Investors should always argue about management fees because of their impact on net performance that can be substantial.This especially for investments,like real estate,which require intensive management.However,differ...Investors should always argue about management fees because of their impact on net performance that can be substantial.This especially for investments,like real estate,which require intensive management.However,different from traditional mutual funds that are usually related to the gross value of the assets under management,but similar to other financial industry sectors(e.g.hedge funds and private equity funds),REIT managers’compensation structure typically provides a basically fixed payment based alternatively on gross asset value(GAV)or net asset value(NAV).In addition,managers usually also gain a performance fee.The paper analyses how the two alternative compensation schemes influence REITs’investment decisions and capital structure and,consequently,REITs’share value and performance.The final issue addressed is whether—and under which conditions—one compensation scheme is superior to the other.Due to the(usual)market price discount on NAVs,both fee structures incentivise managers to leverage—even in a tax-free environment—in order to maximize the management fees.However,the leverage motivation is stronger for GAV-based than for NAV-based REITs,which are also expected to be more selective in investment decisions.Overall,considering initial fee percentage,GAV-based REITs are expected to execute higher management fees than NAV-based REITs due to the relevant leverage effect.Moreover,debt recourse produces different effects on share value if measured upon market price or net asset value.The empirical analysis focuses on public Italian REITs(2002-2012).The results seem to support the theoretical expectations.GAV-based REITs experience higher debt trends and levels than NAV-based REITs.At the same time,GAV-based REITs register lower real estate asset returns gross and net of management fees for both current and growth yields.Differences in the returns lead to permanent higher performances over total return indexes of NAV-based REITs compared to GAV-based REITs.展开更多
In this paper, a command filter-based adaptive fuzzy predefined-time event-triggered tracking control problem is investigated for uncertain nonlinear systems with time-varying full-state constraints. By designing a sl...In this paper, a command filter-based adaptive fuzzy predefined-time event-triggered tracking control problem is investigated for uncertain nonlinear systems with time-varying full-state constraints. By designing a sliding mode differentiator, the inherent computational complexity problem within the predefined-time backstepping framework is solved. Different from the existing command filter-based finite-time and fixed-time control strategies that the convergence time of the filtering error is adjusted through the system initial value or numerous parameters, a novel command filtering error compensation method is presented,which tunes one control parameter to make the filtering error converge in the predefined time, thereby reducing the complexity of design and analysis of processing the filtering error. Then, an improved event-triggered mechanism(ETM) that builds upon the switching threshold strategy, in which an inverse cotangent function is designed to replace the residual term of the ETM,is proposed to gradually release the controller's dependence on the residual term with increasing time. Furthermore, a tan-type nonlinear mapping technique is applied to tackle the time-varying full-state constraints problem. By the predefined-time stability theory, all signals in the uncertain nonlinear systems exhibit predefined-time stability. Finally, the feasibility of the proposed algorithm is substantiated through two simulation results.展开更多
针对现有深度学习光流计算模型在运动遮挡和大位移等场景下光流计算的准确性与鲁棒性问题,本文提出一种联合遮挡约束与残差补偿的特征金字塔光流计算方法.首先,构造基于遮挡掩模的光流约束模块,通过预测遮挡掩模特征图抑制变形特征的边...针对现有深度学习光流计算模型在运动遮挡和大位移等场景下光流计算的准确性与鲁棒性问题,本文提出一种联合遮挡约束与残差补偿的特征金字塔光流计算方法.首先,构造基于遮挡掩模的光流约束模块,通过预测遮挡掩模特征图抑制变形特征的边缘伪影,克服运动遮挡区域的图像边缘模糊问题.然后,采用特征图变形策略构建基于特征变形的光流残差补偿模块,利用该模块学习到的残差光流细化原始光流场,改善大位移运动区域的光流计算效果.最后,采用特征金字塔框架构建联合遮挡约束与残差补偿的光流计算网络模型,提升大位移和运动遮挡场景下的光流计算精度.分别采用MPI-Sintel(Max-Planck Institute and Sintel)和KITTI(Karlsruhe Institute of Technology and Toyota Technological Institute)数据集对本文方法和代表性传统光流计算方法、深度学习光流计算方法进行综合对比分析,实验结果表明本文方法相对于其他方法能够有效提升大位移和运动遮挡场景下的光流计算精度与鲁棒性.展开更多
