Related papers: Generalized Vector Dominance Model up to 2 GeV

We consider models of gradient type, which are the densities of a collection of real-valued random variables $\phi :=\{\phi_x: x \in \Lambda\}$ given by $Z^{-1}\exp({-\sum\nolimits_{j \sim k}V(\phi_j-\phi_k)})$. We focus our study on the…

We present the Mixed Likelihood Gaussian process latent variable model (GP-LVM), capable of modeling data with attributes of different types. The standard formulation of GP-LVM assumes that each observation is drawn from a Gaussian…

While momentum-based methods, in conjunction with stochastic gradient descent (SGD), are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods. In this work,…

We compute the production rate of the energy density carried by gravitational waves emitted by a Standard Model plasma in thermal equilibrium, consistently to leading order in coupling constants for momenta $k\sim \pi T$. Summing up the…

The process e+ e- -> n gamma with n>=2 is studied at centre-of-mass energies ranging from \root(s)=192 to 208 GeV. The data sample corresponds to a total integrated luminosity of 427 1/pb. The total and differential cross sections are found…

The coupled pi+N, eta+N, gamma+N systems are described by a K-matrix method. The parameters in this model are adjusted to get an optimal fit to pi+N -> pi+N, pi+N -> eta+N, gamma+N -> pi+N and gamma+N -> eta+N data in an energy range of…

Many machine learning tasks can be formulated as Regularized Empirical Risk Minimization (R-ERM), and solved by optimization algorithms such as gradient descent (GD), stochastic gradient descent (SGD), and stochastic variance reduction…

The nondiagonal generalized vector dominance model (GVDM) of photoabsorption is elaborated using QCD-motivated picture of the (gamma)-(q,anti q) - transition and subsequent meson dominated scattering of the (q, anti q)-pair on the nucleon.…

Rich and complex time-series data, such as those generated from engineering systems, financial markets, videos or neural recordings, are now a common feature of modern data analysis. Explaining the phenomena underlying these diverse data…

We search for the signature of universal properties of extreme events, theoretically predicted for Axiom A flows, in a chaotic and high dimensional dynamical system by studying the convergence of GEV (Generalized Extreme Value) and GP…

The optical conductivity contains relevant information on the properties of correlated electron systems. In infinite dimensions, where dynamical mean field theory becomes exact, vertex corrections can be neglected and the conductivity…

The reaction e+ e- -> e+ e- gamma* gamma* -> e+ e- hadrons is analysed for quasi-real virtual photons using data collected by the L3 detector during the LEP high energy runs at sqrt(s) = 183 and 189 GeV. Preliminary results on the cross…

The process e^+e^- -> gamma gamma (gamma) is studied using data recorded with the OPAL detector at LEP. The data sample corresponds to a total integrated luminosity of 25.38 pb^{-1} taken at centre-of-mass energies of 130-172 GeV. The…

With the data samples taken at center-of-mass energies from 2.00 to 3.08 GeV with the BESIII detector at the BEPCII collider, a partial wave analysis on the $e^{+}e^{-}\rightarrow\pi^{+}\pi^{-}\pi^{0}$ process is performed. The Born cross…

We consider a class of semi-parametric dynamic models with strong white noise errors. This class of processes includes the standard Vector Autoregressive (VAR) model, the nonfundamental structural VAR, the mixed causal-noncausal models, as…

This paper reports measurements of processes: e+e- -> gamma KsK+pi-, e+e- -> gamma K+K-pi0, e+e- -> gamma phi eta, and e+e- -> gamma phi pi0. The initial state radiated photon allows to cover the hadronic final state in the energy range…

Writing data in parallel is a common operation in some computing environments and a good proxy for a number of other parallel processing patterns. The duration of time taken to write data in large-scale compute environments can vary…

We perform a detailed study of the process $e^+e^-\to \mu^+\mu^-\nu_l\bar\nu_l$ including all contributions. The contributions other than from real gauge boson production leads to a rich phenomenology. We explore the use of the process as a…

About 11 200 e^+e^- -> omega -> pi^+pi^-pi^0 events selected in the center of mass energy range from 760 to 810 MeV were used for the measurement of the \omega meson parameters. The following results have been obtained: sigma _{0}=(1457 \pm…

Deep Learning Gaussian Processes (DL-GP) are proposed as a methodology for analyzing (approximating) computer models that produce heteroskedastic and high-dimensional output. Computer simulation models have many areas of applications,…