In this article, a normalized biholomorphic mapping f defined on bounded starlike circular domain in Cn is considered, where z = 0 is a zero of order k ＋ 1 of f（z） - z. The sharp growth, covering theorems for almos...In this article, a normalized biholomorphic mapping f defined on bounded starlike circular domain in Cn is considered, where z = 0 is a zero of order k ＋ 1 of f（z） - z. The sharp growth, covering theorems for almost starlike mappings of order α and starlike mappings of order α are established. Meanwhile, the construction of the above mappings on bounded starlike circular domain in Cn is also discussed, it provides the extremal mappings for the growth, covering theorems of the above mappings.展开更多
Unsupervised methods based on density representation have shown their abilities in anomaly detection,but detection performance still needs to be improved.Specifically,approaches using normalizing flows can accurately ...Unsupervised methods based on density representation have shown their abilities in anomaly detection,but detection performance still needs to be improved.Specifically,approaches using normalizing flows can accurately evaluate sample distributions,mapping normal features to the normal distribution and anomalous features outside it.Consequently,this paper proposes a Normalizing Flow-based Bidirectional Mapping Residual Network(NF-BMR).It utilizes pre-trained Convolutional Neural Networks(CNN)and normalizing flows to construct discriminative source and target domain feature spaces.Additionally,to better learn feature information in both domain spaces,we propose the Bidirectional Mapping Residual Network(BMR),which maps sample features to these two spaces for anomaly detection.The two detection spaces effectively complement each other’s deficiencies and provide a comprehensive feature evaluation from two perspectives,which leads to the improvement of detection performance.Comparative experimental results on the MVTec AD and DAGM datasets against the Bidirectional Pre-trained Feature Mapping Network(B-PFM)and other state-of-the-art methods demonstrate that the proposed approach achieves superior performance.On the MVTec AD dataset,NF-BMR achieves an average AUROC of 98.7%for all 15 categories.Especially,it achieves 100%optimal detection performance in five categories.On the DAGM dataset,the average AUROC across ten categories is 98.7%,which is very close to supervised methods.展开更多
With the rapid urbanization process,ground collapses caused by anthropogenic activities occur frequently.Accurate susceptibility mapping is of great significance for disaster prevention and control.In this study,1198 ...With the rapid urbanization process,ground collapses caused by anthropogenic activities occur frequently.Accurate susceptibility mapping is of great significance for disaster prevention and control.In this study,1198 ground collapse cases in Shenzhen from 2017 to 2020 were collected.Eight effective factors(elevation,relief,clay proportion,average annual precipitation,distance from water,land use type,building density,and road density)were selected to construct the evaluation index system.Ground collapse susceptibility was analyzed and mapped using the normalized frequency ratio(NFR),logistic regression(LR),and NFR-LR coupling models.Finally,the result rationality and performance of the three models were compared through frequency ratio(FR)and ROC curve.The results indicate that all three models can effectively evaluate the ground collapse susceptibility(AUC>0.7),and the NFR-LR model result is more rational and has the best performance(AUC=0.791).The very high and high susceptibility zones cover a total area of 545.68 km^(2) and involve Nanshan,Luohu,and Futian District,as well as some areas of Baoan,Guangming,and Longgang District.The ground collapses in Shenzhen mainly occurred in the built-up areas,and the greater intensity of anthropogenic activities,the more susceptible to the disaster.展开更多
In this paper, we generalize an, inequality of meromorphic mappings to quasimeromorphic ones. Applying the results here, we can establish a normal criterion of quasimeromorphic mappings.
