What we don't know about time

String theory has transformed our understanding of geometry, topology and spacetime. Thus, for this special issue of Foundations of Physics commemorating "Forty Years of String Theory", it seems appropriate to step back and ask what we do not understand. As I will discuss, time remains the least understood concept in physical theory. While we have made significant progress in understanding space, our understanding of time has not progressed much beyond the level of a century ago when Einstein introduced the idea of space-time as a combined entity. Thus, I will raise a series of open questions about time, and will review some of the progress that has been made as a roadmap for the future.Comment: 15 pages; Essay for a special issue of Foundations of Physics commemorating "Forty years of string theory

Dynamic Display of Changing Posterior in Bayesian Survival Analysis: The Software

We consider the problem of estimating an unknown distribution function in the presence of censoring under the conditions that a parametric model is believed to hold approximately. We use a Bayesian approach, in which the prior on is a mixture of Dirichlet distributions. A hyperparameter of the prior determines the extent to which this prior concentrates its mass around the parametric family. A Gibbs sampling algorithm to estimate the posterior distributions of the parameters of interest is reviewed. An importance sampling scheme enables us to use the output of the Gibbs sampler to very quickly recalculate the posterior when we change the hyperparameters of the prior. The calculations can be done sufficiently fast to enable the dynamic display of the changing posterior as the prior hyperparameters are varied. This paper provides a literate program completely documenting the code for performing the dynamic graphics.

Spacetime and the Holographic Renormalization Group

Anti-de Sitter (AdS) space can be foliated by a family of nested surfaces homeomorphic to the boundary of the space. We propose a holographic correspondence between theories living on each surface in the foliation and quantum gravity in the enclosed volume. The flow of observables between our ``interior'' theories is described by a renormalization group equation. The dependence of these flows on the foliation of space encodes bulk geometry.Comment: 12 page

Drag of two-dimensional small-amplitude symmetric and asymmetric wavy walls in turbulent boundary layers

Included are results of an experimental investigation of low-speed turbulent flow over multiple two-dimensional transverse rigid wavy surfaces having a wavelength on the order of the boundary-layer thickness. Data include surface pressure and total drag measurements on symmetric and asymmetric wall waves under a low-speed turbulent boundary-layer flow. Several asymmetric wave configurations exhibited drag levels below the equivalent symmetric (sine) wave. The experimental results compare favorably with numerical predictions from a Reynolds-averaged Navier-Stokes spectral code. The reported results are of particular interest for the estimation of drag, the minimization of fabrication waviness effects, and the study of wind-wave interactions

C4Synth: Cross-Caption Cycle-Consistent Text-to-Image Synthesis

Generating an image from its description is a challenging task worth solving because of its numerous practical applications ranging from image editing to virtual reality. All existing methods use one single caption to generate a plausible image. A single caption by itself, can be limited, and may not be able to capture the variety of concepts and behavior that may be present in the image. We propose two deep generative models that generate an image by making use of multiple captions describing it. This is achieved by ensuring 'Cross-Caption Cycle Consistency' between the multiple captions and the generated image(s). We report quantitative and qualitative results on the standard Caltech-UCSD Birds (CUB) and Oxford-102 Flowers datasets to validate the efficacy of the proposed approach.Comment: To appear in the proceedings of IEEE Winter Conference on Applications of Computer Vision, WACV-201