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Accurate segmentation and classification of nuclei in histology images is critical but challenging due to nuclei heterogeneity, staining variations, and tissue complexity. Existing methods often struggle with limited dataset variability,…
The present uncertainties in the knowledge of the neutrino cross sections for E_nu \sim 1 GeV, that is in the energy range most important for atmospheric and long baseline accelerator neutrinos, are large. These uncertainties do not play a…
Conformational dynamics is crucial for ribonucleic acid (RNA) function. Techniques such as nuclear magnetic resonance, cryo-electron microscopy, small- and wide-angle X-ray scattering, chemical probing, single-molecule F\"orster resonance…
We study the possibility of low energy nuclear fusion assisted by a low energy resonance. We use a simple potential model and replace repulsive Coulomb barrier with a step potential barrier. This is convenient since it allows us to perform…
An accurate impact parameter determination in a heavy ion collision is crucial for almost all further analysis. The capabilities of an artificial neural network are investigated to that respect. A novel input generation for the network is…
This note proposes a method, which can be applied to searches and more in general to any cross section measurement, to maximize the analysis sensitivity.
The cross section for the $A(e,e'\gamma)A$ reaction is calculated, investigating the contribution from the nuclear target with respect to the radiative corrections from the electron. The reaction mechanism is studied for photon emission in…
We demonstrate that the present superaccurate measurements of transition processes between atomic states in hydrogen atom reached the limit of accuracy when transition frequency cannot be defined anymore in a unique way. This was predicted…
The nuclear reactions in a plasma system with energy distribution deviated from Maxwellian are proved to have some unique characteristics including those in their product energy spectrum. Based on this, a new nuclear diagnostic scheme for…
Deep learning based image segmentation methods have achieved great success, even having human-level accuracy in some applications. However, due to the black box nature of deep learning, the best method may fail in some situations. Thus…
Atomic-resolution imaging with scanning transmission electron microscopy is a powerful tool for characterizing the nanoscale structure of materials, in particular features such as defects, local strains, and symmetry-breaking distortions.…
Measurements of atomic transitions in different isotopes offer key information on the nuclear charge radius. The anticipated high-precision experimental techniques, augmented by atomic calculations, will soon enable extraction of the…
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…
To facilitate the relativistic heavy-ion calculations based on transport equations, the binary collisions involving a $\Delta$ resonance in either the entrance channel or the exit channel are investigated within a Hamiltonian formulation of…
Our objective is to estimate the unknown compositional input from its output response through an unknown system after estimating the inverse of the original system with a training set. The proposed methods using artificial neural networks…
Noise is an inherent part of neuronal dynamics, and thus of the brain. It can be observed in neuronal activity at different spatiotemporal scales, including in neuronal membrane potentials, local field potentials, electroencephalography,…
Energy resolved neutron imaging (ERNI) is an advanced neutron radiography technique capable of non-destructively extracting spatial isotopic information within a given material. Energy-dependent radiography image sequences can be created by…
This paper investigates nonlinear panel regression models with interactive fixed effects and introduces a general framework for parameter estimation under potentially non-convex objective functions. We propose a computationally feasible…
Open neutrino physics issues require precision studies, both theoretical and experimental ones, and towards this aim coherent neutral current neutrino-nucleus scattering events are expected to be observed soon. In this work, we explore…
We investigate whether a neural network approach can reproduce and predict the electron-nucleus cross sections in the kinematical domain of present and future accelerator-based neutrino oscillation experiments. For this purpose, we consider…