scholarly journals Robust Extraction and Simplification of 2D Symmetric Tensor Field Topology

2021 ◽  
Author(s):  
Jochen Jankowai ◽  
Bei Wang ◽  
Ingrid Hotz

In this work, we propose a controlled simplification strategy for degenerated points in symmetric 2D tensor fields that is based on the topological notion of robustness. Robustness measures the structural stability of the degenerate points with respect to variation in the underlying field. We consider an entire pipeline for generating a hierarchical set of degenerate points based on their robustness values. Such a pipeline includes the following steps: the stable extraction and classification of degenerate points using an edge labeling algorithm, the computation and assignment of robustness values to the degenerate points, and the construction of a simplification hierarchy. We also discuss the challenges that arise from the discretization and interpolation of real world data.

2021 ◽  
Vol 20 (1) ◽  
pp. 56-63
Author(s):  
T. A. Goldina ◽  
A. S. Kolbin ◽  
D. Yu. Belousov ◽  
V. G. Borovskaya

The article defines the terms «real-world data» (RWD) and «real-world evidence» (RWE); classification of RWD, advantages, disadvantages and their overcoming are outlined; provides a description of the purpose of collecting RWD.


PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0247059
Author(s):  
Yoshitake Kitanishi ◽  
Masakazu Fujiwara ◽  
Bruce Binkowitz

Health insurance and acute hospital-based claims have recently become available as real-world data after marketing in Japan and, thus, classification and prediction using the machine learning approach can be applied to them. However, the methodology used for the analysis of real-world data has been hitherto under debate and research on visualizing the patient journey is still inconclusive. So far, to classify diseases based on medical histories and patient demographic background and to predict the patient prognosis for each disease, the correlation structure of real-world data has been estimated by machine learning. Therefore, we applied association analysis to real-world data to consider a combination of disease events as the patient journey for depression diagnoses. However, association analysis makes it difficult to interpret multiple outcome measures simultaneously and comprehensively. To address this issue, we applied the Topological Data Analysis (TDA) Mapper to sequentially interpret multiple indices, thus obtaining a visual classification of the diseases commonly associated with depression. Under this approach, the visual and continuous classification of related diseases may contribute to precision medicine research and can help pharmaceutical companies provide appropriate personalized medical care.


2016 ◽  
Vol 37 (1) ◽  
Author(s):  
Vaidas Balys ◽  
Rimantas Rudzkis

The problem of classification of scientific texts is considered. Models and methods based on probabilistic distributions of scientific terms in text are discussed. The comparative study of proposed and a few of popular alternative algorithms was performed. The results of experimental study over real-world data are reported.


2016 ◽  
Vol 22 ◽  
pp. 219
Author(s):  
Roberto Salvatori ◽  
Olga Gambetti ◽  
Whitney Woodmansee ◽  
David Cox ◽  
Beloo Mirakhur ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document