Related papers: Fast Maximum Likelihood Positioning for a Staggere…
Machine-learned potentials (MLPs) trained on ab initio data combine the computational efficiency of classical interatomic potentials with the accuracy and generality of the first-principles method used in the creation of the respective…
Image superresolution methods process an input image sequence of a scene to obtain a still image with increased resolution. Classical approaches to this problem involve complex iterative minimization procedures, typically with high…
Precision calorimetry using highly granular sampling calorimeters is being developed based on the particle flow concept within the CALICE collaboration. One design option of a hadron calorimeter is based on silicon photomultipliers (SiPMs)…
The response of a position-sensitive Li-glass based scintillation detector to focused beams of 2.5 MeV protons and deuterons has been investigated. The beams were scanned across the detector in 0.5 mm horizontal and vertical steps…
Machine-learned interatomic potentials can offer near first-principles accuracy but are computationally expensive, limiting their application to large-scale molecular dynamics simulations. Inspired by quantum mechanics/molecular mechanics…
Visible light positioning (VLP) is widely believed to be a cost-effective answer to the growing demanded for robot indoor positioning. Considering that some extreme environments require robot to be equipped with a precise and…
Advancements in adapting deep convolution architectures for Spiking Neural Networks (SNNs) have significantly enhanced image classification performance and reduced computational burdens. However, the inability of Multiplication-Free…
Detailed Monte-Carlo simulations were used to evaluate the performance of a liquid neon scintillation detector for dark matter and low-energy solar neutrino interactions. A maximum-likelihood event vertex fitter including PMT time…
In the context of particle therapy monitoring, we are developing a gamma-ray detector to determine the ion range in vivo from the measurement of particle time-of-flight. For this application, a beam monitor capable to tag in time the…
In chemistry tabulations and Flamelet combustion models, the Flamelet Generated Manifold (FGM) is recognized for its precision and physical representation. The practical implementation of FGM requires a significant allocation of memory…
Scintillator detectors electronics is recalibrated against the datasheet given by the manufacturer. Optimal and mutual dependent values of (a) high voltage at PMT (Photomultiplier Tube), (b) amplifier gain, (c) average time to count the…
We developed machine learning algorithms for distinguishing scintillation signals from a plastic-liquid coupled detector known as a phoswich. The challenge lies in discriminating signals from organic scintillators with similar shapes and…
This paper presents an optimal calibration scheme and a weighted least squares (LS) localization algorithm for received signal strength (RSS) based visible light positioning (VLP) systems, focusing on the often overlooked impact of light…
Plastic scintillation detectors represent an efficient, cost-effective, customizable solution for assessment of gamma radiation fields in the context of nuclear security, environmental radiation survey, radiological and nuclear incidents…
Scintillation light, produced alongside ionisation charge from particle interactions, plays a critical role in liquid argon time projection chamber (LArTPC) detectors. A detailed understanding of its production and detection mechanisms is…
Aiming at drastic speedup for point-feature embeddings at test time, we propose a new framework that uses a pair of multi-layer perceptrons (MLP) and a lookup table (LUT) to transform point-coordinate inputs into high-dimensional features.…
Combining the efficiency of semi-empirical potentials with the accuracy of quantum mechanical methods, machine-learning interatomic potentials (MLIPs) have significantly advanced atomistic modeling in computational materials science and…
Detecting and analyzing the local environment is crucial for investigating the dynamical processes of crystal nucleation and shape colloidal particle self-assembly. Recent developments in machine learning provide a promising avenue for…
A new type of neutron detector, named Stack Structure Solid organic Scintillator (S$^4$), consisting of multi-layer plastic scintillators with capability to suppress low-energy $\gamma$ rays under high-counting rate has been constructed and…
The illumination of improperly exposed photographs has been widely corrected using deep convolutional neural networks or Transformers. Despite with promising performance, these methods usually suffer from large parameter amounts and heavy…