Related papers: Nuclear Data Needs and Capabilities for Applicatio…
Automating data extraction from clinical documents offers significant potential to improve efficiency in healthcare settings, yet deploying Natural Language Processing (NLP) solutions presents practical challenges. Drawing upon our…
In this paper I explore a number of issues in the analysis of data requirements for statistical NLP systems. A preliminary framework for viewing such systems is proposed and a sample of existing works are compared within this framework. The…
Volume II of two. This document could be of interest to anyone who wants to have a comprehensive inside information about the research in nuclear physics from its early beginnings to later years. It describes highlights of my research work…
In this paper, we take the first steps towards a novel unified framework for the analysis of perturbations in both the Time and Frequency domains. The identification of type and source of such perturbations is fundamental for monitoring…
Quantization of Deep Neural Network (DNN) activations is a commonly used technique to reduce compute and memory demands during DNN inference, which can be particularly beneficial on resource-constrained devices. To achieve high accuracy,…
Nucleic acids such as mRNA have emerged as a promising therapeutic modality with the capability of addressing a wide range of diseases. Lipid nanoparticles (LNPs) as a delivery platform for nucleic acids were used in the COVID-19 vaccines…
From the synthesis and evolution of the elements to the celestial nuclear processes of stellar explosions and neutron star mergers, nuclear physics is the foundation of our understanding of the universe. After more than a century of…
We propose Neural Cellular Automata (NCA) to simulate the microstructure development during the solidification process in metals. Based on convolutional neural networks, NCA can learn essential solidification features, such as preferred…
This work is a sequel to our two 2023 publications [PLB 837 137669, NPA 1039 122725] where fitting 14 1$s_\Lambda$ and 1$p_\Lambda$ single-particle binding energies in hypernuclei across the periodic table led to a well-defined…
We revisit correlations of neutrino oscillation parameters in reactor and long-baseline neutrino oscillation experiments. A framework based on an effective value of $\theta_{13}$ is presented, which can easily reproduce experimental…
Training a high-performing neural decoder can be difficult when only limited data are available from a recording session. To address this challenge, we propose a Task-Conditioned Latent Alignment framework (TCLA) for cross-session neural…
Nuclear density functional theory is the prevalent theoretical framework for accurately describing nuclear properties at the scale of the entire chart of nuclides. Given an energy functional and a many-body scheme (e.g., single- or…
Discussion of the nuclear force, lead by a round table consisting of T. Cohen, E. Epelbaum, R. Machleidt, and F. Gross (chair). After an invited talk by Machleidt, published elsewhere in these proceedings, brief remarks are made by…
Between the years 2015 and 2019, members of the Horizon 2020-funded Innovative Training Network named "AMVA4NewPhysics" studied the customization and application of advanced multivariate analysis methods and statistical learning tools to…
Data analysis is a powerful tool in all experimental sciences. Statistical methods, such as sampling theory, computer technologies necessary for handling large amounts of data, skill in analysing information contained in different types of…
The extraction of neutrino oscillation parameters requires the determination of the neutrino energy from observations of the hadronic final state. The use of nuclear targets then requires the use of event generators to isolate the…
Neural Cellular Automata (NCA) models are trainable variations of traditional Cellular Automata (CA). Emergent motion in the patterns created by NCA has been successfully applied to synthesize dynamic textures. However, the conditions…
We plan to develop a new nuclear database for muon-induced nuclear reactions (muon nuclear data). The database will consist of (1) energies and intensities of the muonic X rays, (2) lifetimes of the muonic atom, (3) production branching…
Driven by the wide adoption of deep neural networks (DNNs) across different application domains, multi-tenancy execution, where multiple DNNs are deployed simultaneously on the same hardware, has been proposed to satisfy the latency…
We review the status of, and prospects for, real-time data processing for collider experiments in experimental High Energy Physics. We discuss the historical evolution of data rates and volumes in the field and place them in the context of…