114612 research outputs found
Sort by
Is political science (still) ignoring religion? An analysis of journal publications, 2011-2020
Get PDFPolitical scientists involved in the study of religion have expressed concerns that religious themes have yet to be fully integrated into the mainstream of the discipline. According to a study of articles published in leading political science journals during the first decade of the twenty-first century, papers engaging with religion were relatively few in number and highly concentrated in only a few thematic and disciplinary areas. This article presents an updated analysis of the extent to which political science has engaged with the topic of religion by examining journal outputs for the period 2011–2020. The study finds no significant change in the patterns identified by the earlier research. Despite an overall increase in the quantity of political science articles on the subject of religion, the overall proportion has been relatively static, and the thematic and disciplinary focus of outputs remains narrow
Empowering early career academics to overcome low confidence
Get PDFHow can academic developers support Early Career Academics (ECAs) to increase in confidence as they step into their new responsibilities? Previous work has demonstrated the negative impacts of ECAs’ low self-efficacy, focusing recommendations on the need for systemic changes. Going further than previous academic development work to apply social cognitive theories of personality, we explore how ECAs can be equipped to understand, reflect on and increase their own confidence. We point to evidence-based approaches and recommend practical steps that can be taken to implement this approach in increasing the confidence of ECAs
Attitudes toward migration and associational activity : evidence from Germany
Get PDFWe explore how associational activity – a key aspect of social capital – affects migration attitudes. It is argued that people’s membership in sports clubs and associations likely leads to more negative views on migration. Exploiting the panel structure of the German Longitudinal Election Data, the empirical analysis provides support for our expectations. We also show that individuals’ political orientation moderates the postulated effect. The findings further our understanding of how public opinion on migration is formed and we add to the literature on social capital by highlighting the potentially negative consequences one of its components can have
Near real-time social distancing estimation in London
Get PDFTo mitigate the current COVID-19 pandemic, policy makers at the Greater London Authority, the regional governance body of London, UK, are reliant upon prompt, accurate and actionable estimations of lockdown and social distancing policy adherence. Transport for London, the local transportation department, reports they implemented over 700 interventions such as greater signage and expansion of pedestrian zoning at the height of the pandemic’s first wave with our platform providing key data for those decisions. Large well-defined heterogeneous compositions of pedestrian footfall and physical proximity are difficult to acquire, yet necessary to monitor city-wide activity (busyness) and consequently discern actionable policy decisions. To meet this challenge, we leverage our existing large-scale data processing urban air quality machine learning infrastructure to process over 900 camera feeds in near real-time to generate new estimates of social distancing adherence, group detection and camera stability. In this work, we describe our development and deployment of a computer vision and machine learning pipeline. It provides near immediate sampling and contextualization of activity and physical distancing on the streets of London via live traffic camera feeds. We introduce a platform for inspecting, calibrating and improving upon existing methods, describe the active deployment on real-time feeds and provide analysis over an 18 month period
Energetic solutions to rate-independent large-strain elasto-plastic evolutions driven by discrete dislocation flow
No full textThis work rigorously implements a recent model of large-strain elasto-plastic evolution in single crystals where the plastic flow is driven by the movement of discrete dislocation lines. The model is geometrically and elastically nonlinear, that is, the total deformation gradient splits multiplicatively into elastic and plastic parts, and the elastic energy density is polyconvex. There are two internal variables: The system of all dislocations is modeled via one-dimensional boundaryless integral currents, whereas the history of plastic flow is encoded in a plastic distortion matrix-field. As our main result, we construct an energetic solution in the case of a rate-independent flow rule. Besides the classical stability and energy balance conditions, our notion of solution also accounts for the movement of dislocations and the resulting plastic flow. Because of the path-dependence of plastic flow, a central role is played by so-called “slip trajectories”, that is, the surfaces traced out by moving dislocations, which we represent as integral 2-currents in space-time. The proof of our main existence result further crucially rests on careful a priori estimates via a nonlinear Gronwall-type lemma and a rescaling of time. In particular, we have to account for the fact that the plastic flow may cause the coercivity of the elastic energy functional to decay along the evolution, and hence the solution may blow up in finite time
ESGN : efficient stereo geometry network for fast 3D object detection
Get PDFFast stereo based 3D object detectors have made great progress recently. However, they suffer from the inferior accuracy. We argue that the main reason is due to the poor geometry-aware feature representation in 3D space. To solve this problem, we propose an efficient stereo geometry network (ESGN). The key in our ESGN is an efficient geometry-aware feature generation (EGFG) module. Our EGFG module first uses a stereo correlation and reprojection module to construct multi-scale stereo volumes in camera frustum space, second employs a multi-scale bird’s eye view (BEV) projection and fusion module to generate multiple geometry-aware features. In these two steps, we adopt deep multi-scale information fusion for discriminative geometry-aware feature generation, without any complex aggregation networks. In addition, we introduce a deep geometry-aware feature distillation scheme to guide stereo feature learning with a LiDAR-based detector. The experiments are performed on the classical KITTI dataset. On KITTI test set, our ESGN outperforms the fast state-of-art-art detector YOLOStereo3D by 5.14% on mAP3d at 62ms. To the best of our knowledge, our ESGN achieves a best trade-off between accuracy and speed. We hope that our efficient stereo geometry network can provide more possible directions for fast 3D object detection
The effect of language on income smoothing : cross-country evidence
Get PDFWe examine whether and how the time-oriented tendency embedded in languages influences income smoothing. Separating languages into weak- versus strong-future time reference (FTR) groups, we find that firms in weak-FTR countries tend to smooth earnings more. We also find that relationships with major stakeholders (i.e., debtholders, suppliers, and employees) amplify the effect of the FTR of languages on income smoothing. Additional analyses suggest that income smoothing driven by the FTR of languages enhances earnings informativeness. These findings provide new insights on the role that language plays in financial reporting decisions and on how relationships with major stakeholders influence the relation between an important feature of language and corporate income smoothing behavior
Mechanisms of metagovernance as structural challenges to levelling up in England
Get PDFAt the time of writing, the UK government is attempting to tackle place-based inequality through its ‘levelling up’ agenda. To be effective, such interventions require local institutions with the capacity, powers, and budgets to develop and implement long-term strategies. Multi-level metagovernance, the ongoing reorganisation of local governance systems by the central state, has become a salient political process in England, characterised by fragmented system design, distorted local strategies, micromanagement and mistrustful central–local relations. These various problems are underpinned by a problematic combination of quasi-markets and state hierarchy. Together, these metagovernance mechanisms significantly constrain local capacity to deliver economic development
Using knowledge gradient in Bayesian optimization when searching for robust solutions
Get PDFThis article considers the use of Bayesian optimization to identify robust solutions, where robust means having a high expected performance given disturbances over the decision variables and independent noise in the output. A variant of the well-known knowledge gradient acquisition function is proposed specifically to search for robust solutions, with analytic expressions for uniformly and normally distributed disturbances. An empirical evaluation on a number of test problems demonstrates that the new acquisition function outperforms alternative approaches
