Related papers: MUPen2DTool: a Matlab Tool for 2D Nuclear Magnetic…
Nuclear Magnetic Resonance (NMR) is a tool of choice to characterize molecular motions. In biological macromolecules, pico- to nano-second motions, in particular, can be probed by nuclear spin relaxation rates which depend on the time…
Travel time tomography for the velocity structure of a medium is a highly non-linear and non-unique inverse problem. Monte Carlo methods are becoming increasingly common choices to provide probabilistic solutions to tomographic problems but…
We study the applicability of tools developed by the computer vision community for features learning and semantic image inpainting to perform data reconstruction of fluid turbulence configurations. The aim is twofold. First, we explore on a…
Liquid state NMR is a powerful tool for the analysis of complex mixtures of unknown molecules. This capacity has been used in many analytical approaches: metabolomics, identification of active compounds in natural extracts, characterization…
Recent advancements in neutron and X-ray sources, instrumentation and data collection modes have significantly increased the experimental data size (which could easily contain 10$^{8}$ -- 10$^{10}$ data points), so that conventional…
Neutron cross section matrices for fission and scattering data are required for each material, temperature, and enrichment level to calculate the neutron transport equation accurately. This information can be a limiting factor when using…
The application of machine learning in materials presents a unique challenge of dealing with scarce and varied materials data - both experimental and theoretical. Nevertheless, several state-of-the-art machine learning models for materials…
Purpose: Conventional MRI is relying on the assumption of the magnetic field being homogeneous in direction and amplitude. However, with the growing interest in portable, affordable point-of-care MRI systems, these assumptions do not…
2D materials find promising applications in next-generation devices, however, large-scale, low-defect, and reproducible synthesis of 2D materials remains a challenging task. To assist in the selection of suitable substrates for the…
This paper investigates reduced complexity neural network (NN) based architectures for equalization over the two-dimension magnetic recording (TDMR) digital communication channel for data storage. We use realistic waveforms measured from a…
This paper addresses the challenge of geometric quality assurance in manufacturing, particularly when human assessment is required. It proposes using Blender, an open-source simulation tool, to create synthetic datasets for machine learning…
This paper describes a coplanar non invasive non destructive capacitive imaging device. We first introduce a mathematical model for its output, and discuss some of its theoretical capabilities. We show that the data obtained from this…
Empirical potential structure refinement (EPSR) is a neutron scattering data analysis algorithm and a software package. It was developed by the British spallation neutron source (ISIS) Disordered Materials Group in 1980s, and aims to…
Sophisticated visualization tools are essential for the presentation and exploration of human neuroimaging data. While two-dimensional orthogonal views of neuroimaging data are conventionally used to display activity and statistical…
The Deep Material Network (DMN) has emerged as a powerful framework for multiscale materials modeling, enabling efficient and accurate prediction of material behavior across different length scales. Unlike conventional data-driven…
Nuclear Magnetic Resonance (NMR) is one of the main experimental tools to evaluate the production potential of porous rocks in oil wells. From the relative areas and mean values obtained from relaxation time distribution curves, information…
Manifold learning-based encoders have been playing important roles in nonlinear dimensionality reduction (NLDR) for data exploration. However, existing methods can often fail to preserve geometric, topological and/or distributional…
The thickness of 2D materials not only plays a crucial role in determining the performance of nanoelectronic and optoelectronic devices but also introduces complexities in predicting volume-dependent properties such as energy storage…
The concept of deep learning is employed for the inversion of NMR signals and it is shown that NMR signal inversion can be considered as an image-to-image regression problem, which can be treated with a convolutional neural net. It is…
Nuclear magnetic resonance (NMR) spectroscopy provides detailed information pertaining to dynamic processes through line-shape changes, which have been traditionally limited to equilibrium conditions. However, there is a wealth of…