerial vehicles(UAVs).Nowadays,people actively use quadrotor UAVs in essential missions such as search and rescue,counter-terrorism,firefighting,surveillance,and cargo transportation.While performing these tasks,quadrotors must operate in noisy environments.Therefore,a robust controller design that can control the altitude and attitude of the quadrotor in noisy environments is of great importance.Many researchers have focused only on white Gaussian noise in their studies,whereas researchers need to consider the effects of all colored noises during the operation of the quadrotor.This study aims to design a robust controller that is resistant to all colored noises.Firstly,a nonlinear quadrotormodel was created with MATLAB.Then,a backstepping controller resistant to colored noises was designed.Thedesigned backstepping controller was tested under Gaussian white,pink,brown,blue,and purple noises.PID and Lyapunov-based controller designswere also carried out,and their time responses(rise time,overshoot,settling time)were compared with those of the backstepping controller.In the simulations,time was in seconds,altitude was in meters,and roll,pitch,and yaw references were in radians.Rise and settling time values were in seconds,and overshoot value was in percent.When the obtained values are examined,simulations prove that the proposed backstepping controller has the least overshoot and the shortest settling time under all noise types.展开更多
In engineering practice,it is often necessary to determine functional relationships between dependent and independent variables.These relationships can be highly nonlinear,and classical regression approaches cannot al...In engineering practice,it is often necessary to determine functional relationships between dependent and independent variables.These relationships can be highly nonlinear,and classical regression approaches cannot always provide sufficiently reliable solutions.Nevertheless,Machine Learning(ML)techniques,which offer advanced regression tools to address complicated engineering issues,have been developed and widely explored.This study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic materials.The ML-based regression methods of Artificial Neural Networks(ANNs),Support Vector Machine(SVM),Decision Tree Regression(DTR),and Gaussian Process Regression(GPR)are applied to mathematically describe hot flow stress curve datasets acquired experimentally for a medium-carbon steel.Although the GPR method has not been used for such a regression task before,the results showed that its performance is the most favorable and practically unrivaled;neither the ANN method nor the other studied ML techniques provide such precise results of the solved regression analysis.展开更多
To further understand the performance of the energy harvesters under the influence of the wind force and the random excitation,this paper investigates the stochastic response of the bio-inspired energy harvesters subj...To further understand the performance of the energy harvesters under the influence of the wind force and the random excitation,this paper investigates the stochastic response of the bio-inspired energy harvesters subjected to Gaussian white noise and galloping excitation,simulating the flapping pattern of a seagull and its interaction with wind force.The equivalent linearization method is utilized to convert the original nonlinear model into the Itôstochastic differential equation by minimizing the mean squared error.Then,the second-order steady-state moments about the displacement,velocity,and voltage are derived by combining the moment analysis theory.The theoretical results are simulated numerically to analyze the stochastic response performance under different noise intensities,wind speeds,stiffness coefficients,and electromechanical coupling coefficients,time domain analysis is also conducted to study the performance of the harvester with different parameters.The results reveal that the mean square displacement and voltage increase with increasing the noise intensity and wind speed,larger absolute values of stiffness coefficient correspond to smaller mean square displacement and voltage,and larger electromechanical coupling coefficients can enhance the mean square voltage.Finally,the influence of wind speed and electromechanical coupling coefficient on the stationary probability density function(SPDF)is investigated,revealing the existence of a bimodal distribution under varying environmental conditions.展开更多
In this work,we investigate disordered Dirac fermions from the perspective of quantum entanglement,which provides a different angle compared to the ordinary perturbative renormalization group(RG)analysis.We consider D...In this work,we investigate disordered Dirac fermions from the perspective of quantum entanglement,which provides a different angle compared to the ordinary perturbative renormalization group(RG)analysis.We consider Dirac fermions subjected to random hopping and random flux,which respectively fall into the chiral Gaussian orthogonal ensemble(cGOE)and chiral Gaussian unitary ensemble(cGUE)universality classes.Existing studies based on perturbative calculations suggest that both types of randomness are marginal.Here,through numerical simulations of the corresponding lattice models,we find that these two different types of randomness exhibit distinct entanglement features,signaling completely different properties in contrast to the perturbative RG analysis.In particular,although the entropy area-law is generally held for both types of randomness,we identify that the subleading term of the entanglement entropy is enhanced by random flux but not by random hopping.This subleading term is known as the entropic F-function in the clean limit without disorder.Our observations indicate that disordered theories in cGOE and cGUE are essentially different,which recalls careful analysis on the RG calculations.展开更多