Edge detection is an image processing technique for finding the boundaries of objects within images. It is typically used to interpret gravity and magnetic data, and find the horizontal boundaries of geological bodies...Edge detection is an image processing technique for finding the boundaries of objects within images. It is typically used to interpret gravity and magnetic data, and find the horizontal boundaries of geological bodies. Large deviations between model and true edges are common because of the interference of depth and errors in computing the derivatives; thus, edge detection methods cannot provide information about the depth of the source. To simultaneously obtain the horizontal extent and depth of geophysical anomalies, we use normalized edge detection filters, which normalize the edge detection function at different depths, and the maxima that correspond to the location of the source. The errors between model and actual edges are minimized as the depth of the source decreases and the normalized edge detection method recognizes the extent of the source based on the maxima, allowing for reliable model results. We demonstrate the applicability of the normalized edge detection filters in defining the horizontal extent and depth using synthetic and actual aeromagnetic data.展开更多
This paper presents an explicit upper bound for the linear dilatation of K- quasiregular (K-qr) mappings, which improves S. Rickman's [6, P.37] corresponding re- sult for K-qr mappings and generalizes P. Seittenra...This paper presents an explicit upper bound for the linear dilatation of K- quasiregular (K-qr) mappings, which improves S. Rickman's [6, P.37] corresponding re- sult for K-qr mappings and generalizes P. Seittenranta's [7, Theorem 1.5] result for K- quasiconformal (K-qc) maps.展开更多
We propose a multi-sensor multi-spectral and bi-temporal dual-polarimetric Synthetic Aperture Radar(SAR) data integration scheme for dry/wet snow mapping using Sentinel-2 and Sentinel-1 data which are freely available...We propose a multi-sensor multi-spectral and bi-temporal dual-polarimetric Synthetic Aperture Radar(SAR) data integration scheme for dry/wet snow mapping using Sentinel-2 and Sentinel-1 data which are freely available to the research community. The integration is carried out by incorporating the information retrieved from ratio images of the conventional method for wet snow mapping and the multispectral data in two different frameworks. Firstly, a simple differencing scheme is employed for dry/wet snow mapping, where the snow cover area is derived using the Normalized Differenced Snow Index(NDSI). In the second framework, the ratio images are stacked with the multispectral bands and this stack is used for supervised and unsupervised classification using support vector machines for dry/wet snow mapping. We also investigate the potential of a state of the art backscatter model for the identification of dry/wet snow using Sentinel-1 data. The results are validated using a reference map derived from RADARSAT-2 full polarimetric SAR data. A good agreement was observed between the results and the reference data with an overall accuracy greater than 0.78 for the different blending techniques examined. For all the proposed frameworks, the wet snow was better identified. The coefficient of determination between the snow wetness derived from the backscatter model and the reference based on RADARSAT-2 data was observed to be 0.58 with a significantly higher root mean square error of 1.03 % by volume.展开更多
This article gives a normal criterion for families of holomorphic mappings of several complex variables into P N(C)for moving hypersurfaces in pointwise general position,related to an Eremenko’s theorem.
Gravity anomalies illuminate subsurface lithology and geological structure in three dimensions,which is vital for studies of concealed faults,sedimentary basins,basement lithology,and other geological targets.Although...Gravity anomalies illuminate subsurface lithology and geological structure in three dimensions,which is vital for studies of concealed faults,sedimentary basins,basement lithology,and other geological targets.Although not all geological contacts correspond to lithological contacts,the contact mapping provides key information on structural regimes,deformation styles and trends.Many techniques for contact mapping have been developed.Here,we