Related papers: Deep Learning Analysis of Deeply Virtual Exclusive…

Measuring DVCS on a neutron target is a necessary step to deepen our understanding of the structure of the nucleon in terms of Generalized Parton Distributions (GPDs). The combination of neutron and proton targets allows to perform a flavor…

Collisions at high-energy particle colliders are a traditionally fruitful source of exotic particle discoveries. Finding these rare particles requires solving difficult signal-versus-background classification problems, hence machine…

Deep convolutional neural networks have led to breakthrough results in numerous practical machine learning tasks such as classification of images in the ImageNet data set, control-policy-learning to play Atari games or the board game Go,…

A new generation of experiments is expected to shed light on the elusive parton structure of the bound proton. One of the most promising directions is incoherent deeply virtual Compton scattering, which can provide a tomographic view of the…

The computational complexity of calculating phase diagrams for multi-parameter models significantly limits the ability to select parameters that correspond to experimental data. This work presents a machine learning method for solving the…

This work proposes a new end-to-end DCNN based approach for motion segmentation, especially for video sequences captured with such non-static cameras, called MOSNET. While other approaches focus on spatial or temporal context only, the…

This paper addresses the task of set prediction using deep learning. This is important because the output of many computer vision tasks, including image tagging and object detection, are naturally expressed as sets of entities rather than…

In this work we study the inverse quantum scattering via deep learning regression, which is implemented via a Multilayer Perceptron. A step-by-step method is provided in order to obtain the potential parameters. A circular boundary-wall…

Deep convolutional neural networks (CNNs) have achieved breakthrough performance in many pattern recognition tasks such as image classification. However, the development of high-quality deep models typically relies on a substantial amount…

We report on the first measurement of the beam-spin asymmetry in the exclusive process of coherent deeply virtual Compton scattering off a nucleus. The experiment used the 6 GeV electron beam from the CEBAF accelerator at Jefferson Lab…

We establish a series of deep convolutional neural networks to automatically analyze position averaged convergent beam electron diffraction patterns. The networks first calibrate the zero-order disk size, center position, and rotation…

We present final results on the photon electroproduction ($\vec{e}p\rightarrow ep\gamma$) cross section in the deeply virtual Compton scattering (DVCS) regime and the valence quark region from Jefferson Lab experiment E00-110. Results from…

For four decades statistical physics has been providing a framework to analyse neural networks. A long-standing question remained on its capacity to tackle deep learning models capturing rich feature learning effects, thus going beyond the…

Many large scale problems in computational fluid dynamics such as uncertainty quantification, Bayesian inversion, data assimilation and PDE constrained optimization are considered very challenging computationally as they require a large…

Deep convolutional neural networks (DCNN) have enjoyed great successes in many signal processing applications because they can learn complex, non-linear causal relationships from input to output. In this light, DCNNs are well suited for the…

Effective learning of asymmetric and local features in images and other data observed on multi-dimensional grids is a challenging objective critical for a wide range of image processing applications involving biomedical and natural images.…

A core problem in machine learning is to learn expressive latent variables for model prediction on complex data that involves multiple sub-components in a flexible and interpretable fashion. Here, we develop an approach that improves…

Optical neural networks are emerging as a powerful and versatile tool for processing optical signals directly in the optical domain with superior speed, integrability, and functionality. Their application to optical polarization enables…

We discuss all-order factorization for the virtual Compton process at next-to-leading power (NLP) in the $\Lambda_{\rm QCD}/Q$ and $\sqrt{-t}/Q$ expansion (twist-3), both in the double-deeply-virtual case and the single-deeply-virtual case.…

Camera model identification has earned paramount importance in the field of image forensics with an upsurge of digitally altered images which are constantly being shared through websites, media, and social applications. But, the task of…