University of Lincoln

About: University of Lincoln is a education organization based out in Lincoln, Lincolnshire, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 2341 authors who have published 7025 publications receiving 124797 citations.

Measurement of retinal vessel widths from fundus images based on 2-D modeling

Abstract: Changes in retinal vessel diameter are an important sign of diseases such as hypertension, arteriosclerosis and diabetes mellitus. Obtaining precise measurements of vascular widths is a critical and demanding process in automated retinal image analysis as the typical vessel is only a few pixels wide. This paper presents an algorithm to measure the vessel diameter to subpixel accuracy. The diameter measurement is based on a two-dimensional difference of Gaussian model, which is optimized to fit a two-dimensional intensity vessel segment. The performance of the method is evaluated against Brinchmann-Hansen's half height, Gregson's rectangular profile and Zhou's Gaussian model. Results from 100 sample profiles show that the presented algorithm is over 30% more precise than the compared techniques and is accurate to a third of a pixel.

Beyond self-report: tools to compare estimated and real-world smartphone use

Abstract: Psychologists typically rely on self-report data when quantifying mobile phone usage, despite little evidence of its validity. In this paper we explore the accuracy of using self-reported estimates when compared with actual smartphone use. We also include source code to process and visualise these data. We compared 23 participants’ actual smartphone use over a two-week period with self-reported estimates and the Mobile Phone Problem Use Scale. Our results indicate that estimated time spent using a smartphone may be an adequate measure of use, unless a greater resolution of data are required. Estimates concerning the number of times an individual used their phone across a typical day did not correlate with actual smartphone use. Neither estimated duration nor number of uses correlated with the Mobile Phone Problem Use Scale. We conclude that estimated smartphone use should be interpreted with caution in psychological research.

The design and function of birds' nests

Abstract: All birds construct nests in which to lay eggs and/or raise offspring. Traditionally, it was thought that natural selection and the requirement to minimize the risk of predation determined the design of completed nests. However, it is becoming increasingly apparent that sexual selection also influences nest design. This is an important development as while species such as bowerbirds build structures that are extended phenotypic signals whose sole purpose is to attract a mate, nests contain eggs and/or offspring, thereby suggesting a direct trade-off between the conflicting requirements of natural and sexual selection. Nest design also varies adaptively in order to both minimize the detrimental effects of parasites and to create a suitable microclimate for parents and developing offspring in relation to predictable variation in environmental conditions. Our understanding of the design and function of birds' nests has increased considerably in recent years, and the evidence suggests that nests have four nonmutually exclusive functions. Consequently, we conclude that the design of birds' nests is far more sophisticated than previously realized and that nests are multifunctional structures that have important fitness consequences for the builder/s.

DOC: Deep Open Classification of Text Documents

Abstract: Traditional supervised learning makes the closed-world assumption that the classes appeared in the test data must have appeared in training. This also applies to text learning or text classification. As learning is used increasingly in dynamic open environments where some new/test documents may not belong to any of the training classes, identifying these novel documents during classification presents an important problem. This problem is called open-world classification or open classification. This paper proposes a novel deep learning based approach. It outperforms existing state-of-the-art techniques dramatically.