Global dimensions for the recognition of prototypical urban roads in large-scale vector topographic maps

CISRG discussion paper ; 1

Teaching schools evaluation. Research Brief

This Research Brief reports the findings from a two-year study (2013-15) in to the work of teaching schools and their alliances commissioned by the National College for Teaching and Leadership (NCTL). The broad aim of the study was to investigate the effectiveness and impact of teaching schools on improvement, and identify the quality and scope of external support that are required to enhance these . This was achieved through combining qualitative and quantitative data collection and analysis derived from three research activities: case studies of 26 teaching schools alliances (TSAs), a national survey of the first three cohorts of 345 TSAs, and secondary research and analysis of national performance and inspection results

Word frequency predicts translation asymmetry

Bilingualism studies report asymmetries in word processing across languages. Access to L2 words is slower and sensitive to semantic blocking. These observations inform influential models of bilingual processing, which propose autonomous lexicons with different processing routes. In a series of experiments, we explored an alternative hypothesis that the asymmetries are due to frequency of use. Using a within-language ‘translation’ task, involving high/low frequency (HF/LF) synonyms, we obtained parallel results to bilingual studies. Experiment 1 revealed that HF synonyms were accessed faster than LF ones. Experiment 2 showed that semantic blocking slowed retrieval only of LF synonyms, while form blocking produced powerful interference of both HF and LF words. Experiment 3 examined translation speed and sensitivity to blocking in two groups of Russian-English bilinguals who differed in frequency of use of their languages. Translation asymmetries were modulated by frequency of use. The results support an integrated lexicon model of bilingual processing

A new approach to analysing HST spatial scans: the transmission spectrum of HD 209458 b

The Wide Field Camera 3 (WFC3) on Hubble Space Telescope (HST) is currently one of the most widely used instruments for observing exoplanetary atmospheres, especially with the use of the spatial scanning technique. An increasing number of exoplanets have been studied using this technique as it enables the observation of bright targets without saturating the sensitive detectors. In this work we present a new pipeline for analyzing the data obtained with the spatial scanning technique, starting from the raw data provided by the instrument. In addition to commonly used correction techniques, we take into account the geometric distortions of the instrument, whose impact may become important when combined to the scanning process. Our approach can improve the photometric precision for existing data and also push further the limits of the spatial scanning technique, as it allows the analysis of even longer spatial scans. As an application of our method and pipeline, we present the results from a reanalysis of the spatially scanned transit spectrum of HD 209458 b. We calculate the transit depth per wavelength channel with an average relative uncertainty of 40 ppm. We interpret the final spectrum with T-Rex, our fully Bayesian spectral retrieval code, which confirms the presence of water vapor and clouds in the atmosphere of HD 209458 b. The narrow wavelength range limits our ability to disentangle the degeneracies between the fitted atmospheric parameters. Additional data over a broader spectral range are needed to address this issue.Comment: 13 pages, 15 figures, 7 tables, Accepted for publication in Ap

3D freeform surfaces from planar sketches using neural networks

A novel intelligent approach into 3D freeform surface reconstruction from planar sketches is proposed. A multilayer perceptron (MLP) neural network is employed to induce 3D freeform surfaces from planar freehand curves. Planar curves were used to represent the boundaries of a freeform surface patch. The curves were varied iteratively and sampled to produce training data to train and test the neural network. The obtained results demonstrate that the network successfully learned the inverse-projection map and correctly inferred the respective surfaces from fresh curves