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Large-scale data analysis is becoming an important source of information for mobile network operators (MNOs). MNOs can now investigate the feasibility of possible new technological advances such as storage/memory utilization, context awareness, and edge/cloud computing using analytic platforms designed for big data processing. Within this context, studying caching from a mobile data traffic analytical perspective can offer rich insights on evaluating the potential benefits and gains of proactive caching at base stations. In this chapter, we study how data collected from MNOs can be leveraged using machine learning tools in order to infer insights into the benefits of caching. Through our practical architecture, vast amount of data can be harnessed for content popularity estimations and placing strategic contents at base stations (BSs). Our results demonstrate several gains in terms of both content demand satisfaction and backhaul offloading rates while utilizing real-world data sets collected from a major MNO.
This chapter describes the adoption of the Cobb–Douglas regression by agricultural economists over the 1940-1960 period. These economists used the regression to explore a set of long-standing questions specific to agricultural economics. As a result, their defense and development of the method and the criticisms they attracted from their colleagues, while drawing on the prewar literature surrounding the Cobb–Douglas regression, had different emphases, and controversial questions that surrounded Douglas's applications of the regression became irrelevant. The agricultural economists were also the first to estimate the Cobb–Douglas regression using data generated by individual firms, as opposed to the more highly aggregated data used by Douglas and his coauthors, and they embedded it in a probability-based statistical framework. They also developed general models for applying regression analysis to panel data, models that had a profound impact on empirical economic research. All of this contributed to the process through which the Cobb–Douglas regression came to be seen as an empirical tool potentially suited to a broad list of applications.
We live in an algorithmic world. There is currently no area of our lives that has not been touched by computation and its language and tools. Since when, in the early 1940s, a small group of people led by John von Neumann gathered to turn into reality the vision of a universal computing machine, humankind is experiencing a sort of permanent revolution in which our understanding of the world and our ways of acting on it are steadily transformed by the steps forward we make in processing information. Such a condition is vividly depicted by Alan Turing in one of the founding documents of the quest for artificial intelligence (AI): “in attempting to construct machines … we are providing mansions for the souls.”1 Computers and algorithms can be seen as the building blocks of a new, ever-expanding building – a cathedral, to use George Dyson’s metaphor2 – in which every human activity is going to be shaped by the digital architecture hosting it.
Chapter 6 examines the case of the EU controversy on data protection. In the context of the launch of the Digital Agenda and the introduction of sensitive security and defence areas in EU-funded research, EU policymakers asked ethics experts to issue two opinions on the ethics of information and communication technologies. But as the EGE experts started their work, the Commission also launched the data protection legislative reform; in this context data privacy became the object of a controversy, affecting the role ethics experts played in policy. First, the EGE became a terrain of competition between the various Commission DGs involved in data protection issues. Second, and as the policy debate became increasingly polarised, the EGE’s work was also increasingly perceived as a tool to unlock policy deadlock. The EGE experts calibrated their findings in order to deconstruct the binary positions that had been evoked in the debate and elaborate a more consensual policy narrative. The case brings to light that when policy conflicts intensify, the role of ethics experts shifts from conflict containment to conflict manoeuvring.
This chapter first provides a framework for understanding recent local government approaches to aligning Uber and Lyft operations with urban transportation policy goals—including improving street safety, improving transportation access, and reducing greenhouse gas emissions. Many of these approaches to setting policy and designing streets are not regulatory per se, though they can and have been used as de facto regulatory strategies. This “implicit” regulatory approach has arisen in part because most local governments in the U.S. lack the formal authority to regulate Uber and Lyft. Furthermore, most local governments also lack the data necessary to develop and/or enforce appropriate regulations of the app-enabled for-hire vehicle industry.
The chapter continues with a case study of how the San Francisco County Transportation Authority, in partnership with researchers at Northeastern University, developed a creative and partnership-driven approach to policy-making in the face of a severe data deficit. Agency staff and University researchers scraped data from the Uber and Lyft application programming interfaces and used those data to better understand how people move in San Francisco County. This work demonstrates the importance of innovative, goal-oriented problem-solving approaches to inform the regulation of increasingly complex city streets.
