Introduction: Nationalism’s Futures

At a time when nationalist sentiment is on the rise, this special issue takes stock of how sociology can contribute to understanding the past, present and future of nationalism. In contrast to declarations of ‘the end of history’, which was also meant to herald increasing integration due to a lowering of cultural and national barriers, nationalism never went away. The articles in this collection engage with the question of nationalism at a theoretical and empirical level and in different regional contexts, assessing how national boundaries are drawn and policed, how national identities are formed and the myriad political and everyday consequences of nationalism

Alternative derivation of the Feigel effect and call for its experimental verification

A recent theory by Feigel [Phys. Rev. Lett. {\bf 92}, 020404 (2004)] predicts the finite transfer of momentum from the quantum vacuum to a fluid placed in strong perpendicular electric and magnetic fields. The momentum transfer arises because of the optically anisotropic magnetoelectric response induced in the fluid by the fields. After summarising Feigel's original assumptions and derivation (corrected of trivial mistakes), we rederive the same result by a simpler route, validating Feigel's semi-classical approach. We then derive the stress exerted by the vacuum on the fluid which, if the Feigel hypothesis is correct, should induce a Poiseuille flow in a tube with maximum speed $\approx 100\mu$m/s (2000 times larger than Feigel's original prediction). An experiment is suggested to test this prediction for an organometallic fluid in a tube passing through the bore of a high strength magnet. The predicted flow can be measured directly by tracking microscopy or indirectly by measuring the flow rate ($\approx 1$ml/min) corresponding to the Poiseuille flow. A second experiment is also proposed whereby a `vacuum radiometer' is used to test a recent prediction that the net force on a magnetoelectric slab in the vacuum should be zero.Comment: 20 pages, 1 figures. revised and improved versio

Smart cities need smart villages

© 2018 Economic and Political Weekly. All rights reserved. The current Smart Cities Mission needs to be linked to India's villages. The lacuna in the current mission mandate can be filled by directly addressing the opportunities provided by renewable off-grid production to increase employment and diversification in the rural economy, with a particular focus on India's rural youth.Templeton Foundatio

Languages and Learning at Key Stage 2: A Longitudinal Study Final Report

In 2006, The Open University, the University of Southampton and Canterbury Christ Church University were commissioned by the then Department for Education and Skills (DfES), now Department for Children, Schools and Families (DCSF) to conduct a three-year longitudinal study of languages learning at Key Stage 2 (KS2). The qualitative study was designed to explore provision, practice and developments over three school years between 2006/07 and 2008/09 in a sample of primary schools and explore children’s achievement in oracy and literacy, as well as the possible broader cross-curricular impact of languages learning

Entanglement witness operator for quantum teleportation

The ability of entangled states to act as resource for teleportation is linked to a property of the fully entangled fraction. We show that the set of states with their fully entangled fraction bounded by a threshold value required for performing teleportation is both convex and compact. This feature enables for the existence of hermitian witness operators the measurement of which could distinguish unknown states useful for performing teleportation. We present an example of such a witness operator illustrating it for different classes of states.Comment: Minor revisions to match the published version. Accepted for publication in Physical Review Letter

Ab Initio Liquid Hydrogen Muon Cooling Simulations with ELMS in ICOOL

This paper presents new theoretical results on the passage of muons through liquid hydrogen which have been confirmed in a recent experiment. These are used to demonstrate that muon bunches may be compressed by ionisation cooling more effectively than suggested by previous calculations. Muon cooling depends on the differential cross section for energy loss and scattering of muons. We have calculated this cross section for liquid H2 from first principles and atomic data, avoiding traditional assumptions. Thence, 2-D probability maps of energy loss and scattering in mm-scale thicknesses are derived by folding, and stored in a database. Large first-order correlations between energy loss and scattering are found for H2, which are absent in other simulations. This code is named ELMS, Energy Loss & Multiple Scattering. Single particle trajectories may then be tracked by Monte Carlo sampling from this database on a scale of 1 mm or less. This processor has been inserted into the cooling code ICOOL. Significant improvements in 6-D muon cooling are predicted compared with previous predictions based on GEANT. This is examined in various geometries. The large correlation effect is found to have only a small effect on cooling. The experimental scattering observed for liquid H2 in the MUSCAT experiment has recently been reported to be in good agreement with the ELMS prediction, but in poor agreement with GEANT simulation.Comment: 6 pages, 3 figure

Eliciting students' preferences for the use of their data for learning analytics. A crowdsourcing approach.

Research on student perspectives of learning analytics suggests that students are generally unaware of the collection and use of their data by their learning institutions, and they are often not involved in decisions about whether and how their data are used. To determine the influence of risks and benefits awareness on students’ data use preferences for learning analytics, we designed two interventions: one describing the possible privacy risks of data use for learning analytics and the second describing the possible benefits. These interventions were distributed amongst 447 participants recruited using a crowdsourcing platform. Participants were randomly assigned to one of three experimental groups – risks, benefits, and risks and benefits – and received the corresponding intervention(s). Participants in the control group received a learning analytics dashboard (as did participants in the experimental conditions). Participants’ indicated the motivation for their data use preferences. Chapter 11 will discuss the implications of our findings in relation to how to better support learning institutions in being more transparent with students about the practice of learning analytics

Quasi-Bayesian Nonparametric Density Estimation via Autoregressive Predictive Updates

Bayesian methods are a popular choice for statistical inference in small-data regimes due to the regularization effect induced by the prior. In the context of density estimation, the standard nonparametric Bayesian approach is to target the posterior predictive of the Dirichlet process mixture model. In general, direct estimation of the posterior predictive is intractable and so methods typically resort to approximating the posterior distribution as an intermediate step. The recent development of quasi-Bayesian predictive copula updates, however, has made it possible to perform tractable predictive density estimation without the need for posterior approximation. Although these estimators are computationally appealing, they struggle on non-smooth data distributions. This is due to the comparatively restrictive form of the likelihood models from which the proposed copula updates were derived. To address this shortcoming, we consider a Bayesian nonparametric model with an autoregressive likelihood decomposition and a Gaussian process prior. While the predictive update of such a model is typically intractable, we derive a quasi-Bayesian update that achieves state-of-the-art results in small-data regimes

Eliciting students' preferences for the use of their data for learning analytics. A crowdsourcing approach.

Research on student perspectives of learning analytics suggests that students are generally unaware of the collection and use of their data by their learning institutions, and they are often not involved in decisions about whether and how their data are used. To determine the influence of risks and benefits awareness on students’ data use preferences for learning analytics, we designed two interventions: one describing the possible privacy risks of data use for learning analytics and the second describing the possible benefits. These interventions were distributed amongst 447 participants recruited using a crowdsourcing platform. Participants were randomly assigned to one of three experimental groups – risks, benefits, and risks and benefits – and received the corresponding intervention(s). Participants in the control group received a learning analytics dashboard (as did participants in the experimental conditions). Participants’ indicated the motivation for their data use preferences. Chapter 11 will discuss the implications of our findings in relation to how to better support learning institutions in being more transparent with students about the practice of learning analytics