Related papers: Bayesian optimization and nonlocal effects method …
A novel method for estimating Bayesian network (BN) parameters from data is presented which provides improved performance on test data. Previous research has shown the value of representing conditional probability distributions (CPDs) via…
While Bayesian neural networks (BNNs) have drawn increasing attention, their posterior inference remains challenging, due to the high-dimensional and over-parameterized nature. To address this issue, several highly flexible and scalable…
This study investigates the application of quantum neural networks (QNNs) for propensity score estimation to address selection bias in comparing survival outcomes between laparoscopic and open surgical techniques in a cohort of 1177…
Cosmic microwave background (CMB) observations suggest the possibility of an extra dark radiation component, while the current evidence from big bang nucleosynthesis (BBN) is more ambiguous. Dark radiation from a decaying particle can…
Accurate survival prediction in head and neck cancer (HNC) is essential for guiding clinical decision-making and optimizing treatment strategies. Traditional models, such as Cox proportional hazards, have been widely used but are limited in…
The $\mathcal{\alpha}$ decay half-life of unknown nucleus $^{297}$Og is predicted within two-potential approach, and $\mathcal{\alpha}$ preformation probabilities of 64 odd-$A$ nuclei in the region of proton numbers $82<Z<126$ and neutron…
The alpha decay half-lives of recently synthesized superheavy nuclei (SHN) are calculated by applying a new approach which estimates them with the help of their neighbors based on some simple formulas. The estimated half-life values are in…
Several experimental groups reported the evidence of multiple periodic modulations of nuclear decay constants which amplitudes are of the order .1% and periods of one year, 24 hours or about one month. We argue that such deviations from…
Bayesian neural networks (BNNs) have received an increased interest in the last years. In BNNs, a complete posterior distribution of the unknown weight and bias parameters of the network is produced during the training stage. This…
Deep convolutional neural networks (DCNNs) have dominated the recent developments in computer vision through making various record-breaking models. However, it is still a great challenge to achieve powerful DCNNs in resource-limited…
Within the Coulomb and proximity potential model (CPPM) the cluster decay process in {199-226}^Fr, {206-232}^Ac, {209-237}^Th, {212-238}^Pa, {217-241}^U, {225-242}^Np, {225-244}^Pu, {231-246}^Am, {202-230}^Ra and {233-249}^Cm isotopes…
We developed an efficient classifier that sorts alpha-decay events from various vertex-like objects in nuclear emulsion using a convolutional neural network (CNN). Alpha-decay events in the emulsion are standard calibration sources for the…
Scientific Machine Learning (SciML) integrates physics and data into the learning process, offering improved generalization compared with purely data-driven models. Despite its potential, applications of SciML in prognostics remain limited,…
An intense study of the alpha decay properties of the isotopes of superheavy element Z=113 have been performed within the Coulomb and proximity potential model for deformed nuclei (CPPMDN) within the wide range 255 l.e. A l.e. 314. The…
We discuss the nonmesonic decay of deformed p-shell hypernuclei. The Nilsson model with angular momentum projection is employed in order to take into account the deformation effects. The nonmesonic decay rate and the intrinsic $\Lambda$…
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…
The low-energy nuclear structure and two-neutrino double-$\beta$ ($2\nu\beta\beta$) decay are studied within the interacting boson model (IBM) that is based on the nuclear energy density functional (EDF). The IBM Hamiltonian describing the…
Lithium-ion batteries are a key energy storage technology driving revolutions in mobile electronics, electric vehicles and renewable energy storage. Capacity retention is a vital performance measure that is frequently utilized to assess…
With the increased use of data-driven approaches and machine learning-based methods in material science, the importance of reliable uncertainty quantification (UQ) of the predicted variables for informed decision-making cannot be…
Active learning optimizes the exploration of large parameter spaces by strategically selecting which experiments or simulations to conduct, thus reducing resource consumption and potentially accelerating scientific discovery. A key…