In this paper,we investigate the convergence of the generalized Bregman alternating direction method of multipliers(ADMM)for solving nonconvex separable problems with linear constraints.This algorithm relaxes the requ...In this paper,we investigate the convergence of the generalized Bregman alternating direction method of multipliers(ADMM)for solving nonconvex separable problems with linear constraints.This algorithm relaxes the requirement of global Lipschitz continuity of differentiable functions that is often seen in many researches,and it incorporates the acceleration technique of the proximal point algorithm(PPA).As a result,the scope of application of the algorithm is broadened and its performance is enhanced.Under the assumption that the augmented Lagrangian function satisfies the Kurdyka-Lojasiewicz inequality,we demonstrate that the iterative sequence generated by the algorithm converges to a critical point of its augmented Lagrangian function when the penalty parameter in the augmented Lagrangian function is sufficiently large.Finally,we analyze the convergence rate of the algorithm.展开更多
Statistical inference is developed for the analysis of generalized type-Ⅱ hybrid censoring data under exponential competing risks model. In order to solve the problem that approximate methods make unsatisfactory perf...Statistical inference is developed for the analysis of generalized type-Ⅱ hybrid censoring data under exponential competing risks model. In order to solve the problem that approximate methods make unsatisfactory performances in the case of small sample size,we establish the exact conditional distributions of estimators for parameters by conditional moment generating function(CMGF). Furthermore, confidence intervals(CIs) are constructed by exact distributions, approximate distributions as well as bootstrap method respectively,and their performances are evaluated by Monte Carlo simulations. And finally, a real data set is analyzed to illustrate all the methods developed here.展开更多
The study aims to explore the impact of governance and macroeconomic conditions on financial stability in developed and emerging countries.The study sample comprised 122 countries from 2013 to 2020,and a comprehensive...The study aims to explore the impact of governance and macroeconomic conditions on financial stability in developed and emerging countries.The study sample comprised 122 countries from 2013 to 2020,and a comprehensive set of variables was used to construct the financial stability index(FSI).The results of the two-step system GMM analysis,robust with D–K regression,indicate that interest rate,GDP growth,voice and accountability,political stability and absence of violence/terrorism,government effectiveness,regulatory quality,and control of corruption have a positive and statistically significant impact on financial stability.However,inflation,money supply,and the rule of law have adverse and insignificant effects on financial stability.Notably,the findings vary between developed and emerging countries due to differences in governance and macroeconomic conditions and their role in financial stability.The study concludes that regulatory governance and macroeconomic conditions are crucial for financial stability.These outcomes are significant for central banks,academia,and policymakers,as they emphasize the need for stable financial systems and sustainable,balanced growth through governance and macroeconomic conditions.展开更多
The conditional generative adversarial network(CGAN)is used in this paper for empirical Bayes(EB)analysis of road crash hotspots.EB is a well-known method for estimating the expected crash frequency of sites(e.g.road ...The conditional generative adversarial network(CGAN)is used in this paper for empirical Bayes(EB)analysis of road crash hotspots.EB is a well-known method for estimating the expected crash frequency of sites(e.g.road segments,intersections)and then prioritising these sites to identify a subset of high priority sites(e.g.hotspots)for additional safety audits/improvements.In contrast to the conventional EB approach,which employs a statis tical model such as the negative binomial model(NB-EB)to model crash frequency data,the recently developed CGAN-EB approach uses a conditional generative adversarial net work,a form of deep neural network,that can model any form of distributions of the crash frequency data.Previous research has shown that the CGAN-EB performs as well as or bet ter than NB-EB,however that work considered only a small range of crash data character istics and did not examine the spatial and temporal transferability.In this paper a series of simulation experiments are devised and carried out to assess the CGAN-EB performance across a wide range of conditions and compares it to the NB-EB.The simulation results show that CGAN-EB performs as well as NB-EB when conditions favor the NB-EB model(i.e.data conform to the assumptions of the NB model)and outperforms NB-EB in experi ments reflecting conditions frequently encountered in practice(i.e.low sample mean crash rates,and when crash frequency does not follow a log-linear relationship with covariates).Also,temporal and spatial transferability of both approaches were evaluated using field data and both CGAN-EB and NB-EB approaches were found to have similar performance.展开更多
