DTLS Performance in Duty-Cycled Networks

The Datagram Transport Layer Security (DTLS) protocol is the IETF standard for securing the Internet of Things. The Constrained Application Protocol, ZigBee IP, and Lightweight Machine-to-Machine (LWM2M) mandate its use for securing application traffic. There has been much debate in both the standardization and research communities on the applicability of DTLS to constrained environments. The main concerns are the communication overhead and latency of the DTLS handshake, and the memory footprint of a DTLS implementation. This paper provides a thorough performance evaluation of DTLS in different duty-cycled networks through real-world experimentation, emulation and analysis. In particular, we measure the duration of the DTLS handshake when using three duty cycling link-layer protocols: preamble-sampling, the IEEE 802.15.4 beacon-enabled mode and the IEEE 802.15.4e Time Slotted Channel Hopping mode. The reported results demonstrate surprisingly poor performance of DTLS in radio duty-cycled networks. Because a DTLS client and a server exchange more than 10 signaling packets, the DTLS handshake takes between a handful of seconds and several tens of seconds, with similar results for different duty cycling protocols. Moreover, because of their limited memory, typical constrained nodes can only maintain 3-5 simultaneous DTLS sessions, which highlights the need for using DTLS parsimoniously.Comment: International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC - 2015), IEEE, IEEE, 2015, http://pimrc2015.eee.hku.hk/index.htm

On the path to AI

This open access book explores machine learning and its impact on how we make sense of the world. It does so by bringing together two ‘revolutions’ in a surprising analogy: the revolution of machine learning, which has placed computing on the path to artificial intelligence, and the revolution in thinking about the law that was spurred by Oliver Wendell Holmes Jr in the last two decades of the 19th century. Holmes reconceived law as prophecy based on experience, prefiguring the buzzwords of the machine learning age—prediction based on datasets. On the path to AI introduces readers to the key concepts of machine learning, discusses the potential applications and limitations of predictions generated by machines using data, and informs current debates amongst scholars, lawyers and policy makers on how it should be used and regulated wisely. Technologists will also find useful lessons learned from the last 120 years of legal grappling with accountability, explainability, and biased data

Shadow epitaxy for in-situ growth of generic semiconductor/superconductor devices

Uniform, defect-free crystal interfaces and surfaces are crucial ingredients for realizing high-performance nanoscale devices. A pertinent example is that advances in gate-tunable and topological superconductivity using semiconductor/superconductor electronic devices are currently built on the hard proximity-induced superconducting gap obtained from epitaxial indium arsenide/aluminium heterostructures. Fabrication of devices requires selective etch processes; these exist only for InAs/Al hybrids, precluding the use of other, potentially superior material combinations. We present a crystal growth platform -- based on three-dimensional structuring of growth substrates -- which enables synthesis of semiconductor nanowire hybrids with in-situ patterned superconductor shells. This platform eliminates the need for etching, thereby enabling full freedom in choice of hybrid constituents. We realise and characterise all the most frequently used architectures in superconducting hybrid devices, finding increased yield and electrostatic stability compared to etched devices, along with evidence of ballistic superconductivity. In addition to aluminium, we present hybrid devices based on tantalum, niobium and vanadium. This is the submitted version of the manuscript. The accepted, peer reviewed version is available from Advanced Materials: http://doi.org/10.1002/adma.201908411 Previous title: Shadow lithography for in-situ growth of generic semiconductor/superconductor device

On the path to AI

This open access book explores machine learning and its impact on how we make sense of the world. It does so by bringing together two ‘revolutions’ in a surprising analogy: the revolution of machine learning, which has placed computing on the path to artificial intelligence, and the revolution in thinking about the law that was spurred by Oliver Wendell Holmes Jr in the last two decades of the 19th century. Holmes reconceived law as prophecy based on experience, prefiguring the buzzwords of the machine learning age—prediction based on datasets. On the path to AI introduces readers to the key concepts of machine learning, discusses the potential applications and limitations of predictions generated by machines using data, and informs current debates amongst scholars, lawyers and policy makers on how it should be used and regulated wisely. Technologists will also find useful lessons learned from the last 120 years of legal grappling with accountability, explainability, and biased data

Achieving Teacher Professional Growth Through Professional Experimentation and Changes in Pedagogical Practices

To facilitate the professional learning of teachers and bring about changes in pedagogical practices, it is necessary to understand the process by which teachers grow professionally. Professional growth can be achieved when teachers work together to engage in professional experimentation and see results in terms of salient outcomes for their students. This paper reports on a study of teachers’ pedagogical practices as they introduced adaptations to focus on personalising students’ learning in mathematics. Two cases are presented to demonstrate how teachers in two schools used student mathematics test data to determine students’ strengths and needs, in order to personalise learning experiences. The findings highlight how shared responsibility and purposeful use of student data can lead to positive professional growth for teachers and improved learning outcomes for students

Counting Platelets: a Novel Image Analysis Algorithm for Detecting and Counting Platelet Adhesion on an Assay Pattern

Changes in platelet function is a major complication in patients with cardiovascular disease. The authors have previously designed a portable, easy to use, low cost biochip for monitoring of anti-platelet therapy in cardiovascular disease patients [1]. This method directly measures platelet function by measuring platelet adhesion to platelet-specific protein patterns using a simple, optical counting technique. The patterned surfaces are designed so that a single platelet adheres to a single protein spot and for imaging purposes, the proteins patterned on the surface are labelled with a fluorescent dye, as are the platelets that adhere to the patterned surface. Following image acquisition, the occupied (i.e. covered by a platelet) protein spots in each sample must be enumerated. The result, calculated as percent adhesion, constitutes a direct and straightforward measurement of platelet adhesion and, therefore, platelet function