Publications

AWTY (are we there yet?): a system for graphical exploration of MCMC convergence in Bayesian phylogenetics

Abstract Summary: A key element to a successful Markov chain Monte Carlo (MCMC) inference is the programming and run performance of the Markov chain. However, the explicit use o...

Recognizing indoor scenes

We propose a scheme for indoor place identification based on the recognition of global scene views. Scene views are encoded using a holistic representation that provides low-res...

Almost Sure Convergence.

Alternative factor specifications, security characteristics, and the cross-section of expected stock returns1We are especially grateful to Eugene Fama (a referee), an anonymous referee and Bill Schwert (the editor) for insightful and constructive suggestions. We also thank Wayne Ferson, Ken French, Will Goetzmann, Craig Holden, Ravi Jagannathan, Bob Jennings, Bruce Lehmann, Josef Lakonishok, Richard Roll, participants at the 1997 Meetings of the Western Finance Association, the 1997 UCLA/USC/UC Irvine conference, the November 1997 Asset Pricing Meeting of the National Bureau of Economic Research, the Atlanta Forum, and seminars at Columbia, Indiana, Florida, New York, Tulane, and Yale Universities; Eugene Fama and Ken French for providing part of the data used in this study; and Christoph Schenzler for excellent programming assistance. The second author acknowledges support from the Dean's Fund for Research and the Financial Markets Research Center at Vanderbilt University. We are responsible for remaining errors. This paper was formerly titled `A Re-Examination of Security Return Anomalies'.1

Solvent Effects. 5. Influence of Cavity Shape, Truncation of Electrostatics, and Electron Correlation on ab Initio Reaction Field Calculations

We describe several improvements to the reaction field model for the ab initio determination of solvation effects. First, the simple spherical cavity model is expanded to includ...

HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent

Stochastic Gradient Descent (SGD) is a popular algorithm that can achieve state-of-the-art performance on a variety of machine learning tasks. Several researchers have recently ...

An Experimental Study of Customer Effort, Expectation, and Satisfaction

Results of a laboratory experiment indicate that customer satisfaction with a product is influenced by the effort expended to acquire the product, and the expectations concernin...

Linear Smoothers and Additive Models

We study linear smoothers and their use in building nonparametric regression models. In the first part of this paper we examine certain aspects of linear smoothers for scatterpl...

Consensus Filters for Sensor Networks and Distributed Sensor Fusion

Consensus algorithms for networked dynamic systems provide scalable algorithms for sensor fusion in sensor networks. This paper introduces a distributed filter that allows the n...

National Institutes of Health Consensus Development Conference Statement: Adjuvant Therapy for Breast Cancer, November 1-3, 2000

The panel concludes that decisions regarding adjuvant hormonal therapy should be based on the presence of hormone receptor protein in tumor tissues. Adjuvant hormonal therapy sh...

Local Control Strategies for Groups of Mobile Autonomous Agents

The problem of achieving a specified formation among a group of mobile autonomous agents by distributed control is studied. If convergence to a point is feasible, then more gene...

Discrete Variational Method for the Energy-Band Problem with General Crystal Potentials

A general variational method for efficiently calculating energy bands and charge densities in solids is presented; the method can be viewed as a weighted local-energy procedure ...