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The space-time correlations of streams of photons can provide fundamentally new channels of information about the Universe. Todays astronomical observations essentially measure certain amplitude coherence functions produced by a source. The spatial correlations of wave fields has traditionally been exploited in Michelson-style amplitude interferometry. However the technology of the past was largely incapable of fine timing resolution and recording multiple beams. When time and space correlations are combined it is possible to achieve spectacular measurements that are impossible by any other means. Stellar intensity interferometry is ripe for development and is one of the few unexploited mechanisms to obtain potentially revolutionary new information in astronomy. As we discuss below, the modern use of stellar intensity interferometry can yield unprecedented measures of stellar diameters, binary stars, distance measures including Cepheids, rapidly rotating stars, pulsating stars, and short-time scale fluctuations that have never been measured before.
Dynamic magnetic resonance imaging (MRI) exhibits high correlations in k-space and time. In order to accelerate the dynamic MR imaging and to exploit k-t correlations from highly undersampled data, here we propose a novel deep learning based approach
The promise of any small improvement in the performance of light-harvesting devices, is sufficient to drive enormous experimental efforts. However these efforts are almost exclusively focused on enhancing the power conversion efficiency with specific
The dynamical response of Coulomb-interacting particles in nano-clusters are analyzed at different temperatures characterizing their solid- and liquid-like behavior. Depending on the trap-symmetry, both the spatial and temporal correlations undergo s
Astrophysical measurements away from the 1 AU orbit of Earth can enable several astrophysical science cases that are challenging or impossible to perform from Earthbound platforms, including: building a detailed understanding of the extragalactic bac
We present a spatio-temporal AI framework that concurrently exploits both the spatial and time-variable features of gravitationally lensed supernovae in optical images to ultimately aid in the discovery of such exotic transients in wide-field surveys