Vermeer as Aporia: Indeterminacy, Divergent Narratives, and Ways of Seeing

Although Johannes Vermeer’s paintings have long been labelled “ambiguous” in the canon of Western Art History, this research aims to challenge the notion of ambiguity. By shifting the conception of Vermeer’s works from ambiguity to indeterminacy, divergent narratives emerge which inform a more complex understanding of Vermeer’s oeuvre. These divergent narratives understand Vermeer’s paintings as turning points in stories that extend beyond the canvas; moments where the possibilities of a situation diverge in different directions. Thus, a myriad of narratives might be contained in a single painting, all of which simultaneously have the possibility of existing, but not the actuality. This interpretation of Vermeer takes evidence from seventeenth-century ways of seeing and the iconographic messages suggested by the paintings within paintings that occur across Vermeer’s oeuvre. Here for the first time, an aporetic approach is utilized to explore how contradictions and paradoxes within a system serve to contribute to holistic meaning. By analyzing four of Vermeer’s paintings – The Concert, Woman Holding a Balance, The Music Lesson, and Lady Seated at a Virginal – through an aporetic lens, an alternative to ambiguity can be constructed using indeterminacy and divergent narratives that help explain compositional and iconographical choices

Elliptical slice sampling

Many probabilistic models introduce strong dependencies between variables using a latent multivariate Gaussian distribution or a Gaussian process. We present a new Markov chain Monte Carlo algorithm for performing inference in models with multivariate Gaussian priors. Its key properties are: 1) it has simple, generic code applicable to many models, 2) it has no free parameters, 3) it works well for a variety of Gaussian process based models. These properties make our method ideal for use while model building, removing the need to spend time deriving and tuning updates for more complex algorithms.Comment: 8 pages, 6 figures, appearing in AISTATS 2010 (JMLR: W&CP volume 6). Differences from first submission: some minor edits in response to feedback

Nested sampling for Potts models

Nested sampling is a new Monte Carlo method by Skilling [1] intended for general Bayesian computation. Nested sampling provides a robust alternative to annealing-based methods for computing normalizing constants. It can also generate estimates of other quantities such as posterior expectations. The key technical requirement is an ability to draw samples uniformly from the prior subject to a constraint on the likelihood. We provide a demonstration with the Potts model, an undirected graphical model

GravityCam: Higher Resolution Visible Wide-Field Imaging

The limits to the angular resolution achievable with conventional ground-based telescopes are unchanged over 70 years. Atmospheric turbulence limits image quality to typically ~1 arcsec in practice. We have developed a new concept of ground-based imaging instrument called GravityCam capable of delivering significantly sharper images from the ground than is normally possible without adaptive optics. The acquisition of visible images at high speed without significant noise penalty has been made possible by advances in optical and near IR imaging technologies. Images are recorded at high speed and then aligned before combination and can yield a 3-5 fold improvement in image resolution. Very wide survey fields are possible with widefield telescope optics. We describe GravityCam and detail its application to accelerate greatly the rate of detection of Earth size planets by gravitational microlensing. GravityCam will also improve substantially the quality of weak shear studies of dark matter distribution in distant clusters of galaxies. The microlensing survey will also provide a vast dataset for asteroseismology studies. In addition, GravityCam promises to generate a unique data set that will help us understand of the population of the Kuiper belt and possibly the Oort cloud.This is the author accepted manuscript. The final version is available from http://dx.doi.org/10.1117/12.223090

MCMC for doubly-intractable distributions

Markov Chain Monte Carlo (MCMC) algorithms are routinely used to draw samples from distributions with intractable normalization constants. However, standard MCMC algorithms do not apply to doubly-intractable distributions in which there are additional parameter-dependent normalization terms; for example, the posterior over parameters of an undirected graphical model. An ingenious auxiliary-variable scheme (Møller et al., 2004) offers a solution: exact sampling (Propp and Wilson, 1996) is used to sample from a Metropolis–Hastings proposal for which the acceptance probability is tractable. Unfortunately the acceptance probability of these expensive updates can be low. This paper provides a generalization of Møller et al. (2004) and a new MCMC algorithm, which obtains better acceptance probabilities for the same amount of exact sampling, and removes the need to estimate model parameters before sampling begins

Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities

The inhomogeneous Poisson process is a point process that has varying intensity across its domain (usually time or space). For nonparametric Bayesian modeling, the Gaussian process is a useful way to place a prior distribution on this intensity. The combination of an Poisson process and GP is known as a Gaussian Cox process, or doubly-stochastic Poisson process. Likelihood-based inference in these models requires an intractable integral over an infinite-dimensional random function. In this paper we present the first approach to Gaussian Cox processes in which it is possible to perform inference without introducing approximations or finite-dimensional proxy distributions. We call our method the Sigmoidal Gaussian Cox Process, which uses a generative model for Poisson data to enable tractable inference via Markov chain Monte Carlo. We compare our methods to competing methods on synthetic data and also apply it to several real-world data sets

High cut-off microdialysis catheters to clinically investigate cytokine changes following flap transfer

Background: ‘Choke vessels’ are thought to dilate in the first 72 h when blood flow to an area is disrupted. This study used ‘high cut-off’ microdialysis catheters in clinical research to investigate factors mediating circulatory change within free flaps. Methods: Six patients undergoing DIEP flap breast reconstruction each had three ‘high cut-off’ microdialysis catheters, with a membrane modification allowing molecules as large as 100 kDa to pass, inserted into Hartrampf zones 1, 2 and 4 to assess multiple vascular territories. Microdialysis continued for 72 h post-operatively. Samples were analysed for interleukin-6 (IL-6), tumour necrosis factor alpha (TNFα) and fibroblast growth factor basic (FGFβ). Results: Three hundred and twenty-four samples were analysed for IL-6, FGFβ and TNFα totalling 915 analyses. IL-6 showed an increasing trend until 36 h post-operatively before remaining relatively constant. Overall, there was an increase (p < 0.001) over the time period from 4 to 72 h, fitting a linear trend. TNFα had a peak around 20–24 h before a gradual decrease. There was a significant linear time trend (p = 0.029) between 4 and 76 h, decreasing over the time period. FGFβ concentrations did not appear to have any overall difference in concentration with time. The concentration however appeared to oscillate about a horizontal trend line. There were no differences between the DIEP zones in concentrations of cytokines collected. Conclusion: This study uses high-cut off microdialysis catheters to evaluate changes in cytokines, and requires further research to be undertaken to add to our knowledge of choke vessels and flap physiology

The Gaussian process density sampler

We present the Gaussian Process Density Sampler (GPDS), an exchangeable generative model for use in nonparametric Bayesian density estimation. Samples drawn from the GPDS are consistent with exact, independent samples from a fixed density function that is a transformation of a function drawn from a Gaussian process prior. Our formulation allows us to infer an unknown density from data using Markov chain Monte Carlo, which gives samples from the posterior distribution over density functions and from the predictive distribution on data space. We can also infer the hyperparameters of the Gaussian process. We compare this density modeling technique to several existing techniques on a toy problem and a skullreconstruction task.