Since 2002 Perimeter Institute has been recording seminars, conference talks, public outreach events such as talks from top scientists using video cameras installed in our lecture theatres. Perimeter now has 7 formal presentation spaces for its many scientific conferences, seminars, workshops and educational outreach activities, all with advanced audio-visual technical capabilities.
Recordings of events in these areas are all available and On-Demand from this Video Library and on Perimeter Institute Recorded Seminar Archive (PIRSA). PIRSA is a permanent, free, searchable, and citable archive of recorded seminars from relevant bodies in physics. This resource has been partially modelled after Cornell University's arXiv.org.
Accessibly by anyone with internet, Perimeter aims to share the power and wonder of science with this free library.
Through their observable properties, the first and smallest dark matter halos represent a rare probe of subkiloparsec-scale variations in the density of the early Universe. These density variations could hold clues to the nature of inflation, the postinflationary cosmic history, and the identity of dark matter. However, the dynamical complexity of these microhalos hinders their usage as cosmological probes.
CMB lensing tomography has the potential to map the amplitude and growth of structure over cosmic time, provide some of the most stringent tests of gravity, and break important degeneracies between cosmological parameters. I use the unWISE photometric galaxy catalog to create three samples at median redshifts z~0.6, 1.1, and 1.5, and cross-correlate them with the most recent Planck CMB lensing maps.
In this talk I will review the state of the art in PN gravity, and in particular its significant advancement via the EFT of spinning gravitating objects. First, I will introduce the concept of a tower of EFTs for the binary inspiral problem. I will then present the intricate formulation of the EFT of spinning objects. Finally, I will present some advanced results accomplished within this framework.
I’ll talk about two independent works on classical and quantum neural networks connected by information theory. In the first part of the talk, I’ll treat sequence models as one-dimensional classical statistical mechanical systems and analyze the scaling behavior of mutual information. I'll provide a new perspective on why recurrent neural networks are not good at natural language processing. In the second part of the talk, I’ll study information scrambling dynamics when quantum neural networks are trained by classical gradient descent algorithm.