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Modern neutrino-nucleus cross section predictions need to incorporate sophisticated nuclear models to achieve greater predictive precision. However, the computational complexity of these advanced models often limits their practicality for experimental analyses. To address this challenge, we introduce a new Monte Carlo method utilizing Normalizing Flows to generate surrogate cross sections that closely approximate those of the original model while significantly reducing computational overhead. As a case study, we built a Monte Carlo event generator for the neutrino-nucleus cross section model developed by the Ghent group. This model employs a Hartree-Fock procedure to establish a quantum mechanical framework in which both the bound and scattering nucleon states are solutions to the mean-field nuclear potential. The surrogate cross sections generated by our method demonstrate excellent accuracy with a relative effective sample size of more than $98.4 \%$, providing a computationally efficient alternative to traditional Monte Carlo sampling methods for differential cross sections., Comment: Submitted to Physics Review D

In the rapidly evolving field of healthcare and beyond, the integration of generative AI in Electronic Health Records (EHRs) represents a pivotal advancement, addressing a critical gap in current information extraction techniques. This paper introduces GAMedX, a Named Entity Recognition (NER) approach utilizing Large Language Models (LLMs) to efficiently extract entities from medical narratives and unstructured text generated throughout various phases of the patient hospital visit. By addressing the significant challenge of processing unstructured medical text, GAMedX leverages the capabilities of generative AI and LLMs for improved data extraction. Employing a unified approach, the methodology integrates open-source LLMs for NER, utilizing chained prompts and Pydantic schemas for structured output to navigate the complexities of specialized medical jargon. The findings reveal significant ROUGE F1 score on one of the evaluation datasets with an accuracy of 98\%. This innovation enhances entity extraction, offering a scalable, cost-effective solution for automated forms filling from unstructured data. As a result, GAMedX streamlines the processing of unstructured narratives, and sets a new standard in NER applications, contributing significantly to theoretical and practical advancements beyond the medical technology sphere.

We demonstrate the unique capabilities of the Wigner function, particularly in its positive and negative parts, for exploring the phase diagram of the spin$-(\frac{1}{2\!}-\!\frac{1}{2})$ and spin$-(\frac{1}{2}\!-\!1)$ Ising-Heisenberg chains. We highlight the advantages and limitations of the phase space approach in comparison with the entanglement concurrence in detecting phase boundaries. We establish that the equal angle slice approximation in the phase space is an effective method for capturing the essential features of the phase diagram, but falls short in accurately assessing the negativity of the Wigner function for the homogeneous spin$-(\frac{1}{2}\!-\!\frac{1}{2})$ Ising-Heisenberg chain. In contrast, we find for the inhomogeneous spin$-(\frac{1}{2}\!-\!1)$ chain that an integral over the entire phase space is necessary to accurately capture the phase diagram of the system. This distinction underscores the sensitivity of phase space methods to the homogeneity of the quantum system under consideration., Comment: 15 pages, 6 figures

In this study, we use Rational-Quadratic Neural Spline Flows, a sophisticated parametrization of Normalizing Flows, for inferring posterior probability distributions in scenarios where direct evaluation of the likelihood is challenging at inference time. We exemplify this approach using the T2K near detector as a working example, focusing on learning the posterior probability distribution of neutrino flux binned in neutrino energy. The predictions of the trained model are conditioned at inference time by the momentum and angle of the outgoing muons released after neutrino-nuclei interaction. This conditioning allows for the generation of personalized posterior distributions, tailored to the muon observables, all without necessitating a full retraining of the model for each new dataset. The performances of the model are studied for different shapes of the posterior distributions., Comment: This article was published in Physical Review D

The COVID-19 infodemic, characterized by the rapid spread of misinformation and unverified claims related to the pandemic, presents a significant challenge. This paper presents a comparative analysis of the COVID-19 infodemic in the English and Chinese languages, utilizing textual data extracted from social media platforms. To ensure a balanced representation, two infodemic datasets were created by augmenting previously collected social media textual data. Through word frequency analysis, the thirty-five most frequently occurring infodemic words are identified, shedding light on prevalent discussions surrounding the infodemic. Moreover, topic clustering analysis uncovers thematic structures and provides a deeper understanding of primary topics within each language context. Additionally, sentiment analysis enables comprehension of the emotional tone associated with COVID-19 information on social media platforms in English and Chinese. This research contributes to a better understanding of the COVID-19 infodemic phenomenon and can guide the development of strategies to combat misinformation during public health crises across different languages.

