Unsupervised learning methods for molecular simulation data
Unsupervised learning is becoming an essential tool to analyze the increasingly large
amounts of data produced by atomistic and molecular simulations, in material science, solid …
amounts of data produced by atomistic and molecular simulations, in material science, solid …
Generalized out-of-distribution detection: A survey
Abstract Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of
machine learning systems. For instance, in autonomous driving, we would like the driving …
machine learning systems. For instance, in autonomous driving, we would like the driving …
Cross-scale characterization of the elasticity of shales: Statistical nanoindentation and data analytics
Shales are a class of multiscale, multiphase, hybrid inorganic-organic composite materials
exhibiting both frictional and cohesive behavior, and it is very challenging to characterize …
exhibiting both frictional and cohesive behavior, and it is very challenging to characterize …
The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis
FDG-PET-derived textural features describing intra-tumor heterogeneity are increasingly
investigated as imaging biomarkers. As part of the process of quantifying heterogeneity …
investigated as imaging biomarkers. As part of the process of quantifying heterogeneity …
Rigorous home range estimation with movement data: a new autocorrelated kernel density estimator
Quantifying animals' home ranges is a key problem in ecology and has important
conservation and wildlife management applications. Kernel density estimation (KDE) is a …
conservation and wildlife management applications. Kernel density estimation (KDE) is a …
[图书][B] Permutation tests: a practical guide to resampling methods for testing hypotheses
P Good - 2013 - books.google.com
In 1982, I published several issues of a samdizat scholarly journal called Random ization
with the aid of an 8-bit, I-MH personal computer with 48K of memory (upgraded to 64K later …
with the aid of an 8-bit, I-MH personal computer with 48K of memory (upgraded to 64K later …
Improved optimization for the robust and accurate linear registration and motion correction of brain images
M Jenkinson, P Bannister, M Brady, S Smith - Neuroimage, 2002 - Elsevier
Linear registration and motion correction are important components of structural and
functional brain image analysis. Most modern methods optimize some intensity-based cost …
functional brain image analysis. Most modern methods optimize some intensity-based cost …
A global optimisation method for robust affine registration of brain images
M Jenkinson, S Smith - Medical image analysis, 2001 - Elsevier
Registration is an important component of medical image analysis and for analysing large
amounts of data it is desirable to have fully automatic registration methods. Many different …
amounts of data it is desirable to have fully automatic registration methods. Many different …
[图书][B] Effect sizes for research: Univariate and multivariate applications
RJ Grissom, JJ Kim - 2012 - taylorfrancis.com
Noted for its comprehensive coverage, this greatly expanded new edition now covers the
use of univariate and multivariate effect sizes. Many measures and estimators are reviewed …
use of univariate and multivariate effect sizes. Many measures and estimators are reviewed …
Springer Series in Statistics
Hidden Markov models—most often abbreviated to the acronym “HMMs”—are one of the
most successful statistical modelling ideas that have came up in the last forty years: the use …
most successful statistical modelling ideas that have came up in the last forty years: the use …