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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…
Predictions of nuclear models guide the design of nuclear facilities to ensure their safe and efficient operation. Because nuclear models often do not perfectly reproduce available experimental data, decisions based on their predictions may…
We present a modified Richardson-Lucy (RL) algorithm tailored for image reconstruction in MeV gamma-ray observations, focusing on its application to the upcoming Compton Spectrometer and Imager (COSI) mission. Our method addresses key…
Sparse principal component analysis (PCA) is an important technique for dimensionality reduction of high-dimensional data. However, most existing sparse PCA algorithms are based on non-convex optimization, which provide little guarantee on…
A pulse-shape discrimination method based on artificial neural networks was applied to pulses simulated for different background, signal and signal-like interactions inside a germanium detector. The simulated pulses were used to investigate…
Active Target Time Projection Chambers (AT-TPCs) are state-of-the-art tools in the field of low-energy nuclear physics, particularly suitable for experiments using low-intensity radioactive ion beams or gamma rays. The Fudan Multi-purpose…
Recent discoveries of multiple long-period pulsars (periods ${\sim}10\,$s or larger) are starting to challenge the conventional notion that coherent radio emission cannot be produced by objects that are below the many theorised death lines.…
Despite advances in the programmable logic capabilities of modern trigger systems, a significant bottleneck remains in the amount of data to be transported from the detector to off-detector logic where trigger decisions are made. We…
Multispectral fusion object detection is a critical task for edge-based maritime surveillance and remote sensing, demanding both high inference efficiency and robust feature representation for high-resolution inputs. However, current State…
We evaluate the performance of four different machine learning (ML) algorithms: an Artificial Neural Network Multi-Layer Perceptron (ANN MLP ), Adaboost, Gradient Boosting Classifier (GBC), XGBoost, for the separation of pulsars from radio…
Multivariate time series anomaly detection is a crucial problem in many industrial and research applications. Timely detection of anomalies allows, for instance, to prevent defects in manufacturing processes and failures in cyberphysical…
Next generation online and real-time adaptive radiotherapy workflows require precise particle transport simulations in sub-second times, which is unfeasible with current analytical pencil beam algorithms (PBA) or stochastic Monte Carlo (MC)…
Deep learning (DL) applied to breast tissue segmentation in magnetic resonance imaging (MRI) has received increased attention in the last decade, however, the domain shift which arises from different vendors, acquisition protocols, and…
Organic scintillators exhibit fast timing, high detection efficiency for fast neutrons and pulse shape discrimination (PSD) capability. PSD is essential in mixed radiation fields, where different types of radiation need to be detected and…
A VME-based experiment system for n-{\gamma} discrimination using the charge comparison method was established. A data acquisition program for controlling the programmable modules and processing data online via VME64X bus was developed…
We introduce a new computer aided detection and diagnosis system for lung cancer screening with low-dose CT scans that produces meaningful probability assessments. Our system is based entirely on 3D convolutional neural networks and…
A computational approach by an implementation of the Principle Component Analysis (PCA) with K-means and Gaussian Mixture (GM) clustering methods from Machine Learning (ML) algorithms to identify structural and dynamical heterogeneities of…
Space plasma data analysis and mission operations are aided by the categorization of plasma data between different regions of the magnetosphere and identification of the boundary regions between them. Without computerized automation this…
This study investigates the feasibility of reconstructing the last closed flux surface (LCFS) in the DIII-D tokamak using neural network models trained on reduced input feature sets, addressing an ill-posed task. Two models are compared:…
Molecularly targeted contrast enhanced ultrasound (mCEUS) is a clinically promising approach for early cancer detection through targeted imaging of VEGFR2 (KDR) receptors. We have developed computational enhancement techniques for mCEUS…