Robots are key to expanding the scope of space applications.The end-to-end training for robot vision-based detection and precision operations is challenging owing to constraints such as extreme environments and high c...Robots are key to expanding the scope of space applications.The end-to-end training for robot vision-based detection and precision operations is challenging owing to constraints such as extreme environments and high computational overhead.This study proposes a lightweight integrated framework for grasp detection and imitation learning,named GD-IL;it comprises a grasp detection algorithm based on manipulability and Gaussian mixture model(manipulability-GMM),and a grasp trajectory generation algorithm based on a two-stage robot imitation learning algorithm(TS-RIL).In the manipulability-GMM algorithm,we apply GMM clustering and ellipse regression to the object point cloud,propose two judgment criteria to generate multiple candidate grasp bounding boxes for the robot,and use manipulability as a metric for selecting the optimal grasp bounding box.The stages of the TS-RIL algorithm are grasp trajectory learning and robot pose optimization.In the first stage,the robot grasp trajectory is characterized using a second-order dynamic movement primitive model and Gaussian mixture regression(GMM).By adjusting the function form of the forcing term,the robot closely approximates the target-grasping trajectory.In the second stage,a robot pose optimization model is built based on the derived pose error formula and manipulability metric.This model allows the robot to adjust its configuration in real time while grasping,thereby effectively avoiding singularities.Finally,an algorithm verification platform is developed based on a Robot Operating System and a series of comparative experiments are conducted in real-world scenarios.The experimental results demonstrate that GD-IL significantly improves the effectiveness and robustness of grasp detection and trajectory imitation learning,outperforming existing state-of-the-art methods in execution efficiency,manipulability,and success rate.展开更多
The generation and reconnection of magneticflux ropes in a plasma irradiated by two Laguerre–Gaussian laser pulses with different frequen-cies and opposite topological charges are investigated numerically by particle-...The generation and reconnection of magneticflux ropes in a plasma irradiated by two Laguerre–Gaussian laser pulses with different frequen-cies and opposite topological charges are investigated numerically by particle-in-cell simulations.It is shown that twisted plasma currents and hence magneticflux ropes can be effectively generated as long as the laser frequency difference matches the electron plasma frequency.More importantly,subsequent reconnection of magneticflux ropes can occur.Typical signatures of magnetic reconnection,such as magnetic island formation and plasma heating,are identified in the reconnection of magneticflux ropes.Notably,it is found that a strong axial magneticfield can be generated on the axis,owing to the azimuthal current induced during the reconnection of the ropes.This indicates that in the reconnection of magneticflux ropes,the energy can be transferred not only from the magneticfield to the plasma but also from the plasma current back to the magneticfield.This work opens a new avenue to the study of magneticflux ropes,which helps in understanding magnetic topology changes,and resultant magnetic energy dissipation,plasma heating,and particle acceleration found in solarflares,and magnetic confinement fusion devices.展开更多
Topological band theory has been studied for free fermions for decades,and one of the most profound physical results is the bulk-boundary correspondence.Recently a focus in topological physics is extending topological...Topological band theory has been studied for free fermions for decades,and one of the most profound physical results is the bulk-boundary correspondence.Recently a focus in topological physics is extending topological classification to mixed states.Here,we focus on Gaussian mixed states for which the modular Hamiltonians of the density matrix are quadratic free fermion models with U(1)symmetry and can be classified by topological invariants.The bulk-boundary correspondence is then manifested as stable gapless modes of the modular Hamiltonian and degenerate spectrum of the density matrix.In this article,we show that these gapless modes can be detected by the full counting statistics,mathematically described by a function introduced as F(θ).A divergent derivative atθ=πcan be used to probe the gapless modes in the modular Hamiltonian.Based on this,a topological indicator,whose quantization to unity senses topologically nontrivial mixed states,is introduced.We present the physical intuition of these results and also demonstrate these results with concrete models in both one-and two-dimensions.Our results pave the way for revealing the physical significance of topology in mixed states.展开更多
Dear Editor,This letter presents a joint probabilistic scheduling and resource allocation method(PSRA) for 5G-based wireless networked control systems(WNCSs). As a control-aware optimization method, PSRA minimizes the...Dear Editor,This letter presents a joint probabilistic scheduling and resource allocation method(PSRA) for 5G-based wireless networked control systems(WNCSs). As a control-aware optimization method, PSRA minimizes the linear quadratic Gaussian(LQG) control cost of WNCSs by optimizing the activation probability of subsystems, the number of uplink repetitions, and the durations of uplink and downlink phases. Simulation results show that PSRA achieves smaller LQG control costs than existing works.展开更多
