Daily Rhythms 1: Population Denominators and Spatio-Temporal Crime Hotspots

The patterning of crime varies with the daily rhythms of the city. The ebb and flow of urban populations hold clear impact on the spatio-temporal patterning of crime. Thus, accurate population-at-risk measures are required to quantify crime rates. Utilising resident and ambient (Andresen, 2011) population-at-risk measures, as well as geo and time coded crime data for a major metropolitan area in the UK, this paper seeks to determine statistically significant spatio-temporal hotspots for both property and violent crime. Addressing the association between the temporal patterning of crime hotspots and population-at-risk measures responds to recent calls in the international literature (Malleson and Andresen, 2016). Thus, we explore property and violent crime rates in relation to day-time, night-time, weekday, weekend resident and ambient (workday and mobile phone) population measures. Further, we test the suitability of diverse spatio-temporal clustering methods (E.g., Knox Tests and Kernel Density Estimations) to undertake this task. The results of this research imply the need to develop spatio-temporal specific explanations of crime, to consider the interplay between resident and ambient populations and the locations in which they interact

Daily Rhythms 2: travel purpose, activity spaces and the spatio-temporal patterning of crime

Interpreting the spatio-temporal patterning of crime, it is vital to consider the interplay of travel purpose and the attributes of activity spaces. This task, building on the insights of routine activities theory (Cohen and Felson, 1979), demands the integration of transport, crime and environmental data. In this vein, recent research (Felson and Boivin, 2015; Boivin and Felson 2017) has sought to explore the association between the characteristics of ambient (visitor) populations and crime. It has done so, however, without being able to account for the temporal patterning of crime hotspots nor the specific influence of environmental characteristics on those hotspots. This paper seeks to address this shortfall. It uses a negative binomial model to evaluate the effects of ambient population on crime across a series of time periods. Then, following Mburu and Helbich (2016), it examines the spatial influence of environmental characteristics on crime hotspots through the deployment of eigenvector spatial filtering techniques. The paper demonstrates that the interplay between ambient populations and environmental characteristics is time-dependent and varies according to whether property or violent crime is considered. The results of this research speak to the potential to develop more robust, though particular, explanations of crime

Big Data Driven Customer Insights for SMEs in Redistributed Manufacturing

© 2017 The Authors. Published by Elsevier. Redistributed manufacturing (RdM) refers to manufacturing business models, strategies, systems and technologies that change the economics and organization of manufacturing, particularly related to location and scale. Small-scale manufacturing has the potential to help tailor products to satisfy the specific needs of consumers different in terms of geographical location, cultural roots, improve sustainability and drive the society towards circular economy. While RdM has the great potential to deliver this, currently, very little has been understood on how RdM could help SMEs for gaining economic benefits due to the constraint of their business model, lack of understanding on customers, limited resource commitment on R & D, marketing and sales, supply chain integration, etc. Similarly user-driven design and customer-insights delivered through 'big data' analytics has the potentially to be highly beneficial for manufacturing SME and little is known of how they can be combined with RdM to benefit SMEs. Hence, they may impose risks on the business and operation of SMEs should poor choices be made or systems be implemented badly. The economic importance of SMEs within the UK and Europe is long established with manufacturing SMEs accounting for 60% of all private sector jobs in the UK. Within the European Union the overwhelming majority of companies (trading in the non-financial sectors) were SMEs (99.8%), employing 89.7 million people (67.1% of the workforce). This paper reports some of the results of an initial exploratory survey of manufacturing SMEs within the United Kingdom. Focusing on their background and status, and their current understanding and interests in RdM, big customer analytics and related topics

A Self-Attention Deep Neural Network Regressor for real time blood glucose estimation in paediatric population using physiological signals

With the advent of modern digital technology, the physiological signals (such as electrocardiogram) are being acquired from portable wearable devices which are being used for non-invasive chronic disease management (such as Type 1 Diabetes). The diabetes management requires real-time assessment of blood glucose which is cumbersome for paediatric population due to clinical complexity and invasiveness. Therefore, real-time non-invasive blood glucose estimation is now pivotal for effective diabetes management. In this paper, we propose a Self-Attention Deep Neural Network Regressor for real-time non-invasive blood glucose estimation for paediatric population based on automatically extracted beat morphology. The first stage performs Morphological Extractor based on Self-Attention based Long Short-Term Memory driven by Convolutional Neural Network for highlighting local features based on temporal context. The second stage is based on Morphological Regressor driven by multilayer perceptron with dropout and batch normalization to avoid overfitting. We performed feature selection via logit model followed by Spearman's correlation among features to avoid feature redundancy. We trained as tested our model on publicly available MIT/BIH-Physionet databases and physiological signals acquired from a T1D paediatric population. We performed our evaluation via Clarke's Grid error to analyse estimation accuracy on range of blood values under different glycaemic conditions. The results show that our tool outperformed existing regression models with 89% accuracy under clinically acceptable range. The proposed model based on beat morphology significantly outperformed models based on HRV features