evaluate five methods applied to gridded data.The first two are the horizontal gradient magnitude of the gravity field （GFhgm）,and tilt （TIhgm）.The third and fourth rely on locating maxima of the analytic signal （AS） and the 3D local wavenumber （LW）.The fifth is normalized standard deviation （NSTD） method.In this article,we evaluate the use of these five methods for mapping contacts and compare the results.First,synthetic vertically-sided models are used to quantify the offsets of maxima from the true contact location due to the source effects of finite source thickness,central depth,and width.Second,the effects of contact dip are discussed.Finally,a real data set is used to evaluate the ability of each method to produce maps of coherent contact trends in the presence of noise and gridding artifacts.展开更多
The purpose is by using the viscosity approximation method to study the convergence problem of the iterative scheme for an infinite family of nonexpansive mappings and a given contractive mapping in a reflexive Banach...The purpose is by using the viscosity approximation method to study the convergence problem of the iterative scheme for an infinite family of nonexpansive mappings and a given contractive mapping in a reflexive Banach space. Under suitable conditions, it was proved that the iterative sequence converges strongly to a common fixed point which was also the unique solution of some variational inequality in a reflexive Banach space. The results presented extend and improve some recent results.展开更多
The definitions of quasimeromorphic mappings from Cn to P1n, where P1 C U {∞}, P1n= P1×P1× ×P1(n-times) are introduced. From an inequality of the value distribution of quasimeromorphic functions of s...The definitions of quasimeromorphic mappings from Cn to P1n, where P1 C U {∞}, P1n= P1×P1× ×P1(n-times) are introduced. From an inequality of the value distribution of quasimeromorphic functions of single variable, it follows that a normal criterion for the family of quasimeromorphic functions of several complex variables. Futhermore, a normal criterion for the family of quasimeromorphic mappings from Cn to P1n has been obtained.展开更多
In the frame of landslide susceptibility assessment, a spectral library was created to support the identification of materials confined to a particular region using remote sensing images. This library, called Pakistan...In the frame of landslide susceptibility assessment, a spectral library was created to support the identification of materials confined to a particular region using remote sensing images. This library, called Pakistan spectral library(pklib) version 0.1, contains the analysis data of sixty rock samples taken in the Balakot region in Northern Pakistan.The spectral library is implemented as SQLite database. Structure and naming are inspired by the convention system of the ASTER Spectral Library. Usability, application and benefit of the pklib were evaluated and depicted taking two approaches, the multivariate and the spectral based. The spectral information were used to create indices. The indices were applied to Landsat and ASTER data tosupportthespatial delineation of outcropping rock sequences instratigraphic formations. The application of the indices introduced in this paper helps to identify spots where specific lithological characteristics occur. Especially in areas with sparse or missing detailed geological mapping, the spectral discrimination via remote sensing data can speed up the survey. The library can be used not only to support the improvement of factor maps for landslide susceptibility analysis, but also to provide a geoscientific basisto further analyze the lithological spotin numerous regions in the Hindu Kush.展开更多
时间序列预测在能源管理、交通流量和气象分析等多个实际场景中具有重要应用价值。然而,时间序列数据中存在的分布漂移(Distribution Shift)与长程依赖(Long-term Dependency)仍限制了传统方法与现有深度学习模型在长期预测中的表现。为...时间序列预测在能源管理、交通流量和气象分析等多个实际场景中具有重要应用价值。然而,时间序列数据中存在的分布漂移(Distribution Shift)与长程依赖(Long-term Dependency)仍限制了传统方法与现有深度学习模型在长期预测中的表现。为此,提出了一种名为D-LINet(Dual-Normalization and Linear Integration Network)的创新模型。该模型结合了Dish-TS(Distribution Shift in Time Series Forecasting)框架的分布归一化能力与线性映射的高效性,并采用双向归一化与双线性层的设计,有效缓解输入与输出空间的分布偏移,增强了对周期性与趋势性特征的捕捉能力。在多个真实数据集上对D-LINet的预测性能进行了全面评估。结果显示,在短期与长期预测中,D-LINet的均方误差和平均绝对误差均显著优于主流模型(如Transformer,Informer,Autoformer和DLinear)。此外,实验还探讨了输入窗口长度及先验知识的引入对预测性能的影响,为后续模型优化提供了重要指导。该研究针对复杂分布漂移问题提出了新的解决思路,并有助于提升时间序列预测的精度与稳健性。展开更多
