相关论文: Treating the b quark distribution function with re…
A novel technique to determine invariant curves in nonlinear beam dynamics based on the method of formal series has been developed. It is first shown how the solution of the Hamilton equations of motion describing nonlinear betatron…
Understanding how anatomical shapes evolve in response to developmental covariates and quantifying their spatially varying uncertainties is critical in healthcare research. Existing approaches typically rely on global time-warping…
We introduce the neural network approach to global fits of parton distribution functions. First we review previous work on unbiased parametrizations of deep-inelastic structure functions with faithful estimation of their uncertainties, and…
Mode shape information play the essential role in deciding the spatial pattern of vibratory response of a structure. The uncertainty quantification of mode shape, i.e., predicting mode shape variation when the structure is subjected to…
Measuring the photon energy spectrum in radiative B decays provides essential help for gaining theoretical control over semileptonic B transitions. The hadronic recoil mass distribution in B -> X_u \ell\nu promises the best environment for…
The inclusive spectra of radiative and semi-leptonic B-meson decays near the endpoint is computed taking into account renormalons in the Sudakov exponent (Dressed Gluon Exponentiation). In this framework we demonstrate the factorization of…
The pion structure function is investigated in a simple model, where the pion and its constituent quark fields are coupled through the simplest pseudoscalar coupling. The imaginary part of the forward gamma* pi -> gamma* pi scattering…
At a low resolution scale with $Q^2={\mu}^2$ corresponding to the nucleon bound state; deep inelastic unpolarized structure functions $F_1(x,{\mu}^2)$ and $F_2(x,{\mu}^2)$ are derived with correct support using the symmetric part of the…
Quantifying uncertainty in neural network predictions is essential for high-stakes domains such as autonomous driving, healthcare, and manufacturing. While existing approaches often depend on costly sampling or restrictive distributional…
The generalized parton distributions, introduced nearly a decade ago, have emerged as a universal tool to describe hadrons in terms of quark and gluonic degrees of freedom. They combine the features of form factors, parton densities and…
Inverse problems and, in particular, inferring unknown or latent parameters from data are ubiquitous in engineering simulations. A predominant viewpoint in identifying unknown parameters is Bayesian inference where both prior information…
The heavy quark effective theory makes model independent predictions for semileptonic $\Lambda_b \to \Lambda_c$ decays in terms of a small set of parameters. No subleading Isgur-Wise function occurs at order $\Lambda_{\rm QCD}/m_{c,b}$, and…
Bayesian neural networks (BNNs) have recently regained a significant amount of attention in the deep learning community due to the development of scalable approximate Bayesian inference techniques. There are several advantages of using a…
Uncertainty estimation is critical for cost-sensitive deep-learning applications (i.e. disease diagnosis). It is very challenging partly due to the inaccessibility of uncertainty groundtruth in most datasets. Previous works proposed to…
The slope of the Isgur-Wise function at the normalization point, $\xi^{(1)}(1)$,is one of the basic parameters for the extraction of the $CKM$ matrix element $V_{cb}$ from exclusive semileptonic decay data. A method for measuring this…
Accurately modeling power distribution grids is crucial for designing effective monitoring and decision making algorithms. This paper addresses the partial observability issue of data-driven distribution modeling in order to improve the…
Statistical shape modeling (SSM) has recently taken advantage of advances in deep learning to alleviate the need for a time-consuming and expert-driven workflow of anatomy segmentation, shape registration, and the optimization of…
A new framework is developed to intrinsically analyze sparsely observed Riemannian functional data. It features four innovative components: a frame-independent covariance function, a smooth vector bundle termed covariance vector bundle, a…
Quantifying the effects on electromagnetic waves scattered by objects of uncertain shape is key for robust design, particularly in high precision applications. Assuming small random perturbations departing from a nominal domain, the…
There has been a growing interest in deep learning-based prognostic and health management (PHM) for building end-to-end maintenance decision support systems, especially due to the rapid development of autonomous systems. However, the low…