An Automated Cloud-based Big Data Analytics Platform for Customer Insights

Product reviews have a significant influence on strategic decisions for both businesses and customers on what to produce or buy. However, with the availability of large amounts of online information, manual analysis of reviews is costly and time consuming, as well as being subjective and prone to error. In this work, we present an automated scalable cloud-based system to harness big customer reviews on products for gaining customer insights through data pipeline from data acquisition, analysis to visualisation in an efficient way. The experimental evaluation has shown that the proposed system achieves good performance in terms of accuracy and computing time

Search for new phenomena in final states with an energetic jet and large missing transverse momentum in pp collisions at √ s = 8 TeV with the ATLAS detector

Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses 20.3 fb−1 of √ s = 8 TeV data collected in 2012 with the ATLAS detector at the LHC. Events are required to have at least one jet with pT > 120 GeV and no leptons. Nine signal regions are considered with increasing missing transverse momentum requirements between Emiss T > 150 GeV and Emiss T > 700 GeV. Good agreement is observed between the number of events in data and Standard Model expectations. The results are translated into exclusion limits on models with either large extra spatial dimensions, pair production of weakly interacting dark matter candidates, or production of very light gravitinos in a gauge-mediated supersymmetric model. In addition, limits on the production of an invisibly decaying Higgs-like boson leading to similar topologies in the final state are presente

Measurement of the cross-section of high transverse momentum vector bosons reconstructed as single jets and studies of jet substructure in pp collisions at √s = 7 TeV with the ATLAS detector

This paper presents a measurement of the cross-section for high transverse momentum W and Z bosons produced in pp collisions and decaying to all-hadronic final states. The data used in the analysis were recorded by the ATLAS detector at the CERN Large Hadron Collider at a centre-of-mass energy of √s = 7 TeV;{\rm Te}{\rm V}$and correspond to an integrated luminosity of$4.6\;{\rm f}{{{\rm b}}^{-1}}$. The measurement is performed by reconstructing the boosted W or Z bosons in single jets. The reconstructed jet mass is used to identify the W and Z bosons, and a jet substructure method based on energy cluster information in the jet centre-of-mass frame is used to suppress the large multi-jet background. The cross-section for events with a hadronically decaying W or Z boson, with transverse momentum${{p}_{{\rm T}}}\gt 320\;{\rm Ge}{\rm V}$and pseudorapidity$|\eta |\lt 1.9$, is measured to be${{\sigma }_{W+Z}}=8.5\pm 1.7$ pb and is compared to next-to-leading-order calculations. The selected events are further used to study jet grooming techniques

Search for direct pair production of the top squark in all-hadronic final states in proton-proton collisions at s√=8 TeV with the ATLAS detector

The results of a search for direct pair production of the scalar partner to the top quark using an integrated luminosity of 20.1fb−1 of proton–proton collision data at √s = 8 TeV recorded with the ATLAS detector at the LHC are reported. The top squark is assumed to decay via t˜→tχ˜01 or t˜→ bχ˜±1 →bW(∗)χ˜01 , where χ˜01 (χ˜±1 ) denotes the lightest neutralino (chargino) in supersymmetric models. The search targets a fully-hadronic final state in events with four or more jets and large missing transverse momentum. No significant excess over the Standard Model background prediction is observed, and exclusion limits are reported in terms of the top squark and neutralino masses and as a function of the branching fraction of t˜ → tχ˜01 . For a branching fraction of 100%, top squark masses in the range 270–645 GeV are excluded for χ˜01 masses below 30 GeV. For a branching fraction of 50% to either t˜ → tχ˜01 or t˜ → bχ˜±1 , and assuming the χ˜±1 mass to be twice the χ˜01 mass, top squark masses in the range 250–550 GeV are excluded for χ˜01 masses below 60 GeV

Search for pair-produced long-lived neutral particles decaying to jets in the ATLAS hadronic calorimeter in ppcollisions at √s=8TeV

The ATLAS detector at the Large Hadron Collider at CERN is used to search for the decay of a scalar boson to a pair of long-lived particles, neutral under the Standard Model gauge group, in 20.3fb−1of data collected in proton–proton collisions at √s=8TeV. This search is sensitive to long-lived particles that decay to Standard Model particles producing jets at the outer edge of the ATLAS electromagnetic calorimeter or inside the hadronic calorimeter. No significant excess of events is observed. Limits are reported on the product of the scalar boson production cross section times branching ratio into long-lived neutral particles as a function of the proper lifetime of the particles. Limits are reported for boson masses from 100 GeVto 900 GeV, and a long-lived neutral particle mass from 10 GeVto 150 GeV

Optic Disc and Fovea Localisation in Ultra-widefield Scanning Laser Ophthalmoscope Images Captured in Multiple Modalities

We propose a convolutional neural network for localising the centres of the optic disc (OD) and fovea in ultra-wide field of view scanning laser ophthalmoscope (UWFoV-SLO) images of the retina. Images captured in both reflectance and autofluorescence (AF) modes, and central pole and eyesteered gazes, were used. The method achieved an OD localisation accuracy of 99.4% within one OD radius, and fovea localisation accuracy of 99.1% within one OD radius on a test set comprising of 1790 images. The performance of fovea localisation in AF images was comparable to the variation between human annotators at this task. The laterality of the image (whether the image is of the left or right eye) was inferred from the OD and fovea coordinates with an accuracy of 99.9%