相关论文: Determination of impact parameter for CEE with dig…
We use the recently introduced independent-atom-model pixel counting method to calculate proton-impact net ionization cross sections for a large class of biologically relevant systems including pyrimidines, purines, amino acids, and…
A theoretical analysis using an impact-parameter description of the collisions of deuterons with nuclei is carried out in the high-energy diffraction approximation. It is used to obtain the intensities and integrated cross sections for…
Results of detailed simulations of the charge transfer inefficiency of a prototype serial readout CCD chip are reported. The effect of radiation damage on the chip operating in a particle detector at high frequency at a future accelerator…
We analyze the nearest neighbor spacing distributions of low-lying 2+ levels of even-even nuclei. We grouped the nuclei into classes defined by the quadrupole deformation parameter (Beta2). We calculate the nearest neighbor spacing…
As repositories of large scale data in earth observation (EO) have grown, so have transfer and storage costs for model training and inference, expending significant resources. We introduce Neural Embedding Compression (NEC), based on the…
The experiments at the Large Hadron Collider at CERN generate vast amounts of complex data from high-energy particle collisions. This data presents significant challenges due to its volume and complex reconstruction, necessitating the use…
Objective: Machine learning- and deep learning-based models have recently been employed in motor imagery intention classification from electroencephalogram (EEG) signals. Nevertheless, there is a limited understanding of feature selection…
This work presents the first comprehensive joint analysis of all available Coherent Elastic Neutrino-Nucleus Scattering (CE$\nu$NS) data on germanium: those observed at the Spallation Neutron Source (SNS) by the COHERENT collaboration and…
Recent studies have highlighted the sensitivity of core-collapse supernovae (CCSNe) models to electron-capture (EC) rates on neutron-rich nuclei near the N=50 closed-shell region. In this work, we perform a large suite of one-dimensional…
Precise scientific analysis in collider-based particle physics is possible because of complex simulations that connect fundamental theories to observable quantities. The significant computational cost of these programs limits the scope,…
Interactions of protons with nuclei are modeled in a form that is suitable for Monte Carlo simulation of proton transport. The differential cross section (DCS) for elastic collisions of protons with neutral atoms is expressed as the product…
Over past years, the easy accessibility to the large scale datasets has significantly shifted the paradigm for developing highly accurate prediction models that are driven from Neural Network (NN). These models can be potentially impacted…
The collisions in four different reaction systems using $^{40,48}$Ca and $^{58,64}$Ni isotope beams and a Be target have been simulated using the Heavy Ion Phase Space Exploration and the Antisymmetrized Molecular Dynamics models. The…
We review the status and prospects of theoretical studies of neutrino-nucleus interactions, and discuss the influence of the treatment of nuclear effects on the determination of oscillation parameters. The models developed to describe the…
Distributional treatment effects can be invisible to means: a treatment may preserve average outcomes while changing tails, modes, dispersion, or rare-event probabilities. Kernel tests can detect discrepancies between interventional outcome…
We address the problem of robot guided assembly tasks, by using a learning-based approach to identify contact model parameters for known and novel parts. First, a Variational Autoencoder (VAE) is used to extract geometric features of…
In recent years, epidemic modeling in complex networks has found many applications, including modeling of information or gossip spread in online social networks, modeling of malware spread in communication networks, and the most recent…
Nanowire field-effect sensors have recently been developed for label-free detection of biomolecules. In this work, we introduce a computational technique based on Bayesian estimation to determine the physical parameters of the sensor and,…
Deep learning models have provided huge interpretation power for image-like data. Specifically, convolutional neural networks (CNNs) have demonstrated incredible acuity for tasks such as feature extraction or parameter estimation. Here we…
Causal effect estimation (CEE) provides a crucial tool for predicting the unobserved counterfactual outcome for an entity. As CEE relaxes the requirement for ``perfect'' counterfactual samples (e.g., patients with identical attributes and…