Energy resolution calibration is crucial for gamma-ray spectral analysis,as measured using a scintillation detector.A locally constrained regularization method was proposed to determine the resolution calibration para...Energy resolution calibration is crucial for gamma-ray spectral analysis,as measured using a scintillation detector.A locally constrained regularization method was proposed to determine the resolution calibration parameters.First,a Monte Carlo simulation model consistent with an actual measurement system was constructed to obtain the energy deposition distribution in the scintillation crystal.Subsequently,the regularization objective function is established based on weighted least squares and additional constraints.Additional constraints were designed using a special weighting scheme based on the incident gamma-ray energies.Subsequently,an intelligent algorithm was introduced to search for the optimal resolution calibration parameters by minimizing the objective function.The most appropriate regularization parameter was determined through mathematical experiments.When the regularization parameter was 30,the calibrated results exhibited the minimum RMSE.Simulations and test pit experiments were conducted to verify the performance of the proposed method.The simulation results demonstrate that the proposed algorithm can determine resolution calibration parameters more accurately than the traditional weighted least squares,and the test pit experimental results show that the R-squares between the calibrated and measured spectra are larger than 0.99.The accurate resolution calibration parameters determined by the proposed method lay the foundation for gamma-ray spectral processing and simulation benchmarking.展开更多
With expeditious advancements in AI-driven facial manipulation techniques,particularly deepfake technology,there is growing concern over its potential misuse.Deepfakes pose a significant threat to society,partic-ularl...With expeditious advancements in AI-driven facial manipulation techniques,particularly deepfake technology,there is growing concern over its potential misuse.Deepfakes pose a significant threat to society,partic-ularly by infringing on individuals’privacy.Amid significant endeavors to fabricate systems for identifying deepfake fabrications,existing methodologies often face hurdles in adjusting to innovative forgery techniques and demonstrate increased vulnerability to image and video clarity variations,thereby hindering their broad applicability to images and videos produced by unfamiliar technologies.In this manuscript,we endorse resilient training tactics to amplify generalization capabilities.In adversarial training,models are trained using deliberately crafted samples to deceive classification systems,thereby significantly enhancing their generalization ability.In response to this challenge,we propose an innovative hybrid adversarial training framework integrating Virtual Adversarial Training(VAT)with Two-Generated Blurred Adversarial Training.This combined framework bolsters the model’s resilience in detecting deepfakes made using unfamiliar deep learning technologies.Through such adversarial training,models are prompted to acquire more versatile attributes.Through experimental studies,we demonstrate that our model achieves higher accuracy than existing models.展开更多
We apply methods of algebraic integral geometry to prove a special case of the Gaussian kinematic formula of Adler-Taylor.The idea,suggested already by Adler and Taylor,is to view the GKF as the limit of spherical kin...We apply methods of algebraic integral geometry to prove a special case of the Gaussian kinematic formula of Adler-Taylor.The idea,suggested already by Adler and Taylor,is to view the GKF as the limit of spherical kinematic formulas for spheres of large dimension N and curvature1/N.展开更多
基金supported by the National Natural Science Foundation of China[grant number 62376217]the Young Elite Scientists Sponsorship Program by CAST[grant number 2023QNRC001]the Joint Research Project for Meteorological Capacity Improvement[grant number 24NLTSZ003]。
文摘Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep learning to medium-range regional weather forecasting with limited data remains a significant challenge.In this work,three key solutions are proposed:(1)motivated by the need to improve model performance in data-scarce regional forecasting scenarios,the authors innovatively apply semantic segmentation models,to better capture spatiotemporal features and improve prediction accuracy;(2)recognizing the challenge of overfitting and the inability of traditional noise-based data augmentation methods to effectively enhance model robustness,a novel learnable Gaussian noise mechanism is introduced that allows the model to adaptively optimize perturbations for different locations,ensuring more effective learning;and(3)to address the issue of error accumulation in autoregressive prediction,as well as the challenge of learning difficulty and the lack of intermediate data utilization in one-shot prediction,the authors propose a cascade prediction approach that effectively resolves these problems while significantly improving model forecasting performance.The method achieves a competitive result in The East China Regional AI Medium Range Weather Forecasting Competition.Ablation experiments further validate the effectiveness of each component,highlighting their contributions to enhancing prediction performance.
