In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by re...In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by replacing them with a minimally adequate collection of their linear combinations without loss of information.Recently,regularization methods have been proposed in SIR to incorporate a sparse structure of predictors for better interpretability.However,existing methods consider convex relaxation to bypass the sparsity constraint,which may not lead to the best subset,and particularly tends to include irrelevant variables when predictors are correlated.In this study,we approach sparse SIR as a nonconvex optimization problem and directly tackle the sparsity constraint by establishing the optimal conditions and iteratively solving them by means of the splicing technique.Without employing convex relaxation on the sparsity constraint and the orthogonal constraint,our algorithm exhibits superior empirical merits,as evidenced by extensive numerical studies.Computationally,our algorithm is much faster than the relaxed approach for the natural sparse SIR estimator.Statistically,our algorithm surpasses existing methods in terms of accuracy for central subspace estimation and best subset selection and sustains high performance even with correlated predictors.展开更多
隐私集合交集(private set intersection,PSI)协议一直是解决用户隐私保护需求和合作共享需求间矛盾的有效工具.面对计算资源受限场景下的多方求交计算,本文提出了支持子集匹配且可验证的云辅助多方PSI协议(tag-based and verifiable cl...隐私集合交集(private set intersection,PSI)协议一直是解决用户隐私保护需求和合作共享需求间矛盾的有效工具.面对计算资源受限场景下的多方求交计算,本文提出了支持子集匹配且可验证的云辅助多方PSI协议(tag-based and verifiable cloud-assisted multi-party PSI,TVC-MPSI).首先,TVC-MPSI应用星型网络拓扑结构,增加对单个云服务器的安全要求,仅利用密文交集基数和交集的多项式形式确保了交集的可验证性;其次,当客户端的集合包含多个子集时,引入了Pedersen门限可验证的秘密共享技术来实现对集合子集的匹配,从而实现细粒度的交集运算;除此之外,引入基于RSA的局部可验证签名算法(local verifiable aggregate signatures,LVS),保证云服务器端和客户端身份的不可伪造性;最后,通过正确性和安全性分析,以及全面的性能对比,表明协议在保证安全性的同时拥有较好的性能.展开更多
传统滑坡地表形变监测手段存在着监测范围小、复杂地形信息获取难度高、经济成本投入量大等缺点,且大型复杂滑坡变形时间序列的非线性、不确定性变化特征也一直是滑坡形变监测及预测研究中亟待解决的难题。以三峡库区范家坪滑坡为研究对...传统滑坡地表形变监测手段存在着监测范围小、复杂地形信息获取难度高、经济成本投入量大等缺点,且大型复杂滑坡变形时间序列的非线性、不确定性变化特征也一直是滑坡形变监测及预测研究中亟待解决的难题。以三峡库区范家坪滑坡为研究对象,利用差分干涉测量短基线集时序分析技术(small baseline subset InSAR,SBAS-InSAR),结合地表GPS监测数据进行滑坡形变监测,基于SBAS-InSAR时间序列数据及长短时记忆网络(long short term memory,LSTM)开展滑坡形变预测研究。结果表明:研究时段内,范家坪滑坡SBAS-InSAR形变监测结果与地表GPS监测数据所反映出的形变区域及形变量级基本保持一致,与现场调查情况相吻合;范家坪滑坡的位移变形与坡体的高程分布及库水位条件密切相关,当库水位高于160 m时,滑坡前缘阻滑段主要受“浮托减重”效应影响,当库水位低于160 m时,渗流压力占主导作用,水位下降阶段的位移变形总体明显大于水位上升阶段,库水位下降速率对范家坪滑坡的位移变形产生重要影响,且木鱼包滑坡区相较于谭家河滑坡区对库水位下降速率的变形响应更为强烈;将LSTM神经网络模型与传统神经网络模型的预测结果进行效果对比、置信区间估计及相关性检验,结果显示,LSTM神经网络模型的预测结果始终保持较高的预测精度,验证了InSAR与神经网络结合的滑坡监测与预测方法能够为三峡库区地质灾害防治提供重要的数据参考和信息支撑。展开更多
文摘In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by replacing them with a minimally adequate collection of their linear combinations without loss of information.Recently,regularization methods have been proposed in SIR to incorporate a sparse structure of predictors for better interpretability.However,existing methods consider convex relaxation to bypass the sparsity constraint,which may not lead to the best subset,and particularly tends to include irrelevant variables when predictors are correlated.In this study,we approach sparse SIR as a nonconvex optimization problem and directly tackle the sparsity constraint by establishing the optimal conditions and iteratively solving them by means of the splicing technique.Without employing convex relaxation on the sparsity constraint and the orthogonal constraint,our algorithm exhibits superior empirical merits,as evidenced by extensive numerical studies.Computationally,our algorithm is much faster than the relaxed approach for the natural sparse SIR estimator.Statistically,our algorithm surpasses existing methods in terms of accuracy for central subspace estimation and best subset selection and sustains high performance even with correlated predictors.
文摘传统滑坡地表形变监测手段存在着监测范围小、复杂地形信息获取难度高、经济成本投入量大等缺点,且大型复杂滑坡变形时间序列的非线性、不确定性变化特征也一直是滑坡形变监测及预测研究中亟待解决的难题。以三峡库区范家坪滑坡为研究对象,利用差分干涉测量短基线集时序分析技术(small baseline subset InSAR,SBAS-InSAR),结合地表GPS监测数据进行滑坡形变监测,基于SBAS-InSAR时间序列数据及长短时记忆网络(long short term memory,LSTM)开展滑坡形变预测研究。结果表明:研究时段内,范家坪滑坡SBAS-InSAR形变监测结果与地表GPS监测数据所反映出的形变区域及形变量级基本保持一致,与现场调查情况相吻合;范家坪滑坡的位移变形与坡体的高程分布及库水位条件密切相关,当库水位高于160 m时,滑坡前缘阻滑段主要受“浮托减重”效应影响,当库水位低于160 m时,渗流压力占主导作用,水位下降阶段的位移变形总体明显大于水位上升阶段,库水位下降速率对范家坪滑坡的位移变形产生重要影响,且木鱼包滑坡区相较于谭家河滑坡区对库水位下降速率的变形响应更为强烈;将LSTM神经网络模型与传统神经网络模型的预测结果进行效果对比、置信区间估计及相关性检验,结果显示,LSTM神经网络模型的预测结果始终保持较高的预测精度,验证了InSAR与神经网络结合的滑坡监测与预测方法能够为三峡库区地质灾害防治提供重要的数据参考和信息支撑。
