Related papers: EXFOR-NSR PDF database: a system for nuclear knowl…
We perform a \chi^2-analysis of Nuclear Parton Distribution Functions (NPDFs) using neutral current charged-lepton Deeply Inelastic Scattering (DIS) and Drell-Yan data for several nuclear targets. The nuclear A dependence of the NPDFs is…
Materials data, especially those related to high-temperature properties, pose significant challenges for machine learning models due to extreme skewness, wide feature ranges, modality, and complex relationships. While traditional models…
The comparison of different sets of PDFs structure functions with the description of the whole sets of experimental data of electromagnetic form factors of the proton and neutron is made in the frame work of our model of t-dependence of…
We introduce Nuclear Co-Learned Representations (NuCLR), a deep learning model that predicts various nuclear observables, including binding and decay energies, and nuclear charge radii. The model is trained using a multi-task approach with…
Continuous and reliable access to curated biological data repositories is indispensable for accelerating rigorous scientific inquiry and fostering reproducible research. Centralized repositories, though widely used, are vulnerable to single…
To produce the best physics results, high energy physics experiments require access to calibration and other non-event data during event data processing. These conditions data are typically stored in databases that provide versioning…
Driven by the recent rapid increase in the number of materials databases published (open and commercial), I discuss here some perspectives on the growing need for standardized, interoperable, open databases. The field of computational…
In this contribution we present a status report on the recent progress towards an analysis of nuclear parton distribution functions (nPDFs) using the NNPDF methodology. We discuss how the NNPDF fitting approach can be extended to account…
Across many domains, large swaths of digital assets are being stored across distributed data repositories, e.g., the DANDI Archive [8]. The distribution and diversity of these repositories impede researchers from formally defining…
This paper summarizes the current status of the electromagnetic data libraries, reviews recent experimental validation results, highlights open issues and introduces new perspectives for the future of these data libraries taking shape in…
Parton Distribution Functions (PDFs) model the parton content of the proton. Among the many collaborations which focus on PDF determination, NNPDF pioneered the use of Neural Networks to model the probability of finding partons (quarks and…
We compare the nuclear corrections factors from neutrino deep-inelastic scattering (DIS) with the ones coming from a standard analysis of nuclear parton distribution functions (nPDF). We focus on a discrepancy between the most precise…
Protein-nucleic acid interactions play a very important role in a variety of biological activities. Accurate identification of nucleic acid-binding residues is a critical step in understanding the interaction mechanisms. Although many…
In nuclear collisions, a sizable fraction of the available energy is carried away by baryons. As the baryon number is conserved, the net-baryon $B-\bar{B}$ retains information on the energy-momentum carried by the incoming nuclei. A simple…
Discovering authoritative links between publications and the datasets that they use can be a labor-intensive process. We introduce a natural language processing pipeline that retrieves and reviews publications for informal references to…
Neural networks with deep architectures have demonstrated significant performance improvements in computer vision, speech recognition, and natural language processing. The challenges in information retrieval (IR), however, are different…
This initial version of this document was written back in 2014 for the sole purpose of providing fundamentals of reliability theory as well as to identify the theoretical types of machinery for the prediction of durability/availability of…
PDF files are primarily intended for human reading rather than automated processing. In addition, the heterogeneous content of PDFs, such as text, tables, and images, poses significant challenges for parsing and information extraction. To…
This paper presents an advancement in Question-Answering (QA) systems using a Retrieval Augmented Generation (RAG) framework to enhance information extraction from PDF files. Recognizing the richness and diversity of data within…
Relation extraction (RE) is a well-known NLP application often treated as a sentence- or document-level task. However, a handful of recent efforts explore it across documents or in the cross-document setting (CrossDocRE). This is distinct…