文摘针对现有SLAM算法在渲染真实感、内存占用和复杂场景适应性方面的不足,提出了一种基于3D Gaussians Splatting的密集SLAM算法——TIGO-SLAM(tensor illumination and Gaussian optimization for indoor SLAM)。该算法集成了基于神经网络的张量光照模型、改进的高斯遮罩算法以及网格化神经场的几何和颜色属性表示,具体创新包括:a)基于神经网络的张量光照模型,增强镜面反射与漫反射效果,从而提升了渲染真实感;b)通过冗余高斯剔除机制改进高斯遮罩算法,有效降低了内存消耗并提高了实时性;c)结合网格化神经场的几何与颜色属性表示,采用优化的码本存储方式,显著提高了渲染性能和场景重建精度。实验结果表明,TIGO-SLAM在室内场景渲染、内存优化和复杂场景适应性方面均有显著提升,特别是在动态室内环境中的渲染和重建效果表现突出,为SLAM技术在资源受限设备上的应用提供了新的可能。
文摘Advances in software and hardware technologies have facilitated the production of quadrotor unmanned aerial vehicles(UAVs).Nowadays,people actively use quadrotor UAVs in essential missions such as search and rescue,counter-terrorism,firefighting,surveillance,and cargo transportation.While performing these tasks,quadrotors must operate in noisy environments.Therefore,a robust controller design that can control the altitude and attitude of the quadrotor in noisy environments is of great importance.Many researchers have focused only on white Gaussian noise in their studies,whereas researchers need to consider the effects of all colored noises during the operation of the quadrotor.This study aims to design a robust controller that is resistant to all colored noises.Firstly,a nonlinear quadrotormodel was created with MATLAB.Then,a backstepping controller resistant to colored noises was designed.Thedesigned backstepping controller was tested under Gaussian white,pink,brown,blue,and purple noises.PID and Lyapunov-based controller designswere also carried out,and their time responses(rise time,overshoot,settling time)were compared with those of the backstepping controller.In the simulations,time was in seconds,altitude was in meters,and roll,pitch,and yaw references were in radians.Rise and settling time values were in seconds,and overshoot value was in percent.When the obtained values are examined,simulations prove that the proposed backstepping controller has the least overshoot and the shortest settling time under all noise types.
基金supported by the SP2024/089 Project by the Faculty of Materials Science and Technology,VˇSB-Technical University of Ostrava.
文摘In engineering practice,it is often necessary to determine functional relationships between dependent and independent variables.These relationships can be highly nonlinear,and classical regression approaches cannot always provide sufficiently reliable solutions.Nevertheless,Machine Learning(ML)techniques,which offer advanced regression tools to address complicated engineering issues,have been developed and widely explored.This study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic materials.The ML-based regression methods of Artificial Neural Networks(ANNs),Support Vector Machine(SVM),Decision Tree Regression(DTR),and Gaussian Process Regression(GPR)are applied to mathematically describe hot flow stress curve datasets acquired experimentally for a medium-carbon steel.Although the GPR method has not been used for such a regression task before,the results showed that its performance is the most favorable and practically unrivaled;neither the ANN method nor the other studied ML techniques provide such precise results of the solved regression analysis.
文摘To further understand the performance of the energy harvesters under the influence of the wind force and the random excitation,this paper investigates the stochastic response of the bio-inspired energy harvesters subjected to Gaussian white noise and galloping excitation,simulating the flapping pattern of a seagull and its interaction with wind force.The equivalent linearization method is utilized to convert the original nonlinear model into the Itôstochastic differential equation by minimizing the mean squared error.Then,the second-order steady-state moments about the displacement,velocity,and voltage are derived by combining the moment analysis theory.The theoretical results are simulated numerically to analyze the stochastic response performance under different noise intensities,wind speeds,stiffness coefficients,and electromechanical coupling coefficients,time domain analysis is also conducted to study the performance of the harvester with different parameters.The results reveal that the mean square displacement and voltage increase with increasing the noise intensity and wind speed,larger absolute values of stiffness coefficient correspond to smaller mean square displacement and voltage,and larger electromechanical coupling coefficients can enhance the mean square voltage.Finally,the influence of wind speed and electromechanical coupling coefficient on the stationary probability density function(SPDF)is investigated,revealing the existence of a bimodal distribution under varying environmental conditions.
