Dear Editor,This letter addresses the impulse game problem for a general scope of deterministic,multi-player,nonzero-sum differential games wherein all participants adopt impulse controls.Our objective is to formulate...Dear Editor,This letter addresses the impulse game problem for a general scope of deterministic,multi-player,nonzero-sum differential games wherein all participants adopt impulse controls.Our objective is to formulate this impulse game problem with the modified objective function including interaction costs among the players in a discontinuous fashion,and subsequently,to derive a verification theorem for identifying the feedback Nash equilibrium strategy.展开更多
Let f,g and h be three distinct primitive holomorphic cusp forms of even integral weights k_(1),k_(2)and k_(3)for the full modular groupΓ=SL(2,Z),and denote byλ_(f)(n),λ_(g)(n),λ_(h)(n)the corresponding normalized...Let f,g and h be three distinct primitive holomorphic cusp forms of even integral weights k_(1),k_(2)and k_(3)for the full modular groupΓ=SL(2,Z),and denote byλ_(f)(n),λ_(g)(n),λ_(h)(n)the corresponding normalized Fourier coefficients,respectively.In this paper,we investigate the correlations of triple sums associated to these Fourier coefficientsλ_(f)(n),λ_(g)(n),λ_(h)(n)over certain polynomials,and obtain some power-saving asymptotic estimates which beat the trivial bounds.展开更多
In this paper,by utilizing the Marcinkiewicz-Zygmund inequality and Rosenthal-type inequality of negatively superadditive dependent(NSD)random arrays and truncated method,we investigate the complete f-moment convergen...In this paper,by utilizing the Marcinkiewicz-Zygmund inequality and Rosenthal-type inequality of negatively superadditive dependent(NSD)random arrays and truncated method,we investigate the complete f-moment convergence of NSD random variables.We establish and improve a general result on the complete f-moment convergence for Sung’s type randomly weighted sums of NSD random variables under some general assumptions.As an application,we show the complete consistency for the randomly weighted estimator in a nonparametric regression model based on NSD errors.展开更多
The complete convergence for weighted sums of sequences of independent,identically distributed random variables under sublinear expectation space is studied.By moment inequality and truncation methods,we establish the...The complete convergence for weighted sums of sequences of independent,identically distributed random variables under sublinear expectation space is studied.By moment inequality and truncation methods,we establish the equivalent conditions of complete convergence for weighted sums of sequences of independent,identically distributed random variables under sublinear expectation space.The results complement the corresponding results in probability space to those for sequences of independent,identically distributed random variables under sublinear expectation space.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
Denoising is an important preprocessing step in seismic exploration that improves the signal-to-noise ratio(SNR)and helps identify oil and minerals.Dictionary learning(DL)is a promising method for noise attenuation.Th...Denoising is an important preprocessing step in seismic exploration that improves the signal-to-noise ratio(SNR)and helps identify oil and minerals.Dictionary learning(DL)is a promising method for noise attenuation.The DL extracts sparse features from noisy seismic data using over-complete dictionaries and performs denoising based on a threshold.However,the choice of threshold in DL greatly impacts the denoising results and the improvement in output SNR.Ramanujan’s sum(s)(RS)is a signal processing tool that exhibits derivative behavior and finds applications in edge detection and noise estimation of signals.Hence,we propose a novel DL method with threshold estimation based on RS to improve the output SNR.In this work,we estimate the noise variance of seismic data based on RS and use it as a threshold value for the DL method to perform denoising.We analyze the results of the proposed work on synthetically generated and field data sets.We perform simulations on noisy seismic data across a wide range of SNR values and tabulate the denoised results using the performance metrics SNR and mean squared error.The results indicate that the proposed method provides superior SNR and reduced mean squared error compared to MAD,SURE-based,and adaptive soft-thresholding techniques.展开更多