Given that challenges on the issue of socioeconomic development faced by countries in sub-Saharan Africa(SSA)have been identified as critical to strengthening the inherent link between governance and socioeconomic con...Given that challenges on the issue of socioeconomic development faced by countries in sub-Saharan Africa(SSA)have been identified as critical to strengthening the inherent link between governance and socioeconomic conditions,this study examines the interconnections between governance and socioeconomic conditions in SSA.With a focus on 25 countries in SSA between 2005 and 2019,we conduct the analysis based on the Panel-Corrected Standard Error and System Generalized Method of Moments estimations and panel causality tests.The results show that SSA does not seem to have the means of effective governance to spur improved socioeconomic conditions.Moreover,the pervasiveness of institutional problems in many countries of SSA has been responsible for the poor socioeconomic conditions in the region.Likewise,governance quality and socioeconomic conditions are found to influence each other.An improvement in socioeconomic conditions could result in better governance quality.On the other hand,governance quality is viewed as a vital ingredient in achieving needed socioeconomic development outcomes.Thus,it is suggested that there is a need for countries in SSA to streamline governing systems toward engendering improved well-being.The introduction and implementation of transformative policies through effective governance are also necessary for ensuring critical structural changes and increasing social service provision.Overall,there should be a proactive identification of ineffective policies and procedures by policymakers to enhance meaningful impacts in the region.展开更多
The boundary knot method(BKM) is a boundary-type meshfree method. Only non-singular general solutions are used during the whole solution procedures. The effective condition number(ECN), which depends on the right-hand...The boundary knot method(BKM) is a boundary-type meshfree method. Only non-singular general solutions are used during the whole solution procedures. The effective condition number(ECN), which depends on the right-hand side vector of a linear system, is considered as an alternative criterion to the traditional condition number. In this paper, the effective condition number is used to help determine the position and distribution of the collocation points as well as the quasi-optimal collocation point numbers. During the solution process, we propose an NMN-search algorithm. Numerical examples show that the ECN is reliable to measure the feasibility of the BKM.展开更多
A judgment criterion to guarantee a point to be a Chen' s approximate zero of Newton method for solving nonlinear equation is sought by dominating sequence techniques. The criterion is based on the fact that the d...A judgment criterion to guarantee a point to be a Chen' s approximate zero of Newton method for solving nonlinear equation is sought by dominating sequence techniques. The criterion is based on the fact that the dominating function may have only one simple positive zero, assuming that the operator is weak Lipschitz continuous, which is much more relaxed and can be checked much more easily than Lipschitz continuous in practice. It is demonstrated that a Chen' s approximate zero may not be a Smale' s approximate zero. The error estimate obtained indicated the convergent order when we use |f(x) | < ε to stop computation in software.The result can also be applied for solving partial derivative and integration equations.展开更多
