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

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence. The aim of edge intelligence is to enhance the quality and speed of data processing and protect the privacy and security of the data. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this paper, we present a thorough and comprehensive survey on the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, namely edge caching, edge training, edge inference, and edge offloading, based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare and analyse the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, etc. This survey article provides a comprehensive introduction to edge intelligence and its application areas. In addition, we summarise the development of the emerging research field and the current state-of-the-art and discuss the important open issues and possible theoretical and technical solutions.

Edge Intelligence: Architectures, Challenges, and Applications

There is a critical need for new thinking regarding overload traffic management in sensor networks. It has now become clear that experimental sensor networks (e.g., mote networks) and their applications commonly experience periods of persistent congestion and high packet loss, and in some cases even congestion collapse. This significantly impacts application fidelity measured at the physical sinks, even under light to moderate traffic loads, and is a direct product of the funneling effect; that is, the many-to-one multihop traffic pattern that characterizes sensor network communications. Existing congestion control schemes are effective at mitigating congestion through rate control and packet drop mechanisms, but do so at the cost of significantly reducing application fidelity measured at the sinks. To address this problem we propose to exploit the availability of a small number of all wireless, multiradio virtual sinks that can be randomly distributed or selectively placed across the sensor field. Virtual sinks are capable of siphoning off data events from regions of the sensor field that are beginning to show signs of high traffic load. In this paper, we present the design, implementation, and evaluation of Siphon, a set of fully distributed algorithms that support virtual sink discovery and selection, congestion detection, and traffic redirection in sensor networks. Siphon is based on a Stargate implementation of virtual sinks that uses a separate longer range radio network (based on IEEE 802.11) to siphon events to one or more physical sinks, and a short-range mote radio to interact with the sensor field at siphon points. Results from analysis, simulation and an experimental 48 Mica2 mote testbed show that virtual sinks can scale mote networks by effectively managing growing traffic demands while minimizing any negative impact on application fidelity. Additionally, we show the scheme is competitive with respect to energy consumption compared to a network composed of only motes.

/pdf/overload-traffic-management-for-sensor-networks-33p08m6964.pdf

Overload traffic management for sensor networks

Solving sparse systems of linear equations is at the heart of scientific computing. Large sparse systems often arise in science and engineering problems. One such problem we consider in this paper is the steadystate analysis of Continuous Time Markov Chains (CTMCs). CTMCs are a widely used formalism for the performance analysis of computer and communication systems. A large variety of useful performance measures can be derived from a CTMC via the computation of its steady-state probabilities. A CTMC may be represented by a set of states and a transition rate matrix containing state transition rates as coefficients, and can be analysed using probabilistic model checking. However, CTMC models for realistic systems are very large. We address this largeness problem in this paper, by considering parallelisation of symbolic methods. In particular, we consider Multi-Terminal Binary Decision Diagrams (MTBDDs) to store CTMCs, and, using Jacobi iterative method, present a parallel method for the CTMC steady-state solution. Employing a 24-node processor bank, we report results of the sparse systems with over a billion equations and eighteen billion nonzeros.

/pdf/parallel-iterative-solution-method-for-large-sparse-linear-r1i2k54vsn.pdf

Parallel iterative solution method for large sparse linear equation systems

Optimal network planning is crucial to ensure viable investments. However, engineering analysis and cost assessment frequently occur independently of each other. Whereas considerable research has been undertaken on 5G networks, there is a lack of openly accessible tools that integrate the engineering and cost aspects, in a techno-economic assessment framework capable of providing geospatially-explicit network analytics. Consequently, this paper details an open-source python simulator for integrated modelling of 5G (pysim5G), that enables both engineering and cost metrics to be assessed in a single unified framework. The tool includes statistical analysis of radio interference to assess the system-level performance of 4G and 5G frequency band coexistence (including millimeter wave), while simultaneously quantifying the costs of ultra-dense 5G networks. An example application of this framework explores the techno-economics of 5G infrastructure sharing strategies, finding that total deployment costs can be reduced by 30% using either passive site sharing, or passive backhaul sharing, or by up to 50% via a multi-operator radio access network. The key contribution is a fully-tested, open-source software codebase, allowing users to undertake integrated techno-economic assessment of 5G deployments in a single geospatial framework.

/pdf/an-open-source-techno-economic-assessment-framework-for-5g-1nfi9r4mli.pdf

An Open-Source Techno-Economic Assessment Framework for 5G Deployment

Quality of Service (QoS) has been touted as a technological requirement for many different networks at many different times. However, very few (if any) schemes for providing it have ever been successful, despite a huge amount of research in the area of QoS provision. In this position paper we analyze some of the reasons why so many QoS mechanisms have failed to be widely deployed. We suggest two factors in this failure: the timeliness of QoS mechanisms (they rarely arrive when they are needed), and the inherent contradiction of layering QoS mechanisms over a best-effort network. We also give some thoughts on how future QoS research might increase its chances of successful deployment by better positioning itself relative to other developments in networking.

/pdf/qos-s-downfall-at-the-bottom-or-not-at-all-47cfh6tvwt.pdf

Jon Crowcroft

Papers

Edge Intelligence: Architectures, Challenges, and Applications

Overload traffic management for sensor networks

Parallel iterative solution method for large sparse linear equation systems

An Open-Source Techno-Economic Assessment Framework for 5G Deployment

QoS's downfall: at the bottom, or not at all!