Feature analysis plays a significant role in computer vision and computer graphics.In the task of shape retrieval,shape descriptor is indispensable.In recent years,feature extraction based on deep learning becomes ver...Feature analysis plays a significant role in computer vision and computer graphics.In the task of shape retrieval,shape descriptor is indispensable.In recent years,feature extraction based on deep learning becomes very popular,but the design of geometric shape descriptor is still meaningful due to the contained intrinsic information and interpretability.This paper proposes an effective and robust descriptor of 3D models.The descriptor is constructed based on the probability distribution of the normalized eigenfunctions of the Laplace–Beltrami operator on the surface,and a spectrum method for dimensionality reduction.The distance metric of the descriptor space is learned by utilizing the joint Bayesian model,and we introduce a matrix regularization in the training stage to re-estimate the covariance matrix.Finally,we apply the descriptor to 3D shape retrieval on a public benchmark.Experiments show that our method is robust and has good retrieval performance.展开更多
Survival of HIV/AIDS patients is crucially dependent on comprehensive and targeted medical interventions such as supply of antiretroviral therapy and monitoring disease progression with CD4 T-cell counts. Statistical ...Survival of HIV/AIDS patients is crucially dependent on comprehensive and targeted medical interventions such as supply of antiretroviral therapy and monitoring disease progression with CD4 T-cell counts. Statistical modelling approaches are helpful towards this goal. This study aims at developing Bayesian joint models with assumed generalized error distribution (GED) for the longitudinal CD4 data and two accelerated failure time distributions, Lognormal and loglogistic, for the survival time of HIV/AIDS patients. Data are obtained from patients under antiretroviral therapy follow-up at Shashemene referral hospital during January 2006-January 2012 and at Bale Robe general hospital during January 2008-March 2015. The Bayesian joint models are defined through latent variables and association parameters and with specified non-informative prior distributions for the model parameters. Simulations are conducted using Gibbs sampler algorithm implemented in the WinBUGS software. The results of the analyses of the two different data sets show that distributions of measurement errors of the longitudinal CD4 variable follow the generalized error distribution with fatter tails than the normal distribution. The Bayesian joint GED loglogistic models fit better to the data sets compared to the lognormal cases. Findings reveal that patients’ health can be improved over time. Compared to the males, female patients gain more CD4 counts. Survival time of a patient is negatively affected by TB infection. Moreover, increase in number of opportunistic infection implies decline of CD4 counts. Patients’ age negatively affects the disease marker with no effects on survival time. Improving weight may improve survival time of patients. Bayesian joint models with GED and AFT distributions are found to be useful in modelling the longitudinal and survival processes. Thus we recommend the generalized error distributions for measurement errors of the longitudinal data under the Bayesian joint modelling. Further studies may investigate the models with various types of shared random effects and more covariates with predictions.展开更多
Multiwave seismic technology promotes the application of joint PP–PS amplitude versus offset (AVO) inversion;however conventional joint PP–PS AVO inversioan is linear based on approximations of the Zoeppritz equatio...Multiwave seismic technology promotes the application of joint PP–PS amplitude versus offset (AVO) inversion;however conventional joint PP–PS AVO inversioan is linear based on approximations of the Zoeppritz equations for multiple iterations. Therefore the inversion results of P-wave, S-wave velocity and density exhibit low precision in the faroffset;thus, the joint PP–PS AVO inversion is nonlinear. Herein, we propose a nonlinear joint inversion method based on exact Zoeppritz equations that combines improved Bayesian inference and a least squares support vector machine (LSSVM) to solve the nonlinear inversion problem. The initial parameters of Bayesian inference are optimized via particle swarm optimization (PSO). In improved Bayesian inference, the optimal parameter of the LSSVM is obtained by