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An effective low energy Lagrangian density is applied to nuclear $K^-$-dynamics. The free parameters, local s-wave couplings and SU(3)-symmetry constrained range terms are adjusted to describe elastic and inelastic $K^-$-nucleon scattering…
The accurate extraction of clinical information from electronic medical records is particularly critical to clinical research but require much trained expertise and manual labor. In this study we developed a robust system for automated…
We investigate the $\gamma p \to \phi K^+ \Lambda$ reaction near threshold within an effective Lagrangian approach and the isobar model. Various nucleon resonances caused by the $\pi$ and $\eta$ meson exchanges and background contributions…
We present an optimization scheme that employs a Genetic Algorithm (GA) to determine the properties of low-lying nucleon excitations within a realistic photo-pion production model based upon an effective Lagrangian. We show that with this…
Nuclear reaction data required for astrophysics and applications is incomplete, as not all nuclear reactions can be measured or reliably predicted. Neutron-induced reactions involving unstable targets are particularly challenging, but often…
Resonances are extracted from a number of energy-dependent and single-energy fits to scattering data. The influence of recent, precise EPECUR data is investigated. Results for the single-energy fits are derived using the L+P method of…
Neutron/gamma discrimination has been intensively researched in recent years, due to its unique scientific value and widespread applications. With the advancement of detection materials and algorithms, nowadays we can achieve fairly good…
The capture of K$^-$ mesons on nucleons bound in nuclei offer a chance to study the $\Sigma \pi$ pairs below the kinematic threshold of the $\bar{\mathrm{K}}$N systems. Various hyperon-pion charged combination are presently under…
Kernel methods offer the flexibility to learn complex relationships in modern, large data sets while enjoying strong theoretical guarantees on quality. Unfortunately, these methods typically require cubic running time in the data set size,…
I describe an approach to fitting and comparison of radio spectra based on Bayesian analysis and realised using a new implementation of the nested sampling algorithm. Such an approach improves on the commonly used maximum-likelihood fitting…
Purpose: To develop and evaluate an automated system for extracting structured clinical information from unstructured radiology and pathology reports using open-weights large language models (LMs) and retrieval augmented generation (RAG),…
Feedforward neural networks (FNNs) can be viewed as non-linear regression models, where covariates enter the model through a combination of weighted summations and non-linear functions. Although these models have some similarities to the…
Most of the fundamental, emergent, and phenomenological parameters of particle and nuclear physics are determined through parametric template fits. Simulations are used to populate histograms which are then matched to data. This approach is…
Beyond the genetic code, there is another layer of information encoded as chemical modifications on histone proteins positioned along the DNA. Maintaining these modifications is crucial for survival and identity of cells. How the…
The question of pseudovector versus pseudoscalar coupling schemes for the kaon-hyperon-nucleon interaction is re-examined for the reaction $\gamma p\to K^+ \Lambda$ in several isobaric models. These models typically include Born terms,…
Many scientific and engineering applications require fitting regression models that are nonlinear in the parameters. Advances in computer hardware and software in recent decades have made it easier to fit such models. Relative to fitting…
Several technological applications require the translation of a protein into a nucleic acid that codes for it (``backtranslation''). The degeneracy of the genetic code makes this translation ambiguous; moreover, not every translation is…
We present a novel computational approach for extracting weak signals, whose exact location and width may be unknown, from complex background distributions with an arbitrary functional form. We focus on datasets that can be naturally…
Evolutionary algorithms have long been used for optimization problems where the appropriate size of solutions is unclear a priori. The applicability of this methodology is here investigated on the problem of designing a nano-particle (NP)…
Results of a partial wave analysis of new high-statistics data on $\gamma p\to p\eta$ from MAMI are presented. A fit using known broad resonances and only standard background amplitudes can not describe the relatively narrow peaking…