다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

On robust estimation of the location parameter

We analyze the betweenness centrality (BC) of nodes in large complex networks. In general, the BC is increasing with connectivity as a power law with an exponent $\eta$. We find that for trees or networks with a small loop density $\eta=2$ while a larger density of loops leads to $\eta<2$. For scale-free networks characterized by an exponent $\gamma$ which describes the connectivity distribution decay, the BC is also distributed according to a power law with a non universal exponent $\delta$. We show that this exponent $\delta$ must satisfy the exact bound $\delta\geq (\gamma+1)/2$. If the scale free network is a tree, then we have the equality $\delta=(\gamma+1)/2$.

Betweenness Centrality in Large Complex Networks

The History of Statistics: The Measurement of Uncertainty before 1900

Method and system for combining a 2D image with a 3D point cloud for improved visualization of a common scene as well as interpretation of the success of the registration process. The resulting fused data contains the combined information from the original 3D point cloud and the information from the 2D image. The original 3D point cloud data is color coded in accordance with a color map tagging process. By fusing data from different sensors, the resulting scene has several useful attributes relating to battle space awareness, target identification, change detection within a rendered scene, and determination of registration success.

Fusion of a 2d electro-optical image and 3d point cloud data for scene interpretation and registration performance assessment

Method and system for registration of a two dimensional image data set and a three-dimensional image comprising point cloud data. The method begins by cropping a three-dimensional volume of point cloud data comprising a three-dimensional image data to remove a portion of the point cloud data comprising a ground surface within a scene, and dividing the three-dimensional volume into a plurality of m sub-volumes. Thereafter, the method continues by edge-enhancing a two-dimensional image data. Then, for each qualifying sub-volume, creating a filtered density image, calculating a two-dimensional correlation surface based on the filtered density image and the two-dimensional image data that has been edge enhanced, finding a peak of the two-dimensional correlation surface, determining a corresponding location of the peak within the two-dimensional image, defining a correspondence point set; and storing the correspondence point set in a point set list. Finally, a transformation is determined that minimizes the error between a plurality of the correspondence point sets contained in the point set list.

Registration of 3d point cloud data to 2d electro-optical image data

A computer implemented method is for processing an original digital elevation model (DEM) including data for terrain (52) and a plurality of objects (54, 56) thereon. The method includes generating a lower-resolution DEM (104) from the original DEM (102), and generating an objects-only DEM (108) based upon a comparison of the lower-resolution DEM (104) and the original DEM (102). The method further includes reducing noise in the objects-only DEM (114, 116).

Method and apparatus for enhancing a digital elevation model (dem) for topographical modeling

A geospatial modeling system (20) may include a geospatial model database (21) and a processor (22). The processor (22) may cooperate with the geospatial model database (21) for inpainting data into at least one void (41a) in geospatial model terrain data (42a) based upon propagating contour data from outside the at least one void into the at least one void.

Geospatial modeling system providing non-linear inpainting for voids in geospatial model cultural feature data and related methods

It is abundantly clear that time dependent data is a vital source of information in the world. The challenge has been for applications in machine learning to gain access to a considerable amount of quality data needed for algorithm development and analysis. Modeling synthetic data using a Generative Adversarial Network (GAN) has been at the heart of providing a viable solution. Our work focuses on one dimensional times series and explores the few shot approach, which is the ability of an algorithm to perform well with limited data. This work attempts to ease the frustration by proposing a new architecture, Time Series GAN (TSGAN), to model realistic time series data. We evaluate TSGAN on 70 data sets from a benchmark time series database. Our results demonstrate that TSGAN performs better than the competition both quantitatively using the Frechet Inception Score (FID) metric, and qualitatively when classification is used as the evaluation criteria.

Anthony O'neil Smith

Papers

Fusion of a 2d electro-optical image and 3d point cloud data for scene interpretation and registration performance assessment

Registration of 3d point cloud data to 2d electro-optical image data

Method and apparatus for enhancing a digital elevation model (dem) for topographical modeling

Geospatial modeling system providing non-linear inpainting for voids in geospatial model cultural feature data and related methods

Conditional GAN for timeseries generation