相关论文: Deep Inelastic Structure Functions in Bag-Like Mod…
Irregular conformal block is an important tool to study a new type of conformal theories, which can be constructed as the colliding limit of the regular conformal block. The irregular conformal block is realized as the $\beta$-deformed…
We study nuclear effect in the $F^A_3(x)$ structure function in the deep inelastic neutrino reactions on iron by taking into account Fermi motion, binding, target mass correction, shadowing and anti-shadowing corrections. Calculations have…
This paper investigates the learnability of the nonlinearity property of Boolean functions using neural networks. We train encoder style deep neural networks to learn to predict the nonlinearity of Boolean functions from examples of…
Since its foundations, more than one hundred years ago, the field of structural biology has strived to understand and analyze the properties of molecules and their interactions by studying the structure that they take in 3D space. However,…
We have studied the color dipole picture for the description of the deep inelastic process, mainly the structure functions which are driven directly by the gluon distribution. Estimates for those functions are obtained using the effective…
We propose a functional linear model to predict a response using multiple functional and longitudinal predictors and to estimate the effect lags of predictors. The coefficient functions are written as the expansion of a basis system (e.g.…
This paper integrates deep neural networks (DNNs) into structural economic models to increase flexibility and capture rich heterogeneity while preserving interpretability. Economic structure and machine learning are complements in empirical…
Nuclear structure functions at small x and small or moderate $Q^2$ are studied using the relation with diffraction on nucleons which arises from Gribov's Reggeon Calculus. A reasonable description of experimental data is obtained with no…
Bayesian neural networks (BNNs) augment deep networks with uncertainty quantification by Bayesian treatment of the network weights. However, such models face the challenge of Bayesian inference in a high-dimensional and usually…
A role of different components in the wave function of the weakly bound light nuclei states was studied within the framework of the cluster model, taking into account of orbitals "polarization". It was shown that a limited number of…
We construct a parametrization of the deep-inelastic structure function of the proton F_2 based on all available experimental information from charged lepton deep-inelastic scattering experiments. The parametrization effectively provides a…
We review the present status of polarized structure functions measured in deep-inelastic scattering. We discuss the x and Q^2 dependence of the structure function g_1, and how it can be used to test perturbative QCD at next-to-leading order…
The long range structure of the nucleon is discussed starting from the old model of a quark bag with a pion cloud (``cloudy bag'') carrying on to the more recent ideas of the parton model of the nucleon. On the basis of the most recent…
We study the space of functions computed by random-layered machines, including deep neural networks and Boolean circuits. Investigating the distribution of Boolean functions computed on the recurrent and layer-dependent architectures, we…
In the context of noncommutative space-time, we investigate the nucleon structure functions which plays an important role to identify the internal structure of nucleons. We use the corrected vertices and employ new vertices that appear in…
The present paper is comprised of two parts. First, we give a brief survey of the theoretical framework for microscopic nuclear structure calculations starting from a free nucleon-nucleon potential. Then, we present some selected results of…
We study the power corrections (infrared renormalon contributions) to the coefficient functions for non-singlet deep inelastic structure functions due to gluon vacuum polarization insertions in one-loop graphs. Remarkably, for all the…
After the initial discovery of the so-called "spin crisis in the parton model" in the 1980's, a large set of polarization data in deep inelastic lepton-nucleon scattering was collected at labs like SLAC, DESY and CERN. More recently, new…
Probabilistic generative deep learning for molecular design involves the discovery and design of new molecules and analysis of their structure, properties and activities by probabilistic generative models using the deep learning approach.…
New experimental results on the spin dependent structure functions g_1 and g_2 which are determined from deep-inelastic scattering experiments at CERN, SLAC and DESY are reported. These results are used to evaluate the Bjorken sum rule and…