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Convolutional Neural Networks (CNNs) are models that are utilized extensively for the hierarchical extraction of features. Vision transformers (ViTs), through the use of a self-attention mechanism, have recently achieved superior modeling…
This paper describes the design and performance of a 50 liter, two-segment $^{6}$Li-loaded liquid scintillator detector that was designed and operated as prototype for the PROSPECT (Precision Reactor Oscillation and Spectrum) Experiment.…
Holographic light potentials generated by phase-modulating liquid-crystal spatial light modulators (SLMs) are widely used in quantum technology applications. Accurate calibration of the wavefront and intensity profile of the laser beam at…
Machine learning potentials (MLPs) for atomistic simulations have an enormous prospective impact on materials modeling, offering orders of magnitude speedup over density functional theory (DFT) calculations without appreciably sacrificing…
Fast timing detectors are an essential element in the experimental setup for time-of-flight (ToF) mass measurements of unstable nuclei. We have upgraded the scintillator detectors used in experiments at the National Superconducting…
The calibration process for the hybrid array pixel detector designed for High Energy Photon Source in China, we called HEPS-BPIX, is presented in this paper. Based on the threshold scanning, the relationship between energy and threshold is…
Particle identification using the energy loss in silicon detectors is a powerful technique for probing the Standard Model (SM) as well as searching for new particles beyond the SM. Traditionally, such techniques use the truncated mean of…
Muon tomography is a non-destructive imaging technique that uses cosmic-ray muons to probe dense materials. Bar scintillator and scintillating fiber detectors equipped with one-dimensional SiPM arrays offer compact, high-resolution…
Recently, Multilayer Perceptron (MLP) becomes the hotspot in the field of computer vision tasks. Without inductive bias, MLPs perform well on feature extraction and achieve amazing results. However, due to the simplicity of their…
Link prediction (LP) has been recognized as an important task in graph learning with its broad practical applications. A typical application of LP is to retrieve the top scoring neighbors for a given source node, such as the friend…
Plastic scintillators are widely used in particle detectors when precise timing information is required. As a basis for comparing different detectors we use such a characteristic as the time resolution per 1MeV detected energy, i.e. the…
Solidification governs the microstructure and, therefore, the mechanical response of metal components, yet the atomistic details of nucleation and defect formation are often difficult to determine experimentally. Molecular dynamics can…
Smoothed Particle Hydrodynamics (SPH) is essential for modeling complex large-deformation problems across various applications, requiring significant computational power. A major portion of SPH computation time is dedicated to the Nearest…
A new supervised learning algorithm, SNN/LP, is proposed for Spiking Neural Networks. This novel algorithm uses limited precision for both synaptic weights and synaptic delays; 3 bits in each case. Also a genetic algorithm is used for the…
A combination of scintillator, wavelength shifting (WLS) fiber, and silicon photomultiplier (SiPM) shows an excellent performance in the `$K_{L}$ and $\mu$ detector (KLM)' of the Belle II experiment. In this study, we present the R&D…
We describe here an efficient machine-learning based approach for the optimization of parameters used for extracting the arrival time of waveforms, in particular those generated by the detection of 511 keV annihilation gamma-rays by a 60…
LIDAR 3D object detection is one of the important tasks for autonomous vehicles. Ensuring that this task operates in real-time is crucial. Toward this, model quantization can be used to accelerate the runtime. However, directly applying…
Machine learning potentials (MLPs) have become an indispensable tool in large-scale atomistic simulations because of their ability to reproduce ab initio potential energy surfaces (PESs) very accurately at a fraction of computational cost.…
Self-supervised masked image modeling (MIM) methods have shown promising performances on analyzing natural images. However, directly applying such methods to medical image segmentation tasks still cannot achieve satisfactory results. The…
We have developed a new laser-based time calibration system for highly segmented scintillator counters like the MEG II pixelated Timing Counter (pTC), consisting of 512-centimeter scale scintillator counters read out by silicon…