The empirical Bayes(EB)method based on parametric statistical models such as the negative binomial(NB)has been widely used for ranking sites in the road network safety screening process.In this paper a novel non-param...The empirical Bayes(EB)method based on parametric statistical models such as the negative binomial(NB)has been widely used for ranking sites in the road network safety screening process.In this paper a novel non-parametric EB method for modeling crash frequency data based on Conditional Generative Adversarial Networks(CGAN)is proposed and evaluated over a real-world crash data set.Unlike parametric approaches,there is no need for a pre-specified underlying relationship between dependent and independent variables in the proposed CGAN-EB and they are able to model any types of distributions.The proposed methodology is applied to real-world and simulated crash data sets.The performance of CGAN-EB in terms of model fit,predictive performance and network screening outcomes is compared with the conventional approach(NB-EB)as a benchmark.The results indicate that the proposed CGAN-EB approach outperforms NB-EB in terms of prediction power and hotspot identification tests.展开更多
考虑到海上风电出力的随机性以及日益突出的生态环境问题,以含柔性直流输电技术(voltagesource converter high voltage direct current,VSC-HVDC)的交直流系统为研究对象,提出了考虑条件风险价值(conditional valueatrisk,CVaR)的两阶...考虑到海上风电出力的随机性以及日益突出的生态环境问题,以含柔性直流输电技术(voltagesource converter high voltage direct current,VSC-HVDC)的交直流系统为研究对象,提出了考虑条件风险价值(conditional valueatrisk,CVaR)的两阶段分布鲁棒低碳经济优化模型,构建了基于Kullback-Leibler(KL)散度的概率分布模糊集,同时利用条件风险价值量化了极端场景下的尾部风险,使得模型能够同时考虑概率分布不确定性以及处于最坏概率分布中极端场景下的尾部损失;此外,将阶梯型碳交易机制并入所提分布鲁棒模型中,通过合理利用柔性资源和储能装置,增强系统运行的灵活性,在兼顾运行风险的前提下,降低碳排放量的目标。再者,为了提高计算效率,在列和约束生成算法(column-and-constraint generation method,C&CG)和Multi-cut Benders分解算法的基础上提出了双循环分解算法。最后,在基于改进的IEEE RTS 79测试系统中验证了所提模型及算法的有效性。展开更多
Elastography is a non-invasive medical imaging technique to map the spatial variation of elastic properties of soft tissues.The quality of reconstruction results in elastography is highly sensitive to the noise induce...Elastography is a non-invasive medical imaging technique to map the spatial variation of elastic properties of soft tissues.The quality of reconstruction results in elastography is highly sensitive to the noise induced by imaging measurements and processing.To address this issue,we propose a deep learning(DL)model based on conditional Generative Adversarial Networks(cGANs)to improve the quality of nonhomogeneous shear modulus reconstruction.To train this model,we generated a synthetic displacement field with finite element simulation under known nonhomogeneous shear modulus distribution.Both the simulated and experimental displacement fields are used to validate the proposed method.The reconstructed results demonstrate that the DL model with synthetic training data is able to improve the quality of the reconstruction compared with the well-established optimization method.Moreover,we emphasize that our DL model is only trained on synthetic data.This might provide a way to alleviate the challenge of obtaining clinical or experimental data in elastography.Overall,this work addresses several fatal issues in applying the DL technique into elastography,and the proposed method has shown great potential in improving the accuracy of the disease diagnosis in clinical medicine.展开更多
文摘In this paper,we investigate the convergence of the generalized Bregman alternating direction method of multipliers(ADMM)for solving nonconvex separable problems with linear constraints.This algorithm relaxes the requirement of global Lipschitz continuity of differentiable functions that is often seen in many researches,and it incorporates the acceleration technique of the proximal point algorithm(PPA).As a result,the scope of application of the algorithm is broadened and its performance is enhanced.Under the assumption that the augmented Lagrangian function satisfies the Kurdyka-Lojasiewicz inequality,we demonstrate that the iterative sequence generated by the algorithm converges to a critical point of its augmented Lagrangian function when the penalty parameter in the augmented Lagrangian function is sufficiently large.Finally,we analyze the convergence rate of the algorithm.
基金Supported by the National Natural Science Foundation of China（71401134, 71571144, 71171164） Supported by the Natural Science Basic Research Program of Shaanxi Province（2015JM1003）+1 种基金 Sup- ported by the Program of International Cooperation and Exchanges in Science and Technology Funded of Shaanxi Province（2016KW-033） Supported by the Scholarship Program of Shanxi Province（2016-015）
文摘Statistical inference is developed for the analysis of generalized type-Ⅱ hybrid censoring data under exponential competing risks model. In order to solve the problem that approximate methods make unsatisfactory performances in the case of small sample size,we establish the exact conditional distributions of estimators for parameters by conditional moment generating function(CMGF). Furthermore, confidence intervals(CIs) are constructed by exact distributions, approximate distributions as well as bootstrap method respectively,and their performances are evaluated by Monte Carlo simulations. And finally, a real data set is analyzed to illustrate all the methods developed here.
