3D printing using robots has garnered significant interest in manufacturing and construction in recent years. A robot’s versatility paired with the design freedom of 3D printing offers promising opportunities for

# Geospatial Transformations for Ground-Based Sky Imaging Systems. (arXiv:2103.02066v1 [astro-ph.IM])

Sky imaging systems use lenses to acquire images concentrating light beams in an imager. The light beams received by the imager have an elevation angle with respect to the normal

# Deep J-Sense: Accelerated MRI Reconstruction via Unrolled Alternating Optimization. (arXiv:2103.02087v1 [eess.SP])

Accelerated multi-coil magnetic resonance imaging reconstruction has seen a substantial recent improvement combining compressed sensing with deep learning. However, most of these methods rely on estimates of the coil sensitivity

# Not engaging with problems in the lab: Students’ navigation of conflicting data and models. (arXiv:2103.02032v1 [physics.ed-ph])

With the adoption of instructional laboratories (labs) that require students to make their own decisions, there is a need to better understand students’ activities as they make sense of their

# Ultrasound Matrix Imaging. II. The distortion matrix for aberration correction over multiple isoplanatic patches. (arXiv:2103.02036v1 [eess.IV])

This is the second article in a series of two which report on a matrix approach for ultrasound imaging in heterogeneous media. This article describes the quantification and correction of

# Implementation of Quantum Machine Learning for Electronic Structure Calculations of Periodic Systems on Quantum Computing Devices. (arXiv:2103.02037v1 [physics.comp-ph])

Quantum machine learning algorithms, the extensions of machine learning to quantum regimes, are believed to be more powerful as they leverage the power of quantum properties. Quantum machine learning methods

# Parametric Complexity Bounds for Approximating PDEs with Neural Networks. (arXiv:2103.02138v1 [cs.LG])

Recent empirical results show that deep networks can approximate solutions to high dimensional PDEs, seemingly escaping the curse of dimensionality. However many open questions remain regarding the theoretical basis for

# Two-Stage Framework for Seasonal Time Series Forecasting. (arXiv:2103.02144v1 [cs.LG])

Seasonal time series Forecasting remains a challenging problem due to the long-term dependency from seasonality. In this paper, we propose a two-stage framework to forecast univariate seasonal time series. The

# Ridge-penalized adaptive Mantel test and its application in imaging genetics. (arXiv:2103.02156v1 [stat.ME])

We propose a ridge-penalized adaptive Mantel test (AdaMant) for evaluating the association of two high-dimensional sets of features. By introducing a ridge penalty, AdaMant tests the association across many metrics

# Preliminaries on the Accurate Estimation of the Hurst Exponent Using Time Series. (arXiv:2103.02091v1 [eess.SP])

This article explores the required amount of time series points from a high-speed computer network to accurately estimate the Hurst exponent. The methodology consists in designing an experiment using estimators