Related papers: Generalized Vector Dominance Model up to 2 GeV
The cross section of reaction e^+e^-\to\pi^+\pi^-\pi^+\pi^- is calculated for energies 0.65\leq \sqrt{s}\leq1 GeV in the framework of the generalized hidden local symmetry model. The calculations are compared with the data of CMD-2 and…
Gaussian processes (GPs) are popular nonparametric statistical models for learning unknown functions and quantifying the spatiotemporal uncertainty in data. Recent works have extended GPs to model scalar and vector quantities distributed…
The process e^+e^- -> Z gamma gamma -> q q~ gamma gamma is studied in 0.5 fb-1 of data collected with the L3 detector at centre-of-mass energies between 130.1 GeV and 201.7 GeV. Cross sections are measured and found to be consistent with…
The Gaussian process state-space model (GPSSM) has attracted extensive attention for modeling complex nonlinear dynamical systems. However, the existing GPSSM employs separate Gaussian processes (GPs) for each latent state dimension,…
While deep learning has expanded the possibilities for highly expressive variational families, the practical benefits of these tools for variational inference (VI) are often limited by the minimization of the traditional Kullback-Leibler…
Deep generative models often perform poorly in real-world applications due to the heterogeneity of natural data sets. Heterogeneity arises from data containing different types of features (categorical, ordinal, continuous, etc.) and…
The generic matrix multiply (GEMM) function is the core element of high-performance linear algebra libraries used in many computationally-demanding digital signal processing (DSP) systems. We propose an acceleration technique for GEMM based…
New results of the e+e- -> pi+pi-, K+K-, K_SK_L, pi0/gamma, eta0/gamma processes cross section measurements are presented. The results are based on the 30 pb^-1 data, accumulated by SND detector at VEPP-2M e+e- collider in the energy range…
Deep generative models such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models, and Transformers, have shown great promise in a variety of applications, including image and speech synthesis, natural…
In this paper we introduce deep Gaussian process (GP) models. Deep GPs are a deep belief network based on Gaussian process mappings. The data is modeled as the output of a multivariate GP. The inputs to that Gaussian process are then…
The technique of discrimination of the $e^+e^-\to e^+e^-$ and $e^+e^-\to \pi^+\pi^-$ events in energy range $0.5 < \sqrt{s} < 1$ GeV by energy deposition in the calorimeter of SND detector was developed by applying machine learning method.…
A Finite Energy QCD sum rule at non-zero temperature is used to determine the $q^2$- and the T-dependence of the $\rho \pi \pi$ vertex function in the space-like region. A comparison with an independent QCD determination of the…
We present an analysis of the process $e^+e^-\to V^*\to\pi\pi\gamma$, where $V=\gamma$ or $V=Z$ boson, in the kinematical region where $\sqrt{s}$, the c.m. energy of the $e^+e^-$ pair, is large but much below the $Z$-pole. The subprocess…
A partial wave analysis on the process $e^{+}e^{-}\rightarrow\omega\pi^{+}\pi^{-}$ is performed using 647 pb$^{-1}$ of data sample collected by using the BESIII detector operating at the BEPCII storage ring at center-of-mass (c.m.) energies…
We study the reaction e+e- --> e+e-pi0 and measure the gamma gamma* --> pi0 transition form factor in the momentum transfer range from 4 to 40 GeV^2. The analysis is based on 442 fb^-1 of integrated luminosity collected at PEP-II with the…
We present a unified description of the vector meson and dilepton production in elementary and in heavy ion reactions. The production of vector mesons ($\rho,\omega$) is described via the excitation of nuclear resonances ($R$). The…
We review the generalized vector dominance (GVD) approach to DIS at small values of the scaling variable, x. In particular, we concentrate on a recent formulation of GVD that explicitly incorporates the configuration of the gamma^* -> q…
Variational autoencoders (VAE) often use Gaussian or category distribution to model the inference process. This puts a limit on variational learning because this simplified assumption does not match the true posterior distribution, which is…
The modeling and simulation of dynamical systems is a necessary step for many control approaches. Using classical, parameter-based techniques for modeling of modern systems, e.g., soft robotics or human-robot interaction, is often…
The differential cross section for the process $\gamma \gamma \to \eta \pi^0$ has been measured in the kinematic range $0.84 \GeV < W < 4.0 \GeV$, $|\cos \theta^*|<0.8$, where $W$ and $\theta^*$ are the energy and $\pi^0$ (or $\eta$)…