Attentional demand estimation with attentive driving models

The task of driving can sometimes require the processing of large amounts of visual information; such situations can overload the perceptual systems of human drivers leading to ‘inattentional blindness’, where potentially critical visual information is overlooked. This phenomenon of ‘looking but failing to see’ is the third largest contributor to traffic accidents in the UK. In this work we develop a method to identify these particularly demanding driving scenes using an end-to-end driving architecture, imbued with a spatial attention mechanism and trained to mimic ground-truth driving controls from video input. At test time, the network’s attention distribution is segmented to identify relevant items in the driving scene which are used to estimate the attentional demand on the driver according to an established model in cognitive neuroscience. Without collecting any ground-truth attentional demand data - instead using readily available odometry data in a novel way - our approach is shown to outperform several baselines on a new dataset of 1200 driving scenes labelled for attentional demand in driving

SchiNet: Automatic Estimation of Symptoms of Schizophrenia from Facial Behaviour Analysis.

Patients with schizophrenia often display impairments in the expression of emotion and speech and those are observed in their facial behaviour. Automatic analysis of patients' facial expressions that is aimed at estimating symptoms of schizophrenia has received attention recently. However, the datasets that are typically used for training and evaluating the developed methods, contain only a small number of patients (4-34) and are recorded while the subjects were performing controlled tasks such as listening to life vignettes, or answering emotional questions. In this paper, we use videos of professional-patient interviews, in which symptoms were assessed in a standardised way as they should/may be assessed in practice, and which were recorded in realistic conditions (i.e. varying illumination levels and camera viewpoints) at the patients' homes or at mental health services. We automatically analyse the facial behaviour of 91 out-patients - this is almost 3 times the number of patients in other studies - and propose SchiNet, a novel neural network architecture that estimates expression-related symptoms in two different assessment interviews. We evaluate the proposed SchiNet for patient-independent prediction of symptoms of schizophrenia. Experimental results show that some automatically detected facial expressions are significantly correlated to symptoms of schizophrenia, and that the proposed network for estimating symptom severity delivers promising results.Comment: 13 pages, IEEE Transactions on Affective Computin

Microwave Assisted Synthesis of Py-Im Polyamides

Microwave synthesis was utilized to rapidly build Py-Im polyamides in high yields and purity using Boc-protection chemistry on Kaiser oxime resin. A representative polyamide targeting the 5′-WGWWCW-3′ (W = A or T) subset of the consensus Androgen and Glucocorticoid Response Elements was synthesized in 56% yield after 20 linear steps and HPLC purification. It was confirmed by Mosher amide derivatization of the polyamide that a chiral α-amino acid does not racemize after several additional coupling steps

FMR1 locus, Fibroblast cell line established from individual with fragile X syndrome, double-stranded DNA methylation patterns

Batchstamp- and barcode-authenticated, double-stranded DNA sequence sets collected from the human loci indicated, using hairpin-bisulfite PCR