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

Information-centric networks are an interesting new paradigm for distributing content on the Internet. They bring up many research challenges, such as addressing content by name, securing content, and wide-spread caching of content. Caching has caught a lot of attention in the research community, but a lot of the work suffers from a poor understanding of the different metrics with which caching performance can be measured. In this paper we not only present a comprehensive overview of different caching metrics that have been proposed for information-centric networks, but also propose the coupling factor as a new metric to capture the relation- ship between content popularity and network topology. As we show, many commonly used metrics have several failure modes which are largely ignored in literature. We identify these problems and propose remedies and new metrics to address these failures. Our work highlights the fundamental differences between information-centric caches and "traditional" cache networks and we demonstrate the need for a systematic understanding of the metrics for information- centric caching. We also discuss how experimental work should be done when evaluating networks of caches.

/pdf/you-really-need-a-good-ruler-to-measure-caching-performance-4gc65wl8o1.pdf

You Really Need A Good Ruler to Measure Caching Performance in Information-Centric Networks.

Recently academia and industry has growing interest in the sixth generation network, which aims to support a rich range of applications with higher capacity and greater coverage than existing 5G connections. One of such promising applications that can benefit from 6G is Decentralised Federated Learning, a privacy-preserving machine learning paradigm. Also, it relies heavily on peer-to-peer mobile connection among edge and mobile devices, instead of a powerful central server on the cloud. However, the data and device heterogeneity, and highly dynamic environment in mobile networks pose challenges to the performance of federated learning. In this paper, we propose a data redistribution phase that balances the data distribution on different participating devices to a certain degree, which can further increase the system performance in the training phase. To derive our method, we first model this problem as a bargaining game, the equilibrium of which is formalised as an optimisation problem. Then we propose two algorithms to solve it: a centralised one, and a decentralised one that each participant executes without centralised coordination. We further improve the energy efficiency of the decentralised algorithm by introducing several heuristics. We evaluate the proposed system with both simulation and DNN training tasks on large scale FEMNIST-based datasets.

Energy-Efficient and Fair IoT Data Distribution in Decentralised Federated Learning

Recent years have witnessed several initiatives on enabling Internet access to the next three billion people. Access to the Internet necessarily translates to access to its services. This means that the goal of providing Internet access requires ac- cess to its critical service infrastructure, which are currently hosted in the cloud. However, recent works have pointed out that the current cloud centric nature of the Internet is a fundamental barrier for Internet access in rural/remote areas as well as in developing regions. It is important to explore (low cost) solutions such as micro cloud infrastructures that can provide services at the edge of the network (potentially on demand), right near the users. In this paper, we present Cloudrone- a preliminary idea of deploying a lightweight micro cloud infrastructure in the sky using indigenously built low cost drones, single board computers and lightweight Operating System virtualization technologies. Our paper lays out the preliminary ideas on such a system that can be instantaneously deployed on demand. We describe an initial design of the Cloudrone and provide a preliminary evaluation of the proposed system mainly focussed on the scalability issues of supporting multiple services and users.

Cloudrone: Micro Clouds in the Sky

Low-Power Wide Area Network (LPWAN) technologies are characterized by
very low physical layer bit and message transmission rates. Moreover,
a response to a message sent by an LPWAN device may often only be
received after a significant delay. As a result, Round-Trip Time (RTT)
values in LPWAN are often (sometimes, significantly) greater than
typical default values of Retransmission TimeOut (RTO) algorithms.
Furthermore, buffering at network elements such as radio gateways may
interact negatively with LPWAN technology transmission mechanisms,
potentially exacerbating RTTs by up to several orders of magnitude.
This document provides guidance for RTO settings in LPWAN, and
describes an experimental dual RTO algorithm for LPWAN.

RTO considerations in LPWAN

IP multicasting (Cheriton and Deering, 1985) appears to achieve a level of simplicity by extending the IP unicast addressing model (historically the class A, B, and C net numbers) from the mask and longest match schemes of CIDR (Braun et al. 1993), with a new classful address space, class D. The routing systems have been also built in a deceptively simple way in one of three manners-either broadcast and prune, destination list based tree computation or single centered trees. The multicast service creates the illusion of a spectrum that one can tune in to, as an application writer. Thus, many have seen the multicast pilot service, Mbone, as a worldwide Ethernet, where simple distributed algorithms can be used to allocate "wavelengths" and advertise them through "broadcasting" on a channel associated with a spectrum. These three pieces of the picture have tempted people to construct a distributed architecture for a number of next level services that cannot work at more than a modest scale, since they ignore the basic spirit of location independence for senders and receivers of IP packets. The problem is that many of these services are attempting to group activities at source, when it is only at join time that user grouping becomes apparent. This memo proposes approaches to solve some current multicast problems rather statically with DNS (Postel 1994) and the URL based approach, and avoid the pitfalls of trying to use address allocation to implement traffic aggregation for different sources or aggregation of multicast route policy control through control of such aggregated sources.

Jon Crowcroft

Papers

You Really Need A Good Ruler to Measure Caching Performance in Information-Centric Networks.

Energy-Efficient and Fair IoT Data Distribution in Decentralised Federated Learning

Cloudrone: Micro Clouds in the Sky

RTO considerations in LPWAN

Static Internet multicast