文摘The study aims to explore the impact of governance and macroeconomic conditions on financial stability in developed and emerging countries.The study sample comprised 122 countries from 2013 to 2020,and a comprehensive set of variables was used to construct the financial stability index(FSI).The results of the two-step system GMM analysis,robust with D–K regression,indicate that interest rate,GDP growth,voice and accountability,political stability and absence of violence/terrorism,government effectiveness,regulatory quality,and control of corruption have a positive and statistically significant impact on financial stability.However,inflation,money supply,and the rule of law have adverse and insignificant effects on financial stability.Notably,the findings vary between developed and emerging countries due to differences in governance and macroeconomic conditions and their role in financial stability.The study concludes that regulatory governance and macroeconomic conditions are crucial for financial stability.These outcomes are significant for central banks,academia,and policymakers,as they emphasize the need for stable financial systems and sustainable,balanced growth through governance and macroeconomic conditions.
文摘The conditional generative adversarial network(CGAN)is used in this paper for empirical Bayes(EB)analysis of road crash hotspots.EB is a well-known method for estimating the expected crash frequency of sites(e.g.road segments,intersections)and then prioritising these sites to identify a subset of high priority sites(e.g.hotspots)for additional safety audits/improvements.In contrast to the conventional EB approach,which employs a statis tical model such as the negative binomial model(NB-EB)to model crash frequency data,the recently developed CGAN-EB approach uses a conditional generative adversarial net work,a form of deep neural network,that can model any form of distributions of the crash frequency data.Previous research has shown that the CGAN-EB performs as well as or bet ter than NB-EB,however that work considered only a small range of crash data character istics and did not examine the spatial and temporal transferability.In this paper a series of simulation experiments are devised and carried out to assess the CGAN-EB performance across a wide range of conditions and compares it to the NB-EB.The simulation results show that CGAN-EB performs as well as NB-EB when conditions favor the NB-EB model(i.e.data conform to the assumptions of the NB model)and outperforms NB-EB in experi ments reflecting conditions frequently encountered in practice(i.e.low sample mean crash rates,and when crash frequency does not follow a log-linear relationship with covariates).Also,temporal and spatial transferability of both approaches were evaluated using field data and both CGAN-EB and NB-EB approaches were found to have similar performance.
文摘Given that challenges on the issue of socioeconomic development faced by countries in sub-Saharan Africa(SSA)have been identified as critical to strengthening the inherent link between governance and socioeconomic conditions,this study examines the interconnections between governance and socioeconomic conditions in SSA.With a focus on 25 countries in SSA between 2005 and 2019,we conduct the analysis based on the Panel-Corrected Standard Error and System Generalized Method of Moments estimations and panel causality tests.The results show that SSA does not seem to have the means of effective governance to spur improved socioeconomic conditions.Moreover,the pervasiveness of institutional problems in many countries of SSA has been responsible for the poor socioeconomic conditions in the region.Likewise,governance quality and socioeconomic conditions are found to influence each other.An improvement in socioeconomic conditions could result in better governance quality.On the other hand,governance quality is viewed as a vital ingredient in achieving needed socioeconomic development outcomes.Thus,it is suggested that there is a need for countries in SSA to streamline governing systems toward engendering improved well-being.The introduction and implementation of transformative policies through effective governance are also necessary for ensuring critical structural changes and increasing social service provision.Overall,there should be a proactive identification of ineffective policies and procedures by policymakers to enhance meaningful impacts in the region.
