Tootfinder

This https://arxiv.org/abs/2404.03004 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Validation of the DESI 2024 Ly$α$ forest BAO analysis using synthetic datasets
The first year of data from the Dark Energy Spectroscopic Instrument (DESI) contains the largest set of Lyman-$α$ (Ly$α$) forest spectra ever observed. This data, collected in the DESI Data Release 1 (DR1) sample, has been used to measure the Baryon Acoustic Oscillation (BAO) feature at redshift $z=2.33$. In this work, we use a set of 150 synthetic realizations of DESI DR1 to validate the DESI 2024 Ly$α$ forest BAO measurement. The synthetic data sets are based on Gaussian random fields usin…

Validation of the DESI 2024 Ly$\alpha$ forest BAO analysis using synthetic datasets
Andrei Cuceu, Hiram K. Herrera-Alcantar, Calum Gordon, Paul Martini, Julien Guy, Andreu Font-Ribera, Alma X. Gonzalez-Morales, M. Abdul Karim, J. Aguilar, S. Ahlen, E. Armengaud, A. Bault, D. Brooks, T. Claybaugh, A. de la Macorra, P. Doel, K. Fanning, S. Ferraro, J. E. Forero-Romero, E. Gazta\~naga, S. Gontcho A Gontcho, G. Gutierrez, K. Honscheid, C. Howlett, N. G. Kara\c{c}ayl{\i}, D. Kirkby, A. Krem…

Validation of the DESI 2024 Ly$α$ forest BAO analysis using synthetic datasets
The first year of data from the Dark Energy Spectroscopic Instrument (DESI) contains the largest set of Lyman-$α$ (Ly$α$) forest spectra ever observed. This data, collected in the DESI Data Release 1 (DR1) sample, has been used to measure the Baryon Acoustic Oscillation (BAO) feature at redshift $z=2.33$. In this work, we use a set of 150 synthetic realizations of DESI DR1 to validate the DESI 2024 Ly$α$ forest BAO measurement. The synthetic data sets are based on Gaussian random fields usin…

Validation of the DESI 2024 Ly$\alpha$ forest BAO analysis using synthetic datasets
Andrei Cuceu, Hiram K. Herrera-Alcantar, Calum Gordon, Paul Martini, Julien Guy, Andreu Font-Ribera, Alma X. Gonzalez-Morales, M. Abdul Karim, J. Aguilar, S. Ahlen, E. Armengaud, A. Bault, D. Brooks, T. Claybaugh, A. de la Macorra, P. Doel, K. Fanning, S. Ferraro, J. E. Forero-Romero, E. Gazta\~naga, S. Gontcho A Gontcho, G. Gutierrez, K. Honscheid, C. Howlett, N. G. Kara\c{c}ayl{\i}, D. Kirkby, A. Krem…

Validation of the DESI 2024 Ly$α$ forest BAO analysis using synthetic datasets
The first year of data from the Dark Energy Spectroscopic Instrument (DESI) contains the largest set of Lyman-$α$ (Ly$α$) forest spectra ever observed. This data, collected in the DESI Data Release 1 (DR1) sample, has been used to measure the Baryon Acoustic Oscillation (BAO) feature at redshift $z=2.33$. In this work, we use a set of 150 synthetic realizations of DESI DR1 to validate the DESI 2024 Ly$α$ forest BAO measurement. The synthetic data sets are based on Gaussian random fields usin…

Constructive vs Predicative data: #Programmers, but especially my

On Bootstrapping Lasso in Generalized Linear Models and the Cross Validation
Mayukh Choudhury, Debraj Das
https://arxiv.org/abs/2403.19515 https://<…

On Bootstrapping Lasso in Generalized Linear Models and the Cross Validation
Generalized linear models or GLM constitutes an important set of models which generalizes the ordinary linear regression by connecting the response variable with the covariates through arbitrary link functions. On the other hand, Lasso is a popular and easy to implement penalization method in regression when all the covariates are not relevant. However, Lasso generally has non-tractable asymptotic distribution and hence development of an alternative method of distributional approximation is req…

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…

On Bootstrapping Lasso in Generalized Linear Models and the Cross Validation
Mayukh Choudhury, Debraj Das
https://arxiv.org/abs/2403.19515 https://<…

On Bootstrapping Lasso in Generalized Linear Models and the Cross Validation
Generalized linear models or GLM constitutes an important set of models which generalizes the ordinary linear regression by connecting the response variable with the covariates through arbitrary link functions. On the other hand, Lasso is a popular and easy to implement penalization method in regression when all the covariates are not relevant. However, Lasso generally has non-tractable asymptotic distribution and hence development of an alternative method of distributional approximation is req…

Advancing COVID-19 Detection in 3D CT Scans
Qingqiu Li, Runtian Yuan, Junlin Hou, Jilan Xu, Yuejie Zhang, Rui Feng, Hao Chen
https://arxiv.org/abs/2403.11953

Advancing COVID-19 Detection in 3D CT Scans
To make a more accurate diagnosis of COVID-19, we propose a straightforward yet effective model. Firstly, we analyse the characteristics of 3D CT scans and remove the non-lung parts, facilitating the model to focus on lesion-related areas and reducing computational cost. We use ResNeSt50 as the strong feature extractor, initializing it with pretrained weights which have COVID-19-specific prior knowledge. Our model achieves a Macro F1 Score of 0.94 on the validation set of the 4th COV19D Competi…

Global prediction of nuclear charge density distributions using deep neural network
Tian Shuai Shang, Hui Hui Xie, Jian Li, Haozhao Liang
https://arxiv.org/abs/2404.09558

Global prediction of nuclear charge density distributions using deep neural network
A deep neural network (DNN) has been developed to generate the distributions of nuclear charge density, utilizing the training data from the relativistic density functional theory and incorporating available experimental charge radii of 1014 nuclei into the loss function. The DNN achieved a root-mean-square (rms) deviation of 0.0193 fm for charge radii on its validation set. Furthermore, the DNN can improve the description in both the tail and central regions of the charge density, enhancing ag…

Gradient-flow adaptive importance sampling for Bayesian leave one out cross-validation for sigmoidal classification models
Joshua C Chang, Xiangting Li, Shixin Xu, Hao-Ren Yao, Julia Porcino, Carson Chow
https://arxiv.org/abs/2402.08151

Gradient-flow adaptive importance sampling for Bayesian leave one out cross-validation for sigmoidal classification models
We introduce a set of gradient-flow-guided adaptive importance sampling (IS) transformations to stabilize Monte-Carlo approximations of point-wise leave one out cross-validated (LOO) predictions for Bayesian classification models. One can leverage this methodology for assessing model generalizability by for instance computing a LOO analogue to the AIC or computing LOO ROC/PRC curves and derived metrics like the AUROC and AUPRC. By the calculus of variations and gradient flow, we derive two simp…