基金supported by the National Key Research and Development Program(Grant No.2022YFA1402204)the National Natural Science Foundation[Grant Nos.22373095(QL),52471020(WC),and 12474144(WZ)]+2 种基金the Innovation Program for Quantum Science and Technology[Grant No.2021ZD0303306(QL)]the Fundamental Research Funds for the Central Universities[Grant No.JZ2025HGQA0310(WC)]the Science Research Foundation for High-Level Talents of Anhui University of Science and Technology[Grant No.YJ20240002(WL)].
文摘In this work,we investigate disordered Dirac fermions from the perspective of quantum entanglement,which provides a different angle compared to the ordinary perturbative renormalization group(RG)analysis.We consider Dirac fermions subjected to random hopping and random flux,which respectively fall into the chiral Gaussian orthogonal ensemble(cGOE)and chiral Gaussian unitary ensemble(cGUE)universality classes.Existing studies based on perturbative calculations suggest that both types of randomness are marginal.Here,through numerical simulations of the corresponding lattice models,we find that these two different types of randomness exhibit distinct entanglement features,signaling completely different properties in contrast to the perturbative RG analysis.In particular,although the entropy area-law is generally held for both types of randomness,we identify that the subleading term of the entanglement entropy is enhanced by random flux but not by random hopping.This subleading term is known as the entropic F-function in the clean limit without disorder.Our observations indicate that disordered theories in cGOE and cGUE are essentially different,which recalls careful analysis on the RG calculations.
基金Supported by National Natural Science Foundation of China(Grant No.52475280)Shaanxi Provincial Natural Science Basic Research Program(Grant No.2025SYSSYSZD-105).
文摘Robots are key to expanding the scope of space applications.The end-to-end training for robot vision-based detection and precision operations is challenging owing to constraints such as extreme environments and high computational overhead.This study proposes a lightweight integrated framework for grasp detection and imitation learning,named GD-IL;it comprises a grasp detection algorithm based on manipulability and Gaussian mixture model(manipulability-GMM),and a grasp trajectory generation algorithm based on a two-stage robot imitation learning algorithm(TS-RIL).In the manipulability-GMM algorithm,we apply GMM clustering and ellipse regression to the object point cloud,propose two judgment criteria to generate multiple candidate grasp bounding boxes for the robot,and use manipulability as a metric for selecting the optimal grasp bounding box.The stages of the TS-RIL algorithm are grasp trajectory learning and robot pose optimization.In the first stage,the robot grasp trajectory is characterized using a second-order dynamic movement primitive model and Gaussian mixture regression(GMM).By adjusting the function form of the forcing term,the robot closely approximates the target-grasping trajectory.In the second stage,a robot pose optimization model is built based on the derived pose error formula and manipulability metric.This model allows the robot to adjust its configuration in real time while grasping,thereby effectively avoiding singularities.Finally,an algorithm verification platform is developed based on a Robot Operating System and a series of comparative experiments are conducted in real-world scenarios.The experimental results demonstrate that GD-IL significantly improves the effectiveness and robustness of grasp detection and trajectory imitation learning,outperforming existing state-of-the-art methods in execution efficiency,manipulability,and success rate.
基金supported by the National Natural Science Foundation of China(Grant Nos.12375236 and 12135009)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant Nos.XDA25050100 and XDA25010100).
文摘The generation and reconnection of magneticflux ropes in a plasma irradiated by two Laguerre–Gaussian laser pulses with different frequen-cies and opposite topological charges are investigated numerically by particle-in-cell simulations.It is shown that twisted plasma currents and hence magneticflux ropes can be effectively generated as long as the laser frequency difference matches the electron plasma frequency.More importantly,subsequent reconnection of magneticflux ropes can occur.Typical signatures of magnetic reconnection,such as magnetic island formation and plasma heating,are identified in the reconnection of magneticflux ropes.Notably,it is found that a strong axial magneticfield can be generated on the axis,owing to the azimuthal current induced during the reconnection of the ropes.This indicates that in the reconnection of magneticflux ropes,the energy can be transferred not only from the magneticfield to the plasma but also from the plasma current back to the magneticfield.This work opens a new avenue to the study of magneticflux ropes,which helps in understanding magnetic topology changes,and resultant magnetic energy dissipation,plasma heating,and particle acceleration found in solarflares,and magnetic confinement fusion devices.
