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Psychometric functions typically characterize binary sensory decisions along a single stimulus dimension. However, real-life sensory tasks vary along a greater variety of dimensions (e.g. color, contrast and luminance for visual stimuli).…

Neurons and Cognition · Quantitative Biology 2023-02-03 Stephen Keeley , Benjamin Letham , Chase Tymms , Craig Sanders , Michael Shvartsman

As with parton distributions, flexible phenomenological parameterizations of generalized parton distributions (GPDs) are essential for their extraction from data. The large number of constraints imposed on GPDs make simple Lorentz covariant…

High Energy Physics - Phenomenology · Physics 2017-08-30 Brian C. Tiburzi , Gaurav Verma

Two types of parameter dependent generalizations of classical matrix ensembles are defined by their probability density functions (PDFs). As the parameter is varied, one interpolates between the eigenvalue PDF for the superposition of two…

Mathematical Physics · Physics 2007-05-23 Peter J. Forrester , Eric M. Rains

Divergence measures play a central role and become increasingly essential in deep learning, yet efficient measures for multiple (more than two) distributions are rarely explored. This becomes particularly crucial in areas where the…

Machine Learning · Computer Science 2024-06-07 Mingfei Lu , Chenxu Li , Shujian Yu , Robert Jenssen , Badong Chen

We investigate the relations between transverse momentum dependent parton distributions (TMDs) and generalized parton distributions (GPDs) in a light-front quark-diquark model motivated by soft wall AdS/QCD. Many relations are found to have…

High Energy Physics - Phenomenology · Physics 2021-11-10 Bheemsehan Gurjar , Dipankar Chakrabarti , Poonam Choudhary , Asmita Mukherjee , Pulak Talukdar

The Beta Rank Function (BRF) $x(u) =A(1-u)^b/u^a$, where $u$ is the normalized and continuous rank of an observation $x$, has wide applications in fitting real-world data from social science to biological phenomena. The underlying…

Methodology · Statistics 2019-10-15 Oscar Fontanelli , Pedro Miramontes , Ricardo Mansilla , Germinal Cocho , Wentian Li

This paper investigates general and generalized differentiation properties of the optimal value function associated with perturbed optimization problems. Fundamental results on nearly convex sets and functions in infinite-dimensional spaces…

Optimization and Control · Mathematics 2025-10-24 V. S. T. Long , B. S. Mordukhovich , N. M. Nam , L. White

Existing bounds on the generalization error of deep networks assume some form of smooth or bounded dependence on the input variable, falling short of investigating the mechanisms controlling such factors in practice. In this work, we…

Machine Learning · Computer Science 2025-07-24 Matteo Gamba , Hossein Azizpour , Mårten Björkman

We find candidate macroscopic gravity duals for scale-invariant but non-Lorentz invariant fixed points, which do not have particle number as a conserved quantity. We compute two-point correlation functions which exhibit novel behavior…

High Energy Physics - Theory · Physics 2010-04-06 Shamit Kachru , Xiao Liu , Michael Mulligan

Double parton distribution functions (dPDF), accessible in high energy proton-proton and proton nucleus collisions, encode information on how partons inside a proton are correlated among each other and could represent a tool to explore the…

High Energy Physics - Phenomenology · Physics 2015-09-30 Matteo Rinaldi , Sergio Scopetta , Marco Traini , Vicente Vento

A range of issues pertaining to the use of Wilson lines in integrated and transverse-momentum dependent (TMD) parton distribution functions (PDF) is discussed. The relation between gauge invariance and the renormalization properties of the…

High Energy Physics - Phenomenology · Physics 2008-11-26 I. O. Cherednikov , N. G. Stefanis

We prove that the marginal densities of a global probability mass function in a primal normal factor graph and the corresponding marginal densities in the dual normal factor graph are related via local mappings. The mapping depends on the…

Machine Learning · Statistics 2020-07-15 Mehdi Molkaraie

The normal distribution is used as a unified probability distribution, however, our researcher found that it is not good agreed with the real-life dynamical system's data. We collected and analyzed representative naturally occurring data…

Dynamical Systems · Mathematics 2020-11-06 Wei Ping Cheng , Zhi Hong Zhang , Pu Wang

Geometric quantiles are popular location functionals to build rank-based statistical procedures in multivariate settings. They are obtained through the minimization of a non-smooth convex objective function. As a result, the singularity of…

Statistics Theory · Mathematics 2026-02-11 Dimitri Konen , Gilles Stupfler

We discuss the current developments by the European Twisted Mass Collaboration in extracting parton distribution functions from the quasi-PDF approach. We concentrate on the non-perturbative renormalization prescription recently developed…

The results on polarized parton densities (PDFs) obtained using different methods of QCD analysis of the present polarized DIS data are discussed. Their dependence on the method used in the analysis, accounting or not for the kinematic and…

High Energy Physics - Phenomenology · Physics 2009-11-05 Elliot Leader , Aleksander V. Sidorov , Dimiter B. Stamenov

We discuss the recent progress in extracting partonic functions from the quasi-distribution approach, using twisted mass fermions. This concerns, among others, the investigation of several sources of systematic effects. Their careful…

The generalized master equation with two times, introduced in earlier, applies to the problem of diffusion in an time-dependent (in general inhomogeneous) external field. We consider the case of the quasi Fokker-Planck approximation, when…

Soft Condensed Matter · Physics 2007-05-23 S. A. Trigger

We present a calculation of the generalized parton distributions (GPDs) of the photon when the helicity of the initial photon is different from the final photon. We calculate the GPDs using overlaps of photon light-front wave functions…

High Energy Physics - Phenomenology · Physics 2015-06-12 Asmita Mukherjee , Sreeraj Nair , Vikash Kumar Ojha

Douglas-Rachford splitting and its equivalent dual formulation ADMM are widely used iterative methods in composite optimization problems arising in control and machine learning applications. The performance of these algorithms depends on…

Optimization and Control · Mathematics 2019-06-28 Jacob H. Seidman , Mahyar Fazlyab , Victor M. Preciado , George J. Pappas
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