Exploiting the Path Propagation Time in Multipath Transmission with FEC

We consider a transmission of a delay-sensitive data stream from a single source to a single destination. The reliability of this transmission may suffer from bursty packet losses - the predominant type of failures in today's Internet. An effective and well studied solution to this problem is to protect the data by a Forward Error Correction (FEC) code and send the FEC packets over multiple paths. In this paper we show that the performance of such a multipath FEC scheme can often be further improved. Our key observation is that the propagation times on the available paths often significantly differ, usually by 10-100ms. We propose to exploit these differences by appropriate packet scheduling that we call `Spread'. We evaluate our solution with a precise, analytical formulation and trace-driven simulations. Our studies show that Spread substantially outperforms the state-of-the-art solutions. It typically achieves two- to five-fold improvement (reduction) in the effective loss rate. Or conversely, keeping the same level of effective loss rate, Spread significantly decreases the observed delays and helps fighting the delay jitter

Error and Attack Tolerance of Layered Complex Networks

Many complex systems may be described not by one, but by a number of complex networks mapped one on the other in a multilayer structure. The interactions and dependencies between these layers cause that what is true for a distinct single layer does not necessarily reflect well the state of the entire system. In this paper we study the robustness of three real-life examples of two-layer complex systems that come from the fields of communication (the Internet), transportation (the European railway system) and biology (the human brain). In order to cover the whole range of features specific to these systems, we focus on two extreme policies of system's response to failures, no rerouting and full rerouting. Our main finding is that multilayer systems are much more vulnerable to errors and intentional attacks than they seem to be from a single layer perspective.Comment: 5 pages, 3 figure

How Much Confidence Do We Have in a MRI Tractography Experiment?

When performing a tractography experiment it is essential to know whether a reconstructed tract results from the diffusion signal itself or from some random effect or noise. In this study, we introduce a way to associate to every connection a confidence level. The reason why the latter greatly varies with the length of the tract is analyzed. We use this method to filter out the connections likely to be the result of noise and show the effect on the connectivity of the human visual system

Imaging the Brain Neuronal Network with Diffusion MRI: A Way to Understand Its Global Architecture

In order to better understand the high complexity of the brain, the detailed study of its individual components clearly seems insufficient. The backbone of complexity in the nervous system is composed of the large scale architectural characteristics of the neuronal network. Newly, by the advent of MR tractography, its investigation is accessible. We report on two important network characteristics that were already guessed from functional investigations and animal ex vivo studies, but never directly addressed in the human subject, ie the small world and hierarchical architecture of the human long-range brain axonal network

Enhancing Stratified Graph Sampling Algorithms based on Approximate Degree Distribution

Sampling technique has become one of the recent research focuses in the graph-related fields. Most of the existing graph sampling algorithms tend to sample the high degree or low degree nodes in the complex networks because of the characteristic of scale-free. Scale-free means that degrees of different nodes are subject to a power law distribution. So, there is a significant difference in the degrees between the overall sampling nodes. In this paper, we propose an idea of approximate degree distribution and devise a stratified strategy using it in the complex networks. We also develop two graph sampling algorithms combining the node selection method with the stratified strategy. The experimental results show that our sampling algorithms preserve several properties of different graphs and behave more accurately than other algorithms. Further, we prove the proposed algorithms are superior to the off-the-shelf algorithms in terms of the unbiasedness of the degrees and more efficient than state-of-the-art FFS and ES-i algorithms.Comment: 10 pages, 23 figures, the concept of approximate degree distribution, scale-free networks, graph sampling methods, stratified technolog

Investigating the topology of interacting networks - Theory and application to coupled climate subnetworks

Network theory provides various tools for investigating the structural or functional topology of many complex systems found in nature, technology and society. Nevertheless, it has recently been realised that a considerable number of systems of interest should be treated, more appropriately, as interacting networks or networks of networks. Here we introduce a novel graph-theoretical framework for studying the interaction structure between subnetworks embedded within a complex network of networks. This framework allows us to quantify the structural role of single vertices or whole subnetworks with respect to the interaction of a pair of subnetworks on local, mesoscopic and global topological scales. Climate networks have recently been shown to be a powerful tool for the analysis of climatological data. Applying the general framework for studying interacting networks, we introduce coupled climate subnetworks to represent and investigate the topology of statistical relationships between the fields of distinct climatological variables. Using coupled climate subnetworks to investigate the terrestrial atmosphere's three-dimensional geopotential height field uncovers known as well as interesting novel features of the atmosphere's vertical stratification and general circulation. Specifically, the new measure "cross-betweenness" identifies regions which are particularly important for mediating vertical wind field interactions. The promising results obtained by following the coupled climate subnetwork approach present a first step towards an improved understanding of the Earth system and its complex interacting components from a network perspective

Expression of homothorax and extradenticle mRNA in the legs of the crustacean Parhyale hawaiensis: evidence for a reversal of gene expression regulation in the pancrustacean lineage

In Drosophila leg development, the extradenticle (exd) gene is expressed ubiquitously and its co-factor homothorax (hth) is restricted to the proximal leg portion. This condition is conserved in other insect species but is reversed in chelicerates and myriapods. As the region of co-expression does not differ in the two groups and transcripts from both are necessary for function, this difference in expression is likely to be functionally neutral. Here, we report the expression patterns of exd and hth in a crustacean, the amphipod shrimp Parhyale hawaiensis. The patterns in P. hawaiensis are similar to the insect patterns, supporting the close relationship between crustaceans and insects in the taxon Tetraconata. However, mRNA expression of exd in P. hawaiensis is weak in the distal leg parts, thus being intermediate between the complete lack of distal exd expression in chelicerates and myriapods and the strong distal exd expression in insects. Our data suggest that the reversal of the gene expression regulation of hth and exd occurred in the pancrustacean lineage

The Glial Regenerative Response to Central Nervous System Injury Is Enabled by Pros-Notch and Pros-NFκB Feedback

Organisms are structurally robust, as cells accommodate changes preserving structural integrity and function. The molecular mechanisms underlying structural robustness and plasticity are poorly understood, but can be investigated by probing how cells respond to injury. Injury to the CNS induces proliferation of enwrapping glia, leading to axonal re-enwrapment and partial functional recovery. This glial regenerative response is found across species, and may reflect a common underlying genetic mechanism. Here, we show that injury to the Drosophila larval CNS induces glial proliferation, and we uncover a gene network controlling this response. It consists of the mutual maintenance between the cell cycle inhibitor Prospero (Pros) and the cell cycle activators Notch and NFκB. Together they maintain glia in the brink of dividing, they enable glial proliferation following injury, and subsequently they exert negative feedback on cell division restoring cell cycle arrest. Pros also promotes glial differentiation, resolving vacuolization, enabling debris clearance and axonal enwrapment. Disruption of this gene network prevents repair and induces tumourigenesis. Using wound area measurements across genotypes and time-lapse recordings we show that when glial proliferation and glial differentiation are abolished, both the size of the glial wound and neuropile vacuolization increase. When glial proliferation and differentiation are enabled, glial wound size decreases and injury-induced apoptosis and vacuolization are prevented. The uncovered gene network promotes regeneration of the glial lesion and neuropile repair. In the unharmed animal, it is most likely a homeostatic mechanism for structural robustness. This gene network may be of relevance to mammalian glia to promote repair upon CNS injury or disease