文摘Investors should always argue about management fees because of their impact on net performance that can be substantial.This especially for investments,like real estate,which require intensive management.However,different from traditional mutual funds that are usually related to the gross value of the assets under management,but similar to other financial industry sectors(e.g.hedge funds and private equity funds),REIT managers’compensation structure typically provides a basically fixed payment based alternatively on gross asset value(GAV)or net asset value(NAV).In addition,managers usually also gain a performance fee.The paper analyses how the two alternative compensation schemes influence REITs’investment decisions and capital structure and,consequently,REITs’share value and performance.The final issue addressed is whether—and under which conditions—one compensation scheme is superior to the other.Due to the(usual)market price discount on NAVs,both fee structures incentivise managers to leverage—even in a tax-free environment—in order to maximize the management fees.However,the leverage motivation is stronger for GAV-based than for NAV-based REITs,which are also expected to be more selective in investment decisions.Overall,considering initial fee percentage,GAV-based REITs are expected to execute higher management fees than NAV-based REITs due to the relevant leverage effect.Moreover,debt recourse produces different effects on share value if measured upon market price or net asset value.The empirical analysis focuses on public Italian REITs(2002-2012).The results seem to support the theoretical expectations.GAV-based REITs experience higher debt trends and levels than NAV-based REITs.At the same time,GAV-based REITs register lower real estate asset returns gross and net of management fees for both current and growth yields.Differences in the returns lead to permanent higher performances over total return indexes of NAV-based REITs compared to GAV-based REITs.
基金supported by the Revitalization of Liaoning Talents Program(Grant No.XLYC2203201)。
文摘In this paper, a command filter-based adaptive fuzzy predefined-time event-triggered tracking control problem is investigated for uncertain nonlinear systems with time-varying full-state constraints. By designing a sliding mode differentiator, the inherent computational complexity problem within the predefined-time backstepping framework is solved. Different from the existing command filter-based finite-time and fixed-time control strategies that the convergence time of the filtering error is adjusted through the system initial value or numerous parameters, a novel command filtering error compensation method is presented,which tunes one control parameter to make the filtering error converge in the predefined time, thereby reducing the complexity of design and analysis of processing the filtering error. Then, an improved event-triggered mechanism(ETM) that builds upon the switching threshold strategy, in which an inverse cotangent function is designed to replace the residual term of the ETM,is proposed to gradually release the controller's dependence on the residual term with increasing time. Furthermore, a tan-type nonlinear mapping technique is applied to tackle the time-varying full-state constraints problem. By the predefined-time stability theory, all signals in the uncertain nonlinear systems exhibit predefined-time stability. Finally, the feasibility of the proposed algorithm is substantiated through two simulation results.
文摘针对现有深度学习光流计算模型在运动遮挡和大位移等场景下光流计算的准确性与鲁棒性问题,本文提出一种联合遮挡约束与残差补偿的特征金字塔光流计算方法.首先,构造基于遮挡掩模的光流约束模块,通过预测遮挡掩模特征图抑制变形特征的边缘伪影,克服运动遮挡区域的图像边缘模糊问题.然后,采用特征图变形策略构建基于特征变形的光流残差补偿模块,利用该模块学习到的残差光流细化原始光流场,改善大位移运动区域的光流计算效果.最后,采用特征金字塔框架构建联合遮挡约束与残差补偿的光流计算网络模型,提升大位移和运动遮挡场景下的光流计算精度.分别采用MPI-Sintel(Max-Planck Institute and Sintel)和KITTI(Karlsruhe Institute of Technology and Toyota Technological Institute)数据集对本文方法和代表性传统光流计算方法、深度学习光流计算方法进行综合对比分析,实验结果表明本文方法相对于其他方法能够有效提升大位移和运动遮挡场景下的光流计算精度与鲁棒性.
