Benchmarking Measures of Network Influence

Identifying key agents for the transmission of diseases (ideas, technology, etc.) across social networks has predominantly relied on measures of centrality on a static base network or a temporally flattened graph of agent interactions. Various measures have been proposed as the best trackers of influence, such as degree centrality, betweenness, and $k$-shell, depending on the structure of the connectivity. We consider SIR and SIS propagation dynamics on a temporally-extruded network of observed interactions and measure the conditional marginal spread as the change in the magnitude of the infection given the removal of each agent at each time: its temporal knockout (TKO) score. We argue that the exhaustive approach of the TKO score makes it an effective benchmark measure for evaluating the accuracy of other, often more practical, measures of influence. We find that none of the common network measures applied to the induced flat graphs are accurate predictors of network propagation influence on the systems studied; however, temporal networks and the TKO measure provide the requisite targets for the hunt for effective predictive measures

Can Terahertz Provide High-Rate Reliable Low Latency Communications for Wireless VR?

Wireless virtual reality (VR) imposes new visual and haptic requirements that are directly linked to the quality-of-experience (QoE) of VR users. These QoE requirements can only be met by wireless connectivity that offers high-rate and high-reliability low latency communications (HRLLC), unlike the low rates usually considered in vanilla ultra-reliable low latency communication scenarios. The high rates for VR over short distances can only be supported by an enormous bandwidth, which is available in terahertz (THz) frequency bands. Guaranteeing HRLLC requires dealing with the uncertainty that is specific to the THz channel. To explore the potential of THz for meeting HRLLC requirements, a quantification of the risk for an unreliable VR performance is conducted through a novel and rigorous characterization of the tail of the end-to-end (E2E) delay. Then, a thorough analysis of the tail-value-atrisk (TVaR) is performed to concretely characterize the behavior of extreme wireless events crucial to the real-time VR experience. System reliability for scenarios with guaranteed line-of-sight (LoS) is then derived as a function of THz network parameters after deriving a novel expression for the probability distribution function of the THz transmission delay. Numerical results show that abundant bandwidth and low molecular absorption are necessary to improve the reliability. However, their effect remains secondary compared to the availability of LoS, which significantly affects the THz HRLLC performance. In particular, for scenarios with guaranteed LoS, a reliability of 99.999% (with an E2E delay threshold of 20 ms) for a bandwidth of 15 GHz along with data rates of 18.3 Gbps can be achieved by the THz network (operating at a frequency of 1 THz), compared to a reliability of 96% for twice the bandwidth, when blockages are considered.Comment: arXiv admin note: text overlap with arXiv:1905.0765

Channel Dynamics and SNR Tracking in Millimeter Wave Cellular Systems

The millimeter wave (mmWave) frequencies are likely to play a significant role in fifth-generation (5G) cellular systems. A key challenge in developing systems in these bands is the potential for rapid channel dynamics: since mmWave signals are blocked by many materials, small changes in the position or orientation of the handset relative to objects in the environment can cause large swings in the channel quality. This paper addresses the issue of tracking the signal to noise ratio (SNR), which is an essential procedure for rate prediction, handover and radio link failure detection. A simple method for estimating the SNR from periodic synchronization signals is considered. The method is then evaluated using real experiments in common blockage scenarios combined with outdoor statistical models

Police Knowledge Exchange: Full Report 2018

[Executive Summary] This report was commissioned to explore the enablers and barriers to sharing within and between police forces and between police forces and partners, including the public. This was completed from an interdisciplinary review of international literature covering sharing, knowledge exchange, learning and organisational learning. The literature broke down into four main factors; who, why, what and how. An introduction to the literature is presented with ‘Who’ is sharing which considers both personal identity and different institutional issues. The ‘Why’ literature covers issues of cultural and community motivators and barriers. The ‘What’ segment reviews concepts of data, information and knowledge and related legislative issues. Finally, the ‘how’ section spans face to face sharing approaches to technologies that produce both enablers and barriers. A series of 42 in-depth interviews and focus groups were completed and combined with 47 survey responses . The aim of the interviews, focus groups and survey was to show perceptions and beliefs around knowledge sharing from a small sample across policing in order to complement the findings from the literature review. The survey was adapted from a standardised questionnaire (Biggs, 1987). The Biggs questionnaire focused on what motivated students to learn and how they approached their learning. Our adapted survey looked at what motivated police to share, and how they approached sharing. The responses showed a trend, across the police, towards a motivation for sharing to develop a deeper understanding of issues. However, the approaches and the strategies they used to share with others, which were primarily driven by achieving and surface approaches (to get promoted and get the job done). According to Biggs (1987) this could leave them discontented as they never progress to a deeper understanding of issues. Scaffolding sharing within the police through processes that are clearly defined, effective and valued could help to overcome these issues. Within the interviews and focus group findings a similar structured approach to sharing was adopted. Within the ‘who’ section some key aspects around personal relationships, reciprocity and reputation were identified. The ‘why’ the police share was one of the largest discussion points. Not only was there a deep motivation to solve key policing issues there was an approach of reciprocity. Police sharing was deeply motivated to support ‘good practice’ in the prevention and detection of crime. However, a sharing barrier was identified in the parity of value given to different types of knowledge for example between professional judgement and research evidence knowledge. Sharing was achieved when there were reciprocal benefits, in particular with personal networks or face to face sharing which was noted as ‘safe’. Again, this was inhibited by misunderstandings around the ‘risks’ of sharing, frequently attributed to data protection legislation; producing cautious reactions and as an avoidance tactic to save time and effort sharing. However, a divide was noted between technical users and those who avoided any online systems for sharing; often due to poorly designed systems and a lack of confidence in how to use systems. The police culture was identified as being risk-adverse, and competitive due to multiple factors, a lack of supported time to share, Her Majesty’s Inspectorate of Constabulary (HMIC) reviews and promotion criteria. The result was perceived to be a poor cultural ability to learn from mistakes and a likelihood to repeat errors. A set of strategic recommendations are given and include the use of a sharing authorised professional practice for HMIC reviews, sharing networks and training. A further set of operational recommendations are given such as; sharing impact cases for evidence based practice, data sharing officers and evaluating mechanisms for sharing. This full report is supported by the Police Knowledge Exchange Summary Report 2018 which gives an overview of the findings and recommendations