A low-background γ spectrometer named the Gamma spectrometer for Nuclear Activation Studies(GNAS)was developed to detect scarce γ radioactivity,with a special focus on conducting activation experiments in nuclear as...A low-background γ spectrometer named the Gamma spectrometer for Nuclear Activation Studies(GNAS)was developed to detect scarce γ radioactivity,with a special focus on conducting activation experiments in nuclear astrophysics.It consisted of a well-type HPGe detector surrounded by optimized multi-layer shielding,which reduced the laboratory background counting rate by 99.5%and enabled a sensitivity edge as low as 0.044 Bq for the 477.6 KeV γ line of ^(7)Be.The near 4π geometry of the HPGe detector introduces a severe true coincidence summing(TCS)effect along with its high detection efficiency.To determine the intrinsic detection efficiency and correct for the TCS effect,a Monte Carlo simulation method was developed with the Geant4 toolkit.The detector model was optimized by matching the simulated full energy peak(FEP)statistics with those of a ^(137)Cs monoenergetic source and calibrated ^(55,57,58)Co sources produced by low-energy proton beam bombardment of natural iron.The intrinsic detection efficiency curve was obtained,and an algorithm for the correction of the TCS effect was programmed using decay data from the ENSDF library and Nuclear Wallet Cards.The GNAS fulfills the requirements of the ongoing activation measurement of proton-and alpha-induced reactions in nuclear astrophysics on the ground and at the Jinping Underground Nuclear Astrophysics(JUNA)facility.展开更多
Sensitivity encoding(SENSE)is a parallel magnetic resonance imaging(MRI)reconstruction model by utilizing the sensitivity information of receiver coils to achieve image reconstruction.The existing SENSE-based reconstr...Sensitivity encoding(SENSE)is a parallel magnetic resonance imaging(MRI)reconstruction model by utilizing the sensitivity information of receiver coils to achieve image reconstruction.The existing SENSE-based reconstruction algorithms usually used nonadaptive sparsifying transforms,resulting in a limited reconstruction accuracy.Therefore,we proposed a new model for accurate parallel MRI reconstruction by combining the L0 norm regularization term based on the efficient sum of outer products dictionary learning(SOUPDIL)with the SENSE model,called SOUPDIL-SENSE.The SOUPDIL-SENSE model is mainly solved by utilizing the variable splitting and alternating direction method of multipliers techniques.The experimental results on four human datasets show that the proposed algorithm effectively promotes the image sparsity,eliminates the noise and artifacts of the reconstructed images,and improves the reconstruction accuracy.展开更多
基金supported in part by the National Natural Science Foundation of China(62173051)the Fundamental Research Funds for the Central Universities(2024CDJCGJ012,2023CDJXY-010)+1 种基金the Chongqing Technology Innovation and Application Development Special Key Project(CSTB2022TIADCUX0015,CSTB2022TIAD-KPX0162)the China Postdoctoral Science Foundation(2024M763865)
文摘Dear Editor,This letter addresses the impulse game problem for a general scope of deterministic,multi-player,nonzero-sum differential games wherein all participants adopt impulse controls.Our objective is to formulate this impulse game problem with the modified objective function including interaction costs among the players in a discontinuous fashion,and subsequently,to derive a verification theorem for identifying the feedback Nash equilibrium strategy.
基金Supported in part by NSFC(Nos.12401011,12201214)National Key Research and Development Program of China(No.2021YFA1000700)+3 种基金Shaanxi Fundamental Science Research Project for Mathematics and Physics(No.23JSQ053)Science and Technology Program for Youth New Star of Shaanxi Province(No.2025ZC-KJXX-29)Natural Science Basic Research Program of Shaanxi Province(No.2025JC-YBQN-091)Scientific Research Foundation for Young Talents of WNU(No.2024XJ-QNRC-01)。
文摘Let f,g and h be three distinct primitive holomorphic cusp forms of even integral weights k_(1),k_(2)and k_(3)for the full modular groupΓ=SL(2,Z),and denote byλ_(f)(n),λ_(g)(n),λ_(h)(n)the corresponding normalized Fourier coefficients,respectively.In this paper,we investigate the correlations of triple sums associated to these Fourier coefficientsλ_(f)(n),λ_(g)(n),λ_(h)(n)over certain polynomials,and obtain some power-saving asymptotic estimates which beat the trivial bounds.
基金supported by the National Social Science Fundation(Grant No.21BTJ040)the Project of Outstanding Young People in University of Anhui Province(Grant Nos.2023AH020037,SLXY2024A001).
文摘In this paper,by utilizing the Marcinkiewicz-Zygmund inequality and Rosenthal-type inequality of negatively superadditive dependent(NSD)random arrays and truncated method,we investigate the complete f-moment convergence of NSD random variables.We establish and improve a general result on the complete f-moment convergence for Sung’s type randomly weighted sums of NSD random variables under some general assumptions.As an application,we show the complete consistency for the randomly weighted estimator in a nonparametric regression model based on NSD errors.
