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The reparameterization gradient has become a widely used method to obtain Monte Carlo gradients to optimize the variational objective. However, this technique does not easily apply to commonly used distributions such as beta or gamma…

Machine Learning · Statistics 2016-10-20 Francisco J. R. Ruiz , Michalis K. Titsias , David M. Blei

This work introduces ``generalized meshes", a type of meshes suited for the discretization of partial differential equations in non-regular geometries. Generalized meshes extend regular simplicial meshes by allowing for overlapping elements…

Numerical Analysis · Mathematics 2023-01-02 Martin Averseng , Xavier Claeys , Ralf Hiptmair

Hard pQCD processes taking place in ultrarelativistic heavy-ion collisions are a well-calibrated probe. It is believed that the interaction with the surrounding medium of outgoing partons from a hard vertex is capable of revealing details…

High Energy Physics - Phenomenology · Physics 2008-11-26 Thorsten Renk

The generalised vector meson dominance model (GVDM) gives a good description of F_2 data at very low Q^2. At intermediate Q^2 a GVDM component avoids problems when applying the large-Q^2 DIS formalism, such as a negative gluon distribution…

High Energy Physics - Phenomenology · Physics 2007-05-23 Johan Alwall , Gunnar Ingelman

We provide a complete and unified description of translational symmetry breaking in a simple holographic model. In particular, we focus on the distinction and the interplay between explicit and spontaneous breaking. We consider a class of…

High Energy Physics - Theory · Physics 2020-01-08 Martin Ammon , Matteo Baggioli , Amadeo Jiménez-Alba

This paper is concerned with a rate-distortion theory for sequences of i.i.d. random variables with general distribution supported on general sets including manifolds and fractal sets. Manifold structures are prevalent in data science,…

Information Theory · Computer Science 2018-04-25 Erwin Riegler , Günther Koliander , Helmut Bölcskei

This paper discusses a general framework for smoothing parameter estimation for models with regular likelihoods constructed in terms of unknown smooth functions of covariates. Gaussian random effects and parametric terms may also be…

Methodology · Statistics 2016-05-10 Simon N. Wood , Natalya Pya , Benjamin Säfken

We extend the study of integrable structures and analyticity of the spectrum in large $N_c$ QCD$_2$ to a broad class of theories called the generalized QCD, which are given by the Lagrangian $\mathcal{L}\propto {\rm tr}\,B\wedge F- {\rm…

High Energy Physics - Theory · Physics 2024-12-18 Federico Ambrosino , Shota Komatsu

The review is based on the author's papers since 1985 in which a new approach to the separation of variables (\SoV) has being developed. It is argued that \SoV, understood generally enough, could be the most universal tool to solve…

solv-int · Physics 2016-09-08 E. K. Sklyanin

Vector vortex beams, featuring independent spatial modes in orthogonal polarization components, offer an increase in information density for emerging applications in both classical and quantum communication technology. Recent advances in…

We consider the scattering of massless particles coupled to an abelian gauge field in 2n-dimensional Minkowski spacetime. Weinberg's soft photon theorem is recast as Ward identities for infinitely many new nontrivial symmetries of the…

High Energy Physics - Theory · Physics 2014-12-10 Daniel Kapec , Vyacheslav Lysov , Andrew Strominger

A simple, but fully-covariant model for describing neutral Vector Mesons, in both light and heavy sectors, is briefly illustrated. The main ingredients of our relativistic constituent model are i) an Ansatz for the Bethe-Salpeter vertex for…

High Energy Physics - Phenomenology · Physics 2015-05-18 T. Frederico , E. Pace , S. Pisano , G. Salme'

Many widely studied graphical models with latent variables lead to nontrivial constraints on the distribution of the observed variables. Inspired by the Bell inequalities in quantum mechanics, we refer to any linear inequality whose…

Artificial Intelligence · Computer Science 2011-07-21 Greg Ver Steeg , Aram Galstyan

Assuming that naturalness should be modified by ignoring quadratic divergences, we propose a simple extension of the Standard Model where the weak scale is dynamically generated together with an automatically stable vector. Identifying it…

High Energy Physics - Phenomenology · Physics 2013-10-02 Thomas Hambye , Alessandro Strumia

In an exact conformal theory there is no particle. The excitations have continuum spectra and are called "unparticles" by Georgi. We consider supersymmetric extensions of the Standard Model with approximate conformal sectors. The conformal…

High Energy Physics - Phenomenology · Physics 2010-11-15 Hsin-Chia Cheng

In this work we consider anomalous and linear holographic hard wall (HW) models for light unflavored mesons inspired by the AdS/CFT correspondence. The anomalous dimensions depend on the logarithm of the spin S of the meson state and come…

High Energy Physics - Phenomenology · Physics 2024-11-07 Rafael A. Costa-Silva , Henrique Boschi-Filho

Starting from a nonmarkovian conserving relaxation time approximation for collisions we derive coupled dispersion relations for asymmetric nuclear matter. The isovector and isoscalar modes are coupled due to asymmetric nuclear meanfield…

Nuclear Theory · Physics 2008-11-26 K. Morawetz , R. Walke , U. Fuhrmann

We completely characterize General Gauge Mediation (GGM) at the weak scale by solving all IR constraints over the full parameter space. This is made possible through a combination of numerical and analytical methods, based on a set of…

High Energy Physics - Phenomenology · Physics 2016-04-20 Simon Knapen , Diego Redigolo , David Shih

A reorganized perturbation expansion with a propagator of soft infrared behavior is used to study the critical behavior of the mass gap. The condition of relativistic covariance fixes the form of the soft propagator. Finite approximants to…

High Energy Physics - Theory · Physics 2009-10-22 Peter Suranyi

The support vector machine (SVM) and minimum Euclidean norm least squares regression are two fundamentally different approaches to fitting linear models, but they have recently been connected in models for very high-dimensional data through…

Machine Learning · Computer Science 2021-10-28 Navid Ardeshir , Clayton Sanford , Daniel Hsu
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