maximizing the posterior probability of the hyperparameters, thus improving the learning and generalization abilities of LSSVM. Then, an optimal nonlinear LSSVM model that defi nes the relationship between seismic refl ection amplitude and elastic parameters is established to improve the precision of the joint PP–PS AVO inversion. Further, the nonlinear problem of joint inversion can be solved through a single training of the nonlinear inversion model. The results of the synthetic data suggest that the precision of the estimated parameters is higher than that obtained via Bayesian linear inversion with PP-wave data and via approximations of the Zoeppritz equations. In addition, results using synthetic data with added noise show that the proposed method has superior anti-noising properties. Real-world application shows the feasibility and superiority of the proposed method, as compared with Bayesian linear inversion.展开更多
A joint two-dimensional(2D)direction-of-arrival(DOA)and radial Doppler frequency estimation method for the L-shaped array is proposed in this paper based on the compressive sensing(CS)framework.Revised from the conven...A joint two-dimensional(2D)direction-of-arrival(DOA)and radial Doppler frequency estimation method for the L-shaped array is proposed in this paper based on the compressive sensing(CS)framework.Revised from the conventional CS-based methods where the joint spatial-temporal parameters are characterized in one large scale matrix,three smaller scale matrices with independent azimuth,elevation and Doppler frequency are introduced adopting a separable observation model.Afterwards,the estimation is achieved by L1-norm minimization and the Bayesian CS algorithm.In addition,under the L-shaped array topology,the azimuth and elevation are separated yet coupled to the same radial Doppler frequency.Hence,the pair matching problem is solved with the aid of the radial Doppler frequency.Finally,numerical simulations corroborate the feasibility and validity of the proposed algorithm.展开更多
We propose a flexible joint longitudinal-survival framework to examine the association between longitudinally collected biomarkers and a time-to-event endpoint. More specifically, we use our method for analyzing the s...We propose a flexible joint longitudinal-survival framework to examine the association between longitudinally collected biomarkers and a time-to-event endpoint. More specifically, we use our method for analyzing the survival outcome of end-stage renal disease patients with time-varying serum albumin measurements. Our proposed method is robust to common parametric assumptions in that it avoids explicit specification of the distribution of longitudinal responses and allows for a subject-specific baseline hazard in the survival component. Fully joint estimation is performed to account for uncertainty in the estimated longitudinal biomarkers that are included in the survival model.展开更多
[目的]探讨雌二醇(estradiol,E2)水平动态变化与乳腺癌患者生存预后的潜在关联,比较新辅助治疗与无新辅助治疗下乳腺癌患者生存率的差异性。[方法]基于2015—2019年新疆医科大学附属肿瘤医院随访的女性乳腺癌患者的临床数据,首先在不同...[目的]探讨雌二醇(estradiol,E2)水平动态变化与乳腺癌患者生存预后的潜在关联,比较新辅助治疗与无新辅助治疗下乳腺癌患者生存率的差异性。[方法]基于2015—2019年新疆医科大学附属肿瘤医院随访的女性乳腺癌患者的临床数据,首先在不同分位数下(=0.10,0.25,0.50,0.75)分别建立线性分位数混合模型拟合E2水平的动态变化,并通过赤池信息量准则(akaike information criterion,AIC)与贝叶斯信息准则(Bayesian information criteria,BIC)从中选择最优模型作为联合模型的纵向子模型。其次,基于扩展的Cox比例风险模型建立生存子模型;进一步通过共享随机效应建立纵向与生存数据的贝叶斯分位数联合模型,并通过马尔科夫链蒙特卡洛(Markov Chain Monte Carlo,MCMC)方法估计其关联系数()。[结果]最优子模型筛选结果显示,=0.50时,纵向子模型的AIC与BIC值最小。在=0.50下构建贝叶斯分位数联合模型。联合模型结果显示,E2水平的动态变化与乳腺癌患者的生存结局显著性相关(=0.59,HR=1.80,95%CI:1.47~2.24)。新辅助治疗是乳腺癌患者的保护因素(HR=0.155,95%CI:0.047~0.384),能够降低乳腺癌患者84.5%死亡风险。[结论]乳腺癌患者E2水平增加与不良生存预后相关,新辅助治疗可降低乳腺癌患者的死亡风险,并改善其生存预后。乳腺癌患者应采取积极治疗手段控制雌二醇水平升高、抑制肿瘤的生长和扩散,从而提高患者的生存率。展开更多
基于Bayes反演理论(Tarantola,1987,2005),在接收函数非线性复谱比反演方法基础上(刘启元等,1996),本文讨论了接收函数与地震环境噪声Rayleigh波相速度频散的联合反演.本文采用修正后的快速广义反射/透射系数方法(Pei et al.,2008,2009...基于Bayes反演理论(Tarantola,1987,2005),在接收函数非线性复谱比反演方法基础上(刘启元等,1996),本文讨论了接收函数与地震环境噪声Rayleigh波相速度频散的联合反演.本文采用修正后的快速广义反射/透射系数方法(Pei et al.,2008,2009)计算Rayleigh波相速度频散,并引入地壳泊松比的全局性搜索.数值检验表明:(1)接收函数与环境噪声的联合反演能够有效地解决反演结果对初始模型依赖的问题,即使对地壳速度结构仅有非常粗略的初始估计(例如,垂向均匀模型),本文方法仍能给出模型参数的可靠估计;(2)由于环境噪声与接收函数在频带上的适配性明显优于地震面波,接收函数与环境噪声的非线性联合反演能更好地约束台站下方近地表的速度结构;对于周期范围为2～40s的环境噪声相速度频散,利用本文方法能够可靠推测台站下方0～80km深度范围的S波速度结构,其浅表速度结构的分辨率可达到1km(3)本文方法能够可靠地估计地壳泊松比,泊松比的全局性搜索有助于合理解释接收函数和环境噪声的面波频散数据.利用本文方法对川西台阵KWC05台站观测的接收函数与环境噪声的联合反演表明,该台站下方地壳厚度为44km,上地壳具有明显的高速结构,24～42km范围的中下地壳具有低速结构.该台站下方地壳的平均泊松比为0.262,壳内低速带的泊松比为0.27.展开更多
基金the National Natural Science Foundation of China under Grant Nos.61872316,61932018.
