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
