Northumbria University

About: Northumbria University is a education organization based out in Newcastle upon Tyne, United Kingdom. It is known for research contribution in the topics: Context (language use) & Population. The organization has 5624 authors who have published 17423 publications receiving 381949 citations. The organization is also known as: University of Northumbria at Newcastle.

Instantaneous Antarctic ice sheet mass loss driven by thinning ice shelves

Abstract: Recent observations show that the rate at which the Antarctic Ice Sheet (AIS) is contributing to sea level rise is increasing. Increasing ice-ocean heat exchange has the potential to induce substantial mass loss through the melting of its ice shelves. Lack of data and limitations in modelling, however, have made it challenging to quantify the importance of ocean-induced changes in ice-shelf thickness as a driver for ongoing mass loss. Here, we use a numerical ice-sheet model in combination with satellite observations of ice-shelf thinning from 1994 to 2017 to quantify instantaneous changes in ice flow across all AIS grounding lines, resulting from changes in ice-shelf buttressing alone. Our process-based predictions are in good agreement with observed spatial patterns of ice loss, providing support for the notion that a significant portion of the current ice loss of the AIS is ocean driven and caused by a reduction in ice-shelf buttressing.

Impressions of an annual report: an experimental study

Abstract: Considerable research has sought to establish the use of impression management in corporate annual reports (CARS), especially in depicting the reporting organisation in as favourable a light as possible. Whether there is a useful outcome from the perspective of influencing those being managed has not been addressed. Based on the evidence that impression management mainly occurs in the front half, an experiment was conducted to examine whether readers’ perceptions of a company's performance differed depending on their assigned reading. Four similar groups rated the performance of an organisation in terms of several dimensions after completing their reading task, but no significant differences were found. Respondents in three groups approximate what may be termed “sophisticated users”, perhaps not easily impressed by the front half. The fourth group, homogeneous at university level but without the same financial background, ranked financial performance higher. Implications are drawn about the effectiveness of impression management.

Scratching beneath the surface of communities of (mal)practice

Abstract: Purpose – This paper seeks to surface less positive aspects of communities of practice (CoPs), regardless of emergent or organisationally managed, grounded in political‐power interactions. Examples are provided from the authors' experiences of a research‐based CoP within UK higher education.Design/methodology/approach – The paper is primarily theoretical with empirical examples drawn from a descriptive CoP case study.Findings – The paper discusses the following themes: the impact of timing on CoP development; the impact of CoP leaders and managers in “managed” CoPs; the power‐political interrelationship between emergent CoPs and formal organisation; the impact of dominant actors with position power; emotional containment and emotion work within CoPs; power implications of novices and masters and the implications when CoP practices diverge from organisational practices. It finds that to ignore such issues of power within CoPs is to limit the knowledge creation process.Research limitations/implications – Fu...

Learning to Hash With Optimized Anchor Embedding for Scalable Retrieval

Abstract: Sparse representation and image hashing are powerful tools for data representation and image retrieval respectively. The combinations of these two tools for scalable image retrieval, i.e., sparse hashing (SH) methods, have been proposed in recent years and the preliminary results are promising. The core of those methods is a scheme that can efficiently embed the (high-dimensional) image features into a low-dimensional Hamming space, while preserving the similarity between features. Existing SH methods mostly focus on finding better sparse representations of images in the hash space. We argue that the anchor set utilized in sparse representation is also crucial, which was unfortunately underestimated by the prior art. To this end, we propose a novel SH method that optimizes the integration of the anchors, such that the features can be better embedded and binarized, termed as Sparse Hashing with Optimized Anchor Embedding. The central idea is to push the anchors far from the axis while preserving their relative positions so as to generate similar hashcodes for neighboring features. We formulate this idea as an orthogonality constrained maximization problem and an efficient and novel optimization framework is systematically exploited. Extensive experiments on five benchmark image data sets demonstrate that our method outperforms several state-of-the-art related methods.