在不改变译码性能的条件下,为了加快最大后验概率(Maximum A Posteriori Probability,MAP)译码器状态信息更新的速度和降低算法的复杂度,提出了一种用于Turbo码的MAP译码器的免归一化处理算法。算法采用二进制补码加法器和减法器将MAP...在不改变译码性能的条件下,为了加快最大后验概率(Maximum A Posteriori Probability,MAP)译码器状态信息更新的速度和降低算法的复杂度,提出了一种用于Turbo码的MAP译码器的免归一化处理算法。算法采用二进制补码加法器和减法器将MAP译码过程中的状态信息投影到一个归一化圆上,当状态信息更新时所有的状态信息在归一化圆上移动,通过保持归一化圆上状态信息的正确关系来计算似然比。归一化过程中不用搜索或估计状态信息的最大值,通过简化状态信息归一化过程加速了MAP译码器的状态信息更新并降低了复杂度。所提算法在与传统算法译码性能相同的情况下,可以降低36.2%的计算复杂度和17.4%的关键路径延迟,达到MAP译码器实现中的高速、低复杂度目标。展开更多
基金The research was supported by the National Nat ural Science Foundation of China(10571164)Specialized Research Fund for the Doctoral Program of Higher Education(20050358052)+1 种基金Guangdong Natural Science Foundation(06301315)the Doctoral Foundation of Zhanjiang Normal University(Z0420)
文摘In this article, a normalized biholomorphic mapping f defined on bounded starlike circular domain in Cn is considered, where z = 0 is a zero of order k ＋ 1 of f（z） - z. The sharp growth, covering theorems for almost starlike mappings of order α and starlike mappings of order α are established. Meanwhile, the construction of the above mappings on bounded starlike circular domain in Cn is also discussed, it provides the extremal mappings for the growth, covering theorems of the above mappings.
基金This work was supported in part by the National Key R&D Program of China 2021YFE0110500in part by the National Natural Science Foundation of China under Grant 62062021in part by the Guiyang Scientific Plan Project[2023]48-11.
文摘Unsupervised methods based on density representation have shown their abilities in anomaly detection,but detection performance still needs to be improved.Specifically,approaches using normalizing flows can accurately evaluate sample distributions,mapping normal features to the normal distribution and anomalous features outside it.Consequently,this paper proposes a Normalizing Flow-based Bidirectional Mapping Residual Network(NF-BMR).It utilizes pre-trained Convolutional Neural Networks(CNN)and normalizing flows to construct discriminative source and target domain feature spaces.Additionally,to better learn feature information in both domain spaces,we propose the Bidirectional Mapping Residual Network(BMR),which maps sample features to these two spaces for anomaly detection.The two detection spaces effectively complement each other’s deficiencies and provide a comprehensive feature evaluation from two perspectives,which leads to the improvement of detection performance.Comparative experimental results on the MVTec AD and DAGM datasets against the Bidirectional Pre-trained Feature Mapping Network(B-PFM)and other state-of-the-art methods demonstrate that the proposed approach achieves superior performance.On the MVTec AD dataset,NF-BMR achieves an average AUROC of 98.7%for all 15 categories.Especially,it achieves 100%optimal detection performance in five categories.On the DAGM dataset,the average AUROC across ten categories is 98.7%,which is very close to supervised methods.
基金jointed supported by the National Natural Science Foundation of China(Nos.41920104007,41731284)。
文摘With the rapid urbanization process,ground collapses caused by anthropogenic activities occur frequently.Accurate susceptibility mapping is of great significance for disaster prevention and control.In this study,1198 ground collapse cases in Shenzhen from 2017 to 2020 were collected.Eight effective factors(elevation,relief,clay proportion,average annual precipitation,distance from water,land use type,building density,and road density)were selected to construct the evaluation index system.Ground collapse susceptibility was analyzed and mapped using the normalized frequency ratio(NFR),logistic regression(LR),and NFR-LR coupling models.Finally,the result rationality and performance of the three models were compared through frequency ratio(FR)and ROC curve.The results indicate that all three models can effectively evaluate the ground collapse susceptibility(AUC>0.7),and the NFR-LR model result is more rational and has the best performance(AUC=0.791).The very high and high susceptibility zones cover a total area of 545.68 km^(2) and involve Nanshan,Luohu,and Futian District,as well as some areas of Baoan,Guangming,and Longgang District.The ground collapses in Shenzhen mainly occurred in the built-up areas,and the greater intensity of anthropogenic activities,the more susceptible to the disaster.
