Related papers: Re-optimization of a deep neural network model for…
Deep neural networks are increasingly being used for the analysis of medical images. However, most works neglect the uncertainty in the model's prediction. We propose an uncertainty-aware deep kernel learning model which permits the…
While Deep Neural Networks (DNNs) achieve state-of-the-art accuracy in various applications, they often fall short in accurately estimating their predictive uncertainty and, in turn, fail to recognize when these predictions may be wrong.…
The real-time quantification of the effect of a wireless channel on the transmitting signal is crucial for the analysis and the intelligent design of wireless communication systems for various services. Recent mechanisms to model channel…
We present a detailed phenomenological study of charged-current-mediated deep-inelastic scattering off longitudinally polarized nucleons at a future Electron-Ion Collider. A new version of the event generator package DJANGOH, extended by…
Recently proposed Transverse Enhancement Model of nuclear effects in Charge Current Quasi-Elastic neutrino scattering [A. Bodek, H. S. Budd, and M. E. Christy, Eur. Phys. J. C{\bf 71} (2011) 1726] is confronted with the MiniBooNE high…
This is the 6th paper in the series developing the formalism to manage the effective scattering theory of strong interactions. Relying on the theoretical scheme suggested in our previous publications we concentrate here on the practical…
Recently, deep neural networks have expanded the state-of-art in various scientific fields and provided solutions to long standing problems across multiple application domains. Nevertheless, they also suffer from weaknesses since their…
The transport of excess electrons in liquid argon driven out of equilibrium by an applied electric field is revisited using a multi-term solution of Boltzmann's equation together with ab initio liquid phase cross-sections calculated using…
We introduce a deep neural network to model in a symmetry preserving way the environmental dependence of the centers of the electronic charge. The model learns from ab-initio density functional theory, wherein the electronic centers are…
A review of recent experimental and theoretical studies of characteristics of neutrino deep-inelastic scattering is presented. Special attentition is paid to the determination of $\alpha_s$ and $1/Q^2$ non-perturbative effects from QCD fits…
We present a novel approach to the regression of quantum mechanical energies based on a scattering transform of an intermediate electron density representation. A scattering transform is a deep convolution network computed with a cascade of…
The availability of the double-differential charged-current neutrino cross section, measured by the MiniBooNE collaboration using a carbon target, allows for a systematic comparison of nuclear effects in quasi-elastic electron and neutrino…
We present a deep learning framework for modeling and analyzing the small-angle scattering data of polydisperse hard-rod systems, a widely used models for anisotropic colloidal particles. We use a variational autoencoder-based neural…
The semi-exclusive averaged reduced cross sections for (anti)neutrino charged current quasi-elastic scattering on carbon, oxygen, and argon are analyzed within the relativistic distorted wave impulse approximation. We found that these cross…
In this work, we introduce a novel Deep Learning-based method to perceive the environment of a vehicle based on radar scans while accounting for uncertainties in its predictions. The environment of the host vehicle is segmented into equally…
The neutrino-electron scattering process is a powerful tool to explore new physics beyond the standard model. Recently the possibility of DUNE Near Detector (ND) to constrain various new physics scenarios using this process have been…
Particle scattering is a powerful tool to unveil the nature of various subatomic phenomena. The key quantity is the scattering amplitude whose analytic structure carries the information of the quantum states. In this work, we demonstrate…
The next generation of long-baseline neutrino experiments will be capable of precision measurements of neutrino oscillation parameters, precision neutrino-nucleus scattering, and unprecedented sensitivity to physics beyond the Standard…
This paper investigates deep learning enabled beamforming design for ultra-dense wireless networks by integrating prior knowledge and graph neural network (GNN), named model-based GNN. A energy efficiency (EE) maximization problem is…
This study proposes a novel analytical framework that integrates DBSCAN clustering with the Elastic Net regression model to address multifactorial problems characterized by structural complexity and multicollinearity, exemplified by carbon…