This series covers all areas of research at Perimeter Institute, as well as those outside of PI's scope.
The cosmic microwave background radiation has been an indispensable tool for learning about the origins and evolution of our Observable Universe. Satellites and ground based experiments measuring the temperature and polarization anisotropies with ever increasing angular resolution and sensitivity have established the standard cosmological model, LCDM, and constrained or ruled out a huge variety of theoretical models of the early Universe.
Sexual harassment and sexual assault in the workplace is almost always a severe betrayal of trust. I will describe research and theory that my students and I have developed over the last 25 years regarding interpersonal and institutional betrayals of trust. My presentation will include an explanation of betrayal trauma theory and information about institutional betrayal. I will present data from some of our research studies, including results from a study of sexual harassment of graduate students. Included will be research-based recommendations for how to respond well to disclosures o
From earliest infancy, we live in and learn to function in a world of causes and effects. Yet science has had an ambivalent, even hostile attitude toward causation for more than a century. Statistics courses teach us that “correlation is not causation,” yet they are strangely silent about what is causation.
While it’s undeniably sexy to work with infinite-dimensional categories “model-independently,” we contend there is a categorical imperative to familiarize oneself with at least one concrete model in order to check that proposed model-independent constructions interpret correctly. With this aim in mind, we recount the n-complicial sets model of (∞,n)-categories for 0 ≤ n ≤ ∞, the combinatorics of which are quite similar to its low-dimensional special cases: quasi-categories (n=1) and Kan complexes (n=0).
The basic geometry of the Solar System - the shapes, spacings, and
orientations of the planetary orbits - has long been a subject of
fascination as well as inspiration for planet-formation theories. For
exoplanetary systems, those same properties have only recently come
into focus. I will review our current knowledge of the occurrence of
planets around other stars, their orbital distances and
eccentricities, the orbital spacings and mutual inclinations in
multiplanet systems, the orientation of the host star's rotation axis,
I will discuss the status and future of numerical lattice Quantum Chromodynamics (QCD) calculations for nuclear physics. With advances in supercomputing, we are beginning to quantitatively understand nuclear structure and interactions directly from the fundamental quark and gluon degrees of freedom of the Standard Model. Recent studies provide insight into the neutrino-nucleus interactions relevant to long-baseline neutrino experiments, double beta decay, and nuclear sigma terms needed for theory predictions of dark matter cross-sections at underground detectors.
While the term “wide-field telescope” might sound like an oxymoron, a strong argument can be made that wide-field instruments lie behind much of the success of Canadian astronomy. Furthermore, despite the large size of the optical-IR community in Canada, this success has been made possible by considering multiple wavelength windows, from gamma to radio, and access to a suite of facilities.
Recently there have been several proposals of low-energy precision experiments that can search for new particles, new forces, and the Dark Matter of the Universe in a way that is complementary to collider searches. In this talk, I will present some examples involving atomic clocks, nuclear magnetic resonance, and astrophysical black holes accessible to LIGO.
The general purpose computer can run any program we can express in
symbolic logic; that makes it the go-to tool for accomplishing any task
that can be reduced to a computable function, and that's why software is
eating the world and cars and colliders and airplanes and pacemakers and
toasters are all just turning into computers in fancy cases.
The surprising success of learning with deep neural networks poses two fundamental challenges: understanding why these networks work so well and what this success tells us about the nature of intelligence and our biological brain. Our recent Information Theory of Deep Learning shows that large deep networks achieve the optimal tradeoff between training size and accuracy, and that this optimality is achieved through the noise in the learning process.