13,389 research outputs found

    Active Topology Inference using Network Coding

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    Our goal is to infer the topology of a network when (i) we can send probes between sources and receivers at the edge of the network and (ii) intermediate nodes can perform simple network coding operations, i.e., additions. Our key intuition is that network coding introduces topology-dependent correlation in the observations at the receivers, which can be exploited to infer the topology. For undirected tree topologies, we design hierarchical clustering algorithms, building on our prior work. For directed acyclic graphs (DAGs), first we decompose the topology into a number of two-source, two-receiver (2-by-2) subnetwork components and then we merge these components to reconstruct the topology. Our approach for DAGs builds on prior work on tomography, and improves upon it by employing network coding to accurately distinguish among all different 2-by-2 components. We evaluate our algorithms through simulation of a number of realistic topologies and compare them to active tomographic techniques without network coding. We also make connections between our approach and alternatives, including passive inference, traceroute, and packet marking

    Mining Network Events using Traceroute Empathy

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    In the never-ending quest for tools that enable an ISP to smooth troubleshooting and improve awareness of network behavior, very much effort has been devoted in the collection of data by active and passive measurement at the data plane and at the control plane level. Exploitation of collected data has been mostly focused on anomaly detection and on root-cause analysis. Our objective is somewhat in the middle. We consider traceroutes collected by a network of probes and aim at introducing a practically applicable methodology to quickly spot measurements that are related to high-impact events happened in the network. Such filtering process eases further in- depth human-based analysis, for example with visual tools which are effective only when handling a limited amount of data. We introduce the empathy relation between traceroutes as the cornerstone of our formal characterization of the traceroutes related to a network event. Based on this model, we describe an algorithm that finds traceroutes related to high-impact events in an arbitrary set of measurements. Evidence of the effectiveness of our approach is given by experimental results produced on real-world data.Comment: 8 pages, 7 figures, extended version of Discovering High-Impact Routing Events using Traceroutes, in Proc. 20th International Symposium on Computers and Communications (ISCC 2015

    Electron tomography provides a direct link between the Payne effect and the inter-particle spacing of rubber composites.

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    Rubber-filler composites are a key component in the manufacture of tyres. The filler provides mechanical reinforcement and additional wear resistance to the rubber, but it in turn introduces non-linear mechanical behaviour to the material which most likely arises from interactions between the filler particles, mediated by the rubber matrix. While various studies have been made on the bulk mechanical properties and of the filler network structure (both imaging and by simulations), there presently does not exist any work directly linking filler particle spacing and mechanical properties. Here we show that using STEM tomography, aided by a machine learning image analysis procedure, to measure silica particle spacings provides a direct link between the inter-particle spacing and the reduction in shear modulus as a function of strain (the Payne effect), measured using dynamic mechanical analysis. Simulations of filler network formation using attractive, repulsive and non-interacting potentials were processed using the same method and compared with the experimental data, with the net result being that an attractive inter-particle potential is the most accurate way of modelling styrene-butadiene rubber-silica composite formation.L.S. and P.A.M thank Michelin for funding. The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement 291522-3DIMAGE.This is the final published version. It first appeared at http://www.nature.com/srep/2014/141209/srep07389/full/srep07389.html

    Classification of Radiology Reports Using Neural Attention Models

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    The electronic health record (EHR) contains a large amount of multi-dimensional and unstructured clinical data of significant operational and research value. Distinguished from previous studies, our approach embraces a double-annotated dataset and strays away from obscure "black-box" models to comprehensive deep learning models. In this paper, we present a novel neural attention mechanism that not only classifies clinically important findings. Specifically, convolutional neural networks (CNN) with attention analysis are used to classify radiology head computed tomography reports based on five categories that radiologists would account for in assessing acute and communicable findings in daily practice. The experiments show that our CNN attention models outperform non-neural models, especially when trained on a larger dataset. Our attention analysis demonstrates the intuition behind the classifier's decision by generating a heatmap that highlights attended terms used by the CNN model; this is valuable when potential downstream medical decisions are to be performed by human experts or the classifier information is to be used in cohort construction such as for epidemiological studies

    State Reform Dominates Boston Health Care Market Dynamics

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    Examines the impact of Massachusetts' 2006 healthcare reform, the recession, and other developments on Boston's healthcare market dynamics, including cost containment efforts, rate increases for the small group market, and pressure on the safety net
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