Jon Crowcroft

Bio: Jon Crowcroft is an academic researcher from University of Cambridge. The author has contributed to research in topics: The Internet & Multicast. The author has an hindex of 87, co-authored 672 publications receiving 38848 citations. Previous affiliations of Jon Crowcroft include Memorial University of Newfoundland & Information Technology University.

Demo: PhoneLets: offloading the phone off your phone for energy, cost and network load optimization

Abstract: This demo presents how phone functionality can be offloaded from a smartphone over wireless link to a PhoneLet by sharing one SIM card across multiple devices. This can lead to significant cost and network load reductions by decreasing the number of simultaneously connected mobile clients. Furthermore, it can save energy for the mobile user when connected to a powered PhoneLet by offloading phone functionality. It absorbs the energy cost of online presence and inefficient mobile applications' communication patterns, instead providing connectivity for the user over a WiFi link.

SpinThrift: Saving energy in viral workloads

Abstract: This paper looks at optimising the energy costs for data storage when the work load is highly skewed by a large number of accesses from a few popular articles, but whose popularity varies dynamically. A typical example of such a work load is news article access, where the most popular is highly accessed, but which article is most popular keeps changing. The properties of dynamically changing popular content are investigated using a trace drawn from a social news web site. It is shown that a) popular content have a much larger window of interest than non-popular articles. i.e. popular articles typically have a more sustained interest rather than a brief surge of interest. b) popular content are accessed by multiple unrelated users. In contrast, articles whose accesses spread only virally, i.e. from friend to friend, are shown to have a tendency not to be popular. Using this data, we improve upon Popular Data Concentration (PDC), a technique which is used to save energy by spinning down disks that do not contain popular data. PDC requires keeping the data ordered by their popularity, which involves significant amount of data migration, when the most popular articles keep changing. In contrast, our technique, SpinThrift, detects popular data by the proportion of non-viral accesses made, and results in lesser data migration, whilst using a similar amount of energy as PDC.

Outdoor Air Pollutant Concentration and COVID-19 Infection in Wuhan, China

Abstract: COVID-19 infection, first reported in Wuhan, China in December 2019, has become a global pandemic, causing significantly high infections and mortalities in Italy, the UK, the US, and other parts of the world. Based on the statistics reported by John Hopkins University, 4.7M people worldwide and 84,054 people in China have been confirmed positive and infected with COVID-19, as of 18 May 2020. Motivated by the previous studies which show that the exposures to air pollutants may increase the risk of influenza infection, our study examines if such exposures will also affect Covid-19 infection. To the best of our understanding, we are the first group in the world to rigorously explore the effects of outdoor air pollutant concentrations, meteorological conditions and their interactions, and lockdown interventions, on Covid-19 infection in China. Since the number of confirmed cases is likely to be under-reported due to the lack of testing capacity, the change in confirmed case definition, and the undiscovered and unreported asymptotic cases, we use the rate of change in the daily number of confirmed infection cases instead as our dependent variable. Even if the number of reported infections is under-reported, the rate of change will still accurately reflect the relative change in infection, provided that the trend of under-reporting remains the same. In addition, the rate of change in daily infection cases can be distorted by the government imposed public health interventions, including the lockdown policy, inter-city and intra-city mobility, and the change in testing capacity and case definition. Hence, the effects of the lockdown policy and the inter-city and intra-city mobility, and the change in testing capacity and case definition are all taken into account in our statistical modelling. Furthermore, we adopt the generalized linear regression models covering both the Negative Binomial Regression and the Poisson Regression. These two regression models, when combined with different time-lags (to reflect the COVID-19 incubation period and delay due to official confirmation) in air pollutant exposure (PM2.5), are used to fit the COVID-19 infection model. Our statistical study has shown that higher PM2.5 concentration is significantly correlated with a higher rate of change in the daily number of confirmed infection cases in Wuhan, China (p

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Community detection in graphs

Abstract: The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.