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.

/pdf/community-detection-in-graphs-34rogm3r6p.pdf

Community detection in graphs

Knowledge of Wi-Fi networks helps to guide future engineering and spectrum policy decisions. However, due to its unlicensed nature, the deployment of Wi-Fi Access Points is undocumented meaning researchers are left making educated guesses as to the prevalence of these assets through remotely collected or passively sensed measurements. One commonly used method is referred to as `wardriving` essentially where a vehicle is used to collect geospatial statistical data on wireless networks to inform mobile computing and networking security research. Surprisingly, there has been very little examination of the statistical issues with wardriving data, despite the vast number of analyses being published in the literature using this approach. In this paper, a sample of publicly collected wardriving data is compared to a predictive model for Wi-Fi Access Points. The results demonstrate several statistical issues which future wardriving studies must account for, including selection bias, sample representativeness and the modifiable areal unit problem.

Wi-Fi Wardriving Studies Must Account for Important Statistical Issues.

There is an obvious trend that more and more data and computation are migrating into networks nowadays. Combining mature virtualization technologies with service-centric net- working, we are entering into an era where countless services reside in an ISP network to provide low-latency access. Such services are often computation intensive and are dynamically created and destroyed on demands everywhere in the network to perform various tasks. Consequently, these ephemeral in-network services introduce a new type of congestion in the network which we refer to as "computation congestion". The service load need to be effectively distributed on different nodes in order to maintain the funtionality and responsiveness of the network, which calls for a new design rather than reusing the centralised scheduler designed for cloud-based services. In this paper, we study both passive and proactive control strategies, based on the proactive control we further propose a fully distributed solution which is low complexity, adaptive, and responsive to network dynamics.

C3PO: Computation Congestion Control (PrOactive) - an algorithm for dynamic diffusion of ephemeral in-network services

Electronic social networks are a relatively new pervasive phenomenon that has changed the way in which we communicate and interact. They are now supporting new applications, leading to new trends and posing new challenges. The workshop titled "Future of Social Networking: Experts from Industry and Academia" took place in Cambridge on November 18, 2010 to expose how the future of social networking may develop and be exploited in new technologies and systems. We provide a summary of this event and some observations on the key outcomes.

/pdf/message-from-the-workshop-on-the-future-of-social-networking-5bqkzn33ex.pdf

Message from the workshop on the future of social networking

Not all research leads to fruitful results; trying new ways or methods may surpass the state of the art, but sometimes the hypothesis is not proven or the improvement is insignificant. In a systems discipline like pervasive computing, there are many sources of errors, from hardware issues over communication channels to heterogeneous software environments. However, failure to succeed is not a failure to progress. It is essential to create platforms for sharing insights, experiences, and lessons learned when conducting research in pervasive computing so that the same mistakes are not repeated. And sometimes, a problem is a symptom of discovering new research challenges. Based on the collective input of the First International Workshop on Negative Results in Pervasive Computing (PerFail 2022), co-located with the 20th International Conference on Pervasive Computing and Communications (PerCom 2022), this paper presents a comprehensive discussion on perspectives on publishing negative results and lessons learned in pervasive computing.

Perspectives on Negative Research Results in Pervasive Computing

It is often claimed that Internet Traffic patterns are interesting because the Internet puts few constraints on sources. This leads to innovation. It also makes the study of Internet traffic, what we might cal the search for the Internet Erlang, very difficult. At the same time, traffic control (congestion control) and engineering are both hot topics. What if “flash crowds” (a.k.a. slashdot), cascades, epidemics and so on are the norm? What if the trend continues for network link capacity to become flatter, with more equal capacity in the access and core, or even more capacity in the access than the core (as in the early 1980s with 10Mbps LANs versus Kbps links in the ARPANET)? How could we cope? This is a paper about the use of field equations (e.g. gravitational, electrical, magnetic, strong and weak atomic and so forth) as a future model for managing network traffic. We believe that in the future, one could move from this model to a very general prescriptive technique for designing network control on different timescales, including traffic engineering and the set of admission and congestion control laws. We also speculate about the use of the same idea in wireless networks.

/pdf/towards-a-field-theory-for-networks-570lge97q6.pdf

Jon Crowcroft

Papers

Wi-Fi Wardriving Studies Must Account for Important Statistical Issues.

C3PO: Computation Congestion Control (PrOactive) - an algorithm for dynamic diffusion of ephemeral in-network services

Message from the workshop on the future of social networking

Perspectives on Negative Research Results in Pervasive Computing

Towards a Field Theory for Networks