33,558 research outputs found
Hidden geometric correlations in real multiplex networks
Real networks often form interacting parts of larger and more complex
systems. Examples can be found in different domains, ranging from the Internet
to structural and functional brain networks. Here, we show that these multiplex
systems are not random combinations of single network layers. Instead, they are
organized in specific ways dictated by hidden geometric correlations between
the individual layers. We find that these correlations are strong in different
real multiplexes, and form a key framework for answering many important
questions. Specifically, we show that these geometric correlations facilitate:
(i) the definition and detection of multidimensional communities, which are
sets of nodes that are simultaneously similar in multiple layers; (ii) accurate
trans-layer link prediction, where connections in one layer can be predicted by
observing the hidden geometric space of another layer; and (iii) efficient
targeted navigation in the multilayer system using only local knowledge, which
outperforms navigation in the single layers only if the geometric correlations
are sufficiently strong. Our findings uncover fundamental organizing principles
behind real multiplexes and can have important applications in diverse domains.Comment: Supplementary Materials available at
http://www.nature.com/nphys/journal/v12/n11/extref/nphys3812-s1.pd
Polyphonic Sound Event Detection by using Capsule Neural Networks
Artificial sound event detection (SED) has the aim to mimic the human ability
to perceive and understand what is happening in the surroundings. Nowadays,
Deep Learning offers valuable techniques for this goal such as Convolutional
Neural Networks (CNNs). The Capsule Neural Network (CapsNet) architecture has
been recently introduced in the image processing field with the intent to
overcome some of the known limitations of CNNs, specifically regarding the
scarce robustness to affine transformations (i.e., perspective, size,
orientation) and the detection of overlapped images. This motivated the authors
to employ CapsNets to deal with the polyphonic-SED task, in which multiple
sound events occur simultaneously. Specifically, we propose to exploit the
capsule units to represent a set of distinctive properties for each individual
sound event. Capsule units are connected through a so-called "dynamic routing"
that encourages learning part-whole relationships and improves the detection
performance in a polyphonic context. This paper reports extensive evaluations
carried out on three publicly available datasets, showing how the CapsNet-based
algorithm not only outperforms standard CNNs but also allows to achieve the
best results with respect to the state of the art algorithms
CliqueStream: an efficient and fault-resilient live streaming network on a clustered peer-to-peer overlay
Several overlay-based live multimedia streaming platforms have been proposed
in the recent peer-to-peer streaming literature. In most of the cases, the
overlay neighbors are chosen randomly for robustness of the overlay. However,
this causes nodes that are distant in terms of proximity in the underlying
physical network to become neighbors, and thus data travels unnecessary
distances before reaching the destination. For efficiency of bulk data
transmission like multimedia streaming, the overlay neighborhood should
resemble the proximity in the underlying network. In this paper, we exploit the
proximity and redundancy properties of a recently proposed clique-based
clustered overlay network, named eQuus, to build efficient as well as robust
overlays for multimedia stream dissemination. To combine the efficiency of
content pushing over tree structured overlays and the robustness of data-driven
mesh overlays, higher capacity stable nodes are organized in tree structure to
carry the long haul traffic and less stable nodes with intermittent presence
are organized in localized meshes. The overlay construction and fault-recovery
procedures are explained in details. Simulation study demonstrates the good
locality properties of the platform. The outage time and control overhead
induced by the failure recovery mechanism are minimal as demonstrated by the
analysis.Comment: 10 page
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