Modern machine learning and computer science conferences are experiencing a surge in the number of submissions that challenges quality peer review as competent reviewers is growing at much slower rate. To curb this trend reduce burden on reviewers, several have started encouraging or even requiring authors to declare previous submission history their papers. Such initiatives been met with skept...

Neutron stars provide a unique physical laboratory to study the properties of matter at high density. We diagnostic composition high-density matter, namely, g-mode oscillations, which are driven by buoyancy forces. These oscillations can be excited tidal forces and couple gravitational waves. extend prior results for spectrum cold neutron star temperatures that expected achieved in mergers usin...

The recent literature on deep learning offers new tools to learn a rich probability distribution over high dimensional data such as images or sounds. In this work we investigate the possibility of learning the prior distribution over neural network parameters using such tools. Our resulting variational Bayes algorithm generalizes well to new tasks, even when very few training examples are provi...

$K$-frames as a generalization of frames were introduced by L. Gu{a}vruc{t}a to study atomic systems on Hilbert spaces which allows, in a stable way, to reconstruct elements from the range of the bounded linear operator $K$ in a Hilbert space. Recently some generalizations of this concept are introduced and some of its difference with ordinary frames are studied. In this paper, we give a new ge...

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