Related papers: Deep Learning Analysis of Deeply Virtual Exclusive…
We give predictions for the leading order amplitudes for deeply virtual Compton scattering and hard meson electroproduction reactions at large Q^2 in the valence region in terms of skewed quark distributions. We give first estimates for the…
We give a partonic interpretation for the deeply virtual Compton scattering (DVCS) measurements of the H1 and ZEUS collaborations in the small-x_B region in terms of generalized parton distributions. Thereby we have a closer look at the…
This chapter presents deep neural network based methods for enhancing the sensitivity of X-ray telescopic observations with imaging polarimeters. Deep neural networks can be used to determine photoelectron emission directions, photon…
Defect detection is a basic and essential task in automatic parts production, especially for automotive engine precision parts. In this paper, we propose a new idea to construct a deep convolutional network combining related knowledge of…
Contactless and non-invasive estimation of mechanical properties of physical media from optical observations is of interest for manifold engineering and biomedical applications, where direct physical measurements are not possible.…
In meta-learning approaches, it is difficult for a practitioner to make sense of what kind of representations the model employs. Without this ability, it can be difficult to both understand what the model knows as well as to make meaningful…
Deeply virtual meson scattering cross sections and asymmetries for the pi^0 and eta exclusive electroproduction in a very wide kinematic range of Q^2, t and x_B have been measured with CLAS (JLab). Initial analysis is already showing…
The longitudinal target-spin asymmetry A_UL for the exclusive electroproduction of high energy photons was measured for the first time in p(e,e'p\gamma). The data have been accumulated at Jefferson Lab with the CLAS spectrometer using 5.7…
This paper presents an innovative deep learning pipeline which estimates the relative pose of a spacecraft by incorporating the temporal information from a rendezvous sequence. It leverages the performance of long short-term memory (LSTM)…
Few-shot deep learning is a topical challenge area for scaling visual recognition to open ended growth of unseen new classes with limited labeled examples. A promising approach is based on metric learning, which trains a deep embedding to…
This paper presents MONET -- an end-to-end semi-supervised learning framework for a keypoint detector using multiview image streams. In particular, we consider general subjects such as non-human species where attaining a large scale…
Beta-turn prediction is useful in protein function studies and experimental design. Although recent approaches using machine-learning techniques such as SVM, neural networks, and K-NN have achieved good results for beta-turn pre-diction,…
We investigate the QED Compton process (both elastic and inelastic) in unpolarized and longitudinally polarized electron-proton scattering. The cross section can be expressed in terms of the equivalent photon distribution of the proton. We…
To address the challenge of segmenting noisy images with blurred or fragmented boundaries, this paper presents a robust version of Variational Model Based Tailored UNet (VM_TUNet), a hybrid framework that integrates variational methods with…
We discuss recent attempts to extract deeply virtual Compton scattering form factors with emphasis on their uncertainties, which turn out to be most reliably provided by method of neural networks.
We propose a new representation of visual data that disentangles object position from appearance. Our method, termed Deep Latent Particles (DLP), decomposes the visual input into low-dimensional latent ``particles'', where each particle is…
We present a fit of the virtual-photon scattering asymmetry of polarized Deep Inelastic Scattering which combines a Monte Carlo technique with the use of a redundant parametrization based on Neural Networks. We apply the result to the…
AIMS: Clinical radiographic imaging is seated upon the principle of differential keV photon transmission through an object. At clinical x-ray energies the scattering of photons causes signal noise and is utilized solely for transmission…
Deep learning models extract, before a final classification layer, features or patterns which are key for their unprecedented advantageous performance. However, the process of complex nonlinear feature extraction is not well understood, a…
Position detection of hydraulic cylinder pistons is crucial for numerous industrial automation applications. A typical traditional method is to excite electromagnetic waves in the cylinder structure and analytically solve the piston…