## Investment Advice for Star Destroyers

Chris Matzner

University of Toronto

March 17, 2015

Chris Matzner

University of Toronto

March 17, 2015

Johannes Krause

Professor of Archaeology and Paleogenetics at the University of Tübingen and Director of the Max Plank Institute for the Science of Human History in Jena

March 19, 2015

In this lecture, Krause explores the methods used to investigate European population history about the time of agricultural transition. Using genome data, Krause explains how at least three ancestral groups, the Hunter-Gathers, the Early Farmers and the Ancient North Eurasians, contributed genetic material to present-day Europeans. Krause also discusses these three ancestral populations discovered from this data and explores their connection to present-day Europeans.

Fred Sherry and Sebastian Currier

March 20, 2015

The pianist Peter Serkin and cellist Fred Sherry are both known for their brilliant renderings of classical repertoire, as well as for their passionate advocacy and electrifying performances of contemporary music. They will present Beethoven's last two cellos sonatas, as well as solo works by the American modernist composers Babbitt, Wourinen, and Carter.

Dimitris Achlioptas

University of California, Santa Cruz

March 23, 2015

At the heart of every local search algorithm is a directed graph on candidate solutions (states) such that every unsatisfactory state has at least one outgoing arc. In stochastic local search the hope is that a random walk will reach a satisfactory state (sink) quickly. We give a general algorithmic local lemma by establishing a sufficient condition for this to be true. Our work is inspired by Moser's entropic method proof of the Lovász Local Lemma (LLL) for satisfiability and completely bypasses the Probabilistic Method formulation of the LLL.

Jean Bourgain

IBM von Neumann Professor, School of Mathematics

March 23, 2015

Decoupling inequalities in harmonic analysis permit to bound the Fourier transform of measures carried by hyper surfaces by certain square functions defined using the geometry of the hyper surface. The original motivation has to do with issues in PDE, such as smoothing for the wave equation and Strichartz inequalities for the Schrodinger equation on tori. It turns out however that these decoupling inequalities have surprizing number theoretical consequences,on which we will mainly focus.

Carlos Simpson

University of Nice

March 24, 2015

Starting from an example in which the Hitchin correspondence can be written down explicitly, we look at what might be said relating the incidence complex of the boundary of the character variety, and the Hitchin map.

David Kaplan

University of Wisconsin, Milwaukee

March 24, 2015

Ivan Panin

Steklov Institute of Mathematics, St. Petersburg; Member, School of Mathematics

March 25, 2015

This is joint work with G .Garkusha. Using the machinery of framed sheaves developed by Voevodsky, a triangulated category of framed motives is introduced and studied. To any smooth algebraic variety $X$, the framed motive $M_{fr}(X)$ is associated in that category. Theorem. The bispectrum \[( M_{fr} X, M_{fr}(X)(1), M_{fr}(X)(2), ... ),\] each term of which is a twisted framed motive of $X$, has motivic homotopy type of the suspension bispectrum of $X$. (this result is an $A^1$-homotopy analog of a theorem due to G.Segal).

Bjorn Poonen

Massachusetts Institute of Technology

March 26, 2015

We prove that the probability that a curve of the form $y^2 = f(x)$ over $\mathbb Q$ with $\deg f = 2g + 1$ has no rational point other than the point at infinity tends to 1 as $g$ tends to infinity. This is joint work with Michael Stoll.

Vladimir Vapnik

Columbia University

March 30, 2015

During last fifty years a strong machine learning theory has been developed. This theory includes: 1. The necessary and sufficient conditions for consistency of learning processes. 2. The bounds on the rate of convergence which in general cannot be improved. 3. The new inductive principle (SRM) which always achieves the smallest risk. 4. The effective algorithms, (such as SVM), that realize consistency property of SRM principle.