Anti-Foundational Categorical Structuralism

The aim of this dissertation is to outline and defend the view here dubbed “anti-foundational categorical structuralism” (henceforth AFCS). The program put forth is intended to provide an answer the question “what is mathematics?”. The answer here on offer adopts the structuralist view of mathematics, in that mathematics is taken to be “the science of structure” expressed in the language of category theory, which is argued to accurately capture the notion of a “structural property”. In characterizing mathematical theorems as both conditional and schematic in form, the program is forced to give up claims to securing the truth of its theorems, as well as give up a semantics which involves reference to special, distinguished “mathematical objects”, or which involves quantification over a fixed domain of such objects. One who wishes—contrary to the AFCS view—to inject mathematics with a “standard” semantics, and to provide a secure epistemic foundation for the theorems of mathematics, in short, one who wishes for a foundation for mathematics, will surely find this view lacking. However, I argue that a satisfactory development of the structuralist view, couched in the language of category theory, accurately represents our best understanding of the content of mathematical theorems and thereby obviates the need for any foundational program

Spectral approximations in machine learning

In many areas of machine learning, it becomes necessary to find the eigenvector decompositions of large matrices. We discuss two methods for reducing the computational burden of spectral decompositions: the more venerable Nystom extension and a newly introduced algorithm based on random projections. Previous work has centered on the ability to reconstruct the original matrix. We argue that a more interesting and relevant comparison is their relative performance in clustering and classification tasks using the approximate eigenvectors as features. We demonstrate that performance is task specific and depends on the rank of the approximation.Comment: 11 pages, 4 figure

A gyrokinetic analysis of electron plasma waves at resonance in magnetic field gradients

To produce nuclear fusion in a Tokamak reactor requires the heating of a plasma to a temperature of the order of 10 keV. Electron cyclotron resonant heating (ECRH), in which the plasma is heated by radio waves in resonance with the Larmor frequency of the plasma's electrons, is one scheme under consideration for achieving this. A description of such a heating scheme requires a theory to explain the propagation and absorption of high frequency waves in a plasma in the presence of a magnetic field gradient. A WKB analysis can describe some of the processes involved but a complete explanation requires the use of full wave equations. In this thesis we shall develop a technique for deriving such equations which will be shown to be simpler and more general than calculations performed by earlier workers. The technique relies on including the effect of the magnetic gradient across the Larmor orbit of the electrons in the resonance condition of the wave, the so called Gyrokinetic correction, which has been ignored in calculations by previous workers. Once derived, the equations are solved numerically and the results applied to a number of experiments currently being performed on Tokamak fusion. In addition, we shall also look at the energy loss processes of runaway electrons, which have been shown experimentally to be shorter than would be expected