Technological advances continue to produce massive amounts of information from a variety of sources about our everyday lives. The simple use of a smartphone, for example, can generate data on individuals through telephone records (including location data), social media activity, Internet browsing, e-commerce transactions, and email communications. Much attention has been given to expectations of privacy in light of this data collection, especially consumer privacy. Much attention has also been given to how and when government agencies collect and use this data to monitor the activities of individuals.
It has become of key interest in the insurance industry to understand and extract information from telematics car driving data. Telematics car driving data of individual car drivers can be summarised in so-called speed–acceleration heatmaps. The aim of this study is to cluster such speed–acceleration heatmaps to different categories by analysing similarities and differences in these heatmaps. Making use of local smoothness properties, we propose to process these heatmaps as RGB images. Clustering can then be achieved by involving supervised information via a transfer learning approach using the pre-trained AlexNet to extract discriminative features. The K-means algorithm is then applied on these extracted discriminative features for clustering. The experiment results in an improvement of heatmap clustering compared to classical approaches.
The potential relationship between service demands and remanufacturing services (RMS) is essential to make the decision of a RMS plan accurately and improve the efficiency and benefit. In the traditional association rule mining methods, a large number of candidate sets affect the mining efficiency, and the results are not easy for customers to understand. Therefore, a mining method based on binary particle swarm optimization ant colony algorithm to discover service demands and remanufacture services association rules is proposed. This method preprocesses the RMS records, converts them into a binary matrix, and uses the improved ant colony algorithm to mine the maximum frequent itemset. Because the particle swarm algorithm determines the initial pheromone concentration of the ant colony, it avoids the blindness of the ant colony, effectively enhances the searchability of the algorithm, and makes association rule mining faster and more accurate. Finally, a set of historical RMS record data of straightening machine is used to test the validity and feasibility of this method by extracting valid association rules to guide the design of RMS scheme for straightening machine parts.
Based on the public data from the health departments of Tianjin and Shenzhen, we conducted a comparative analysis of the corona virus disease 2019 (COVID-19) epidemic situation between these two cities. The aim of this study was to evaluate the role of public data in epidemic prevention and control of COVID-19, providing a scientific advice for the subsequent mitigation and containment of COVID-19 prevalence.
Chapter 3 introduces a range of multidisciplinary data sources available to study disasters and history and outlines some of the methodologies through which we can interpret and analyze these sources. The underpinning argument is that we can use history as a laboratory to better understand disasters – testing hypotheses rather than merely describing conspicuous phenomena, albeit with a recognition of what this also demands of us as historians. In particular, we discuss the production of suitable measures and methods to understand hazards and their effects, whilst also keeping in mind the limitations of the historical record and the need for a critical approach to sources. We consider, therefore, state-of-the-art challenges in historical disaster research such as how we can compensate for lacunae in the historical record by incorporating rapidly increasing volumes of data from the natural sciences, and the opportunities and pitfalls of historical ‘big data’. The chapter concludes by arguing for the importance of systematic comparative methodologies in moving beyond the descriptive and towards the analytical, which requires that we pay particular attention to scale and context.
Sibling resemblance in crime may be due to genetic relatedness, shared environment, and/or the interpersonal influence of siblings on each other. This latter process can be understood as a type of ‘peer effect’ in that it is based on social learning between individuals occupying the same status in the social system (family). Building on prior research, we hypothesized that sibling pairs that resemble peer relationships the most, i.e., same-sex siblings close in age, exhibit the most sibling resemblance in crime.
Drawing on administrative microdata covering Finnish children born in 1985–97, we examined 213 911 sibling pairs, observing the recorded criminality of each sibling between ages 11 and 20. We estimated multivariate regression models controlling for individual and family characteristics, and employed fixed-effects models to analyze the temporal co-occurrence of sibling delinquency.
Among younger siblings with a criminal older sibling, the adjusted prevalence estimates of criminal offending decreased from 32 to 25% as the age differences increased from less than 13 months to 25–28 months. The prevalence leveled off at 23% when age difference reached 37–40 months or more. These effects were statistically significant only among same-sex sibling pairs (p < 0.001), with clear evidence of contemporaneous offending among siblings with minimal age difference.