文摘Feature analysis plays a significant role in computer vision and computer graphics.In the task of shape retrieval,shape descriptor is indispensable.In recent years,feature extraction based on deep learning becomes very popular,but the design of geometric shape descriptor is still meaningful due to the contained intrinsic information and interpretability.This paper proposes an effective and robust descriptor of 3D models.The descriptor is constructed based on the probability distribution of the normalized eigenfunctions of the Laplace–Beltrami operator on the surface,and a spectrum method for dimensionality reduction.The distance metric of the descriptor space is learned by utilizing the joint Bayesian model,and we introduce a matrix regularization in the training stage to re-estimate the covariance matrix.Finally,we apply the descriptor to 3D shape retrieval on a public benchmark.Experiments show that our method is robust and has good retrieval performance.
文摘Survival of HIV/AIDS patients is crucially dependent on comprehensive and targeted medical interventions such as supply of antiretroviral therapy and monitoring disease progression with CD4 T-cell counts. Statistical modelling approaches are helpful towards this goal. This study aims at developing Bayesian joint models with assumed generalized error distribution (GED) for the longitudinal CD4 data and two accelerated failure time distributions, Lognormal and loglogistic, for the survival time of HIV/AIDS patients. Data are obtained from patients under antiretroviral therapy follow-up at Shashemene referral hospital during January 2006-January 2012 and at Bale Robe general hospital during January 2008-March 2015. The Bayesian joint models are defined through latent variables and association parameters and with specified non-informative prior distributions for the model parameters. Simulations are conducted using Gibbs sampler algorithm implemented in the WinBUGS software. The results of the analyses of the two different data sets show that distributions of measurement errors of the longitudinal CD4 variable follow the generalized error distribution with fatter tails than the normal distribution. The Bayesian joint GED loglogistic models fit better to the data sets compared to the lognormal cases. Findings reveal that patients’ health can be improved over time. Compared to the males, female patients gain more CD4 counts. Survival time of a patient is negatively affected by TB infection. Moreover, increase in number of opportunistic infection implies decline of CD4 counts. Patients’ age negatively affects the disease marker with no effects on survival time. Improving weight may improve survival time of patients. Bayesian joint models with GED and AFT distributions are found to be useful in modelling the longitudinal and survival processes. Thus we recommend the generalized error distributions for measurement errors of the longitudinal data under the Bayesian joint modelling. Further studies may investigate the models with various types of shared random effects and more covariates with predictions.
基金supported by the Fundamental Research Funds for the Central Universities of China(No.2652017438)the National Science and Technology Major Project of China(No.2016ZX05003-003)
文摘Multiwave seismic technology promotes the application of joint PP–PS amplitude versus offset (AVO) inversion;however conventional joint PP–PS AVO inversioan is linear based on approximations of the Zoeppritz equations for multiple iterations. Therefore the inversion results of P-wave, S-wave velocity and density exhibit low precision in the faroffset;thus, the joint PP–PS AVO inversion is nonlinear. Herein, we propose a nonlinear joint inversion method based on exact Zoeppritz equations that combines improved Bayesian inference and a least squares support vector machine (LSSVM) to solve the nonlinear inversion problem. The initial parameters of Bayesian inference are optimized via particle swarm optimization (PSO). In improved Bayesian inference, the optimal parameter of the LSSVM is obtained by maximizing the posterior probability of the hyperparameters, thus improving the learning and generalization abilities of LSSVM. Then, an optimal nonlinear LSSVM model that defi nes the relationship between seismic refl ection amplitude and elastic parameters is established to improve the precision of the joint PP–PS AVO inversion. Further, the nonlinear problem of joint inversion can be solved through a single training of the nonlinear inversion model. The results of the synthetic data suggest that the precision of the estimated parameters is higher than that obtained via Bayesian linear inversion with PP-wave data and via approximations of the Zoeppritz equations. In addition, results using synthetic data with added noise show that the proposed method has superior anti-noising properties. Real-world application shows the feasibility and superiority of the proposed method, as compared with Bayesian linear inversion.
