We consider the problem of finding the Discrete Fourier Transform (DFT) of $N-$ length signals with known frequency support of size $k$. When $N$ is a power of 2 and

# Prior Image-Constrained Reconstruction using Style-Based Generative Models. (arXiv:2102.12525v1 [eess.IV])

Obtaining an accurate and reliable estimate of an object from highly incomplete imaging measurements remains a holy grail of imaging science. Deep learning methods have shown promise in learning object

# Estimation and Distributed Eradication of SIR Epidemics on Networks. (arXiv:2102.12549v1 [eess.SY])

This work examines the discrete-time networked SIR (susceptible-infected-recovered) epidemic model, where the infection and recovery parameters may be time-varying. We provide a sufficient condition for the SIR model to converge

# Energy-consistent finite difference schemes for compressible hydrodynamics and magnetohydrodynamics using nonlinear filtering. (arXiv:2102.12476v1 [physics.comp-ph])

In this paper, an energy-consistent finite difference scheme for the compressible hydrodynamic and magnetohydrodynamic (MHD) equations is introduced. For the compressible magnetohydrodynamics, an energy-consistent finite difference formulation is derived using

# Nested sampling with any prior you like. (arXiv:2102.12478v1 [astro-ph.IM])

Nested sampling is an important tool for conducting Bayesian analysis in Astronomy and other fields, both for sampling complicated posterior distributions for parameter inference, and for computing marginal likelihoods for

# Decoding Photons: Physics in the Latent Space of a BIB-AE Generative Network. (arXiv:2102.12491v1 [physics.ins-det])

Given the increasing data collection capabilities and limited computing resources of future collider experiments, interest in using generative neural networks for the fast simulation of collider events is growing. In

# Nested sampling with any prior you like. (arXiv:2102.12478v1 [astro-ph.IM])

Nested sampling is an important tool for conducting Bayesian analysis in Astronomy and other fields, both for sampling complicated posterior distributions for parameter inference, and for computing marginal likelihoods for

# Symmetric distinguishability as a quantum resource. (arXiv:2102.12512v1 [quant-ph])

We develop a resource theory of symmetric distinguishability, the fundamental objects of which are elementary quantum information sources, i.e., sources that emit one of two possible quantum states with given

# Optimized Diffusion Imaging for Brain Structural Connectome Analysis. (arXiv:2102.12526v1 [stat.AP])

High angular resolution diffusion imaging (HARDI), a type of diffusion magnetic resonance imaging (dMRI) that measures diffusion signals on a sphere in q-space, is widely used in data acquisition for

# Maximum Likelihood Constraint Inference from Stochastic Demonstrations. (arXiv:2102.12554v1 [eess.SY])

When an expert operates a perilous dynamic system, ideal constraint information is tacitly contained in their demonstrated trajectories and controls. The likelihood of these demonstrations can be computed, given the