相关论文: Statistical Global Modeling of Beta-Decay Halflive…
The neutrinoless double-$\beta$ decay ($0\nu\beta\beta$) of nuclei is one of the major research subjects of neutrino physics nowadays because of its influence on particle physics and astrophysics. The predicted nuclear matrix elements…
A systematic study of the $\beta$-decay of neutron-deficient nuclei has been carried out and has provided spectroscopic information of importance for both nuclear structure and nuclear astrophysics. Following an overview of the most…
Statistical clustering in dynamic networks aims to identify groups of nodes with similar or distinct internal connectivity patterns as the network evolves over time. While early research primarily focused on static Stochastic Block Models…
This paper describes the architecture and systems built towards solving the SemEval 2023 Task 2: MultiCoNER II (Multilingual Complex Named Entity Recognition) [1]. We evaluate two approaches (a) a traditional Conditional Random Fields model…
In this work, we present a machine learning approach for reducing the error when numerically solving time-dependent partial differential equations (PDE). We use a fully convolutional LSTM network to exploit the spatiotemporal dynamics of…
Spontaneous fission and alpha decay are the main decay modes for superheavy nuclei. The superheavy nuclei which have small alpha decay half-life compared to spontaneous fission half-life will survive fission and can be detected in the…
Support vector machines (SVMs) appeared in the early nineties as optimal margin classifiers in the context of Vapnik's statistical learning theory. Since then SVMs have been successfully applied to real-world data analysis problems, often…
The paper investigates nonlinear system identification using system output data at various linearized operating points. A feed-forward multi-layer Artificial Neural Network (ANN) based approach is used for this purpose and tested for two…
Dementia is the fifth cause of death worldwide with 10 million new cases every year. Healthcare applications using machine learning techniques have almost reached the physical limits while more data is becoming available resulting from the…
This research introduces an extended application of neural networks for solving nonlinear partial differential equations (PDEs). A neural network, combined with a pseudo-arclength continuation, is proposed to construct bifurcation diagrams…
We study a special case of the problem of statistical learning without the i.i.d. assumption. Specifically, we suppose a learning method is presented with a sequence of data points, and required to make a prediction (e.g., a classification)…
The article provides a theoretical substantiation for a significant increase in the level of accuracy in determining the neutron lifetime using an alternative concept of neutron beta decay. Neutrons are distributed among different subsets…
We evaluate the allowed $\beta^-$-decay properties of nuclei with $Z = 8 - 15$ systematically under the framework of the nuclear shell model with the use of the valence space Hamiltonians derived from modern $ab~intio$ methods, such as…
Capturing aleatoric uncertainty is a critical part of many machine learning systems. In deep learning, a common approach to this end is to train a neural network to estimate the parameters of a heteroscedastic Gaussian distribution by…
The $\alpha$- decay half-lives of the superheavy nuclei are systematically studied using different versions of proximity potential and a exact method to calculate Coulomb potential between spherical and deformed nuclei in the framework of…
This paper considers distributed optimization algorithms, with application in binary classification via distributed support-vector-machines (D-SVM) over multi-agent networks subject to some link nonlinearities. The agents solve a…
We investigate the half-life expectations for neutrinoless double beta decay by applying statistical distributions of neutrino mixing observables, neutrino mass constraints from cosmology and nuclear matrix elements. The analysis is…
A method for the general analysis of the sensitivities of neutron beta-decay experiments to manifestations of possible deviations from the Standard model is proposed. In a consistent fashion, we take into account all known (radiative and…
Downscaling techniques are one of the most prominent applications of Deep Learning (DL) in Earth System Modeling. A robust DL downscaling model can generate high-resolution fields from coarse-scale numerical model simulations, saving the…
Machine Learning (ML) applications on healthcare can have a great impact on people's lives helping deliver better and timely treatment to those in need. At the same time, medical data is usually big and sparse requiring important…