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This paper develops a Deep Reinforcement Learning (DRL)-agent for navigation and control of autonomous surface vessels (ASV) on inland waterways. Spatial restrictions due to waterway geometry and the resulting challenges, such as high flow…
Ground penetrating radar (GPR) is one of the most popular and successful sensing modalities that has been investigated for landmine and subsurface threat detection. Many of the detection algorithms applied to this task are supervised and…
New developments have been brought to our energy-, spin- and parity-dependent nuclear level densities based on the microscopic combinatorial method. Our new calculation is based on the BSkG3 mean-field model which relies on a…
Accurate Remaining Useful Life (RUL) prediction is a key requirement for effective Prognostics and Health Management (PHM) in safety-critical systems such as aero-engines. Existing deep learning approaches, particularly LSTM-based models,…
Neutrino nucleus elastic scattering ({\nu}Ael) with reactor neutrinos is an interaction under full quantum-mechanical coherence. It has not yet been experimentally observed. We present new results on the studies of {\nu}Ael cross section…
Airborne Gamma-Ray Spectrometry (AGRS) is a critical tool for radiological emergency response, enabling the rapid identification and quantification of hazardous terrestrial radionuclides over large areas. However, existing calibration…
Recent advances in self-supervised learning (SSL) in computer vision are primarily comparative, whose goal is to preserve invariant and discriminative semantics in latent representations by comparing siamese image views. However, the…
Despite its versatility and high chemical specificity, conventional NMR spectroscopy is limited in measurement throughput due to the need for high-homogeneity magnetic fields, necessitating sequential sample analysis, and bulky devices.…
Many subsurface formations, including some of those under consideration for large-scale geological carbon storage, include extensive faults that can strongly impact fluid flow. In this study, we develop a new recurrent transformer U-Net…
Controlling and measuring the concentration of para-D2 is an essential step toward realizing solid deuterium as an intense ultra-cold neutron (UCN) source. To this end, we implemented an experimental technique to convert para- to…
Recently deep reinforcement learning (DRL) has achieved outstanding success on solving many difficult and large-scale RL problems. However the high sample cost required for effective learning often makes DRL unaffordable in resource-limited…
Taking advantage of both vehicle-to-everything (V2X) communication and automated driving technology, connected and automated vehicles are quickly becoming one of the transformative solutions to many transportation problems. However, in a…
The Copernican principle, the notion that we are not at a special location in the Universe, is one of the cornerstones of modern cosmology and its violation would invalidate the Friedmann-Lema\^{\i}tre-Robertson-Walker (FLRW) metric,…
Relative localization (RL) is essential for the successful operation of micro air vehicle (MAV) swarms. Achieving accurate 3-D RL in infrastructure-free and GPS-denied environments with only distance information is a challenging problem…
Evaluated nuclear data uncertainties are often perceived as unrealistic, most often because they are thought to be too small. The impact of this issue in applied nuclear science has been discussed widely in recent years. Commonly suggested…
We present a Markov-Chain Monte-Carlo (MCMC) forecast for the precision of neutrino mass and cosmological parameter measurements with a Euclid-like galaxy clustering survey. We use a complete perturbation theory model for the galaxy…
We perform a global sensitivity analysis of the binding energy and the charge radius of the nucleus $^{16}$O to identify the most influential low-energy constants in the next-to-next-to-leading order chiral Hamiltonian with two- and…
Autonomously controlling quadrotors in large-scale subterranean environments is applicable to many areas such as environmental surveying, mining operations, and search and rescue. Learning-based controllers represent an appealing approach…
Scaling RL for LLMs is computationally expensive, largely due to multi-sampling for policy optimization and evaluation, making efficient data selection crucial. Inspired by the Zone of Proximal Development (ZPD) theory, we hypothesize LLMs…
Energy storage solutions play an increasingly important role in modern infrastructure and lead-acid batteries are among the most commonly used in the rechargeable category. Due to normal degradation over time, correctly determining the…