基金supported by the National Key R&D Program of China(Grant No.2023YFA1406702)the Innovation Program for Quantum Science and Technology 2021ZD0302005+1 种基金the XPLORER Prizepartly supported by the Start-up Research Fund of Southeast University(RF1028624190)。
文摘Topological band theory has been studied for free fermions for decades,and one of the most profound physical results is the bulk-boundary correspondence.Recently a focus in topological physics is extending topological classification to mixed states.Here,we focus on Gaussian mixed states for which the modular Hamiltonians of the density matrix are quadratic free fermion models with U(1)symmetry and can be classified by topological invariants.The bulk-boundary correspondence is then manifested as stable gapless modes of the modular Hamiltonian and degenerate spectrum of the density matrix.In this article,we show that these gapless modes can be detected by the full counting statistics,mathematically described by a function introduced as F(θ).A divergent derivative atθ=πcan be used to probe the gapless modes in the modular Hamiltonian.Based on this,a topological indicator,whose quantization to unity senses topologically nontrivial mixed states,is introduced.We present the physical intuition of these results and also demonstrate these results with concrete models in both one-and two-dimensions.Our results pave the way for revealing the physical significance of topology in mixed states.
基金supported by the Liaoning Revitalization Talents Program(XLYC2203148)
文摘Dear Editor,This letter presents a joint probabilistic scheduling and resource allocation method(PSRA) for 5G-based wireless networked control systems(WNCSs). As a control-aware optimization method, PSRA minimizes the linear quadratic Gaussian(LQG) control cost of WNCSs by optimizing the activation probability of subsystems, the number of uplink repetitions, and the durations of uplink and downlink phases. Simulation results show that PSRA achieves smaller LQG control costs than existing works.
基金supported by the National Natural Science Foundation of China(No.41804141)。
文摘Energy resolution calibration is crucial for gamma-ray spectral analysis,as measured using a scintillation detector.A locally constrained regularization method was proposed to determine the resolution calibration parameters.First,a Monte Carlo simulation model consistent with an actual measurement system was constructed to obtain the energy deposition distribution in the scintillation crystal.Subsequently,the regularization objective function is established based on weighted least squares and additional constraints.Additional constraints were designed using a special weighting scheme based on the incident gamma-ray energies.Subsequently,an intelligent algorithm was introduced to search for the optimal resolution calibration parameters by minimizing the objective function.The most appropriate regularization parameter was determined through mathematical experiments.When the regularization parameter was 30,the calibrated results exhibited the minimum RMSE.Simulations and test pit experiments were conducted to verify the performance of the proposed method.The simulation results demonstrate that the proposed algorithm can determine resolution calibration parameters more accurately than the traditional weighted least squares,and the test pit experimental results show that the R-squares between the calibrated and measured spectra are larger than 0.99.The accurate resolution calibration parameters determined by the proposed method lay the foundation for gamma-ray spectral processing and simulation benchmarking.
基金supported by King Saud University,Riyadh,Saudi Arabia,through the Researchers Supporting Project under Grant RSP2025R493。
文摘With expeditious advancements in AI-driven facial manipulation techniques,particularly deepfake technology,there is growing concern over its potential misuse.Deepfakes pose a significant threat to society,partic-ularly by infringing on individuals’privacy.Amid significant endeavors to fabricate systems for identifying deepfake fabrications,existing methodologies often face hurdles in adjusting to innovative forgery techniques and demonstrate increased vulnerability to image and video clarity variations,thereby hindering their broad applicability to images and videos produced by unfamiliar technologies.In this manuscript,we endorse resilient training tactics to amplify generalization capabilities.In adversarial training,models are trained using deliberately crafted samples to deceive classification systems,thereby significantly enhancing their generalization ability.In response to this challenge,we propose an innovative hybrid adversarial training framework integrating Virtual Adversarial Training(VAT)with Two-Generated Blurred Adversarial Training.This combined framework bolsters the model’s resilience in detecting deepfakes made using unfamiliar deep learning technologies.Through such adversarial training,models are prompted to acquire more versatile attributes.Through experimental studies,we demonstrate that our model achieves higher accuracy than existing models.
文摘We apply methods of algebraic integral geometry to prove a special case of the Gaussian kinematic formula of Adler-Taylor.The idea,suggested already by Adler and Taylor,is to view the GKF as the limit of spherical kinematic formulas for spheres of large dimension N and curvature1/N.
