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

Notice of Violation of IEEE Publication Principles: Data Trading with Competitive Social Platforms: Outcomes are Mostly Privacy Welfare Damaging

ln-app advertising is a multi-billion dollar industry that is an essential part of the current digital ecosystem, and is amenable to sensitive consumer information often being sold downstream without the knowledge of consumers, and in many cases to their annoyance. While this practice, in cases, may result in long-term benefits for the consumers, it can result in serious information privacy (IP) breaches of very significant impact (e.g., breach of genetic data) in the short term. The question we raise through this article is: does the type of information being traded downstream play a role in the degree of IP risks generated? We investigate two general (one-many) information trading market structures between a single data aggregating seller (e.g., enterprise app) and multiple competing buyers (e.g., ad-networks, retailers), distinguished by mutually exclusive and privacy sanitized aggregated consumer data (information) types: (i) data entailing strategically complementary actions among buyers and (ii) data entailing strategically substituting actions among buyers. Our primary question of interest here is: trading which type of data might pose less information privacy risks for society? To this end, we show that at market equilibrium IP trading markets exhibiting strategic substitutes between buying firms pose lesser risks for IP in society, primarily because the ‘substitutes’ setting, in contrast to the ‘complements’ setting, economically incentivizes appropriate consumer data distortion by the seller in addition to restricting the proportion of buyers to which it sells. Moreover, we also show that irrespective of the data type traded by the seller, the likelihood of improved IP in society is higher if there is purposeful or free-riding based transfer/leakage of data between buying firms. This is because the seller finds itself economically incentivized to restrict the release of sanitized consumer data with respect to the span of its buyer space, as well as in improved data quality.

Privacy Risk is a Function of Information Type: Learnings for the Surveillance Capitalism Age

Peer-to-peer distributed virtual environments (DVE's) distribute state tracking and state transitions. Many DVE's - such as online games - require ways to fairly determine the outcome of probabilistic events. While trivial when a trusted third party is involved, resolving these actions fairly between adversaries without a trusted third party is much more difficult. This paper proposes the Pairwise Random Protocol (PRP), which uses secure coin flipping to enable adversaries to fairly determine the result of a probabilistic event without a trusted third party. Three different variations of PRP are presented, and the time impact and network overhead are examined. We conclude that PRP enables DVE's to distribute the work of determining probabilistic events between adversaries without loss of security or fairness, and with acceptable overhead.

https://www.microsoft.com/en-us/research/wp-content/uploads/2009/06/MillerCrowcroft-PRP2009.pdf

Probabilistic event resolution with the pairwise random protocol

This paper examines the window mechanism that has been widely used for flow and congestion control. The analysis shows that the mono-window model, which combines flow control and congestion control in a single window, can cause pipe breakdown after packet losses. A dual-window model is then presented, in which flow control and congestion control are decoupled. The dual-window model allows the traffic flow to be sustained while retransmission is being carried out. The dual-window model is particularly suitable for high-speed and long-delay networks where the pipelining of the packets is more crucial. The estimation of the window sizes, advance of the window edges, acknowledgement and retransmission are discussed in detail. Finally the performance of the dual-window model is examined with illustrated examples and by simulation.

http://iopscience.iop.org/article/10.1088/0967-1846/1/3/004/pdf

A dual-window model for flow and congestion control

This report documents the program and the outcomes of Dagstuhl Seminar 16281 "Network Latency Control in Data Centres". This seminar explored existing and future techniques for controlling data centre latency and thus explores research directions in the new field of data centre latency control in networking research. This need for a new research direction is motivated by the fact that traditional networking equipment and TCP-IP stacks were designed for wide-area networks, where the goal is to maximize throughput, and the control loop between end systems is measured in 10s of milliseconds. Consequently, this seminar discussed new research direction for data center latency control across the entire software and hardware stack, including in-network solutions, end-host solutions, and others.

Jon Crowcroft

Papers

Notice of Violation of IEEE Publication Principles: Data Trading with Competitive Social Platforms: Outcomes are Mostly Privacy Welfare Damaging

Privacy Risk is a Function of Information Type: Learnings for the Surveillance Capitalism Age

Probabilistic event resolution with the pairwise random protocol

A dual-window model for flow and congestion control

Network Latency Control in Data Centres (Dagstuhl Seminar 16281)