**Abstract:** This paper proposes a novel framework for detecting anomalous terms in Chiral Perturbation Theory (ChPT) expansions using a multi-modal Bayesian inference pipeline. Leveraging recent advances in transformer networks, graph neural networks, and automated theorem proving, our system combines symbolic logic, numerical simulation, and machine learning to identify statistically significant deviations from expected chiral symmetry behavior. The resulting system, "HyperScore," enables rapid, high-precision identification of potential new physics contributions beyond the standard ChPT Lagrangian, promising accelerated discovery in particle physics phenomenology and lattice QCD studies.

La météo de ce week-end du samedi 17 et dimanche 18 janvier s’annonce pluvieuse dans le sud-est de la France avec l’arrivée d’une importance perturbation sur la Corse et le Languedoc-Roussillon. Dans ...

Human T-cell leukemia virus type I (HTLV-1) was the first human pathogenic retrovirus to be discovered. HTLV-1 induces a T-cell malignancy, adult T-ce…

**Abstract:** Predicting quantum tunneling rates in chemical reactions is crucial for understanding reaction dynamics and designing efficient catalysts. Traditional methods like transition state theory (TST) often fall short due to their reliance on classical approximations. This paper introduces a novel framework integrating Density Functional Perturbation Theory (DFPT) calculations with a machine learning (ML) model to accurately predict tunneling rates, achieving substantial speedups and enhanced accuracy compared to standalone DFPT.

arXiv abstract link

arXiv abstract link

FAPROTAX project site

**Abstract:** This paper introduces a novel methodology for analyzing quantum field density perturbations, a critical area for understanding early universe cosmology and quantum gravity. It proposes an Adaptive Multi-Resolution Spectral Decomposition (AMRSD) framework that dynamically adjusts the resolution of Fourier analysis based on local perturbation amplitude, drastically improving accuracy and efficiency compared to traditional methods. By combining spectral decomposition with a sophisticated error estimation and adaptive refinement algorithm, we provide a robust and scalable approach to characterizing quantum fluctuations, enabling unprecedented insights into the emergence of structure in the universe and potential modifications to standard cosmological models.

**Abstract:** AI-driven recruitment systems (AI-RS) demonstrate potential for increased efficiency, yet perpetuate and amplify existing societal biases, resulting in unfair outcomes across demographic groups. This paper introduces a novel framework, Stratified Adversarial Perturbation and Bayesian Meta-Analysis (SAP-BMA), for rigorously auditing AI-RS for bias. SAP-BMA combines stratified adversarial perturbation within feature spaces, generating targeted “unfairness samples” reflecting potential bias triggers, followed by Bayesian meta-analysis of model predictions across these strata.

Generation of 120M+ cells and 225,000 perturbation interactions will accelerate virtual cell model development.