Going Deeper with Lean Point Networks

In this work we introduce Lean Point Networks (LPNs) to train deeper and more accurate point processing networks by relying on three novel point processing blocks that improve memory consumption, inference time, and accuracy: a convolution-type block for point sets that blends neighborhood information in a memory-efficient manner; a crosslink block that efficiently shares information across low- and high-resolution processing branches; and a multiresolution point cloud processing block for faster diffusion of information. By combining these blocks, we design wider and deeper point-based architectures. We report systematic accuracy and memory consumption improvements on multiple publicly available segmentation tasks by using our generic modules as drop-in replacements for the blocks of multiple architectures (PointNet++, DGCNN, SpiderNet, PointCNN).Comment: 16 pages, 11 figures, 9 table

Collective narratives and politics in the contemporary study of work : the new management practices debate

In this article we explore the question of how as sociologists of work we might research those who constitute the substance of our labour process. We approach this question through an examination of the New Management Practices debate, principally in the labour movement where a distinctive and critical view of NMP developed in the late 1980s. Second, we argue that there is a link between this debate and the wider politics of labour process discussion both within and beyond the labour movement which has witnessed a shift away from an earlier engagement with worker interventions. In response we suggest the need to re-evaluate the nature of academic engagement with labour thus reanimating a closer engagement with labour-in-work and collective worker narratives

The Global Networked Value Circle: A new model for best-in-class manufacturing

As companies face deflation, slowing production and declining prices, they will need to assess their entire value chain as they look for ways to keep costs low and improve efficiencies while continuing to innovate. To help address this challenge, this report reflects fresh research undertaken by Capgemini in collaboration with the University of Edinburgh into the ?Best-in-Class Global Manufacturing Value Chain?

Improving Service Delivery Through Provider Training: A Process Evaluation of the Veterans Affairs Palo Alto Health Care System “Commitment to SERVE” Workshop

As the customer-focused management strategies gradually advances into all of the VISNs [Veterans Integrated Service Networks], the Veterans Health Administration in Palo Alto, California implemented a customer service training program for employees to meet the diverse and complex needs of its customers. This research will analyze whether participants in this training, known as Commitment to SERVE, believe that it is achieving its goal. In other words, does the Veterans Affairs Palo Alto Health Care System (VAPAHCS) staff perceive the Commitment to SERVE workshop as a beneficial customer service training program

Looking Beyond Appearances: Synthetic Training Data for Deep CNNs in Re-identification

Re-identification is generally carried out by encoding the appearance of a subject in terms of outfit, suggesting scenarios where people do not change their attire. In this paper we overcome this restriction, by proposing a framework based on a deep convolutional neural network, SOMAnet, that additionally models other discriminative aspects, namely, structural attributes of the human figure (e.g. height, obesity, gender). Our method is unique in many respects. First, SOMAnet is based on the Inception architecture, departing from the usual siamese framework. This spares expensive data preparation (pairing images across cameras) and allows the understanding of what the network learned. Second, and most notably, the training data consists of a synthetic 100K instance dataset, SOMAset, created by photorealistic human body generation software. Synthetic data represents a good compromise between realistic imagery, usually not required in re-identification since surveillance cameras capture low-resolution silhouettes, and complete control of the samples, which is useful in order to customize the data w.r.t. the surveillance scenario at-hand, e.g. ethnicity. SOMAnet, trained on SOMAset and fine-tuned on recent re-identification benchmarks, outperforms all competitors, matching subjects even with different apparel. The combination of synthetic data with Inception architectures opens up new research avenues in re-identification.Comment: 14 page

Lean Thinking: Theory, Application and Dissemination

This book was written and compiled by the University of Huddersfield to share the learnings and experiences of seven years of Knowledge Transfer Partnership (KTP) and Economic and Social Research Council (ESRC) funded projects with the National Health Service (NHS). The focus of these projects was the implementation of Lean thinking and optimising strategic decision making processes. Each of these projects led to major local improvements and this book explains how they were achieved and compiles the lessons learnt. The book is split into three chapters; Lean Thinking Theory, Lean Thinking Applied and Lean Thinking Dissemination

Lean and green – a systematic review of the state of the art literature

The move towards greener operations and products has forced companies to seek alternatives to balance efficiency gains and environmental friendliness in their operations and products. The exploration of the sequential or simultaneous deployment of lean and green initiatives is the results of this balancing action. However, the lean-green topic is relatively new, and it lacks of a clear and structured research definition. Thus, this paper’s main contribution is the offering of a systematic review of the existing literature on lean and green, aimed at providing guidance on the topic, uncovering gaps and inconsistencies in the literature, and finding new paths for research. The paper identifies and structures, through a concept map, six main research streams that comprise both conceptual and empirical research conducted within the context of various organisational functions and industrial sectors. Important issues for future research are then suggested in the form of research questions. The paper’s aim is to also contribute by stimulating scholars to further study this area in depth, which will lead to a better understanding of the compatibility and impact on organisational performance of lean and green initiatives. It also holds important implications for industrialists, who can develop a deeper and richer knowledge on lean and green to help them formulate more effective strategies for their deployment