Tootfinder

Efficient Algorithms for the Sensitivities of the Pearson Correlation Coefficient and Its Statistical Significance to Online Data
Marc Harary
https://arxiv.org/abs/2405.14686

On Machine Learning Complete Intersection Calabi-Yau 3-folds
Kaniba Mady Keita
https://arxiv.org/abs/2404.11710 https://arxiv.org/pdf…

On Machine Learning Complete Intersection Calabi-Yau 3-folds
Gaussian Process Regression, Kernel Support Vector Regression, the random forest, extreme gradient boosting and the generalized linear model algorithms are applied to data of Complete Intersection Calabi-Yau 3-folds. It is shown that Gaussian process regression is the most suitable for learning the Hodge number h^(2,1)in terms of h^(1,1). The performance of this regression algorithm is such that the Pearson correlation coefficient for the validation set is R^2 = 0.9999999995 with a Root Mean Sq…

Comparative Image Alignment for Multiple Telescopes: ALMA, IRIS, SDO
Francisco J. Ordo\~nez Araujo, Juan Camilo Guevara G\'omez, Benjam\'in Calvo Mozo
https://arxiv.org/abs/2404.04401

Comparative Image Alignment for Multiple Telescopes: ALMA, IRIS, SDO
This paper introduces a method for aligning solar observations from different telescopes. We utilized helioprojective coordinates from the Solar Dynamics Observatory (SDO) as a reference to align images from ALMA and IRIS. The alignment is based on correlation analysis, employing both the Pearson Correlation Coefficient (PCC) and the Structural Similarity Index (SSIM) for assessing data correlations. PCC was preferred for its effectiveness with diverse datasets and its computational efficiency …

Accurate estimation of the normalized mutual information of multidimensional data
Daniel Nagel, Georg Diez, Gerhard Stock
https://arxiv.org/abs/2405.04980 …

Accurate estimation of the normalized mutual information of multidimensional data
While the linear Pearson correlation coefficient represents a well-established normalized measure to quantify the interrelation of two stochastic variables $X$ and $Y$, it fails for multidimensional variables such as Cartesian coordinates. Avoiding any assumption about the underlying data, the mutual information $I(X, Y)$ does account for multidimensional correlations. However, unlike the normalized Pearson correlation, it has no upper bound ($I \in [0, \infty)$), i.e., it is not clear if say, …