When do young birds disperse? : Tests from studies of golden eagles in Scotland

Peer reviewedPublisher PD

Semi-Supervised Deep Learning for Fully Convolutional Networks

Deep learning usually requires large amounts of labeled training data, but annotating data is costly and tedious. The framework of semi-supervised learning provides the means to use both labeled data and arbitrary amounts of unlabeled data for training. Recently, semi-supervised deep learning has been intensively studied for standard CNN architectures. However, Fully Convolutional Networks (FCNs) set the state-of-the-art for many image segmentation tasks. To the best of our knowledge, there is no existing semi-supervised learning method for such FCNs yet. We lift the concept of auxiliary manifold embedding for semi-supervised learning to FCNs with the help of Random Feature Embedding. In our experiments on the challenging task of MS Lesion Segmentation, we leverage the proposed framework for the purpose of domain adaptation and report substantial improvements over the baseline model.Comment: 9 pages, 6 figure

Enhancement of Specific Immunofluorescent Finding with Use of a Para-Phenylenediamine Mounting Buffer

A recently described immunofluorescence mounting buffer containing para-phenylenediamine prevents fading of specific staining in skin sections during microscopic examination, and allows better appreciation of morphological detail. Examination of slides at high powers with intense illumination, as well as improved photomicrographs, are possible with this reagent

Optimal symmetric flight studies

Several topics in optimal symmetric flight of airbreathing vehicles are examined. In one study, an approximation scheme designed for onboard real-time energy management of climb-dash is developed and calculations for a high-performance aircraft presented. In another, a vehicle model intermediate in complexity between energy and point-mass models is explored and some quirks in optimal flight characteristics peculiar to the model uncovered. In yet another study, energy-modelling procedures are re-examined with a view to stretching the range of validity of zeroth-order approximation by special choice of state variables. In a final study, time-fuel tradeoffs in cruise-dash are examined for the consequences of nonconvexities appearing in the classical steady cruise-dash model. Two appendices provide retrospective looks at two early publications on energy modelling and related optimal control theory

The Distance to Nova V959 Mon from VLA Imaging

Determining reliable distances to classical novae is a challenging but crucial step in deriving their ejected masses and explosion energetics. Here we combine radio expansion measurements from the Karl G. Jansky Very Large Array with velocities derived from optical spectra to estimate an expansion parallax for nova V959 Mon, the first nova discovered through its gamma-ray emission. We spatially resolve the nova at frequencies of 4.5-36.5 GHz in nine different imaging epochs. The first five epochs cover the expansion of the ejecta from 2012 October to 2013 January, while the final four epochs span 2014 February to 2014 May. These observations correspond to days 126 through 199 and days 615 through 703 after the first detection of the nova. The images clearly show a non-spherical ejecta geometry. Utilizing ejecta velocities derived from 3D modelling of optical spectroscopy, the radio expansion implies a distance between 0.9 +/- 0.2 and 2.2 +/- 0.4 kpc, with a most probable distance of 1.4 +/- 0.4 kpc. This distance implies a gamma-ray luminosity much less than the prototype gamma-ray-detected nova, V407 Cyg, possibly due to the lack of a red giant companion in the V959 Mon system. V959 Mon also has a much lower gamma-ray luminosity than other classical novae detected in gamma-rays to date, indicating a range of at least a factor of 10 in the gamma-ray luminosities for these explosions.Comment: 11 pages, 8 figures, 3 tables, submitted to ApJ 2015-01-21, under revie

Differential negative reinforcement of other behavior to increase compliance with wearing an anti-strip suit

Using a changing-criterion design, we replicated and extended a study (Cook, Rapp, & Schulze, 2015) on differential negative reinforcement of other behavior (DNRO). More specifically, educational assistants implemented DNRO to teach a 12-year-old boy with autism spectrum disorder to comply with wearing an anti-strip suit to prevent inappropriate fecal behavior in a school setting. The duration for which the participant wore the suit systematically increased from 2 s at the start of treatment to the entire duration of the school day at the termination of the study. Moreover, these effects were generalized to a new school with novel staff and persisted for more than a year. These findings replicate prior research on DNRO and further support the use of the intervention to increase compliance with wearing protective items, or medical devices, in practical settings