基金supported by Doctoral Scientific Research Starting Foundation of Jingdezhen Ceramic University(Grant No.102/01003002031)Re-accompanying Funding Project of Academic Achievements of Jingdezhen Ceramic University(Grant Nos.215/20506277,215/20506341)。
文摘The complete convergence for weighted sums of sequences of independent,identically distributed random variables under sublinear expectation space is studied.By moment inequality and truncation methods,we establish the equivalent conditions of complete convergence for weighted sums of sequences of independent,identically distributed random variables under sublinear expectation space.The results complement the corresponding results in probability space to those for sequences of independent,identically distributed random variables under sublinear expectation space.
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Denoising is an important preprocessing step in seismic exploration that improves the signal-to-noise ratio(SNR)and helps identify oil and minerals.Dictionary learning(DL)is a promising method for noise attenuation.The DL extracts sparse features from noisy seismic data using over-complete dictionaries and performs denoising based on a threshold.However,the choice of threshold in DL greatly impacts the denoising results and the improvement in output SNR.Ramanujan’s sum(s)(RS)is a signal processing tool that exhibits derivative behavior and finds applications in edge detection and noise estimation of signals.Hence,we propose a novel DL method with threshold estimation based on RS to improve the output SNR.In this work,we estimate the noise variance of seismic data based on RS and use it as a threshold value for the DL method to perform denoising.We analyze the results of the proposed work on synthetically generated and field data sets.We perform simulations on noisy seismic data across a wide range of SNR values and tabulate the denoised results using the performance metrics SNR and mean squared error.The results indicate that the proposed method provides superior SNR and reduced mean squared error compared to MAD,SURE-based,and adaptive soft-thresholding techniques.
基金supported by the National Key Research and Development Project(No.2022YFA1602301)the National Natural Science Foundation of China(Nos.U2267205 and 12275361)the Continuous-Support Basic Scientific Research Project.
文摘A low-background γ spectrometer named the Gamma spectrometer for Nuclear Activation Studies(GNAS)was developed to detect scarce γ radioactivity,with a special focus on conducting activation experiments in nuclear astrophysics.It consisted of a well-type HPGe detector surrounded by optimized multi-layer shielding,which reduced the laboratory background counting rate by 99.5%and enabled a sensitivity edge as low as 0.044 Bq for the 477.6 KeV γ line of ^(7)Be.The near 4π geometry of the HPGe detector introduces a severe true coincidence summing(TCS)effect along with its high detection efficiency.To determine the intrinsic detection efficiency and correct for the TCS effect,a Monte Carlo simulation method was developed with the Geant4 toolkit.The detector model was optimized by matching the simulated full energy peak(FEP)statistics with those of a ^(137)Cs monoenergetic source and calibrated ^(55,57,58)Co sources produced by low-energy proton beam bombardment of natural iron.The intrinsic detection efficiency curve was obtained,and an algorithm for the correction of the TCS effect was programmed using decay data from the ENSDF library and Nuclear Wallet Cards.The GNAS fulfills the requirements of the ongoing activation measurement of proton-and alpha-induced reactions in nuclear astrophysics on the ground and at the Jinping Underground Nuclear Astrophysics(JUNA)facility.
基金the National Natural Science Foundation of China(No.61861023)the Yunnan Fundamental Research Project(No.202301AT070452)。
文摘Sensitivity encoding(SENSE)is a parallel magnetic resonance imaging(MRI)reconstruction model by utilizing the sensitivity information of receiver coils to achieve image reconstruction.The existing SENSE-based reconstruction algorithms usually used nonadaptive sparsifying transforms,resulting in a limited reconstruction accuracy.Therefore,we proposed a new model for accurate parallel MRI reconstruction by combining the L0 norm regularization term based on the efficient sum of outer products dictionary learning(SOUPDIL)with the SENSE model,called SOUPDIL-SENSE.The SOUPDIL-SENSE model is mainly solved by utilizing the variable splitting and alternating direction method of multipliers techniques.The experimental results on four human datasets show that the proposed algorithm effectively promotes the image sparsity,eliminates the noise and artifacts of the reconstructed images,and improves the reconstruction accuracy.
