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

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

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

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

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

Beginning of Viniculture in France

Chemical analyses of ancient organic compounds absorbed into the pottery fabrics of imported Etruscan amphoras (ca. 500-475 B.C.) and into a limestone pressing platform (ca. 425-400 B.C.) at the ancient coastal port site of Lattara in southern France provide the earliest biomolecular archaeological evidence for grape wine and viniculture from this country, which is crucial to the later history of wine in Europe and the rest of the world. The data support the hypothesis that export of wine by ship from Etruria in central Italy to southern Mediterranean France fueled an ever-growing market and interest in wine there, which, in turn, as evidenced by the winepress, led to transplantation of the Eurasian grapevine and the beginning of a Celtic industry in France. Herbal and pine resin additives to the Etruscan wine point to the medicinal role of wine in antiquity, as well as a means of preserving it during marine transport

A New Probe of the Molecular Gas in Galaxies: Application to M101

Recent studies of nearby spiral galaxies suggest that photodissociation regions (PDRs) are capable of producing much of the observed HI in galaxy disks. In that case, measurements of the HI column density and the far-ultraviolet (FUV) photon flux provide a new probe of the volume density of the local underlying H_2. We develop the method and apply it to the giant Scd spiral M101 (NGC 5457). We find that, after correction for the best-estimate gradient of metallicity in the ISM of M101 and for the extinction of the ultraviolet emission, molecular gas with a narrow range of density from 30-1000 cm^-3 is found near star- forming regions at all radii in the disk of M101 out to a distance of 12' (approximately 26 kpc), close to the photometric limit of R_25 = 13.5'. In this picture, the ISM is virtually all molecular in the inner parts of M101. The strong decrease of the HI column density in the inner disk of the galaxy at R_G < 10 kpc is a consequence of a strong increase in the dust-to-gas ratio there, resulting in an increase of the H_2 formation rate on grains and a corresponding disappearance of hydrogen in its atomic form.Comment: accepted for publication in The Astrophysical Journal (1 August 2000); 29 pages including 20 figures (7 gif); AAS LaTex; contact authors for full resolution versions of gif figure

The Ultraviolet Imaging Telescope: Instrument and Data Characteristics

The Ultraviolet Imaging Telescope (UIT) was flown as part of the Astro observatory on the Space Shuttle Columbia in December 1990 and again on the Space Shuttle Endeavor in March 1995. Ultraviolet (1200-3300 Angstroms) images of a variety of astronomical objects, with a 40 arcmin field of view and a resolution of about 3 arcsec, were recorded on photographic film. The data recorded during the first flight are available to the astronomical community through the National Space Science Data Center (NSSDC); the data recorded during the second flight will soon be available as well. This paper discusses in detail the design, operation, data reduction, and calibration of UIT, providing the user of the data with information for understanding and using the data. It also provides guidelines for analyzing other astronomical imagery made with image intensifiers and photographic film.Comment: 44 pages, LaTeX, AAS preprint style and EPSF macros, accepted by PAS

UIT Detection of Hot Stars in the Globular Cluster NGC362

We used the Ultraviolet Imaging Telescope during the March 1995 Astro-2 mission to obtain a deep far-UV image of the globular cluster NGC 362, which was formerly thought to have an almost entirely red horizontal branch (HB). 84 hot (T_eff > 8500 K) stars were detected within a radius of 8'.25 of the cluster center. Of these, 43 have FUV magnitudes consistent with HB stars in NGC 362, and at least 34 are cluster members. The number of cluster members is made uncertain by background contamination from blue stars in the Small Magellanic Cloud (SMC). There are six candidate supra-HB stars which have probably evolved from the HB. We discuss the implications of these results for the production of hot blue stars in stellar populations.Comment: 10 pages AASLaTeX including one postscript figure and one compressed bitmap, .jpg format. To appear in Ap. J. Letters. Postscript version also available at http://www.astro.virginia.edu/~bd4r