Related papers: Deep Learning Analysis of Deeply Virtual Exclusive…
The impact of potential future measurements of beam charge asymmetries on the current knowledge of Compton form factors is evaluated. Elaborating on the results of a global neural network fit to deeply virtual Compton scattering data, a…
The beam spin asymmetries of the reaction ep -> epg in the Bjorken regime were measured over a wide kinematical domain using the CLAS detector and a new lead-tungstate calorimeter. Through the interference of the Bethe-Heitler process with…
We present a detailed next-to-leading order (NLO) leading twist QCD analysis of deeply virtual Compton scattering (DVCS) observables, for several different input scenarios, in the MS-bar scheme. We discuss the size of the NLO effects and…
We consider the azimuthal angle dependence in the cross section of the hard leptoproduction of a photon on a nucleon target. We show that this dependence allows to define observables that isolate the twist-two and twist-three sectors in the…
Learning binary representation is essential to large-scale computer vision tasks. Most existing algorithms require a separate quantization constraint to learn effective hashing functions. In this work, we present Direct Binary Embedding…
The recent impressive results of deep learning-based methods on computer vision applications brought fresh air to the research and industrial community. This success is mainly due to the process that allows those methods to learn…
A multitude of imaging and vision tasks have seen recently a major transformation by deep learning methods and in particular by the application of convolutional neural networks. These methods achieve impressive results, even for…
We review the recent developments of virtual Compton scattering. We focus on the kinematical regimes which look the most promising. The threshold regime gives access to the generalized polarizabilities of the proton. The hard scattering…
Class imbalance is a common problem in the case of real-world object detection and classification tasks. Data of some classes is abundant making them an over-represented majority, and data of other classes is scarce, making them an…
This paper reports the measurement of polarized and unpolarized cross sections for the ep -> e'p' reaction, which is comprised of Deeply Virtual Compton Scattering (DVCS) and Bethe-Heitler (BH) processes, at an electron beam energy of 5.88…
The unitarization of the longitudinal vector boson scattering (VBS) cross section by the Higgs boson is a fundamental prediction of the Standard Model which has not been experimentally verified. One of the most promising ways to measure VBS…
Fluid ferroelectrics, a recently discovered class of liquid crystals that exhibit switchable, long-range polar order, offer opportunities in ultrafast electro-optic technologies, responsive soft matter, and next-generation energy materials.…
Anomaly detection describes methods of finding abnormal states, instances or data points that differ from a normal value space. Industrial processes are a domain where predicitve models are needed for finding anomalous data instances for…
Measuring Deeply Virtual Compton Scattering on the neutron is one of the necessary steps to understand the structure of the nucleon in terms of Generalized Parton Distributions (GPDs). Neutron targets play a complementary role to…
We propose to use the High Momentum Spectrometer of Hall C combined with the Neutral Particle Spectrometer (NPS) to perform high precision measurements of the Deeply Virtual Compton Scattering (DVCS) cross section using a beam of positrons.…
In this contribution we analyse the cross sections for the exclusive vector meson production as well as the deeply virtual Compton scattering (DVCS) relying on the color dipole approach and considering the numerical solution of the…
The ability to recognize objects despite there being differences in appearance, known as Core Object Recognition, forms a critical part of human perception. While it is understood that the brain accomplishes Core Object Recognition through…
Accurate localization of proteins from fluorescence microscopy images is challenging due to the inter-class similarities and intra-class disparities introducing grave concerns in addressing multi-class classification problems. Conventional…
Many real-world problems, e.g. object detection, have outputs that are naturally expressed as sets of entities. This creates a challenge for traditional deep neural networks which naturally deal with structured outputs such as vectors,…
Coherent imaging through scatter is a challenging task in computational imaging. Both model-based and data-driven approaches have been explored to solve the inverse scattering problem. In our previous work, we have shown that a deep…