Counting faces of randomly-projected polytopes when the projection radically lowers dimension

This paper develops asymptotic methods to count faces of random high-dimensional polytopes. Beyond its intrinsic interest, our conclusions have surprising implications - in statistics, probability, information theory, and signal processing - with potential impacts in practical subjects like medical imaging and digital communications. Three such implications concern: convex hulls of Gaussian point clouds, signal recovery from random projections, and how many gross errors can be efficiently corrected from Gaussian error correcting codes.Comment: 56 page

A Technique to Derive Improved Proper Motions for Kepler Objects of Interest

We outline an approach yielding proper motions with higher precision than exists in present catalogs for a sample of stars in the Kepler field. To increase proper motion precision we combine first moment centroids of Kepler pixel data from a single Season with existing catalog positions and proper motions. We use this astrometry to produce improved reduced proper motion diagrams, analogous to a Hertzsprung-Russell diagram, for stars identified as Kepler Objects of Interest. The more precise the relative proper motions, the better the discrimination between stellar luminosity classes. With UCAC4 and PPMXL epoch 2000 positions (and proper motions from those catalogs as quasi-bayesian priors) astrometry for a single test Channel (21) and Season (0) spanning two years yields proper motions with an average per-coordinate proper motion error of 1.0 millisecond of arc per year, over a factor of three better than existing catalogs. We apply a mapping between a reduced proper motion diagram and an HR diagram, both constructed using HST parallaxes and proper motions, to estimate Kepler Object of Interest K-band absolute magnitudes. The techniques discussed apply to any future small-field astrometry as well as the rest of the Kepler field.Comment: Accepted to The Astronomical Journal 15 August 201

A boundary integral formalism for stochastic ray tracing in billiards

Determining the flow of rays or non-interacting particles driven by a force or velocity field is fundamental to modelling many physical processes. These include particle flows arising in fluid mechanics and ray flows arising in the geometrical optics limit of linear wave equations. In many practical applications, the driving field is not known exactly and the dynamics are determined only up to a degree of uncertainty. This paper presents a boundary integral framework for propagating flows including uncertainties, which is shown to systematically interpolate between a deterministic and a completely random description of the trajectory propagation. A simple but efficient discretisation approach is applied to model uncertain billiard dynamics in an integrable rectangular domain

Observed Universality of Phase Transitions in High-Dimensional Geometry, with Implications for Modern Data Analysis and Signal Processing

We review connections between phase transitions in high-dimensional combinatorial geometry and phase transitions occurring in modern high-dimensional data analysis and signal processing. In data analysis, such transitions arise as abrupt breakdown of linear model selection, robust data fitting or compressed sensing reconstructions, when the complexity of the model or the number of outliers increases beyond a threshold. In combinatorial geometry these transitions appear as abrupt changes in the properties of face counts of convex polytopes when the dimensions are varied. The thresholds in these very different problems appear in the same critical locations after appropriate calibration of variables. These thresholds are important in each subject area: for linear modelling, they place hard limits on the degree to which the now-ubiquitous high-throughput data analysis can be successful; for robustness, they place hard limits on the degree to which standard robust fitting methods can tolerate outliers before breaking down; for compressed sensing, they define the sharp boundary of the undersampling/sparsity tradeoff in undersampling theorems. Existing derivations of phase transitions in combinatorial geometry assume the underlying matrices have independent and identically distributed (iid) Gaussian elements. In applications, however, it often seems that Gaussianity is not required. We conducted an extensive computational experiment and formal inferential analysis to test the hypothesis that these phase transitions are {\it universal} across a range of underlying matrix ensembles. The experimental results are consistent with an asymptotic large-$n$ universality across matrix ensembles; finite-sample universality can be rejected.Comment: 47 pages, 24 figures, 10 table

Counting faces of randomly projected polytopes when the projection radically lowers dimension

1.1. Three surprises of high dimensions. This paper develops asymptotic methods to count faces of random high-dimensional polytopes; a seemingly dry and unpromising pursuit. Yet our conclusions have surprising implications- in statistics, probability, information theory, and signal processing- with potential impacts i