The mathematical up-scaling of gas-liquid bubbly flows was carried out under the framework of the volume averaging theory. A two-fluid model and its associated closure problem were deduced. The closure problem was solved for a case study: a liquid flow past an assembly of homogeneous and monodisperse distorted bubbles. To this end, a cylindrical unit cells with concentric bubbles was used as approach of a representative elementary volume. The following conditions were evaluated: 0 < Re <= 675, 0 < {\epsilon}g <= 0.2, 7.15 x 10-9 <= We <= 3.96 and Mo = 2.55 x 10-11. The interfacial force closure was analyzed in terms of the ratio between the interfacial force for bubbly flow and that existing for a single bubble with the equivalent Re (in an unbounded liquid). It was found that, for creeping regime (0.001 <= Re <= 0.1), the ratio between drag coefficients increases as {\epsilon}g increases independently of Re. For intermediate and inertial regimes (1 <= Re <= 650), the ratio also increases as {\epsilon}g increases; however, two different trends were observed with respect to Re: the ratio decreases for the intermediate regime (1 <= Re <= 50) and increases for the inertial regime (50 < Re <= 650). A transition point around Re = 50 was found, which could be related to the boundary layer formation. Similar trends with respect to existing data from direct numerical simulations of bubbly flows were obtained, showing that this approach represents a good alternative, in terms of computing efforts, to obtain closure laws for two-phase flow modeling.

In this paper, we present a new approach for Covid-19 Pandemic spreading simulation based on fuzzy multi agents. The agent parameters consider distribution of the population according to age, and the index of socio-economic fragility. Medical knowledge affirms that the COVID-19 main risk factors are age and obesity. The worst medical situation is caused by the combination of these two risk factors which in almost99% of cases finish in ICU. The appearance of virus variants is another aspect parameter by our simulation through a simplified modeling of the contagiousness. Using real data from people from West Indies (Guadeloupe, F.W.I.), we modeled the infection rate of the risk population, if neither vaccination nor barrier gestures are respected. The results show that hospital capacities are exceeded, and the number of deaths exceeds 2% of the infected population, which is close to the reality.

Forecasts are typically not produced in a vacuum but in a business context, where forecasts are generated on a regular basis and interact with each other. For decisions, it may be important that forecasts do not change arbitrarily, and are stable in some sense. However, this area has received only limited attention in the forecasting literature. In this paper, we explore two types of forecast stability that we call vertical stability and horizontal stability. The existing works in the literature are only applicable to certain base models and extending these frameworks to be compatible with any base model is not straightforward. Furthermore, these frameworks can only stabilise the forecasts vertically. To fill this gap, we propose a simple linear-interpolation-based approach that is applicable to stabilise the forecasts provided by any base model vertically and horizontally. The approach can produce both accurate and stable forecasts. Using N-BEATS, Pooled Regression and LightGBM as the base models, in our evaluation on four publicly available datasets, the proposed framework is able to achieve significantly higher stability and/or accuracy compared to a set of benchmarks including a state-of-the-art forecast stabilisation method across three error metrics and six stability metrics.

The Proton Improvement Plan - II (PIP-II) is a new linear accelerator (LINAC) complex being built at Fermilab. It is based on superconducting radiofrequency cavities and will accelerate H- ions to 800 MeV kinetic energy before injection into the existing Booster ring. Measurements of the profile of the beam along the LINAC must be done by non-intercepting methods due to the superconducting cavities. The method chosen is photoionization of a small number of H- by a focused infrared laser, aka laserwire. The number of ionized electrons is measured as a function of laser position within the H- beam. To aid in the design of the collection mechanism, a simulation was written in MATLAB with input from the commercial electromagnetic simulation, CST. This simulation calculates the number and positions of the liberated electrons and tracks them through the magnetic collection and H- beam fields to the collection point. Results from this simulation for various points along the LINAC will be shown., Comment: 12th international beam instrumentation conference