Loss Tomography in General Topologies with Network Coding

Network tomography infers internal network characteristics by sending and collecting probe packets from the network edge. Traditional tomographic techniques for general topologies typically use a mesh of multicast trees and/or unicast paths to cover the entire graph, which is suboptimal from the point of view of bandwidth efficiency and estimation accuracy. In this paper, we investigate an active probing method for link loss inference in a general topology, where multiple sources and receivers are used and intermediate nodes are equipped with network coding, in addition to unicast and multicast, capabilities. With our approach, each link is traversed by exactly one packet, which is in general a linear combination of the original probes. The receivers infer the loss rate on all links by observing not only the number but also the contents of the received probes. In this paper: (i) we propose an orientation algorithm that creates an acyclic graph with the maximum number of identifiable edges (ii) we define probe combining coding schemes and discuss some of their properties and (iii) we present simulation results over realistic topologies using Belief-Propagation (BP) algorithms

Nitrogenation and sintering of (Nd-Zr)Fe10Si2 tetragonal compounds for permanent magnets applications

International audienceNd(1-x)Zr(x)Fe10Si2 alloys have been prepared in the tetragonal ThMn12-type structure by arc-melting and melt-spinning and then nitrogenated to improve their magnetic properties. For x = 0.4 and 0.6 the Curie temperature and magnetic anisotropy fields increase from 280-300 ºC to about 390 ºC and from 2.8-3 T to 4.5-5 T respectively. The saturation magnetization remains almost unchanged. The nitrogenated powders were processed by spark plasma sintering (SPS) leading to compact pellets, which retain the full nitrogen content and magnetic properties up to 600 ºC, but segregated Fe-Si at elevated temperatures. Nitrogenation and SPS processing are, therefore, appropriate for sintering metastable materials such as (Nd,Zr)Fe10Si2 into compact material without loosing functional properties. This opens a way towards a new family of permanent magnets, lean of critical raw materials

On the Structural Properties of Social Networks and their Measurement-calibrated Synthetic Counterparts

Data-driven analysis of large social networks has attracted a great deal of research interest. In this paper, we investigate 120 real social networks and their measurement-calibrated synthetic counterparts generated by four well-known network models. We investigate the structural properties of the networks revealing the correlation profiles of graph metrics across various social domains (friendship networks, communication networks, and collaboration networks). We find that the correlation patterns differ across domains. We identify a non-redundant set of metrics to describe social networks. We study which topological characteristics of real networks the models can or cannot capture. We find that the goodness-of-fit of the network models depends on the domains. Furthermore, while 2K and stochastic block models lack the capability of generating graphs with large diameter and high clustering coefficient at the same time, they can still be used to mimic social networks relatively efficiently.Comment: To appear in International Conference on Advances in Social Networks Analysis and Mining (ASONAM '19), Vancouver, BC, Canad

High coercivity cobalt carbide nanoparticles processed via polyol reaction: A new permanent magnet material

Cobalt carbide nanoparticles were processed using polyol reduction chemistry that offers high product yields in a cost effective single-step process. Particles are shown to be acicular in morphology and typically assembled as clusters with room temperature coercivities greater than 4 kOe and maximum energy products greater than 20 KJ/m3. Consisting of Co3C and Co2C phases, the ratio of phase volume, particle size, and particle morphology all play important roles in determining permanent magnet properties. Further, the acicular particle shape provides an enhancement to the coercivity via dipolar anisotropy energy as well as offering potential for particle alignment in nanocomposite cores. While Curie temperatures are near 510K at temperatures approaching 700 K the carbide powders experience an irreversible dissociation to metallic cobalt and carbon thus limiting operational temperatures to near room temperature.Comment: Total 28 pages, 10 figures, and 1 tabl

Toward Rare-Earth-Free Permanent Magnets: A Combinatorial Approach Exploiting the Possibilities of Modeling, Shape Anisotropy in Elongated Nanoparticles, and Combinatorial Thin-Film Approach

The objective of the rare-earth free permanent magnets (REFREEPM) project is to develop a new generation of high-performance permanent magnets (PMs) without rare earths. Our approach is based on modeling using a combinatorial approach together with micromagnetic modeling and the realization of the modeled systems (I) by using a novel production of high-aspect-ratio (>5) nanostructrures (nanowires, nanorods, and nanoflakes) by exploiting the magnetic shape anisotropy of the constituents that can be produced via chemical nanosynthesis polyol process or electrodeposition, which can be consolidated with novel processes for a new generation of rare-earth free PMs with energy product in the range of 60 kJ/m3 < (BH)max < 160 kJ/m3 at room temperature, and (II) by using a high-throughput thin-film synthesis and high-throughput characterization approach to identify promising candidate materials that can be stabilized in a tetragonal or hexagonal structure by epitaxial growth on selected substrates, under various conditions of pressure, stoichiometry, and temperature. In this article, we report the progress so far in selected phases.This work is supported by European Commission (REFREEPERMAG project) grant number GA-NMP3-SL-2012-280670

Composition and Structure of a Large Online Social Network in the Netherlands

Limitations in data collection have long been an obstacle in research on friendship networks. Most earlier studies use either a sample of ego-networks, or complete network data on a relatively small group (e.g., a single organization). The rise of online social networking services such as Friendster and Facebook, however, provides researchers with opportunities to study friendship networks on a much larger scale. This study uses complete network data from Hyves, a popular online social networking service in the Netherlands, comprising over eight million members and over 400 million online friendship relations. In the first study of its kind for the Netherlands, I examine the structure of this network in terms of the degree distribution, characteristic path length, clustering, and degree assortativity. Results indicate that this network shares features of other large complex networks, but also deviates in other respects. In addition, a comparison with other online social networks shows that these networks show remarkable similarities

Estimating Systematic Risk in Real-World Networks

Abstract. Social, technical and business connections can all give rise to security risks. These risks can be substantial when individual compro-mises occur in combinations, and difficult to predict when some connec-tions are not easily observed. A significant and relevant challenge is to predict these risks using only locally-derivable information. We illustrate by example that this challenge can be met if some general topological features of the connection network are known. By simulat-ing an attack propagation on two large real-world networks, we identify structural regularities in the resulting loss distributions, from which we can relate various measures of a network’s risks to its topology. While de-riving these formulae requires knowing or approximating the connective structure of the network, applying them requires only locally-derivable information. On the theoretical side, we show that our risk-estimating methodology gives good approximations on randomly-generated scale-free networks with parameters approximating those in our study. Since many real-world networks are formed through preferential attachment mechanisms that yield similar scale-free topologies, we expect this methodology to have a wider range of applications to risk management whenever a large number of connections is involved