基金the National Natural Science Foundation of China (No.198710 64 )
文摘In this paper, we generalize an, inequality of meromorphic mappings to quasimeromorphic ones. Applying the results here, we can establish a normal criterion of quasimeromorphic mappings.
基金supported by the China Postdoctoral Science Foundation (No.2014M551188)the Deep Exploration in China Sinoprobe-09-01 (No.201011078)
文摘Edge detection is an image processing technique for finding the boundaries of objects within images. It is typically used to interpret gravity and magnetic data, and find the horizontal boundaries of geological bodies. Large deviations between model and true edges are common because of the interference of depth and errors in computing the derivatives; thus, edge detection methods cannot provide information about the depth of the source. To simultaneously obtain the horizontal extent and depth of geophysical anomalies, we use normalized edge detection filters, which normalize the edge detection function at different depths, and the maxima that correspond to the location of the source. The errors between model and actual edges are minimized as the depth of the source decreases and the normalized edge detection method recognizes the extent of the source based on the maxima, allowing for reliable model results. We demonstrate the applicability of the normalized edge detection filters in defining the horizontal extent and depth using synthetic and actual aeromagnetic data.
基金This research was partially supported by China NSF (19531060)Doctoral Foundation of the Education Commission of China (97024
文摘This paper presents an explicit upper bound for the linear dilatation of K- quasiregular (K-qr) mappings, which improves S. Rickman's [6, P.37] corresponding re- sult for K-qr mappings and generalizes P. Seittenranta's [7, Theorem 1.5] result for K- quasiconformal (K-qc) maps.
基金partly supported by Project number DST-2016056, funded by the Department of Science and Technology, Government of India
文摘We propose a multi-sensor multi-spectral and bi-temporal dual-polarimetric Synthetic Aperture Radar(SAR) data integration scheme for dry/wet snow mapping using Sentinel-2 and Sentinel-1 data which are freely available to the research community. The integration is carried out by incorporating the information retrieved from ratio images of the conventional method for wet snow mapping and the multispectral data in two different frameworks. Firstly, a simple differencing scheme is employed for dry/wet snow mapping, where the snow cover area is derived using the Normalized Differenced Snow Index(NDSI). In the second framework, the ratio images are stacked with the multispectral bands and this stack is used for supervised and unsupervised classification using support vector machines for dry/wet snow mapping. We also investigate the potential of a state of the art backscatter model for the identification of dry/wet snow using Sentinel-1 data. The results are validated using a reference map derived from RADARSAT-2 full polarimetric SAR data. A good agreement was observed between the results and the reference data with an overall accuracy greater than 0.78 for the different blending techniques examined. For all the proposed frameworks, the wet snow was better identified. The coefficient of determination between the snow wetness derived from the backscatter model and the reference based on RADARSAT-2 data was observed to be 0.58 with a significantly higher root mean square error of 1.03 % by volume.
基金supported in part by the National Natural Science Foundation of China(10371091)
文摘This article gives a normal criterion for families of holomorphic mappings of several complex variables into P N(C)for moving hypersurfaces in pointwise general position,related to an Eremenko’s theorem.
基金supported by the Ph.D. Program Foundation of Ministry of Education of China for Distinguished Young Scholars (No. 200804911523)the Research Foundation for Outstanding Young Teachers,China University of Geosciences (No. CUGQNL0726)
文摘Gravity anomalies illuminate subsurface lithology and geological structure in three dimensions,which is vital for studies of concealed faults,sedimentary basins,basement lithology,and other geological targets.Although not all geological contacts correspond to lithological contacts,the contact mapping provides key information on structural regimes,deformation styles and trends.Many techniques for contact mapping have been developed.Here,we evaluate five methods applied to gridded data.The first two are the horizontal gradient magnitude of the gravity field （GFhgm）,and tilt （TIhgm）.The third and fourth rely on locating maxima of the analytic signal （AS） and the 3D local wavenumber （LW）.The fifth is normalized standard deviation （NSTD） method.In this article,we evaluate the use of these five methods for mapping contacts and compare the results.First,synthetic vertically-sided models are used to quantify the offsets of maxima from the true contact location due to the source effects of finite source thickness,central depth,and width.Second,the effects of contact dip are discussed.Finally,a real data set is used to evaluate the ability of each method to produce maps of coherent contact trends in the presence of noise and gridding artifacts.