文摘A joint two-dimensional(2D)direction-of-arrival(DOA)and radial Doppler frequency estimation method for the L-shaped array is proposed in this paper based on the compressive sensing(CS)framework.Revised from the conventional CS-based methods where the joint spatial-temporal parameters are characterized in one large scale matrix,three smaller scale matrices with independent azimuth,elevation and Doppler frequency are introduced adopting a separable observation model.Afterwards,the estimation is achieved by L1-norm minimization and the Bayesian CS algorithm.In addition,under the L-shaped array topology,the azimuth and elevation are separated yet coupled to the same radial Doppler frequency.Hence,the pair matching problem is solved with the aid of the radial Doppler frequency.Finally,numerical simulations corroborate the feasibility and validity of the proposed algorithm.
文摘We propose a flexible joint longitudinal-survival framework to examine the association between longitudinally collected biomarkers and a time-to-event endpoint. More specifically, we use our method for analyzing the survival outcome of end-stage renal disease patients with time-varying serum albumin measurements. Our proposed method is robust to common parametric assumptions in that it avoids explicit specification of the distribution of longitudinal responses and allows for a subject-specific baseline hazard in the survival component. Fully joint estimation is performed to account for uncertainty in the estimated longitudinal biomarkers that are included in the survival model.
文摘[目的]探讨雌二醇(estradiol,E2)水平动态变化与乳腺癌患者生存预后的潜在关联,比较新辅助治疗与无新辅助治疗下乳腺癌患者生存率的差异性。[方法]基于2015—2019年新疆医科大学附属肿瘤医院随访的女性乳腺癌患者的临床数据,首先在不同分位数下(=0.10,0.25,0.50,0.75)分别建立线性分位数混合模型拟合E2水平的动态变化,并通过赤池信息量准则(akaike information criterion,AIC)与贝叶斯信息准则(Bayesian information criteria,BIC)从中选择最优模型作为联合模型的纵向子模型。其次,基于扩展的Cox比例风险模型建立生存子模型;进一步通过共享随机效应建立纵向与生存数据的贝叶斯分位数联合模型,并通过马尔科夫链蒙特卡洛(Markov Chain Monte Carlo,MCMC)方法估计其关联系数()。[结果]最优子模型筛选结果显示,=0.50时,纵向子模型的AIC与BIC值最小。在=0.50下构建贝叶斯分位数联合模型。联合模型结果显示,E2水平的动态变化与乳腺癌患者的生存结局显著性相关(=0.59,HR=1.80,95%CI:1.47~2.24)。新辅助治疗是乳腺癌患者的保护因素(HR=0.155,95%CI:0.047~0.384),能够降低乳腺癌患者84.5%死亡风险。[结论]乳腺癌患者E2水平增加与不良生存预后相关,新辅助治疗可降低乳腺癌患者的死亡风险,并改善其生存预后。乳腺癌患者应采取积极治疗手段控制雌二醇水平升高、抑制肿瘤的生长和扩散,从而提高患者的生存率。
文摘基于Bayes反演理论(Tarantola,1987,2005),在接收函数非线性复谱比反演方法基础上(刘启元等,1996),本文讨论了接收函数与地震环境噪声Rayleigh波相速度频散的联合反演.本文采用修正后的快速广义反射/透射系数方法(Pei et al.,2008,2009)计算Rayleigh波相速度频散,并引入地壳泊松比的全局性搜索.数值检验表明:(1)接收函数与环境噪声的联合反演能够有效地解决反演结果对初始模型依赖的问题,即使对地壳速度结构仅有非常粗略的初始估计(例如,垂向均匀模型),本文方法仍能给出模型参数的可靠估计;(2)由于环境噪声与接收函数在频带上的适配性明显优于地震面波,接收函数与环境噪声的非线性联合反演能更好地约束台站下方近地表的速度结构;对于周期范围为2～40s的环境噪声相速度频散,利用本文方法能够可靠推测台站下方0～80km深度范围的S波速度结构,其浅表速度结构的分辨率可达到1km(3)本文方法能够可靠地估计地壳泊松比,泊松比的全局性搜索有助于合理解释接收函数和环境噪声的面波频散数据.利用本文方法对川西台阵KWC05台站观测的接收函数与环境噪声的联合反演表明,该台站下方地壳厚度为44km,上地壳具有明显的高速结构,24～42km范围的中下地壳具有低速结构.该台站下方地壳的平均泊松比为0.262,壳内低速带的泊松比为0.27.
