Related papers: Neutron reflectometry analysis: using model-depend…

In naturalistic learning problems, a model's input contains a wide range of features, some useful for the task at hand, and others not. Of the useful features, which ones does the model use? Of the task-irrelevant features, which ones does…

The application of machine learning based decision making systems in safety critical areas requires reliable high certainty predictions. Reject options are a common way of ensuring a sufficiently high certainty of predictions made by the…

In this paper, we consider the inverse problem of state estimation of nuclear power fields in a power plant from a limited number of observations of the neutron flux. For this, we use the Parametrized Background Data Weak approach. The…

Neural Networks (NN) has been used in many areas with great success. When a NN's structure (Model) is given, during the training steps, the parameters of the model are determined using an appropriate criterion and an optimization algorithm…

Interpretable rationales for model predictions are crucial in practical applications. We develop neural models that possess an interpretable inference process for dependency parsing. Our models adopt instance-based inference, where…

Diagnostic reasoning has been characterized logically as consistency-based reasoning or abductive reasoning. Previous analyses in the literature have shown, on the one hand, that choosing the (in general more restrictive) abductive…

An artificial intelligence (AI) model can be viewed as a function that maps inputs to outputs in high-dimensional spaces. Once designed and well trained, the AI model is applied for inference. However, even optimized AI models can produce…

Bulk nuclear observables such as charge radii and binding energies are well described by both nonrelativistic and covariant mean-field models. However, predictions of neutron radii, which are not tightly constrained by reliable data, vary…

Model dependence of the reaction rates for the weak breakup of deuterons by low energy neutrinos is studied starting from the cross sections derived from potential models and also from pionless effective field theory. Choosing the spread of…

Feature based explanations, that provide importance of each feature towards the model prediction, is arguably one of the most intuitive ways to explain a model. In this paper, we establish a novel set of evaluation criteria for such feature…

Modeling of X-ray pulse profiles from millisecond pulsars offers a promising method of inferring the mass-to-radius ratios of neutron stars. Recent observations with NICER resulted in measurements of radii for three neutron stars using this…

We implement a semi-analytic approach for stability analysis, addressing the ongoing uncertainty about stability and structure of neutron star magnetic fields. Applying the energy variational principle, a model system is displaced from its…

Precision tests of the standard model are essential for constraining models of new physics. Neutrino-electron elastic scattering offers a clean probe into many electroweak effects that are complimentary to the more canonical measurements…

The results of analytical measurements performed with solid-sampling techniques are affected by the distribution of the analytes within the matrix. The effect becomes significant in case of determination of trace elements in small…

There is an increasing reliance on mathematical modelling to assist in the design of piezoelectric ultrasonic transducers since this provides a cost-effective and quick way to arrive at a first prototype. Given a desired operating envelope…

Standard Model predictions for neutrino-electron scattering cross-sections, including effects of electroweak radiative corrections, are reviewed. The sensitivity of those quantities to neutrino dipole moments, z' bosons, dynamical symmetry…

This talk compares standard model predictions for solar neutrino experiments with the results of actual observations. Here `standard model' means the combined standard model of minimal electroweak theory plus a standard solar model. I…

A kernel based procedure for correcting experimental data for distortions due to the finite resolution and limited detector acceptance is presented. The unfolding problem is known to be an ill-posed problem that can not be solved without…

We found that temperature-dependent infrared spectroscopy measurements (i.e., reflectance or transmittance) using a Fourier-transform spectrometer can have substantial errors, especially for elevated sample temperatures and collection using…

Recent advancements in machine learning have emphasized the need for transparency in model predictions, particularly as interpretability diminishes when using increasingly complex architectures. In this paper, we propose leveraging…