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Over the past two decades, intense experimental efforts have focused on measuring observables that contribute to a three-dimensional description of the nucleon. Generalized Parton Distributions provide complementary insights into the…

Nuclear Experiment · Physics 2025-09-24 L. Calero Diaz , D. Keller

This article studies the solutions of time-dependent differential inclusions which is motivated by their utility in the modeling of certain physical systems. The differential inclusion is described by a time-dependent set-valued mapping…

Optimization and Control · Mathematics 2021-07-05 Kanat Camlibel , Luigi Iannelli , Aneel Tanwani

We describe an extension to the density matrix renormalization group method incorporating real time evolution into the algorithm. Its application to transport problems in systems out of equilibrium and frequency dependent correlation…

Strongly Correlated Electrons · Physics 2007-05-23 Steven R. White , Adrian E. Feiguin

Motivated by the study of H\"ormander's sums-of-squares operators and their generalizations, we define the convolution algebra of transverse distributions associated to a singular foliation. We prove that this algebra is represented as…

Analysis of PDEs · Mathematics 2020-04-20 Iakovos Androulidakis , Omar Mohsen , Robert Yuncken

Traditional linear methods for forecasting multivariate time series are not able to satisfactorily model the non-linear dependencies that may exist in non-Gaussian series. We build on the theory of learning vector-valued functions in the…

Machine Learning · Computer Science 2017-06-28 Magda Gregorová , Alexandros Kalousis , Stéphane Marchand-Maillet

We present recent results on elastic deuteron Compton scattering calculations for polarised beans and targets up to next-to-leading order within Chiral Effective Field Theory in the Small Scale Expansion variant to implement a dynamical…

Nuclear Theory · Physics 2014-11-20 Harald W. Griesshammer , Deepshikha Shukla

We introduce a class of auto-encoder neural networks tailored to data from the natural exponential family (e.g., count data). The architectures are inspired by the problem of learning the filters in a convolutional generative model with…

Machine Learning · Computer Science 2020-06-30 Bahareh Tolooshams , Andrew H. Song , Simona Temereanca , Demba Ba

The paper deals with homogenization and higher order approximations of solutions to nonlocal evolution equations of convolution type whose coefficients are periodic in the spatial variables and random stationary in time. We assume that the…

Analysis of PDEs · Mathematics 2026-02-11 Marina Kleptsyna , Andrey Piatnitski , Alexandre Popier

In this paper, estimates are proven for convolution kernels associated to multipliers from a reasonably general class of compactly supported two-dimensional functions constructed out of real-analytic functions. These estimates are both for…

Classical Analysis and ODEs · Mathematics 2016-06-28 Michael Greenblatt

We present results for leading-twist azimuthal asymmetries in semi-inclusive lepton-nucleon deep-inelastic scattering due to naively time-reversal odd transverse-momentum dependent parton distribution functions from the light-cone…

High Energy Physics - Phenomenology · Physics 2011-07-08 B. Pasquini , P. Schweitzer

We treat the convergence of Carleman linearization of nonlinear evolutionary equations through the approximation theory of strongly continuous semigroups, by Carleman embedding the underlying nonlinear semigroups as linear semigroups.…

Quantum Physics · Physics 2026-05-06 Sitanshu Gakkhar , Ala Shayeghi , David C. Del Rey Fernández

This thesis work summarizes studies in QCD for applications both at hadron colliders and in exclusive processes. In the first part we focus our attention on the study of the initial state scaling violations and the evolution of the…

High Energy Physics - Phenomenology · Physics 2007-05-23 Marco Guzzi

We carry out the first complete calculation of kinematic power corrections $\sim t/Q^2$ and $\sim m^2/Q^2$ to several key observables in Deeply Virtual Compton Scattering. The issue of convention dependence of the leading twist…

High Energy Physics - Phenomenology · Physics 2014-04-23 Vladimir M. Braun , Alexander N. Manashov , Dieter Mueller , Bjoern M. Pirnay

We introduce a new category of multivariate conditional generative models and demonstrate its performance and versatility in probabilistic time series forecasting and simulation. Specifically, the output of quantile regression networks is…

Machine Learning · Statistics 2019-07-26 Ruofeng Wen , Kari Torkkola

The modified evolution equation for parton distributions of Dokshitzer, Marchesini and Salam is extended to non-singlet Deep Inelastic Scattering coefficient functions and the physical evolution kernels which govern their scaling violation.…

High Energy Physics - Phenomenology · Physics 2014-11-20 Georges Grunberg

Nonlinear integrable equations serve as a foundation for nonlinear dynamics, and fractional equations are well known in anomalous diffusion. We connect these two fields by presenting the discovery of a new class of integrable fractional…

Exactly Solvable and Integrable Systems · Physics 2022-10-21 Mark J. Ablowitz , Joel B. Been , Lincoln D. Carr

We discuss reflection factors for purely elastic scattering theories and relate them to perturbations of specific conformal boundary conditions, using recent results on exact off-critical g-functions. For the non-unitary cases, we support…

High Energy Physics - Theory · Physics 2008-11-26 Patrick Dorey , Anna Lishman , Chaiho Rim , Roberto Tateo

We study a class of nonautonomous, linear, parabolic equations with unbounded coefficients on $\mathbb R^{d}$ which admit an evolution system of measures. It is shown that the solutions of these equations converge to constant functions as…

Analysis of PDEs · Mathematics 2015-08-18 Luca Lorenzi , Alessandra Lunardi , Roland Schnaubelt

We present a complete, next-to-leading-order (NLO), leading-twist QCD analysis of deeply virtual Compton scattering (DVCS) observables, in the ${\bar {MS}}$ scheme, and in the kinematic ranges of the H1, ZEUS and HERMES experiments. We use…

High Energy Physics - Phenomenology · Physics 2009-11-07 A. Freund , M. F. McDermott

Dictionary learning algorithms or supervised deep convolution networks have considerably improved the efficiency of predefined feature representations such as SIFT. We introduce a deep scattering convolution network, with predefined wavelet…

Computer Vision and Pattern Recognition · Computer Science 2015-06-02 Edouard Oyallon , Stéphane Mallat
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