A Probabilistic Analysis of Kademlia Networks

Kademlia is currently the most widely used searching algorithm in P2P (peer-to-peer) networks. This work studies an essential question about Kademlia from a mathematical perspective: how long does it take to locate a node in the network? To answer it, we introduce a random graph K and study how many steps are needed to locate a given vertex in K using Kademlia's algorithm, which we call the routing time. Two slightly different versions of K are studied. In the first one, vertices of K are labelled with fixed IDs. In the second one, vertices are assumed to have randomly selected IDs. In both cases, we show that the routing time is about c*log(n), where n is the number of nodes in the network and c is an explicitly described constant.Comment: ISAAC 201

Knowledge is at the Edge! How to Search in Distributed Machine Learning Models

With the advent of the Internet of Things and Industry 4.0 an enormous amount of data is produced at the edge of the network. Due to a lack of computing power, this data is currently send to the cloud where centralized machine learning models are trained to derive higher level knowledge. With the recent development of specialized machine learning hardware for mobile devices, a new era of distributed learning is about to begin that raises a new research question: How can we search in distributed machine learning models? Machine learning at the edge of the network has many benefits, such as low-latency inference and increased privacy. Such distributed machine learning models can also learn personalized for a human user, a specific context, or application scenario. As training data stays on the devices, control over possibly sensitive data is preserved as it is not shared with a third party. This new form of distributed learning leads to the partitioning of knowledge between many devices which makes access difficult. In this paper we tackle the problem of finding specific knowledge by forwarding a search request (query) to a device that can answer it best. To that end, we use a entropy based quality metric that takes the context of a query and the learning quality of a device into account. We show that our forwarding strategy can achieve over 95% accuracy in a urban mobility scenario where we use data from 30 000 people commuting in the city of Trento, Italy.Comment: Published in CoopIS 201

An Optimal Broadcast Algorithm for Content-Addressable Networks

International audienceStructured peer-to-peer networks are powerful underlying structures for communication and storage systems in large-scale setting. In the context of the Content-Addressable Network (CAN), this paper addresses the following challenge: how to perform an efficient broadcast while the local view of the network is restricted to a set of neighbours? In existing approaches, either the broadcast is inefficient (there are dupli- cated messages) or it requires to maintain a particular structure among neighbours, e.g. a spanning tree. We define a new broadcast primitive for CAN that sends a minimum number of messages while covering the whole network, without any global knowledge. Currently, no other al- gorithm achieves those two goals in the context of CAN. In this sense, the contribution we propose in this paper is threefold. First, we pro- vide an algorithm that sends exactly one message per recipient without building a global view of the network. Second, we prove the absence of duplicated messages and the coverage of the whole network when using this algorithm. Finally, we show the practical benefits of the algorithm throughout experiments

TLR2 and Nod2 Mediate Resistance or Susceptibility to Fatal Intracellular Ehrlichia Infection in Murine Models of Ehrlichiosis

Our murine models of human monocytic ehrlichiosis (HME) have shown that severe and fatal ehrlichiosis is due to generation of pathogenic T cell responses causing immunopathology and multi-organ failure. However, the early events in the liver, the main site of infection, are not well understood. In this study, we examined the liver transcriptome during the course of lethal and nonlethal infections caused by Ixodes ovatus Ehrlichia and Ehrlichia muris, respectively. On day 3 post-infection (p.i.), although most host genes were down regulated in the two groups of infected mice compared to naïve counterparts, lethal infection induced significantly higher expression of caspase 1, caspase 4, nucleotide binding oligomerization domain-containing proteins (Nod1), tumor necrosis factor-alpha, interleukin 10, and CCL7 compared to nonlethal infection. On day 7 p.i., lethal infection induced highly significant upregulation of type-1 interferon, several inflammatory cytokines and chemokines, which was associated with increased expression levels of Toll-like receptor-2 (TLR2), Nod2, MyD88, nuclear factor-kappa B (NF-kB), Caspase 4, NLRP1, NLRP12, Pycard, and IL-1β, suggesting enhanced TLR signals and inflammasomes activation. We next evaluated the participation of TLR2 and Nod2 in the host response during lethal Ehrlichia infection. Although lack of TLR2 impaired bacterial elimination and increased tissue necrosis, Nod2 deficiency attenuated pathology and enhanced bacterial clearance, which correlated with increased interferon-γ and interleukin-10 levels and a decreased frequency of pathogenic CD8+ T cells in response to lethal infection. Thus, these data indicate that Nod2, but not TLR2, contributes to susceptibility to severe Ehrlichia-induced shock. Together, our studies provide, for the first time, insight into the diversity of host factors and novel molecular pathogenic mechanisms that may contribute to severe HME. © 2013 Chattoraj et al

Preparation, characterization and catalytic applications of ZrO2 supported on low cost SBA-15

This work presents some applications of ZrO2 supported over SBA-15 silica as promoter of sulfated zirconia and as support from CuO/CeO 2 catalytic system for preferential oxidation of CO to CO2 in hydrogen rich streams, used as feed for proton exchange membrane fuel cells (PEMFC). Different amounts of ZrO2, from 10 to 30 wt.% were incorporated. These prepared materials were characterized by powder XRD, adsorption-desorption of N2 at 77 K, transmission and scanning electron microscopy (TEM and SEM) and X-rays photoelectron spectroscopy (XPS). The acidity was studied by thermo-programmed desorption of ammonia (NH 3-TPD). These materials were tested, after treatment with H 2SO4, by 2-propanol dehydration and 1-butene isomerization catalytic tests. The samples were found quite good catalyst with strong acid sites, the sample with 20 wt.% of ZrO2 being the better performing sample. Finally this material was successfully used as support for a CuO/CeO2 system, with 6 wt.% of Cu and 20 wt.% of Ce. The resulting catalyst was tested in the preferential oxidation of CO (CO-PROX) attaining conversions close to 100% and high selectivity to CO2