Same-sex siblings very close in age stand out as having the highest sibling resemblance in crime. This finding suggests that a meaningful share of sibling similarity in criminal offending is due to a process akin to peer influence, typically flowing from the older to the younger sibling.
Oral contraceptive use has been previously associated with an increased risk of suicidal behavior in some, but not all, samples. The use of large, representative, longitudinally-assessed samples may clarify the nature of this potential association.
We used Swedish national registries to identify women born between 1991 and 1995 (N = 216 702) and determine whether they retrieved prescriptions for oral contraceptives. We used Cox proportional hazards models to test the association between contraceptive use and first observed suicidal event (suicide attempt or death) from age 15 until the end of follow-up in 2014 (maximum age 22.4). We adjusted for covariates, including mental illness and parental history of suicide.
In a crude model, use of combination or progestin-only oral contraceptives was positively associated with suicidal behavior, with hazard ratios (HRs) of 1.73–2.78 after 1 month of use, and 1.25–1.82 after 1 year of use. Accounting for sociodemographic, parental, and psychiatric variables attenuated these associations, and risks declined with increasing duration of use: adjusted HRs ranged from 1.56 to 2.13 1 month beyond the initiation of use, and from 1.19 to 1.48 1 year after initiation of use. HRs were higher among women who ceased use during the observation period.
Young women using oral contraceptives may be at increased risk of suicidal behavior, but risk declines with increased duration of use. Analysis of former users suggests that women susceptible to depression/anxiety are more likely to cease hormonal contraceptive use. Additional studies are necessary to determine whether the observed association is attributable to a causal mechanism.
Measurement errors are omnipresent in network data. Most studies observe an erroneous network instead of the desired error-free network. It is well known that such errors can have a severe impact on network metrics, especially on centrality measures: a central node in the observed network might be less central in the underlying, error-free network. The robustness is a common concept to measure these effects. Studies have shown that the robustness primarily depends on the centrality measure, the type of error (e.g., missing edges or missing nodes), and the network topology (e.g., tree-like, core-periphery). Previous findings regarding the influence of network size on the robustness are, however, inconclusive. We present empirical evidence and analytical arguments indicating that there exist arbitrary large robust and non-robust networks and that the average degree is well suited to explain the robustness. We demonstrate that networks with a higher average degree are often more robust. For the degree centrality and Erdős–Rényi (ER) graphs, we present explicit formulas for the computation of the robustness, mainly based on the joint distribution of node degrees and degree changes which allow us to analyze the robustness for ER graphs with a constant average degree or increasing average degree.
This article reports on the early results of using behavioural and educational data to evaluate a residential education programme. The programme serves male and female students between 12 and 16 years of age who have been suspended or expelled from school due to behavioural issues or who refused to attend school. Using measures of behavioural and educational progress during care and reporting these changes over time provided empirical evidence that the programme was achieving its primary aims of ‘behaviour change and educational gains.’ Collecting and reporting this data has empowered the programme to increase programme effectiveness through both data-informed decision-making and ongoing programme evaluation.
With over a century of records, we present a detailed analysis of the spatial and temporal occurrence of marine turtle sightings and strandings in the UK and Ireland between 1910 and 2018. Records of hard-shell turtles, including loggerhead turtles (Caretta caretta, N = 240) and Kemp's ridley turtles (Lepidochelys kempii, N = 61), have significantly increased over time. However, in the most recent years there has been a notable decrease in records. The majority of records of hard-shell turtles were juveniles and occurred in the boreal winter months when the waters are coolest in the North-east Atlantic. They generally occurred on the western aspects of the UK and Ireland highlighting a pattern of decreasing records with increasing latitude, supporting previous suggestions that juvenile turtles arrive in these waters via the North Atlantic current systems. Similarly, the majority of the strandings and sightings of leatherback turtles (Dermochelys coriacea, N = 1683) occurred on the western aspects of the UK and the entirety of Ireland's coastline. In contrast to hard-shell turtles, leatherback turtles were most commonly recorded in the boreal summer months with the majority of strandings being adult sized, of which there has been a recent decrease in annual records. The cause of the recent annual decreases in turtle strandings and sightings across all three species is unclear; however, changes to overall population abundance, prey availability, anthropogenic threats and variable reporting effort could all contribute. Our results provide a valuable reference point to assess species range modification due to climate change, identify possible evidence of anthropogenic threats and to assess the future trajectory of marine turtle populations in the North Atlantic.
