Mapping and Developing Service Design Research in the UK.

This report is the outcome of the Service Design Research UK (SDR UK) Network with Lancaster University as primary investigator and London College of Communication, UAL as co-investigator. This project was funded as part of an Arts and Humanities Research Council Network grant. Service Design Research UK (SDR UK), funded by an AHRC Network Grant, aims to create a UK research network in an emerging field in Design that is Service Design. This field has a recent history and a growing, but still small and dispersed, research community that strongly needs support and visibility to consolidate its knowledge base and enhance its potential impact. Services represent a significant part of the UK economy and can have a transformational role in our society as they affect the way we organize, move, work, study or take care of our health and family. Design introduces a more human centred and creative approach to service innovation; this is critical to delivering more effective and novel solutions that have the potential to tackle contemporary challenges. Service Design Research UK reviewed and consolidated the emergence of Service Design within the estalished field of Design

Deep Learning Reconstruction of Ultra-Short Pulses

Ultra-short laser pulses with femtosecond to attosecond pulse duration are the shortest systematic events humans can create. Characterization (amplitude and phase) of these pulses is a key ingredient in ultrafast science, e.g., exploring chemical reactions and electronic phase transitions. Here, we propose and demonstrate, numerically and experimentally, the first deep neural network technique to reconstruct ultra-short optical pulses. We anticipate that this approach will extend the range of ultrashort laser pulses that can be characterized, e.g., enabling to diagnose very weak attosecond pulses

Quality in Measurement: Beyond the deployment barrier

Network measurement stands at an intersection in the development of the science. We explore possible futures for the area and propose some guidelines for the development of stronger measurement techniques. The paper concludes with a discussion of the work of the NLANR and WAND network measurement groups including the NLANR Network Analysis Infrastructure, AMP, PMA, analysis of Voice over IP traffic and separation of HTTP delays into queuing delay, network latency and server delay

Fab Lab Amersfoort, De War: an innovation history

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FABRIC: A National-Scale Programmable Experimental Network Infrastructure

FABRIC is a unique national research infrastructure to enable cutting-edge and exploratory research at-scale in networking, cybersecurity, distributed computing and storage systems, machine learning, and science applications. It is an everywhere-programmable nationwide instrument comprised of novel extensible network elements equipped with large amounts of compute and storage, interconnected by high speed, dedicated optical links. It will connect a number of specialized testbeds for cloud research (NSF Cloud testbeds CloudLab and Chameleon), for research beyond 5G technologies (Platforms for Advanced Wireless Research or PAWR), as well as production high-performance computing facilities and science instruments to create a rich fabric for a wide variety of experimental activities

Road Friction Estimation for Connected Vehicles using Supervised Machine Learning

In this paper, the problem of road friction prediction from a fleet of connected vehicles is investigated. A framework is proposed to predict the road friction level using both historical friction data from the connected cars and data from weather stations, and comparative results from different methods are presented. The problem is formulated as a classification task where the available data is used to train three machine learning models including logistic regression, support vector machine, and neural networks to predict the friction class (slippery or non-slippery) in the future for specific road segments. In addition to the friction values, which are measured by moving vehicles, additional parameters such as humidity, temperature, and rainfall are used to obtain a set of descriptive feature vectors as input to the classification methods. The proposed prediction models are evaluated for different prediction horizons (0 to 120 minutes in the future) where the evaluation shows that the neural networks method leads to more stable results in different conditions.Comment: Published at IV 201