相关论文: Statistical Global Modeling of Beta-Decay Halflive…

In this paper, we present new optimization models for Support Vector Machine (SVM), with the aim of separating data points in two or more classes. The classification task is handled by means of nonlinear classifiers induced by kernel…

For $\alpha$ decay half-life calculations in this work, the Coulomb and proximity potential model with a new semiempirical formula for diffuseness parameter developed in previous work [Phys. Rev. C 100, 024601 (2019)] is used. The present…

In this work, the $\beta$-stable region for Z $\geq$ 90 is proposed. The calculated $\beta$-stable nuclei in the $\beta$-stable region are in good agreement with the ones obtained by M\"{o}ller \emph{et al}.. The half-lives of the nuclei…

This paper reports the first application of a new technique to measure the beta-decay half -lives of exotic nuclei in complex background conditions. Since standard tools were not adapted to extract the relevant information, a new analysis…

Multivariate data analysis techniques have the potential to improve physics analyses in many ways. The common classification problem of signal/background discrimination is one example. The Support Vector Machine learning algorithm is a…

We present a microscopic model for the calculation of alpha-decay half lives employing potentials obtained from relativistic and non-relativistic self-consistent mean-field models. The nuclear and Coulomb potentials are used to obtain the…

The alpha-decay half-lives of recently synthesized superheavy nuclei (SHN) are investigated based on a unified fission model (UFM) where a new method to calculate the assault frequency of alpha-emission is used. The excellent agreement with…

Accurately predicting industrial aging processes makes it possible to schedule maintenance events further in advance, ensuring a cost-efficient and reliable operation of the plant. So far, these degradation processes were usually described…

In this paper we take B-L supersymmetric standard model (B-LSSM) and TeV scale left-right symmetric model (LRSM) as two representations of the two kinds of new physics models to study the nuclear neutrinoless double beta decays…

Using methods of Statistical Physics, we investigate the generalization performance of support vector machines (SVMs), which have been recently introduced as a general alternative to neural networks. For nonlinear classification rules, the…

For radioactive nuclear data, $\beta$ decay is one of the most important information and is applied to various fields. However, some of the $\beta$-decay data are not available due to experimental difficulties. From this respect,…

Based on official estimates, 50 million people worldwide are affected by dementia, and this number increases by 10 million new patients every year. Without a cure, clinical prognostication and early intervention represent the most effective…

The life of the modern world essentially depends on the work of the large artificial homogeneous networks, such as wired and wireless communication systems, networks of roads and pipelines. The support of their effective continuous…

Nuclear double $\beta$-decay with two neutrinos is a rare and important process for natural radioactivity of unstable nuclei. The experimental data of nuclear double $\beta^{-}$-decay with two neutrinos are analyzed and a systematic law to…

Quantum computing leverages quantum effects to build algorithms that are faster then their classical variants. In machine learning, for a given model architecture, the speed of training the model is typically determined by the size of the…

Multilevel models (MLMs) are a central building block of the Bayesian workflow. They enable joint, interpretable modeling of data across hierarchical levels and provide a fully probabilistic quantification of uncertainty. Despite their…

Understanding visual degradations is a critical yet challenging problem in computer vision. While recent Vision-Language Models (VLMs) excel at qualitative description, they often fall short in understanding the parametric physics…

Support vector machine (SVM) is one of the most studied paradigms in the realm of machine learning for classification and regression problems. It relies on vectorized input data. However, a significant portion of the real-world data exists…

Building on the work of E. L. Medeiros [1] and our previous study [2], we generalize the alpha-nucleus nonlocality effect to odd-A and odd-odd nuclei within the two-potential approach (TPA) framework. The coordinate-dependent parameters…

The classical hinge-loss support vector machines (SVMs) model is sensitive to outlier observations due to the unboundedness of its loss function. To circumvent this issue, recent studies have focused on non-convex loss functions, such as…