相关论文: Cusp Kernels for Velocity-Changing Collisions

The aim of the present work is a comparative study of different persistence kernels applied to various classification problems. After some necessary preliminaries on homology and persistence diagrams, we introduce five different kernels…

It is theoretically and experimentally argued that there may exist bubble or toroid-shaped configurations in some nucleus systems. Based on the nuclear transport model, it is shown that compared with the collision of normal nuclei, there is…

A technique to deal with Coulomb electron distortions in the analysis of (e,e'p) reactions is presented. Thereby, no approximations are made. The suggested technique relies on a partial-wave expansion of the electron wave functions and a…

In decision-making systems, it is important to have classifiers that have calibrated uncertainties, with an optimisation objective that can be used for automated model selection and training. Gaussian processes (GPs) provide uncertainty…

An important feature of kernel mean embeddings (KME) is that the rate of convergence of the empirical KME to the true distribution KME can be bounded independently of the dimension of the space, properties of the distribution and smoothness…

This paper presents new methodology for computationally efficient kernel density estimation. It is shown that a large class of kernels allows for exact evaluation of the density estimates using simple recursions. The same methodology can be…

Net proton and negative hadron spectra for central \PbPb collisions at 158 GeV per nucleon at the CERN SPS were measured and compared to spectra from lighter systems. Net baryon distributions were derived from those of net protons,…

Compton scattering between electrons and photons plays a crucial role in astrophysical plasmas. Many important aspects of this process can be captured by using the so-called Compton scattering kernel. For isotropic media, exact analytic…

Experiment NA44 has measured proton and antiproton distributions at mid-rapidity in sulphur and proton collisions with nuclear targets at 200 and 450 GeV/c per nucleon respectively. The inverse slopes of transverse mass distributions…

Kinetics of collision-sticking processes between vapor molecules and molecular clusters of low volatile compounds facilitates the initial steps of atmospheric clustering. Conventional theoretical models are quite inaccurate due to the…

This work proposed kernel selection approaches for probabilistic classifiers based on features produced by the convolutional encoder of a variational autoencoder. Particularly, the developed methodologies allow the selection of the most…

Intermediate energy data of proton-induced fission on 241Am, 238U and 237Np targets were analysed and investigated using the computational simulation code CRISP. Inelastic interactions of protons on heavy nuclei and both symmetric and…

Kernel means are frequently used to represent probability distributions in machine learning problems. In particular, the well known kernel density estimator and the kernel mean embedding both have the form of a kernel mean. Unfortunately,…

As hardware architectures are evolving in the push towards exascale, developing Computational Science and Engineering (CSE) applications depend on performance portable approaches for sustainable software development. This paper describes…

A model describing nuclear collisions at intermediate energies is presented and the results are compared with recently measured double differential cross sections in C-C reactions at 95 MeV/nucleon. Results show the key role played by…

Collision cross sections and rate constants between two ground- state oxygen molecules are investigated theoretically at translational energies below $\sim 1$K and in zero magnetic field. We present calculations for elastic and spin-…

The production of dileptons with an invariant mass in the range 1 GeV < M < 5 GeV provides unique insight into the approach to thermal equilibrium in ultrarelativistic nucleus-nucleus collisions. In this mass range, they are produced…

A novel algorithm is proposed to solve the sample-based optimal transport problem. An adversarial formulation of the push-forward condition uses a test function built as a convolution between an adaptive kernel and an evolving probability…

We consider the problem of improving the efficiency of randomized Fourier feature maps to accelerate training and testing speed of kernel methods on large datasets. These approximate feature maps arise as Monte Carlo approximations to…

Kernels are often developed and used as implicit mapping functions that show impressive predictive power due to their high-dimensional feature space representations. In this study, we gradually construct a series of simple feature maps that…