基金Supported by the Natural Science Foundation of Anhui Province(1908085QA09)Higher Education Department of the Ministry of Education(201802358008)
文摘The boundary knot method(BKM) is a boundary-type meshfree method. Only non-singular general solutions are used during the whole solution procedures. The effective condition number(ECN), which depends on the right-hand side vector of a linear system, is considered as an alternative criterion to the traditional condition number. In this paper, the effective condition number is used to help determine the position and distribution of the collocation points as well as the quasi-optimal collocation point numbers. During the solution process, we propose an NMN-search algorithm. Numerical examples show that the ECN is reliable to measure the feasibility of the BKM.
文摘A judgment criterion to guarantee a point to be a Chen' s approximate zero of Newton method for solving nonlinear equation is sought by dominating sequence techniques. The criterion is based on the fact that the dominating function may have only one simple positive zero, assuming that the operator is weak Lipschitz continuous, which is much more relaxed and can be checked much more easily than Lipschitz continuous in practice. It is demonstrated that a Chen' s approximate zero may not be a Smale' s approximate zero. The error estimate obtained indicated the convergent order when we use |f(x) | < ε to stop computation in software.The result can also be applied for solving partial derivative and integration equations.
文摘The empirical Bayes(EB)method based on parametric statistical models such as the negative binomial(NB)has been widely used for ranking sites in the road network safety screening process.In this paper a novel non-parametric EB method for modeling crash frequency data based on Conditional Generative Adversarial Networks(CGAN)is proposed and evaluated over a real-world crash data set.Unlike parametric approaches,there is no need for a pre-specified underlying relationship between dependent and independent variables in the proposed CGAN-EB and they are able to model any types of distributions.The proposed methodology is applied to real-world and simulated crash data sets.The performance of CGAN-EB in terms of model fit,predictive performance and network screening outcomes is compared with the conventional approach(NB-EB)as a benchmark.The results indicate that the proposed CGAN-EB approach outperforms NB-EB in terms of prediction power and hotspot identification tests.
文摘考虑到海上风电出力的随机性以及日益突出的生态环境问题,以含柔性直流输电技术(voltagesource converter high voltage direct current,VSC-HVDC)的交直流系统为研究对象,提出了考虑条件风险价值(conditional valueatrisk,CVaR)的两阶段分布鲁棒低碳经济优化模型,构建了基于Kullback-Leibler(KL)散度的概率分布模糊集,同时利用条件风险价值量化了极端场景下的尾部风险,使得模型能够同时考虑概率分布不确定性以及处于最坏概率分布中极端场景下的尾部损失;此外,将阶梯型碳交易机制并入所提分布鲁棒模型中,通过合理利用柔性资源和储能装置,增强系统运行的灵活性,在兼顾运行风险的前提下,降低碳排放量的目标。再者,为了提高计算效率,在列和约束生成算法(column-and-constraint generation method,C&CG)和Multi-cut Benders分解算法的基础上提出了双循环分解算法。最后,在基于改进的IEEE RTS 79测试系统中验证了所提模型及算法的有效性。
基金National Natural Science Foundation of China (12002075)National Key Research and Development Project (2021YFB3300601)Natural Science Foundation of Liaoning Province in China (2021-MS-128).
文摘Elastography is a non-invasive medical imaging technique to map the spatial variation of elastic properties of soft tissues.The quality of reconstruction results in elastography is highly sensitive to the noise induced by imaging measurements and processing.To address this issue,we propose a deep learning(DL)model based on conditional Generative Adversarial Networks(cGANs)to improve the quality of nonhomogeneous shear modulus reconstruction.To train this model,we generated a synthetic displacement field with finite element simulation under known nonhomogeneous shear modulus distribution.Both the simulated and experimental displacement fields are used to validate the proposed method.The reconstructed results demonstrate that the DL model with synthetic training data is able to improve the quality of the reconstruction compared with the well-established optimization method.Moreover,we emphasize that our DL model is only trained on synthetic data.This might provide a way to alleviate the challenge of obtaining clinical or experimental data in elastography.Overall,this work addresses several fatal issues in applying the DL technique into elastography,and the proposed method has shown great potential in improving the accuracy of the disease diagnosis in clinical medicine.
