Self‐control in crows, parrots and nonhuman primates

Self‐control is critical for both humans and nonhuman animals because it underlies complex cognitive abilities, such as decision‐making and future planning, enabling goal‐directed behavior. For instance, it is positively associated with social competence and life success measures in humans. We present the first review of delay of gratification as a measure of self‐control in nonhuman primates, corvids (crow family) and psittacines (parrot order): disparate groups that show comparable advanced cognitive abilities and similar socio‐ecological factors. We compare delay of gratification performance and identify key issues and outstanding areas for future research, including finding the best measures and drivers of delayed gratification. Our review therefore contributes to our understanding of both delayed gratification as a measure of self‐control and of complex cognition in animals

Automated Detection of Candidate Subjects With Cerebral Microbleeds Using Machine Learning

Cerebral microbleeds (CMBs) appear as small, circular, well defined hypointense lesions of a few mm in size on T2*-weighted gradient recalled echo (T2*-GRE) images and appear enhanced on susceptibility weighted images (SWI). Due to their small size, contrast variations and other mimics (e.g., blood vessels), CMBs are highly challenging to detect automatically. In large datasets (e.g., the UK Biobank dataset), exhaustively labelling CMBs manually is difficult and time consuming. Hence it would be useful to preselect candidate CMB subjects in order to focus on those for manual labelling, which is essential for training and testing automated CMB detection tools on these datasets. In this work, we aim to detect CMB candidate subjects from a larger dataset, UK Biobank, using a machine learning-based, computationally light pipeline. For our evaluation, we used 3 different datasets, with different intensity characteristics, acquired with different scanners. They include the UK Biobank dataset and two clinical datasets with different pathological conditions. We developed and evaluated our pipelines on different types of images, consisting of SWI or GRE images. We also used the UK Biobank dataset to compare our approach with alternative CMB preselection methods using non-imaging factors and/or imaging data. Finally, we evaluated the pipeline's generalisability across datasets. Our method provided subject-level detection accuracy > 80% on all the datasets (within-dataset results), and showed good generalisability across datasets, providing a consistent accuracy of over 80%, even when evaluated across different modalities

Disentangling water, ion and polymer dynamics in an anion exchange membrane

Semipermeable polymeric anion exchange membranes are essential for separation, filtration and energy conversion technologies including reverse electrodialysis systems that produce energy from salinity gradients, fuel cells to generate electrical power from the electrochemical reaction between hydrogen and oxygen, and water electrolyser systems that provide H2 fuel. Anion exchange membrane fuel cells and anion exchange membrane water electrolysers rely on the membrane to transport OH− ions between the cathode and anode in a process that involves cooperative interactions with H2O molecules and polymer dynamics. Understanding and controlling the interactions between the relaxation and diffusional processes pose a main scientific and critical membrane design challenge. Here quasi-elastic neutron scattering is applied over a wide range of timescales (100–103 ps) to disentangle the water, polymer relaxation and OH− diffusional dynamics in commercially available anion exchange membranes (Fumatech FAD-55) designed for selective anion transport across different technology platforms, using the concept of serial decoupling of relaxation and diffusional processes to analyse the data. Preliminary data are also reported for a laboratory-prepared anion exchange membrane especially designed for fuel cell applications

Towards an Intelligent Tutor for Mathematical Proofs

Computer-supported learning is an increasingly important form of study since it allows for independent learning and individualized instruction. In this paper, we discuss a novel approach to developing an intelligent tutoring system for teaching textbook-style mathematical proofs. We characterize the particularities of the domain and discuss common ITS design models. Our approach is motivated by phenomena found in a corpus of tutorial dialogs that were collected in a Wizard-of-Oz experiment. We show how an intelligent tutor for textbook-style mathematical proofs can be built on top of an adapted assertion-level proof assistant by reusing representations and proof search strategies originally developed for automated and interactive theorem proving. The resulting prototype was successfully evaluated on a corpus of tutorial dialogs and yields good results.Comment: In Proceedings THedu'11, arXiv:1202.453

Negotiating the modern cross-class ‘model home’:domestic experiences in Basil Spence’s Claremont Court

This article investigates the spatial articulation of architecture and home through the exploration of current domestic experiences in Basil Spence’s Claremont Court housing scheme (1959-1962), Edinburgh. How architecture and home are both idealized and lived is the backdrop for a discussion that draws on the concept of “model home,” or physical representation of a domestic ideal. The article reads Claremont Court as an architectural prototype of the modern domestic ideal, before exploring its reception by five of its households through the use of visual methods and semistructured interviews. Receiving the model home involves negotiating between ideal and lived homes. Building on this idea, the article contributes with a focus on the spatiality of such reception, showing how it is modulated according to the architectural affordances that the “model home” represents. The article expands on scholarship on architecture and home with empirical evidence that argues the reciprocal spatiality of home

Statistical Computing on Non-Linear Spaces for Computational Anatomy

International audienceComputational anatomy is an emerging discipline that aims at analyzing and modeling the individual anatomy of organs and their biological variability across a population. However, understanding and modeling the shape of organs is made difficult by the absence of physical models for comparing different subjects, the complexity of shapes, and the high number of degrees of freedom implied. Moreover, the geometric nature of the anatomical features usually extracted raises the need for statistics on objects like curves, surfaces and deformations that do not belong to standard Euclidean spaces. We explain in this chapter how the Riemannian structure can provide a powerful framework to build generic statistical computing tools. We show that few computational tools derive for each Riemannian metric can be used in practice as the basic atoms to build more complex generic algorithms such as interpolation, filtering and anisotropic diffusion on fields of geometric features. This computational framework is illustrated with the analysis of the shape of the scoliotic spine and the modeling of the brain variability from sulcal lines where the results suggest new anatomical findings