University of Princeton
A self-supervised approach to 3D point cloud reconstruction utilizes implicit attention priors and a learnable dictionary within a neural field to capture non-local self-similarity directly from input data. This method, developed by researchers from the University of Cambridge, achieves state-of-the-art accuracy on standard benchmarks and demonstrates robustness to noise and enhanced detail preservation.
Galaxy clusters are filled with hot, diffuse X-ray emitting plasma, with a stochastically tangled magnetic field whose energy is close to equipartition with the energy of the turbulent motions \cite{zweibel1997, Vacca}. In the cluster cores, the temperatures remain anomalously high compared to what might be expected considering that the radiative cooling time is short relative to the Hubble time \cite{cowie1977,fabian1994}. While feedback from the central active galactic nuclei (AGN) \cite{fabian2012,birzan2012,churazov2000} is believed to provide most of the heating, there has been a long debate as to whether conduction of heat from the bulk to the core can help the core to reach the observed temperatures \cite{narayan2001,ruszkowski2002,kunz2011}, given the presence of tangled magnetic fields. Interestingly, evidence of very sharp temperature gradients in structures like cold fronts implies a high degree of suppression of thermal conduction \cite{markevitch2007}. To address the problem of thermal conduction in a magnetized and turbulent plasma, we have created a replica of such a system in a laser laboratory experiment. Our data show a reduction of local heat transport by two orders of magnitude or more, leading to strong temperature variations on small spatial scales, as is seen in cluster plasmas \cite{markevitch2003}.
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