基金the Natural Science Foundation of Yibin University （No.2005Z3）
文摘The purpose is by using the viscosity approximation method to study the convergence problem of the iterative scheme for an infinite family of nonexpansive mappings and a given contractive mapping in a reflexive Banach space. Under suitable conditions, it was proved that the iterative sequence converges strongly to a common fixed point which was also the unique solution of some variational inequality in a reflexive Banach space. The results presented extend and improve some recent results.
文摘The definitions of quasimeromorphic mappings from Cn to P1n, where P1 C U {∞}, P1n= P1×P1× ×P1(n-times) are introduced. From an inequality of the value distribution of quasimeromorphic functions of single variable, it follows that a normal criterion for the family of quasimeromorphic functions of several complex variables. Futhermore, a normal criterion for the family of quasimeromorphic mappings from Cn to P1n has been obtained.
文摘In the frame of landslide susceptibility assessment, a spectral library was created to support the identification of materials confined to a particular region using remote sensing images. This library, called Pakistan spectral library(pklib) version 0.1, contains the analysis data of sixty rock samples taken in the Balakot region in Northern Pakistan.The spectral library is implemented as SQLite database. Structure and naming are inspired by the convention system of the ASTER Spectral Library. Usability, application and benefit of the pklib were evaluated and depicted taking two approaches, the multivariate and the spectral based. The spectral information were used to create indices. The indices were applied to Landsat and ASTER data tosupportthespatial delineation of outcropping rock sequences instratigraphic formations. The application of the indices introduced in this paper helps to identify spots where specific lithological characteristics occur. Especially in areas with sparse or missing detailed geological mapping, the spectral discrimination via remote sensing data can speed up the survey. The library can be used not only to support the improvement of factor maps for landslide susceptibility analysis, but also to provide a geoscientific basisto further analyze the lithological spotin numerous regions in the Hindu Kush.
文摘时间序列预测在能源管理、交通流量和气象分析等多个实际场景中具有重要应用价值。然而,时间序列数据中存在的分布漂移(Distribution Shift)与长程依赖(Long-term Dependency)仍限制了传统方法与现有深度学习模型在长期预测中的表现。为此,提出了一种名为D-LINet(Dual-Normalization and Linear Integration Network)的创新模型。该模型结合了Dish-TS(Distribution Shift in Time Series Forecasting)框架的分布归一化能力与线性映射的高效性,并采用双向归一化与双线性层的设计,有效缓解输入与输出空间的分布偏移,增强了对周期性与趋势性特征的捕捉能力。在多个真实数据集上对D-LINet的预测性能进行了全面评估。结果显示,在短期与长期预测中,D-LINet的均方误差和平均绝对误差均显著优于主流模型(如Transformer,Informer,Autoformer和DLinear)。此外,实验还探讨了输入窗口长度及先验知识的引入对预测性能的影响,为后续模型优化提供了重要指导。该研究针对复杂分布漂移问题提出了新的解决思路,并有助于提升时间序列预测的精度与稳健性。
文摘在不改变译码性能的条件下,为了加快最大后验概率(Maximum A Posteriori Probability,MAP)译码器状态信息更新的速度和降低算法的复杂度,提出了一种用于Turbo码的MAP译码器的免归一化处理算法。算法采用二进制补码加法器和减法器将MAP译码过程中的状态信息投影到一个归一化圆上,当状态信息更新时所有的状态信息在归一化圆上移动,通过保持归一化圆上状态信息的正确关系来计算似然比。归一化过程中不用搜索或估计状态信息的最大值,通过简化状态信息归一化过程加速了MAP译码器的状态信息更新并降低了复杂度。所提算法在与传统算法译码性能相同的情况下,可以降低36.2%的计算复杂度和17.4%的关键路径延迟,达到MAP译码器实现中的高速、低复杂度目标。
