An organisational perspective on social exclusion in higher education : a case study

We explore organisational mechanisms underlying social exclusion in higher education, the latter defined as the underrepresentation of students from lower socio-economic backgrounds. We focus on “decoupling,†which is a central concept in organisational institutionalism referring to the construction of gaps between public commitment and core organisational practices, a common phenomenon in organisations worldwide. In the context of social inclusion this implies that universities are often publicly committed to social inclusion whereas their actual practices reproduce social exclusion. Drawing on an in-depth case study of a Flemish university, we identify four possible antecedents of decoupling: institutional contradictions resulting from the neo-liberalisation of higher education, uncertainty about effective inclusive practices, resistance of key constituencies and resource stringency resulting from experiences of lacking public funding

JALAD: Joint Accuracy- and Latency-Aware Deep Structure Decoupling for Edge-Cloud Execution

Recent years have witnessed a rapid growth of deep-network based services and applications. A practical and critical problem thus has emerged: how to effectively deploy the deep neural network models such that they can be executed efficiently. Conventional cloud-based approaches usually run the deep models in data center servers, causing large latency because a significant amount of data has to be transferred from the edge of network to the data center. In this paper, we propose JALAD, a joint accuracy- and latency-aware execution framework, which decouples a deep neural network so that a part of it will run at edge devices and the other part inside the conventional cloud, while only a minimum amount of data has to be transferred between them. Though the idea seems straightforward, we are facing challenges including i) how to find the best partition of a deep structure; ii) how to deploy the component at an edge device that only has limited computation power; and iii) how to minimize the overall execution latency. Our answers to these questions are a set of strategies in JALAD, including 1) A normalization based in-layer data compression strategy by jointly considering compression rate and model accuracy; 2) A latency-aware deep decoupling strategy to minimize the overall execution latency; and 3) An edge-cloud structure adaptation strategy that dynamically changes the decoupling for different network conditions. Experiments demonstrate that our solution can significantly reduce the execution latency: it speeds up the overall inference execution with a guaranteed model accuracy loss.Comment: conference, copyright transfered to IEE

Sustainable car life cycle design, taking inspiration from natural systems and thermodynamics

This paper exposes the search for a tool and method, which from a systems approach, adopts the rules and logic that govern our physical context (biosphere) in order to provide guidelines that the car industry could use to achieve an ideal state for ecological, economical and social sustainability