A first attempt is made to use recently developed, non-conventional Artificial Neural Network (ANN) models with Multilayer Perceptron (MLP), Radial Basis Function (RBF) and Adaptive Neuro-Fuzzy Interference System (ANFIS) architectures to predict the fuel flow rate of a commercial aircraft using real data obtained from Flight Data Records (FDRs) of the cruise, climb and descent phases. The training of the architectures with a single hidden layer is performed by utilising the Delta-Bar-Delta (DBD), Conjugate Gradient (CG) and Quickprop (QP) algorithms. The optimum network topologies are sought by varying the number of processing elements in the hidden layer of the networks using a trial-and-error method. An evaluation of the approximate fuel intake values against the ideal fuel intake data from the FDRs indicates a good fit for all three ANN models. Thus, more accurate fuel intake estimations can be obtained by applying the RBF-ANN model during the climb and descent flight stages, whereas the MLP-ANN model is more effective for the cruise phase. The best accuracy obtained in terms of the linear correlation coefficient is 0.99988, 0.91946 and 0.95252 for the climb, cruise and descent phase, respectively.
Research on ancient Greek rural settlement and agricultural economies often emphasises political agency as a driving force behind landscape change, with comparatively less attention directed to the potential effects of climate. This study analyses climate variability and the spatial configuration of land use in the north-eastern Peloponnese during the Late Hellenistic and Roman (c. 150 BC–AD 300) periods. A synthesis of archaeological field survey data combined with new palaeoclimatological data provides novel insight into how changing climate influenced land use. The authors argue that although climatic variability alone did not drive socio-economic change, drying conditions may have influenced the relocation of agricultural production.
Isolation is a concept originally conceived in the context of clique enumeration in static networks, mostly used to model communities that do not have much contact to the outside world. Herein, a clique is considered isolated if it has few edges connecting it to the rest of the graph. Motivated by recent work on enumerating cliques in temporal networks, we transform the isolation concept to the temporal setting. We discover that the addition of the time dimension leads to six distinct natural isolation concepts. Our main contribution is the development of parameterized enumeration algorithms for five of these six isolation types for clique enumeration, employing the parameter “degree of isolation.” In a nutshell, this means that the more isolated these cliques are, the faster we can find them. On the empirical side, we implemented and tested these algorithms on (temporal) social network data, obtaining encouraging results.
This chapter begins (1.1) by looking at prescriptive and descriptive approaches to grammar, and at different sources of linguistic data. It goes on to discuss the approach to syntax in traditional grammar, looking at grammatical categories (1.2) and grammatical functions (1.3). 1.4 considers aspects of syntax which are potentially universal before going on to consider the nature of universals, the architecture of grammars, and the Strong Minimalist Thesis. 1.5 examines parameters of variation between languages, before turning to consider the role of parameter-setting in language acquisition, and outlining Principles and Parameters Theory (1.6). The chapter concludes with a summary (1.7), and a set of bibliographical notes (1.8). There is a free-to-download Students’ Workbook that includes a separate set of exercise material for each core section and a Students’ Answerbook. The free-to-download Teachers’ Answerbook provides detailed written answers for every single exercise example. The free-to-download Powerpoints provide a more vivid and visual representation of the material in each core section of the chapter.
This chapter provides an overview of the recent theoretical, methodological and analytical trends in multimodal research, specifically focusing on how different approaches (e.g. critical discourse analysis, conversation analysis, social semiotics, systemic functional linguistics and interaction analysis) have addressed the complex problems arising from studying the integration of language with other resources, such as images, gesture, movement, space and so forth. In doing so, the chapter discusses how traditional divisions in discourse studies have become somewhat blurred, given the evident need to account for resources other than language and the meaning that arises as choices combine in texts, interactions and events. The chapter also explores how various digital approaches have been developed to handle the multidimensional complexity of multimodal analysis, in particular for the analysis of dynamic media such as videos. This discussion includes the development of mixed methods approaches, purpose-built software, automated techniques and the latest trends in big data approaches to multimodal analysis.