Related papers: Extraction of N* information from the limited p(ga…
We present a free and a constrained fitting procedure for quantitative signal extraction of nanomechanical systems in the dynamic range and for physical model testing. We demonstrate that applying the free-fitting procedure to the measured…
We present a simple model for the $\gamma N \to \pi \pi N$ reaction which reproduces the cross sections of the $\pi^+ \pi^- p, \pi^+ \pi^- n, \pi^+ \pi^0 n$ and $\pi^- \pi^0 p$ channels over the range of the energies 0.41-0.85 GeV. We use…
We propose an approach for biomedical information extraction that marries the advantages of machine learning models, e.g., learning directly from data, with the benefits of rule-based approaches, e.g., interpretability. Our approach starts…
Machine Translation models are trained to translate a variety of documents from one language into another. However, models specifically trained for a particular characteristics of the documents tend to perform better. Fine-tuning is a…
Reconstruction of gene regulatory networks is the process of identifying gene dependency from gene expression profile through some computation techniques. In our human body, though all cells pose similar genetic material but the activation…
Gas sampling methods have been crucial for the advancement of combustion science, enabling analysis of reaction kinetics and pollutant formation. However, the measured composition can deviate from the true one because of the potential…
We apply a stochastic method of minimizing the ground state energy in variational calculations of light nuclei using the Refined Resonating Group Model (RRGM). The method utilizes a bit representation of the width parameters to be varied.…
Machine learning provides a broad framework for addressing high-dimensional prediction problems in classification and regression. While machine learning is often applied for imaging problems in medical physics, there are many efforts to…
We review recent progress in our understanding of the nucleon excitation spectrum. Thanks to dedicated efforts at facilities such as ELSA, MAMI and Jefferson Lab, several new nucleon resonances have been discovered, and evidence for…
The associate $K\Lambda(1520)$ photon and hadronic production in the $\gamma p \to K^+\Lambda(1520)$, $p p \to p K^+ \Lambda(1520)$ and $\pi^- p \to K^0 \Lambda(1520)$ reactions are investigated within the effective Lagrangian approach and…
Medical ultrasound imaging relies heavily on high-quality signal processing to provide reliable and interpretable image reconstructions. Conventionally, reconstruction algorithms where derived from physical principles. These algorithms rely…
In material science, models are derived to predict emergent material properties (e.g. elasticity, strength, conductivity) and their relations to processing conditions. A major drawback is the calibration of model parameters that depend on…
We develop, discuss, and compare several inference techniques to constrain theory parameters in collider experiments. By harnessing the latent-space structure of particle physics processes, we extract extra information from the simulator.…
An existing single resonance model with S11, P11 and P13 Breit-Wiegner resonances in the s-channel has been re-applied to the old pi N --> K Lambda data. It has been shown that the standard set of resonant parameters fails to reproduce the…
We present organization and solutions for a new analysis of all $\pi N$ elastic and the major inelastic channels to extract detailed characteristics of the contributing baryon resonances. This work is based on the work of R. Cutkosky and…
We discuss the ways of extracting a low energy scale of an underlying theory using high energy scattering data. Within an exactly solvable model of quantum mechanics we analyze a technique based on introduction of nonperturbative power…
In the theoretical modelling of a physical system a crucial step consists in the identification of those degrees of freedom that enable a synthetic, yet informative representation of it. While in some cases this selection can be carried out…
As is typical in other fields of application of high throughput systems, radiology is faced with the challenge of interpreting increasingly sophisticated predictive models such as those derived from radiomics analyses. Interpretation may be…
Biological structure and function depend on complex regulatory interactions between many genes. A wealth of gene expression data is available from high-throughput genome-wide measurement technologies, but effective gene regulatory network…
Feedforward neural networks with random hidden nodes suffer from a problem with the generation of random weights and biases as these are difficult to set optimally to obtain a good projection space. Typically, random parameters are drawn…