Linear-time list recovery of high-rate expander codes

We show that expander codes, when properly instantiated, are high-rate list recoverable codes with linear-time list recovery algorithms. List recoverable codes have been useful recently in constructing efficiently list-decodable codes, as well as explicit constructions of matrices for compressive sensing and group testing. Previous list recoverable codes with linear-time decoding algorithms have all had rate at most 1/2; in contrast, our codes can have rate $1 - \epsilon$ for any $\epsilon > 0$. We can plug our high-rate codes into a construction of Meir (2014) to obtain linear-time list recoverable codes of arbitrary rates, which approach the optimal trade-off between the number of non-trivial lists provided and the rate of the code. While list-recovery is interesting on its own, our primary motivation is applications to list-decoding. A slight strengthening of our result would implies linear-time and optimally list-decodable codes for all rates, and our work is a step in the direction of solving this important problem

Population size impacts host-pathogen coevolution

Ongoing host–pathogen interactions are characterized by rapid coevolutionary changes forcing species to continuously adapt to each other. The interacting species are often defined by finite population sizes. In theory, finite population size limits genetic diversity and compromises the efficiency of selection owing to genetic drift, in turn constraining any rapid coevolutionary responses. To date, however, experimental evidence for such constraints is scarce. The aim of our study was to assess to what extent population size influences the dynamics of host–pathogen coevolution. We used Caenorhabditus elegans and its pathogen Bacillus thuringiensis as a model for experimental coevolution in small and large host populations, as well as in host populations which were periodically forced through a bottleneck. By carefully controlling host population size for 23 host generations, we found that host adaptation was constrained in small populations and to a lesser extent in the bottlenecked populations. As a result, coevolution in large and small populations gave rise to different selection dynamics and produced different patterns of host–pathogen genotype-by-genotype interactions. Our results demonstrate a major influence of host population size on the ability of the antagonists to co-adapt to each other, thereby shaping the dynamics of antagonistic coevolution

Printing in three dimensions with graphene

Responsive graphene oxide sheets form non‐covalent networks with optimum rheological properties for 3D printing. These networks have shear thinning behavior and sufficiently high elastic shear modulus (G′) to build self‐supporting 3D structures by direct write assembly. Drying and thermal reduction leads to ultra‐light graphene‐only structures with restored conductivity and elastomeric behavior

Understanding Mechanical Response of Elastomeric Graphene Networks

Ultra-light porous networks based on nano-carbon materials (such as graphene or carbon nanotubes) have attracted increasing interest owing to their applications in wide fields from bioengineering to electrochemical devices. However, it is often difficult to translate the properties of nanomaterials to bulk three-dimensional networks with a control of their mechanical properties. In this work, we constructed elastomeric graphene porous networks with well-defined structures by freeze casting and thermal reduction, and investigated systematically the effect of key microstructural features. The porous networks made of large reduced graphene oxide flakes (>20 μm) are superelastic and exhibit high energy absorption, showing much enhanced mechanical properties than those with small flakes (<2 μm). A better restoration of the graphitic nature also has a considerable effect. In comparison, microstructural differences, such as the foam architecture or the cell size have smaller or negligible effect on the mechanical response. The recoverability and energy adsorption depend on density with the latter exhibiting a minimum due to the interplay between wall fracture and friction during deformation. These findings suggest that an improvement in the mechanical properties of porous graphene networks significantly depend on the engineering of the graphene flake that controls the property of the cell walls

Investigating the imagination of possible and 'like-to-avoid' selves among higher education students from different socioeconomic backgrounds at a selective english university

This is the final version. Available on open access from MDPI via the DOI in this recordAccess to and participation in higher education (HE) remains unequal, with social background continuing to influence decisions and experiences. In this paper, we undertake a proof-of-concept design to apply the theory of 'possible selves', as adapted by Harrison and published in Social Sciences (2018), to university students from different socioeconomic backgrounds. In 2019, we conducted semi-structured interviews with 12 first-year students, from different socioeconomic backgrounds, currently studying at a selective English university. We applied a deductive analysis based on Harrison's adaptation of the 'possible selves' model originally put forward by Markus and Nurius in the 1980s. Students from disadvantaged backgrounds had a clear drive to 'avoid' future selves that would emerge without HE. Across all socioeconomic groups, we found a strong sense of agency, and a strong personal belief in success. Overall, our study shows that the model of possible selves is useful for understanding personalised and individualised student experiences, and the interrelation between social structure (socioeconomic condition) and agency. The model also offers a new way for practitioners to plan interventions for enhancing equity in HE access and participation.University of Exeter Centre for Social Mobilit