Since 2002 Perimeter Institute has been recording seminars, conference talks, and public outreach events 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 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.
Recently methods of integrability were shown to be useful for solving gauge theories in various dimensions. I will make an introduction into integrability in two dimensions and demonstrate how the integrability works also for some three and four dimensional gauge theories.
Researchers in quantum foundations claim (D'Ariano, Fuchs, ...):
Quantum = probability theory + x
x = Quantum - probability theory
Guided by the metaphorical analogy:
probability theory / x = flesh / bones
we introduce a notion of quantum measurement within x, which, when flesing it with Hilbert spaces, provides orthodox quantum mechanical probability calculus.
A modified version of PQCD considered in previous works is further investigated in the case of a vanishing gluon condensate, by retaining only the quark one. In this case the Green functions generating functional is expressed in a simple form in which Dirac’s delta functions are now absent from the free propagators. The new expansion implements the dimensional transmutation effect through a single interaction vertex in addition to the standard ones in mass less QCD. The results of an ongoing two loop evaluation of the vacuum energy will be presented.
Solving linear systems of equations is a common problem that arises both on its own and as a subroutine in more complex problems: given a matrix A and a vector b, find a vector x such that Ax=b. Often, one does not need to know the solution x itself, but rather an approximation of the expectation value of some operator associated with x, e.g., x'Mx for some matrix M. In this case, when A is sparse and well-conditioned, with largest dimension N, the best known classical algorithms can find x and estimate x'Mx in O(N * poly(log(N))) time.
Perturbation Theory Out of Equilibrium
An Ecological Model of the US Economy
The NASDAQ Model
The Partecon Model