Autoclass: An automatic classification system

The task of inferring a set of classes and class descriptions most likely to explain a given data set can be placed on a firm theoretical foundation using Bayesian statistics. Within this framework, and using various mathematical and algorithmic approximations, the AutoClass System searches for the most probable classifications, automatically choosing the number of classes and complexity of class descriptions. A simpler version of AutoClass has been applied to many large real data sets, has discovered new independently-verified phenomena, and has been released as a robust software package. Recent extensions allow attributes to be selectively correlated within particular classes, and allow classes to inherit, or share, model parameters through a class hierarchy. The mathematical foundations of AutoClass are summarized

Bayesian classification theory

The task of inferring a set of classes and class descriptions most likely to explain a given data set can be placed on a firm theoretical foundation using Bayesian statistics. Within this framework and using various mathematical and algorithmic approximations, the AutoClass system searches for the most probable classifications, automatically choosing the number of classes and complexity of class descriptions. A simpler version of AutoClass has been applied to many large real data sets, has discovered new independently-verified phenomena, and has been released as a robust software package. Recent extensions allow attributes to be selectively correlated within particular classes, and allow classes to inherit or share model parameters though a class hierarchy. We summarize the mathematical foundations of AutoClass

The RHMC Algorithm for 2 Flavours of Dynamical Staggered Fermions

We describe an implementation of the Rational Hybrid Monte Carlo (RHMC) algorithm for dynamical computations with two flavours of staggered quarks. We discuss several variants of the method, the performance and possible sources of error for each of them, and we compare the performance and results to the inexact R algorithm.Comment: Lattice2003(machine) 3 pages, 1 figure. Added referenc

Non-iterative and exact method for constraining particles in a linear geometry

We present a practical numerical method for evaluating the Lagrange multipliers necessary for maintaining a constrained linear geometry of particles in dynamical simulations. The method involves no iterations, and is limited in accuracy only by the numerical methods for solving small systems of linear equations. As a result of the non-iterative and exact (within numerical accuracy) nature of the procedure there is no drift in the constrained geometry, and the method is therefore readily applied to molecular dynamics simulations of, e.g., rigid linear molecules or materials of non-spherical grains. We illustrate the approach through implementation in the commonly used second-order velocity explicit Verlet method.Comment: 12 pages, 2 figure

The Sinkhorn algorithm, parabolic optimal transport and geometric Monge–Amp\ue8re equations

We show that the discrete Sinkhorn algorithm—as applied in the setting of Optimal Transport on a compact manifold—converges to the solution of a fully non-linear parabolic PDE of Monge–Amp\ue8re type, in a large-scale limit. The latter evolution equation has previously appeared in different contexts (e.g. on the torus it can be be identified with the Ricci flow). This leads to algorithmic approximations of the potential of the Optimal Transport map, as well as the Optimal Transport distance, with explicit bounds on the arithmetic complexity of the construction and the approximation errors. As applications we obtain explicit schemes of nearly linear complexity, at each iteration, for optimal transport on the torus and the two-sphere, as well as the far-field antenna problem. Connections to Quasi-Monte Carlo methods are exploited

Algorithmic abstractions of ‘fashion identity’ and the role of privacy with regard to algorithmic personalisation systems in the fashion domain

This paper delves into the nuances of ‘fashion’ in recommender systems and social media analytics, which shape and define an individual’s perception and self-relationality. Its aim is twofold: first, it supports a different perspective on privacy that focuses on the individual’s process of identity construction considering the social and personal aspects of ‘fashion’. Second, it underlines the limitations of computational models in capturing the diverse meaning of ‘fashion’, whereby the algorithmic prediction of user preferences is based on individual conscious and unconscious associations with fashion identity. I test both of these claims in the context of current concerns over the impact of algorithmic personalisation systems on individual autonomy and privacy: creating ‘filter bubbles’, nudging the user beyond their conscious awareness, as well as the inherent bias in algorithmic decision-making. We need an understanding of privacy that sustains the inherent reduction of fashion identity to literal attributes and protects individual autonomy in shaping algorithmic approximations of the self