Structural information content of networks: Graph entropy based on local vertex functionals

In this paper we define the structural information content of graphs as their corresponding graph entropy. This definition is based on local vertex functionals obtained by calculating-spheres via the algorithm of Dijkstra. We prove that the graph entropy and, hence, the local vertex functionals can be computed with polynomial time complexity enabling the application of our measure for large graphs. In this paper we present numerical results for the graph entropy of chemical graphs and discuss resulting properties. (C) 2007 Elsevier Ltd. All rights reserved.

A Visual Analytics Framework for Strategic Airlift Decision Making

The foundational concept of Network Enabled Capability relies on effective, timely information sharing. This information is used in analysis, trade and scenario studies, and ultimately decision-making. In this paper, the concept of visual analytics is explored as an enabler to facilitate rapid, defensible, and superior decision-making. By coupling analytical reasoning with the exceptional human capability to rapidly internalize and understand visual data, visual analytics allows individual and collaborative decision-making to occur in the face of vast and disparate data, time pressures, and uncertainty. An example visual analytics framework is presented in the form of a decision-making environment centered on the Lockheed C-5A and C-5M aircraft. This environment allows rapid trade studies to be conducted on design, logistics, and capability within the aircraft?s operational roles. Through this example, the use of a visual analytics decision-making environment within a military environment is demonstrated.

The Forgiving Graph: a distributed data structure for low stretch under adversarial attack

We consider the problem of self-healing in peer-to-peer networks that are under repeated attack by an omniscient adversary. We assume that, over a sequence of rounds, an adversary either inserts a node with arbitrary connections or deletes an arbitrary node from the network. The network responds to each such change by quick “repairs,” which consist of adding or deleting a small number of edges. These repairs essentially preserve closeness of nodes after adversarial deletions, without increasing node degrees by too much, in the following sense. At any point in the algorithm, nodes v and w whose distance would have been l in the graph formed by considering only the adversarial insertions (not the adversarial deletions), will be at distance at most l log n in the actual graph, where n is the total number of vertices seen so far. Similarly, at any point, a node v whose degree would have been d in the graph with adversarial insertions only, will have degree at most 3d in the actual graph. Our distributed data structure, which we call the Forgiving Graph, has low latency and bandwidth requirements. The Forgiving Graph improves on the Forgiving Tree distributed data structure from Hayes et al. (2008) in the following ways: 1) it ensures low stretch over all pairs of nodes, while the Forgiving Tree only ensures low diameter increase; 2) it handles both node insertions and deletions, while the Forgiving Tree only handles deletions; 3) it requires only a very simple and minimal initialization phase, while the Forgiving Tree initially requires construction of a spanning tree of the network.

The Forgiving Graph:A distributed data structure for low stretch under adversarial attack

We consider the problem of self-healing in peer-to-peer networks that are under repeated attack by an omniscient adversary. We assume that, over a sequence of rounds, an adversary either inserts a node with arbitrary connections or deletes an arbitrary node from the network. The network responds to each such change by quick "repairs," which consist of adding or deleting a small number of edges. These repairs essentially preserve closeness of nodes after adversarial deletions,without increasing node degrees by too much, in the following sense. At any point in the algorithm, nodes v and w whose distance would have been - in the graph formed by considering only the adversarial insertions (not the adversarial deletions), will be at distance at most - log n in the actual graph, where n is the total number of vertices seen so far. Similarly, at any point, a node v whose degreewould have been d in the graph with adversarial insertions only, will have degree at most 3d in the actual graph. Our distributed data structure, which we call the Forgiving Graph, has low latency and bandwidth requirements. The Forgiving Graph improves on the Forgiving Tree distributed data structure from Hayes et al. (2008) in the following ways: 1) it ensures low stretch over all pairs of nodes, while the Forgiving Tree only ensures low diameter increase; 2) it handles both node insertions and deletions, while the Forgiving Tree only handles deletions; 3) it requires only a very simple and minimal initialization phase, while the Forgiving Tree initially requires construction of a spanning tree of the network. © Springer-Verlag 2012.

Macroscopic bound entanglement in thermal graph states

We address the presence of bound entanglement in strongly interacting spin systems at thermal equilibrium. In particular, we consider thermal graph states composed of an arbitrary number of particles. We show that for a certain range of temperatures no entanglement can be extracted by means of local operations and classical communication, even though the system is still entangled. This is found by harnessing the independence of the entanglement in some bipartitions of such states with the system's size. Specific examples for one- and two-dimensional systems are given. Our results thus prove the existence of thermal bound entanglement in an arbitrary large spin system with finite-range local interactions.

NanoStreams: Advancing the Hardware and Software Stack for Real-Time Analytics on Fast Data Streams

NanoStreams is a consortium project funded by the European Commission under its FP7 programme and is a major effort to address the challenges of processing vast amounts of data in real-time, with a markedly lower carbon footprint than the state of the art. The project addresses both the energy challenge and the high-performance required by emerging applications in real-time streaming data analytics. NanoStreams achieves this goal by designing and building disruptive micro-server solutions incorporating real-silicon prototype micro-servers based on System-on-Chip and reconfigurable hardware technologies.

On the Viability of Microservers for Financial Analytics

Energy consumption and total cost of ownership are daunting challenges for Datacenters, because they scale disproportionately with performance. Datacenters running financial analytics may incur extremely high operational costs in order to meet performance and latency requirements of their hosted applications. Recently, ARM-based microservers have emerged as a viable alternative to high-end servers, promising scalable performance via scale-out approaches and low energy consumption. In this paper, we investigate the viability of ARM-based microservers for option pricing, using the Monte Carlo and Binomial Tree kernels. We compare an ARM-based microserver against a state-of-the-art x86 server. We define application-related but platform-independent energy and performance metrics to compare those platforms fairly in the context of datacenters for financial analytics and give insight on the particular requirements of option pricing. Our experiments show that through scaling out energyefficient compute nodes within a 2U rack-mounted unit, an ARM-based microserver consumes as little as about 60% of the energy per option pricing compared to an x86 server, despite having significantly slower cores. We also find that the ARM microserver scales enough to meet a high fraction of market throughput demand, while consuming up to 30% less energy than an Intel server