Critical static and dynamic measurements of the magnetization of an insulating spinglass – Fractal model interpretation

1987 ◽  
pp. 75-83
Author(s):  
E. Carre ◽  
L. Puech ◽  
J. J. Prejean ◽  
P. Beauvillain ◽  
J. P. Renard
Keyword(s):  
Fractal Model ◽  
Dynamic Measurements ◽  
Model Interpretation

scholarly journals Critical Dynamics in Spin Glasses: Experimental Study and Fractal Model Interpretation

1986 ◽  
Vol 2 (5) ◽  
pp. 415-415 ◽  
Author(s):  
P Beauvillain ◽  
J. P Renard ◽  
M Matecki ◽  
J. J Prejean
Keyword(s):  
Experimental Study ◽  
Spin Glasses ◽  
Fractal Model ◽  
Critical Dynamics ◽  
Model Interpretation

Critical Dynamics in Spin Glasses: Experimental Study and Fractal Model Interpretation

1986 ◽  
Vol 2 (1) ◽  
pp. 23-30 ◽  
Author(s):  
P Beauvillain ◽  
J. P Renard ◽  
M Matecki ◽  
J. J Prejean
Keyword(s):  
Experimental Study ◽  
Spin Glasses ◽  
Fractal Model ◽  
Critical Dynamics ◽  
Model Interpretation

A fractal model for current generation in porous electrodes

2021 ◽  
Vol 880 ◽  
pp. 114883
Author(s):  
Alex Elías-Zúñiga ◽  
Luis Manuel Palacios-Pineda ◽  
Isaac H. Jiménez-Cedeño ◽  
Oscar Martínez-Romero ◽  
Daniel Olvera-Trejo
Keyword(s):  
Fractal Model ◽  
Porous Electrodes ◽  
Current Generation

EpiPen: An R Package to Investigate Two-Locus Epistatic Models

2014 ◽  
Vol 17 (4) ◽  
Author(s):  
Raymond K. Walters ◽  
Charles Laurin ◽  
Gitta H. Lubke
Keyword(s):  
Power Analysis ◽  
R Package ◽  
Simulation Studies ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Epistatic Interactions ◽  
Model Interpretation ◽  
Genome Wide ◽  
Using Data ◽  
Power Analyses

Epistasis is a growing area of research in genome-wide studies, but the differences between alternative definitions of epistasis remain a source of confusion for many researchers. One problem is that models for epistasis are presented in a number of formats, some of which have difficult-to-interpret parameters. In addition, the relation between the different models is rarely explained. Existing software for testing epistatic interactions between single-nucleotide polymorphisms (SNPs) does not provide the flexibility to compare the available model parameterizations. For that reason we have developed an R package for investigating epistatic and penetrance models, EpiPen, to aid users who wish to easily compare, interpret, and utilize models for two-locus epistatic interactions. EpiPen facilitates research on SNP-SNP interactions by allowing the R user to easily convert between common parametric forms for two-locus interactions, generate data for simulation studies, and perform power analyses for the selected model with a continuous or dichotomous phenotype. The usefulness of the package for model interpretation and power analysis is illustrated using data on rheumatoid arthritis.

scholarly journals Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Markus J. Ankenbrand ◽  
Liliia Shainberg ◽  
Michael Hock ◽  
David Lohr ◽  
Laura M. Schreiber
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Cardiac Mri ◽  
Magnetic Resonance Images ◽  
Model Interpretation ◽  
First Case ◽  
Segmentation Models

Abstract Background Image segmentation is a common task in medical imaging e.g., for volumetry analysis in cardiac MRI. Artificial neural networks are used to automate this task with performance similar to manual operators. However, this performance is only achieved in the narrow tasks networks are trained on. Performance drops dramatically when data characteristics differ from the training set properties. Moreover, neural networks are commonly considered black boxes, because it is hard to understand how they make decisions and why they fail. Therefore, it is also hard to predict whether they will generalize and work well with new data. Here we present a generic method for segmentation model interpretation. Sensitivity analysis is an approach where model input is modified in a controlled manner and the effect of these modifications on the model output is evaluated. This method yields insights into the sensitivity of the model to these alterations and therefore to the importance of certain features on segmentation performance. Results We present an open-source Python library (misas), that facilitates the use of sensitivity analysis with arbitrary data and models. We show that this method is a suitable approach to answer practical questions regarding use and functionality of segmentation models. We demonstrate this in two case studies on cardiac magnetic resonance imaging. The first case study explores the suitability of a published network for use on a public dataset the network has not been trained on. The second case study demonstrates how sensitivity analysis can be used to evaluate the robustness of a newly trained model. Conclusions Sensitivity analysis is a useful tool for deep learning developers as well as users such as clinicians. It extends their toolbox, enabling and improving interpretability of segmentation models. Enhancing our understanding of neural networks through sensitivity analysis also assists in decision making. Although demonstrated only on cardiac magnetic resonance images this approach and software are much more broadly applicable.

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