Related papers: Deeply Learning Deep Inelastic Scattering Kinemati…

Previously published data on inclusive neutral and charged current e+p and e-p deep inelastic scattering from HERA have been combined. The new, model-independent method of combining the measured cross sections takes full account of…

In dynamic positron emission tomography (PET) reconstruction, the importance of leveraging the temporal dependence of the data has been well appreciated. Current deep-learning solutions can be categorized in two groups in the way the…

The deep energy method (DEM) has been used to solve the elastic deformation of structures with linear elasticity, hyperelasticity, and strain-gradient elasticity material models based on the principle of minimum potential energy. In this…

Initial state radiation (ISR) in deep inelastic scattering of charged leptons on nucleons can lead to substantial shifts in the reconstruction of $Q^2$ and the scaling variables $x$ and $y$. By measuring at least three out of the four…

The objective of the Cyclotron Radiation Emission Spectroscopy (CRES) technology is to build precise particle energy spectra. This is achieved by identifying the start frequencies of charged particle trajectories which, when exposed to an…

We present the application of a class of deep learning, known as Physics Informed Neural Networks (PINN), to learning and discovery in solid mechanics. We explain how to incorporate the momentum balance and constitutive relations into PINN,…

With the advent of robot-assisted surgery, the role of data-driven approaches to integrate statistics and machine learning is growing rapidly with prominent interests in objective surgical skill assessment. However, most existing work…

Understanding the training dynamics of deep learning models is perhaps a necessary step toward demystifying the effectiveness of these models. In particular, how do data from different classes gradually become separable in their feature…

The radiation pattern within high energy quark- and gluon-initiated jets (jet substructure) is used extensively as a precision probe of the strong force as well as an environment for optimizing event generators with numerous applications in…

We study the application of deep learning techniques to the analysis and classification of ions accelerated at collisionless shocks in hybrid (kinetic ions--fluid electrons) simulations. Ions were classified as thermal, suprathermal, or…

We report the development of deep learning coherent electron diffractive imaging at sub-angstrom resolution using convolutional neural networks (CNNs) trained with only simulated data. We experimentally demonstrate this method by applying…

The cross section and the proton structure function F2 for neutral current deep inelastic e+p scattering have been measured with the ZEUS detector at HERA using an integrated luminosity of 30 pb-1. The data were collected in 1996 and 1997…

We demonstrate the use of deep learning for fast spectral deconstruction of speckle patterns. The artificial neural network can be effectively trained using numerically constructed multispectral datasets taken from a measured spectral…

The cross sections for charged and neutral current deep inelastic scattering in e^+p collisions with a longitudinally polarised positron beam have been measured using the ZEUS detector at HERA. The results, based on data corresponding to an…

This article presents a physics-informed deep learning method for the quantitative estimation of the spatial coordinates of gamma interactions within a monolithic scintillator, with a focus on Positron Emission Tomography (PET) imaging. A…

After a brief review of the kinematics of deep inelastic scattering (DIS) within the so-called $\Sigma$ method, we derive the necessary formulae for the treatment of QED radiative corrections to DIS originating from hard photon radiation.…

Modern laboratory techniques like ultrafast laser excitation and shock compression can bring matter into highly nonequilibrium states with complex structural transformation, metallization and dissociation dynamics. To understand and model…

The production of D+- and D0 mesons has been measured with the ZEUS detector at HERA using an integrated luminosity of 133.6 pb-1. The measurements cover the kinematic range 5 < Q^2 < 1000 GeV^2, 0.02 < y < 0.7, 1.5 < p_T^D < 15 GeV and…

Studies of the Deep Inelastic Scattering (DIS) have provided fundamental information of the nucleon structure for decades. The electron-ion collider (EIC) will be the first collider capable of DIS study with both polarized lepton and…

We present and evaluate the capacity of a deep neural network to learn robust features from EEG to automatically detect seizures. This is a challenging problem because seizure manifestations on EEG are extremely variable both inter- and…