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

Computing is its own virtual world, bound only by its practitioners' imaginations and creativity.

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On the nature of computing

Due to the importance of communication networks to society, it is pertinent that these networks can withstand failures. Improving the robustness of a network usually requires installing redundant resources, which is very costly. Network providers are consequently less inclined to take robustness measures against failures that are unlikely to manifest, like several failures coinciding simultaneously in different geographic regions of their network. Protecting against single regional failures is more realistic. Network robustness, in terms of connectivity properties, also requires survivability algorithms to quickly reroute traffic affected by a network failure. In this paper, we consider a network embedded in a plane and study the problem of finding a circular region with radius r in that plane that would cause the biggest network degradation if all nodes within that particular region were to be destroyed. We propose a polynomial time algorithm for finding such critical regions. In addition, we develop a region-aware network augmentation technique to decrease the impact of a critical region failure. We subsequently consider the region-disjoint paths problem, which asks for two paths with minimum total weight between a source (s) and a destination (d) that cannot both be cut by a single circular regional failure of radius r (unless that failure includes s and d). We prove that the region-disjoint paths problem is NP-hard and propose and evaluate a heuristic algorithm for it.

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Critical regions and region-disjoint paths in a network

Emerging blockchain technology is a promising platform for implementing smart contracts. But there is a large class of applications, where blockchain is inadequate due to performance, scalability, and consistency requirements, and also due to language expressiveness and cost issues that are hard to solve. In this paper we explain that in some situations a centralised approach that does not rely on blockchain is a better alternative due to its simplicity, scalability, and performance. We suggest that in applications where decentralisation and transparency are essential, developers can advantageously combine the two approaches into hybrid solutions where some operations are enforced by enforcers deployed on–blockchains and the rest by enforcers deployed on trusted third parties.

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On and Off-Blockchain Enforcement Of Smart Contracts

In the modern world, the connectivity-as-we-go model is gaining popularity. Internet-of-Things (IoT) envisions a future in which human beings communicate with each other and with devices that have identities and virtual personalities, as well as sensing, processing, and networking capabilities, which will allow the developing of smart environments that operate with little or no human intervention. In such IoT environments, that will have battery-operated sensors and devices, energy efficiency becomes a fundamental concern. Thus, energy-efficient (EE) connectivity is gaining significant attention from the industrial and academic communities. This work aims to provide a comprehensive state-of-the-art survey on the energy efficiency of medium access control (MAC) protocols for cellular IoT. we provide a detailed discussion on the sources of energy dissipation at the MAC layer and then propose solutions. In addition to reviewing the proposed MAC designs, we also provide insights and suggestions that can guide practitioners and researchers in designing EE MAC protocols that extend the battery life of IoT devices. Finally, we identify a range of challenging open problems that should be solved for providing EE MAC services for IoT devices, along with corresponding opportunities and future research ideas to address these challenges.

Energy-Efficient MAC for Cellular IoT: State-of-the-Art, Challenges, and Standardization

Today's smartphones not only serve as a means of personal communication device, but are also fundamentally transforming the traditional understanding of crowdsourcing to an emerging type of participatory, task-oriented applications. It aims to support the so-called Citizen Science efforts for knowledge discovery, to understand the human behavior and measure/evaluate their opinions. In this paper, to facilitate the above scenarios, we propose a novel energy-efficient participatory crowdsourcing framework that meets the quality-of-information (QoI) requirements of the request in a distributed manner. Specifically, we extend the traditional framework of Gur Game for distributed decision-making to recommend the level of information contribution for each participant, by merging the multiple automaton chains into a single chain with multiple steady states. We evaluate the proposed scheme under the MIT social evolution data set, where the QoI requirements of the request are successfully achieved, with a satisfactory level of energy consumption fairness among participants, of negligible computational complexity. Finally, we explore the impact of community structure on the proposed algorithm, and propose a feasible method to facilitate the local data aggregation.

Jon Crowcroft

Papers

On the nature of computing

Critical regions and region-disjoint paths in a network

On and Off-Blockchain Enforcement Of Smart Contracts

Energy-Efficient MAC for Cellular IoT: State-of-the-Art, Challenges, and Standardization

QoI-Aware Energy-Efficient Participatory Crowdsourcing