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 …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Imagined communities: Reflections on the origin and spread of nationalism

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.

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Community detection in graphs

Mobile devices are becoming pervasive to our daily lives: they follow us everywhere and we use them for much more than just communication. These devices are also equipped with a myriad of different sensors that have the potential to allow the tracking of human activities, user patterns, location, direction and much more. Following this direction, many movements including sports, quantified self, and mobile health ones are starting to heavily rely on this technology, making it pivotal that the sensors offer high accuracy. However, heterogeneity in hardware manufacturing, slight substrate differences, electronic interference as well as external disturbances are just few of the reasons that limit sensor output accuracy which in turn hinders sensor usage in applications which need very high granularity and precision, such as quantified-self applications. Although, calibration of sensors is a widely studied topic in literature to the best of our knowledge no publicly available research exists that specifically tackles the calibration of mobile phones and existing methods that can be adapted for use in mobile devices not only require user interaction but they are also not adaptive to changes. Additionally, alternative approaches for performing more granular and accurate sensing exploit body-wide sensor networks using mobile phones and additional sensors; as one can imagine these techniques can be bulky, tedious, and not particularly user friendly. Moreover, existing techniques for performing data corrections post-acquisition can produce inconsistent results as they miss important context information provided from the device itself; which when used, has been shown to produce better results without a imposing a significant power-penalty. In this paper we introduce a novel multiposition calibration scheme that is specifically targeted at mobile devices Our scheme exploits machine learning techniques to perform an adaptive, power-efficient auto-calibration procedure with which achieves high output sensor accuracy when compared to state of the art techniques without requiring any user interaction or special equipment beyond device itself Moreover, the energy costs associated with our approach are lower than the alternatives (such as Kalman filter based solutions) and the overall power penalty is 95% when using neural network classifiers and > 88% when using SVM classifiers where uncalibrated data classification only reaches ~ 85% and ~ 80% respectively This can be a make or break factor in the use of accelerometer and gyroscope data in applications requiring high accuracy e g sports, health, games and others

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You Are Sensing, but Are You Biased?: A User Unaided Sensor Calibration Approach for Mobile Sensing

In the last issue ACM Computer Communication Review, Christophe Diot, the Editor-in-Chief kicked off a series of contributions to CCR by members of our technical community on networking papers that they would recommend to others. Of course, we all have our lists of favourites (I have 4 book lists on Amazon!), but this is more than just stamp collecting. Search engines and citation indexes have several problems, including: it is hard to balance recency of an article with popularity – a single dimension index doesn't really tell you whether a paper is seminal or just popular; some material is not available (too old, or not there any more; the average of everyone's opinion may not be as useful as a subjective view by someone you trust (or distrust); one day, these problems may be solved by contextualizing information that is retrieved and presenting the recommendation network that your retrieval was made by (Some search engines like http://beta.previewseek.com/ are starting to do this). Until that day, lists like this are a good substitute, and they are also fun starting points for discussion. My list is explicitly not my " top ten " papers ever. Rather, it represents a sample made at a snapshot. These are papers that came up in recent discussions in PhD supervision, research project work, and in reviewing papers for conferences. For each paper, I've given some indication of the value I got from the paper. In some cases, I also give the context that I first saw the paper. Here they are, in random order: • " Experience with Grapevine: the growth of a distributed system, " [Schroeder 1984]. This has so many ideas in how to actually do things right (compared say to DNS) and includes some things people have forgotten about 10 times (including later work that used both epidemic models and control theory applied to the update traffic). We used to work on Directory Systems at UCL in the 1980s – we also worked on comparing early DNS implementations (Berkeley BIND and Stanford's DRUID). The baseline for all of these, though, was GrapeVine. Paul Dourish (now at Irvine) visited us in UCL from Xerox's European PARC, round then and told us the stories of epidemic problems that showed up in this paper. • " The Design and Implementation of an Operating System to Support Distributed Multimedia Applications, " [Leslie 2000]. It's a shame so …

10 networking papers: recommended reading

The vision of Ubiquitous Computing [22] creates the world in which information is omnipresent, migrating seamlessly through the environment to be accessible whenever and wherever needed. Such a vision poses substantial challenges to information security and privacy protection.Unlike in traditional, static, execution environments, information in the Ubiquitous world is exposed, throughout its lifetime, to constantly varying security and privacy threats caused by the inherent dynamicity and unpredictability of the new computing environment and its mobility. Existing data protection mechanisms, built for non- or predictably slowly-changing environments, are unable to strike the balance in the information availability vs. security and privacy threat trade-off in the Ubiquitous world thus hindering the feasibility of the overall vision.In this paper, we present our initial work on a novel paradigm for information security and privacy protection in the ubiquitous world. We model security and privacy threats through sets of contextual attributes and mitigate the projected risks through proactive and reactive data format transformations, subsetting and forced migrations while trying to maximize information availability. We also try to make the approach flexible, scalable and infrastructure independent, as required by the very vision of the Ubiquitous Computing.

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Information exposure control through data manipulation for ubiquitous computing

The Computer Laboratory, University of Cambridge hosted a workshop on "Internet on the Move" on September 22, 2012. The objective of the workshop was to bring academia, industry and regulators to discuss the challenges in realizing the notion of ubiquitous mobile Internet. The editorial summarises a general overview of the issues discussed on enabling universal mobile coverage and some of the solutions that have been proposed to alleviate the problem of having ubiquitous mobile connectivity.

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Internet on the move: challenges and solutions

There is substantial interest in the effect of human mobility patterns on opportunistic communications. Inspired by recent work revisiting some of the early evidence for a L\'evy flight foraging strategy in animals, we analyse datasets on human contact from real world traces. By analysing the distribution of inter-contact times on different time scales and using different graphical forms, we find not only the highly skewed distributions of waiting times highlighted in previous studies but also clear circadian rhythm. The relative visibility of these two components depends strongly on which graphical form is adopted and the range of time scales. We use a simple model to reconstruct the observed behaviour and discuss the implications of this for forwarding efficiency.

Jon Crowcroft

Papers

You Are Sensing, but Are You Biased?: A User Unaided Sensor Calibration Approach for Mobile Sensing

10 networking papers: recommended reading

Information exposure control through data manipulation for ubiquitous computing

Internet on the move: challenges and solutions

Rhythm and Randomness in Human Contact