Efficient Ultrasound Image Analysis Models with Sonographer Gaze Assisted Distillation.

Recent automated medical image analysis methods have attained state-of-the-art performance but have relied on memory and compute-intensive deep learning models. Reducing model size without significant loss in performance metrics is crucial for time and memory-efficient automated image-based decision-making. Traditional deep learning based image analysis only uses expert knowledge in the form of manual annotations. Recently, there has been interest in introducing other forms of expert knowledge into deep learning architecture design. This is the approach considered in the paper where we propose to combine ultrasound video with point-of-gaze tracked for expert sonographers as they scan to train memory-efficient ultrasound image analysis models. Specifically we develop teacher-student knowledge transfer models for the exemplar task of frame classification for the fetal abdomen, head, and femur. The best performing memory-efficient models attain performance within 5% of conventional models that are 1000× larger in size

Recent Brachiopods from Malta

Six species of Recent Brachiopoda, obtained by the authors from both shallow and deep waters around Malta, are listed, briefly described and illustrated. Other Mediterranean brachiopod species which might be expected to be present in this region are also listed, in the hope they might be recorded in the future by local naturalists.peer-reviewe

If you can't be with the one you love, love the one you're with: How individual habituation of agent interactions improves global utility

Simple distributed strategies that modify the behaviour of selfish individuals in a manner that enhances cooperation or global efficiency have proved difficult to identify. We consider a network of selfish agents who each optimise their individual utilities by coordinating (or anti-coordinating) with their neighbours, to maximise the pay-offs from randomly weighted pair-wise games. In general, agents will opt for the behaviour that is the best compromise (for them) of the many conflicting constraints created by their neighbours, but the attractors of the system as a whole will not maximise total utility. We then consider agents that act as 'creatures of habit' by increasing their preference to coordinate (anti-coordinate) with whichever neighbours they are coordinated (anti-coordinated) with at the present moment. These preferences change slowly while the system is repeatedly perturbed such that it settles to many different local attractors. We find that under these conditions, with each perturbation there is a progressively higher chance of the system settling to a configuration with high total utility. Eventually, only one attractor remains, and that attractor is very likely to maximise (or almost maximise) global utility. This counterintutitve result can be understood using theory from computational neuroscience; we show that this simple form of habituation is equivalent to Hebbian learning, and the improved optimisation of global utility that is observed results from wellknown generalisation capabilities of associative memory acting at the network scale. This causes the system of selfish agents, each acting individually but habitually, to collectively identify configurations that maximise total utility

The view from elsewhere: perspectives on ALife Modeling

Many artificial life researchers stress the interdisciplinary character of the field. Against such a backdrop, this report reviews and discusses artificial life, as it is depicted in, and as it interfaces with, adjacent disciplines (in particular, philosophy, biology, and linguistics), and in the light of a specific historical example of interdisciplinary research (namely cybernetics) with which artificial life shares many features. This report grew out of a workshop held at the Sixth European Conference on Artificial Life in Prague and features individual contributions from the workshop's eight speakers, plus a section designed to reflect the debates that took place during the workshop's discussion sessions. The major theme that emerged during these sessions was the identity and status of artificial life as a scientific endeavor

A Conscientious Study of Blended Learning and Cardinal Tools

Evaluating the contexts, activities and relationships of participants in education is a complicated structure that necessitates numerous ideologies and levels of assessment, specifically when it comes to technological developments. Blended learning is a method of instruction that integrates offline and virtual-based education. This article highlights the characteristics of technology and features in blended learning, which enhances the learner engagement in higher education. It showcases some of the digital technologies including video encapsulation and online learning systems that may assist in learning and teaching. This paper delineates the consequences faced by the teachers of higher education while explaining the conceptualization of blended learning. It also suggests the implications that can be practiced for the optimization of blended learning evaluating the learner engagement. It also identifies methods to improve the effectiveness of the teachers in evolving demands in blended learning for higher education

Estimating Small Area Income Deprivation: An Iterative Proportional Fitting Approach

Small area estimation and in particular the estimation of small area income deprivation has potential value in the development of new or alternative components of multiple deprivation indices. These new approaches enable the development of income distribution threshold based as opposed to benefit count based measures of income deprivation and so enable the alignment of regional and national measures such as the Households Below Average Income with small area measures. This paper briefly reviews a number of approaches to small area estimation before describing in some detail an iterative proportional fitting based spatial microsimulation approach. This approach is then applied to the estimation of small area HBAI rates at the small area level in Wales in 2003-5. The paper discusses the results of this approach, contrasts them with contemporary ‘official’ income deprivation measures for the same areas and describes a range of ways to assess the robustness of the results

Global and regional left ventricular myocardial deformation measures by magnetic resonance feature tracking in healthy volunteers: comparison with tagging and relevance of gender

This work was funded by a grant from the Engineering and Physical Sciences Research Council (EP/G030693/1) and supported by the Oxford British Heart Foundation Centre of Research Excellence and the National Institute for Health Research Oxford Biomedical Research Centr

Small animal disease surveillance: respiratory disease 2017

This report focuses on surveillance for respiratory disease in companion animals. It begins with an analysis of data from 392 veterinary practices contributing to the Small Animal Veterinary Surveillance Network (SAVSNET) between January and December 2017. The following section describes canine respiratory coronavirus infections in dogs, presenting results from laboratory-confirmed cases across the country between January 2010 and December 2017. This is followed by an update on the temporal trends of three important syndromes in companion animals, namely gastroenteritis, pruritus and respiratory disease, from 2014 to 2017. A fourth section presents a brief update on Streptococcus equi subspecies zooepidemicus in companion animals. The final section summarises some recent developments pertinent to companion animal health, namely eyeworm (Thelazzia callipaeda) infestations in dogs imported to the UK and canine influenza virus in the USA and Canada