Objective Classes for Micro-Facial Expression Recognition

Micro-expressions are brief spontaneous facial expressions that appear on a face when a person conceals an emotion, making them different to normal facial expressions in subtlety and duration. Currently, emotion classes within the CASME II dataset are based on Action Units and self-reports, creating conflicts during machine learning training. We will show that classifying expressions using Action Units, instead of predicted emotion, removes the potential bias of human reporting. The proposed classes are tested using LBP-TOP, HOOF and HOG 3D feature descriptors. The experiments are evaluated on two benchmark FACS coded datasets: CASME II and SAMM. The best result achieves 86.35\% accuracy when classifying the proposed 5 classes on CASME II using HOG 3D, outperforming the result of the state-of-the-art 5-class emotional-based classification in CASME II. Results indicate that classification based on Action Units provides an objective method to improve micro-expression recognition.Comment: 11 pages, 4 figures and 5 tables. This paper will be submitted for journal revie

Going on beyond Modernism in Beckett's Texts for Nothing

The title of Samuel Beckett's thirteen minimalist prose texts from 1954, Texts for Nothing, contrasts strongly with the award of the Nobel Prize for literature and the canonization of the author in 1969. This prestigious recognition can clearly be regarded as a public act of Beckettian irony, as hardly any other author has done more to refute ideas of canonicity and to undermine the foundations of the institution of literature - not to mention principal philosophical positions. Beckett's failure to appear at the ceremonial event seems symbolic of the many elsewheres of his idiosyncratic imagination and the uncanny resistance in his texts to the demands of aesthetics and the claims of logics, to whose reconstructions his diversity of textual experimentalism has contributed so forcefully.publishedVersio

Characterisation of miRNA variation in Small RNA-Seq data

Evidence suggests that miRNA signatures could have clinical applications as biomarkers for diagnostic and prognostic purposes. In standard analyses, miRNAs are considered to exist as well-defined features with a precise start and stop positions. In reality, they exist as a population of similar but slightly different isoforms - isomiRs. The project has developed methods to investigate isomiR populations and studied their variation between healthy and cancer patients. The methods presented here have been implemented in an analysis pipeline that allows standardised and scalable analysis of raw NGS datasets. The value of this approach has been demonstrated by investigating publicly available datasets to identify statistically significant changes in isomiR populations in various cancers.Forskning tilsier at miRNA-signaturer kan ha kliniske applikasjoner i diagnostisk- og prognostisk sammenheng. I konvensjonelle analyser antas miRNA å være veldefinerte enheter med nøyaktige start- og slutt-posisjoner. I virkeligheten eksisterer de som en variert populasjon av lignende, men noe ulike, isomerer. Gjennom dette prosjektet har det blitt utviklet metoder for å undersøke hvordan miRNA-populasjoner varierer mellom friske og syke. Metodene har blitt implementert i eksisterende programvare som muliggjør standardisert og skalerbar analyse av sekvenseringsdata. Verdien av denne tilnærmingen har blitt demonstrert ved å analysere offentlig tilgjengelige datasett for å identifisere statistisk signifikante endringer i miRNA-populasjoner i ulike krefttyper.M-K