Viscous compressible flow about blunt bodies using a numerically generated orthogonal coordinate system

A numerical solution to the Navier-Stokes equations was obtained for blunt axisymmetric entry bodies of arbitrary shape in supersonic flow. These equations are solved on a finite difference mesh obtained from a simple numerical technique which generates orthogonal coordinates between arbitrary boundaries. The governing equations are solved in time dependent form using Stetter's improved stability three step predictor corrector method. For the present application, the metric coefficients were obtained numerically using fourth order accurate, finite difference relations and proved to be totally reliable for the highly stretched mesh used to resolve the thin viscous boundary layer. Solutions are obtained for a range of blunt body nose shapes including concavities

Vitis aestivalis F.Michx.

https://thekeep.eiu.edu/herbarium_specimens_byname/19735/thumbnail.jp

Temporal HeartNet: Towards Human-Level Automatic Analysis of Fetal Cardiac Screening Video

We present an automatic method to describe clinically useful information about scanning, and to guide image interpretation in ultrasound (US) videos of the fetal heart. Our method is able to jointly predict the visibility, viewing plane, location and orientation of the fetal heart at the frame level. The contributions of the paper are three-fold: (i) a convolutional neural network architecture is developed for a multi-task prediction, which is computed by sliding a 3x3 window spatially through convolutional maps. (ii) an anchor mechanism and Intersection over Union (IoU) loss are applied for improving localization accuracy. (iii) a recurrent architecture is designed to recursively compute regional convolutional features temporally over sequential frames, allowing each prediction to be conditioned on the whole video. This results in a spatial-temporal model that precisely describes detailed heart parameters in challenging US videos. We report results on a real-world clinical dataset, where our method achieves performance on par with expert annotations.Comment: To appear in MICCAI, 201

Metalanguage in L1 English-speaking 12-year-olds: which aspects of writing do they talk about?

Traditional psycholinguistic approaches to metalinguistic awareness in L1 learners elicit responses containing metalanguage that demonstrates metalinguistic awareness of pre-determined aspects of language knowledge. This paper, which takes a more ethnographic approach, demonstrates how pupils are able to engage their own focus of metalanguage when reflecting on their everyday learning activities involving written language. What is equally significant is what their metalanguage choices reveal about their understanding and application of written language concepts

PlaNet - Photo Geolocation with Convolutional Neural Networks

Is it possible to build a system to determine the location where a photo was taken using just its pixels? In general, the problem seems exceptionally difficult: it is trivial to construct situations where no location can be inferred. Yet images often contain informative cues such as landmarks, weather patterns, vegetation, road markings, and architectural details, which in combination may allow one to determine an approximate location and occasionally an exact location. Websites such as GeoGuessr and View from your Window suggest that humans are relatively good at integrating these cues to geolocate images, especially en-masse. In computer vision, the photo geolocation problem is usually approached using image retrieval methods. In contrast, we pose the problem as one of classification by subdividing the surface of the earth into thousands of multi-scale geographic cells, and train a deep network using millions of geotagged images. While previous approaches only recognize landmarks or perform approximate matching using global image descriptors, our model is able to use and integrate multiple visible cues. We show that the resulting model, called PlaNet, outperforms previous approaches and even attains superhuman levels of accuracy in some cases. Moreover, we extend our model to photo albums by combining it with a long short-term memory (LSTM) architecture. By learning to exploit temporal coherence to geolocate uncertain photos, we demonstrate that this model achieves a 50% performance improvement over the single-image model

Ratoon Stunting Disease of Sugarcane: Isolation of the Causal Bacterium

A small coryneform bacterium was consistently isolated from sugarcane with ratoon stunting disease and shown to be the causal agent. A similar bacterium was isolated from Bermuda grass. Both strains multiplied in sugarcane and Bermuda grass, but the Bermuda grass strain did not incite the symptoms of ratoon stunting disease in sugarcane. Shoot growth in Bermuda grass was retarded by both strains

A Comparison of Classical Versus Deep Learning Techniques for Abusive Content Detection on Social Media Sites

The automated detection of abusive content on social media websites faces a variety of challenges including imbalanced training sets, the identification of an appropriate feature representation and the selection of optimal classifiers. Classifiers such as support vector machines (SVM), combined with bag of words or ngram feature representation, have traditionally dominated in text classification for decades. With the recent emergence of deep learning and word embeddings, an increasing number of researchers have started to focus on deep neural networks. In this paper, our aim is to explore cutting-edge techniques in automated abusive content detection. We use two deep learning approaches: convolutional neural networks (CNNs) and recurrent neural networks (RNNs). We apply these to 9 public datasets derived from various social media websites. Firstly, we show that word embeddings pre-trained on the same data source as the subsequent classification task improves the prediction accuracy of deep learning models. Secondly, we investigate the impact of different levels of training set imbalances on classifier types. In comparison to the traditional SVM classifier, we identify that although deep learning models can outperform the classification results of the traditional SVM classifier when the associated training dataset is seriously imbalanced, the performance of the SVM classifier can be dramatically improved through the use of oversampling, surpassing the deep learning models. Our work can inform researchers in selecting appropriate text classification strategies in the detection of abusive content, including scenarios where the training datasets suffer from class imbalance

Systematic review of the methods used in economic evaluations of targeted physical activity and sedentary behaviour interventions

The burden of noncommunicable diseases (NCD) on health systems worldwide is substantial. Physical inactivity and sedentary behaviour are major risk factors for NCD. Previous attempts to understand the value for money of preventative interventions targeting physically inactive individuals have proved to be challenging due to key methodological challenges associated with the conduct of economic evaluations in public health. A systematic review was carried out across six databases (Medline, SPORTSDiscus, EconLit, PsychINFO, NHS EED, HTA) along with supplementary searches. The review examines how economic evaluations published between 2009-March 2017 have addressed methodological challenges with the aim of bringing to light examples of good practice for future studies. Fifteen economic evaluations from four high-income countries were retrieved; there is a dearth of studies targeting sedentary behaviour as an independent risk factor from physical activity. Comparability of studies from the healthcare and societal perspectives were limited due to analysts’ choice in cost categories, valuation technique and time horizon differing substantially. The scarcity of and inconsistencies across economic evaluations for these two behaviours have exposed a mismatch between calls for more preventative action to tackle NCD and the lack of information available on how resources may be optimally allocated in practice. Consequently, this paper offers a table of recommendations on how future studies can be improved