Tootfinder

The Wayback Machine never forgets...
"The BadgerPundit account went private sometime in late 2022, but CNN was able to access the since-removed public tweets by using the Wayback Machine, an internet archive."
Exclusive: Key figure in fake electors plot concealed damning posts on secret Twitter account from investigators
https://www.cnn.com/2024/02/26/politics/kenneth-chesebro-secret-twitter-account-kfile/index.html

The Chemical Composition of Ryugu: Prospects as a Reference Material for Solar System Composition
Tetsuya Yokoyama, Nicolas Dauphas, Ryota Fukai, Tomohiro Usui, Shogo Tachibana, Maria Sch\"onb\"achler, Henner Busemann, Masanao Abe, Toru Yada
https://arxiv.org/abs/2405.04500

The Chemical Composition of Ryugu: Prospects as a Reference Material for Solar System Composition
The Hayabusa 2 spacecraft sampled approximately 5.4 g of asteroid material from the Cb-type asteroid Ryugu. Initial analysis of the Ryugu materials revealed a mineralogical, chemical, and isotopic kinship to the CI chondrites. The pristine nature of Ryugu makes the returned samples ideal for constraining the composition of the Solar System. However, some elements (e.g., P, Ca, Mn, and rare earth elements) show large relative dispersions compared to the other elements in the returned materials s…

Neural Network Approach for Predicting Infrared Spectra from 3D Molecular Structure
Saleh Abdul Al, Abdul-Rahman Allouche
https://arxiv.org/abs/2405.05737 …

Neural Network Approach for Predicting Infrared Spectra from 3D Molecular Structure
Accurately predicting infrared (IR) spectra in computational chemistry using ab initio methods remains a challenge. Current approaches often rely on an empirical approach or on tedious anharmonic calculations, mainly adapted to semi-rigid molecules. This limitation motivates us to explore alternative methodologies. Previous studies explored machine-learning techniques for potential and dipolar surface generation, followed by IR spectra calculation using classical molecular dynamics. However, th…

Data-Driven Modeling of Dislocation Mobility from Atomistics using Physics-Informed Machine Learning
Yifeng Tian, Soumendu Bagchi, Liam Myhill, Giacomo Po, Enrique Martinez, Yen Ting Lin, Nithin Mathew, Danny Perez
https://arxiv.org/abs/2403.14015

Data-Driven Modeling of Dislocation Mobility from Atomistics using Physics-Informed Machine Learning
Dislocation mobility, which dictates the response of dislocations to an applied stress, is a fundamental property of crystalline materials that governs the evolution of plastic deformation. Traditional approaches for deriving mobility laws rely on phenomenological models of the underlying physics, whose free parameters are in turn fitted to a small number of intuition-driven atomic scale simulations under varying conditions of temperature and stress. This tedious and time-consuming approach bec…

Neural Network Approach for Predicting Infrared Spectra from 3D Molecular Structure
Saleh Abdul Al, Abdul-Rahman Allouche
https://arxiv.org/abs/2405.05737 …

Neural Network Approach for Predicting Infrared Spectra from 3D Molecular Structure
Accurately predicting infrared (IR) spectra in computational chemistry using ab initio methods remains a challenge. Current approaches often rely on an empirical approach or on tedious anharmonic calculations, mainly adapted to semi-rigid molecules. This limitation motivates us to explore alternative methodologies. Previous studies explored machine-learning techniques for potential and dipolar surface generation, followed by IR spectra calculation using classical molecular dynamics. However, th…

This https://arxiv.org/abs/2401.05394 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Iterative Regularization with k-support Norm: An Important Complement to Sparse Recovery
Sparse recovery is ubiquitous in machine learning and signal processing. Due to the NP-hard nature of sparse recovery, existing methods are known to suffer either from restrictive (or even unknown) applicability conditions, or high computational cost. Recently, iterative regularization methods have emerged as a promising fast approach because they can achieve sparse recovery in one pass through early stopping, rather than the tedious grid-search used in the traditional methods. However, most of…

This https://arxiv.org/abs/2401.05394 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Iterative Regularization with k-support Norm: An Important Complement to Sparse Recovery
Sparse recovery is ubiquitous in machine learning and signal processing. Due to the NP-hard nature of sparse recovery, existing methods are known to suffer either from restrictive (or even unknown) applicability conditions, or high computational cost. Recently, iterative regularization methods have emerged as a promising fast approach because they can achieve sparse recovery in one pass through early stopping, rather than the tedious grid-search used in the traditional methods. However, most of…