The Purple Haze of Eta Carinae: Binary-Induced Variability?

Asymmetric variability in ultraviolet images of the Homunculus obtained with the Advanced Camera for Surveys/High Resolution Camera on the Hubble Space Telescope suggests that Eta Carinae is indeed a binary system. Images obtained before, during, and after the recent ``spectroscopic event'' in 2003.5 show alternating patterns of bright spots and shadows on opposite sides of the star before and after the event, providing a strong geometric argument for an azimuthally-evolving, asymmetric UV radiation field as one might predict in some binary models. The simplest interpretation of these UV images, where excess UV escapes from the secondary star in the direction away from the primary, places the major axis of the eccentric orbit roughly perpendicular to our line of sight, sharing the same equatorial plane as the Homunculus, and with apastron for the hot secondary star oriented toward the southwest of the primary. However, other orbital orientations may be allowed with more complicated geometries. Selective UV illumination of the wind and ejecta may be partly responsible for line profile variations seen in spectra. The brightness asymmetries cannot be explained plausibly with delays due to light travel time alone, so a single-star model would require a seriously asymmetric shell ejection.Comment: 8 pages, fig 1 in color, accepted by ApJ Letter

Bayesian Transductive Markov Random Fields for Interactive Segmentation in Retinal Disorders

In the realm of computer aided diagnosis (CAD) interactive segmentation schemes have been well received by physicians, where the combination of human and machine intelligence can provide improved segmentation efficacy with minimal expert intervention [1-3]. Transductive learning (TL) or semi-supervised learning (SSL) is a suitable framework for learning-based interactive segmentation given the scarce label problem. In this paper we present extended work on Bayesian transduction and regularized conditional mixtures for interactive segmentation [3]. We present a Markov random field model integrating a semi-parametric conditional mixture model within a Bayesian transductive learning and inference setting. The model allows efficient learning and inference in a semi-supervised setting given only minimal approximate label information. Preliminary experimental results on multimodal images of retinal disorders such as drusen, geographic atrophy (GA), and choroidal neovascularisation (CNV) with exudates and subretinal fibrosis show promising segmentation performance

Learning Non-Homogenous Textures and the Unlearning Problem with Application to Drusen Detection in Retinal Images

In this work we present a novel approach for learning non- homogenous textures without facing the unlearning problem. Our learning method mimics the human behavior of selective learning in the sense of fast memory renewal. We perform probabilistic boosting and structural similarity clustering for fast selective learning in a large knowledge domain acquired over different time steps. Applied to non- homogenous texture discrimination, our learning method is the first approach that deals with the unlearning problem applied to the task of drusen segmentation in retinal imagery, which itself is a challenging problem due to high variability of non-homogenous texture appearance. We present preliminary results

PPARs in Alveolar Macrophage Biology

PPARs, most notably PPAR-γ, play a crucial role in regulating the activation of alveolar macrophages, which in turn occupy a pivotal place in the immune response to pathogens and particulates drawn in with inspired air. In this review, we describe the dual role of the alveolar macrophage as both a first-line defender through its phagocytotic activity and a regulator of the immune response. Depending on its state of activation, the alveolar macrophage may either enhance or suppress different aspects of immune function in the lung. We then review the role of PPAR-γ and its ligands in deactivating alveolar macrophages—thus limiting the inflammatory response that, if unchecked, could threaten the essential respiratory function of the alveolus—while upregulating the cell's phagocytotic activity. Finally, we examine the role that inadequate or inappropriate PPAR-γ responses play in specific lung diseases

Retinal vessel segmentation using multi-scale wavelet frame analysis

Fundus imaging is a non-invasive technique for in vivo ophthalmoscopic inspection of retinal disorders. Quantitative information about the vascular network can facilitate clinical diagnosis of retinal diseases [1]. Goal: Segmentation of the vascular network in fundus images for further quantification and post processing as a binary classification into object and background. Approach: We perform an over-complete multi-scale wavelet frame expansion with selective channel rejection in the decomposition tree. Remaining channels undergo wavelet shrinkage and enhancement to separate retinal objects from background. Results: Comparison to expert gradings on a pixel by pixel basis show mean sensitivity and specificity of 0.8 and 0.9

Latitude-dependent effects in the stellar wind of Eta Carinae

The Homunculus reflection nebula around eta Carinae provides a rare opportunity to observe the spectrum of a star from multiple latitudes. We present STIS spectra of several positions in the Homunculus, showing directly that eta Car has an aspherical stellar wind. P Cygni absorption in Balmer lines depends on latitude, with high velocities and strong absorption near the poles. Stronger absorption at high latitudes is surprising, and it suggests higher mass flux toward the poles, perhaps resulting from radiative driving with equatorial gravity darkening on a rotating star. Reflected profiles of He I lines are more puzzling, offering clues to the wind geometry and ionization structure. During eta Car's high-excitation state in March 2000, the wind was fast and dense at the poles, with higher ionization at low latitudes. Older STIS data obtained since 1998 reveal that this global stellar-wind geometry changes during eta Car's 5.5 year cycle, and may suggest that this star's spectroscopic events are shell ejections. Whether or not a companion star triggers these outbursts remains ambiguous. The most dramatic changes in the wind occur at low latitudes, while the dense polar wind remains relatively undisturbed during an event. The apparent stability of the polar wind also supports the inferred bipolar geometry. The wind geometry and its variability have critical implications for understanding the 5.5 year cycle and long-term variability, but do not provide a clear alternative to the binary hypothesis for generating eta Car's X-rays.Comment: Accepted by ApJ. To appear in March 2003. Based on PhD Thesis, Minnesota 200

Interactive Image Analysis in Age-related Macular Degeneration (AMD) and Stargardt Disease (STGD)

The literature of the last three decades is replete with automatic methods for retinal image analysis. Acceptance has been limited due to post-processing or tuning requirements that may be just as time consuming as the original manual methods. The point of view herein is that by taking advantage of the human visual system and expert knowledge from the outset, the promised efficiencies of digital methods can be achieved in practice as well as in theory. Thus, simple labeling of regions of interest that is accepted and easily performed in a few moments by the human can provide enormous advantage to an already well-developed algorithm. Three examples are provided: drusen segmentation, image registration, and geographic atrophy segmentation, with applications to disease understanding

Why my photos look sideways or upside down? Detecting Canonical Orientation of Images using Convolutional Neural Networks

Image orientation detection requires high-level scene understanding. Humans use object recognition and contextual scene information to correctly orient images. In literature, the problem of image orientation detection is mostly confronted by using low-level vision features, while some approaches incorporate few easily detectable semantic cues to gain minor improvements. The vast amount of semantic content in images makes orientation detection challenging, and therefore there is a large semantic gap between existing methods and human behavior. Also, existing methods in literature report highly discrepant detection rates, which is mainly due to large differences in datasets and limited variety of test images used for evaluation. In this work, for the first time, we leverage the power of deep learning and adapt pre-trained convolutional neural networks using largest training dataset to-date for the image orientation detection task. An extensive evaluation of our model on different public datasets shows that it remarkably generalizes to correctly orient a large set of unconstrained images; it also significantly outperforms the state-of-the-art and achieves accuracy very close to that of humans

The Design and Performance of a Distributed Flow Water-Cooled Solar Collector

Design of a flat plate collector which reduces the temperature differential between the absorber plate and the fluid is described. The reduced temperature differences are shown to yield increase collector performance. Flow characteristics of the collector are examined. Collector thermal performance is illustrated for typical operating and environmental conditions. A cost analysis is presented to demonstrate that material and assembly costs are substantially lower than for any collector presently on the market