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

In this paper, we present a vision of the future of heterogeneous cloud computing. Ours is a cleanslate approach, sweeping away decades of accreted system software. We believe the advent of the latest technology discontinuity---the move to the virtual cloud---makes this a necessary step to take, but one capable of delivering significant benefits in the security, reliability and efficiency of our digital infrastructure at all scales.We motivate this vision by presenting two challenges arising in different fields yet with fundamental commonalities best addressed by a unifying software platform supporting devices ranging from virtual servers in the cloud, through desktops, to mobile smartphones. After drawing out this common ground, we describe our solution and its benefits. We then describe the initial steps we have taken toward our solution, the Mirage framework, as well as ongoing future work.

/pdf/multiscale-not-multicore-efficient-heterogeneous-cloud-pdchwifo39.pdf

Multiscale not multicore: efficient heterogeneous cloud computing

We present a federated, asynchronous, and $(\varepsilon, \delta)$-differentially private algorithm for PCA in the memory-limited setting. Our algorithm incrementally computes local model updates using a streaming procedure and adaptively estimates its $r$ leading principal components when only $\mathcal{O}(dr)$ memory is available with $d$ being the dimensionality of the data. We guarantee differential privacy via an input-perturbation scheme in which the covariance matrix of a dataset $\mathbf{X} \in \mathbb{R}^{d \times n}$ is perturbed with a non-symmetric random Gaussian matrix with variance in $\mathcal{O}\left(\left(\frac{d}{n}\right)^2 \log d \right)$, thus improving upon the state-of-the-art. Furthermore, contrary to previous federated or distributed algorithms for PCA, our algorithm is also invariant to permutations in the incoming data, which provides robustness against straggler or failed nodes. Numerical simulations show that, while using limited-memory, our algorithm exhibits performance that closely matches or outperforms traditional non-federated algorithms, and in the absence of communication latency, it exhibits attractive horizontal scalability.

/pdf/federated-principal-component-analysis-28n6vgfyok.pdf

Federated Principal Component Analysis

Forwarding data in scenarios without connectivity, Pocket Switched or Opportunistic networking can be difficult without a mobility model, or a history of node contacts. One of these scenarios is a disaster, where forwarding victim's medical information to a coordination point is critical for the good and fast intervention. "Time To Return" (TTR) forwarding was used in combination with mobile agents in MAETTS to provide early resource allocation during such emergencies. In this paper, we propose to apply TTR forwarding in Haggle to create an Electronic Triage Tag. This approach allows us to take advantage of short connectivity opportunities between nodes.

/pdf/using-haggle-to-create-an-electronic-triage-tag-z7c068f5mf.pdf

Using Haggle to create an electronic triage tag

"Catch-up", or on-demand access of previously broadcast TV content over the public Internet, constitutes a significant fraction of peak time network traffic. This paper analyses consumption patterns of nearly 6 million users of a nationwide deployment of a catch-up TV service, to understand the network support required. We find that catch-up has certain natural scaling properties compared to traditional TV: The on-demand nature spreads load over time, and users have much higher completion rates for content streams than previously reported. Users exhibit strong preferences for serialised content, and for specific genres. Exploiting this, we design a Speculative Content Offloading and Recording Engine (SCORE) that predictively records a personalised set of shows on user-local storage, and thereby offloads traffic that might result from subsequent catch-up access. Evaluations show that even with a modest storage of ~32GB, an oracle with complete knowledge of user consumption can save up to 74% of the energy, and 97% of the peak bandwidth compared to the current IP streaming-based architecture. In the best case, optimising for energy consumption, SCORE can recover more than 60% of the traffic and energy savings achieved by the oracle. Optimising purely for traffic rather than energy can reduce bandwith by an additional 5%.

/pdf/understanding-and-decreasing-the-network-footprint-of-catch-4yw1p0uo7h.pdf

Understanding and decreasing the network footprint of catch-up tv

Naming is a critical component of the internet architecture, and one whose complexity is often overlooked. As a global system, the DNS must satisfy millions of requests per second, while allowing distributed, delegated administration and maintenance. In this paper, we consider the design of the DNS and the widely distributed manner in which DNS records are published. We propose that the robustness and performance of the existing DNS could be dramatically improved by moving towards a centralized architecture while maintaining the existing client interface and delegated administration.

/pdf/the-main-name-system-an-exercise-in-centralized-computing-30pqyhyl44.pdf

Jon Crowcroft

Papers

Multiscale not multicore: efficient heterogeneous cloud computing

Federated Principal Component Analysis

Using Haggle to create an electronic triage tag

Understanding and decreasing the network footprint of catch-up tv

The main name system: an exercise in centralized computing