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Double Deeply Virtual Compton Scattering (DDVCS) is the only experimental channel for the determination of the dependence of the Generalized Parton Distributions (GPDs) on both the average and the transferred momentum independently. The…
We systematically evaluate observables for hard exclusive electroproduction of real photons and compare them to experiment using a set of Generalized Parton Distributions (GPDs) whose parameters are constrained by Deeply Virtual Meson…
Separating shared and independent features is crucial for multi-phase contrast-enhanced (CE) MRI synthesis. However, existing methods use deep autoencoder generators with low parameter efficiency and lack interpretable training strategies.…
Variational Auto-Encoders (VAEs) are capable of learning latent representations for high dimensional data. However, due to the i.i.d. assumption, VAEs only optimize the singleton variational distributions and fail to account for the…
Learning deep discrete latent presentations offers a promise of better symbolic and summarized abstractions that are more useful to subsequent downstream tasks. Inspired by the seminal Vector Quantized Variational Auto-Encoder (VQ-VAE),…
Deeply virtual Compton scattering (DVCS) is the golden exclusive channel for the study of the partonic structure of hadrons, within the universal framework of generalized parton distributions (GPDs). This paper presents the aim and general…
We review the phenomenological framework for accessing Generalized Parton Distributions (GPDs) using measurements of Deeply Virtual Compton Scattering (DVCS) from a proton target. We describe various GPD models and fitting procedures,…
We present a Machine Learning based approach to the cross section and asymmetries for deeply virtual Compton scattering from an unpolarized proton target using both an unpolarized and polarized electron beam. Machine learning methods are…
Positron initiated deeply virtual Compton scattering (DVCS) off $^4$He and $^3$He nuclei is described. The way the so-called $d-$term could be obtained from the real part of the relevant Compton form factor is summarized, and the importance…
Variational Autoencoders (VAEs) are powerful generative models widely used for learning interpretable latent spaces, quantifying uncertainty, and compressing data for downstream generative tasks. VAEs typically rely on diagonal Gaussian…
Variational autoencoders (VAEs) are fundamental for generative modeling and image reconstruction, yet their performance often struggles to maintain high fidelity in reconstructions. This study introduces a hybrid model, quantum variational…
Variational autoencoders (VAEs), that are built upon deep neural networks have emerged as popular generative models in computer vision. Most of the work towards improving variational autoencoders has focused mainly on making the…
Forecasting conditional stochastic nonlinear dynamical systems is a fundamental challenge repeatedly encountered across the biological and physical sciences. While flow-based models can impressively predict the temporal evolution of…
We study the deeply-virtual Compton scattering (DVCS) process $e^- N \to e^- \gamma \pi N$ involving the transition between a nucleon and a nucleon resonance in the $\pi N$ system, within the framework of generalized parton distributions…
Using the helicity amplitudes formalism, we study deeply virtual exclusive electron photo-production off an unpolarized nucleon target, $ep \rightarrow e' p' \gamma$, through a range of kinematics both in the fixed target setting with…
Double deeply virtual Compton scattering (DDVCS) is the process where an electron scatters off a nucleon and produces a lepton pair. The main advantage of this process in contrast with deeply virtual and timelike Compton scatterings (DVCS…
Sampling trajectories from a distribution followed by ranking them based on a specified cost function is a common approach in autonomous driving. Typically, the sampling distribution is hand-crafted (e.g a Gaussian, or a grid). Recently,…
We study recent Deeply Virtual Compton scattering (DVCS) data within a dual parameterization of the Generalized Parton Distributions (GPDs). This parameterization allows to quantify the maximum amount of information, that can be extracted…
Deep latent variable models (DLVMs) are designed to learn meaningful representations in an unsupervised manner, such that the hidden explanatory factors are interpretable by independent latent variables (aka disentanglement). The…
We report on the status of the phenomenological access of generalized parton distributions from photon and meson electroproduction off proton. Thereby, we emphasize the role of HERMES data for deeply virtual Compton scattering, which allows…