122,237 research outputs found
Toward End-to-End, Full-Stack 6G Terahertz Networks
Recent evolutions in semiconductors have brought the terahertz band in the
spotlight as an enabler for terabit-per-second communications in 6G networks.
Most of the research so far, however, has focused on understanding the physics
of terahertz devices, circuitry and propagation, and on studying physical layer
solutions. However, integrating this technology in complex mobile networks
requires a proper design of the full communication stack, to address link- and
system-level challenges related to network setup, management, coordination,
energy efficiency, and end-to-end connectivity. This paper provides an overview
of the issues that need to be overcome to introduce the terahertz spectrum in
mobile networks, from a MAC, network and transport layer perspective, with
considerations on the performance of end-to-end data flows on terahertz
connections.Comment: Published on IEEE Communications Magazine, THz Communications: A
Catalyst for the Wireless Future, 7 pages, 6 figure
Temporal Evolution of Both Premotor and Motor Cortical Tuning Properties Reflect Changes in Limb Biomechanics
A prevailing theory in the cortical control of limb movement posits that premotor cortex initiates a high-level motor plan that is transformed by the primary motor cortex (MI) into a low-level motor command to be executed. This theory implies that the premotor cortex is shielded from the motor periphery and therefore its activity should not represent the low-level features of movement. Contrary to this theory, we show that both dorsal (PMd) and ventral premotor (PMv) cortices exhibit population-level tuning properties that reflect the biomechanical properties of the periphery similar to those observed in M1. We recorded single-unit activity from M1, PMd, and PMv and characterized their tuning properties while six rhesus macaques performed a reaching task in the horizontal plane. Each area exhibited a bimodal distribution of preferred directions during execution consistent with the known biomechanical anisotropies of the muscles and limb segments. Moreover, these distributions varied in orientation or shape from planning to execution. A network model shows that such population dynamics are linked to a change in biomechanics of the limb as the monkey begins to move, specifically to the state-dependent properties of muscles. We suggest that, like M1, neural populations in PMd and PMv are more directly linked with the motor periphery than previously thought
Towards a Realistic Model for Failure Propagation in Interdependent Networks
Modern networks are becoming increasingly interdependent. As a prominent
example, the smart grid is an electrical grid controlled through a
communications network, which in turn is powered by the electrical grid. Such
interdependencies create new vulnerabilities and make these networks more
susceptible to failures. In particular, failures can easily spread across these
networks due to their interdependencies, possibly causing cascade effects with
a devastating impact on their functionalities.
In this paper we focus on the interdependence between the power grid and the
communications network, and propose a novel realistic model, HINT
(Heterogeneous Interdependent NeTworks), to study the evolution of cascading
failures. Our model takes into account the heterogeneity of such networks as
well as their complex interdependencies. We compare HINT with previously
proposed models both on synthetic and real network topologies. Experimental
results show that existing models oversimplify the failure evolution and
network functionality requirements, resulting in severe underestimations of the
cascading failures.Comment: 7 pages, 6 figures, to be published in conference proceedings of IEEE
International Conference on Computing, Networking and Communications (ICNC
2016), Kauai, US
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Large-effect flowering time mutations reveal conditionally adaptive paths through fitness landscapes in Arabidopsis thaliana.
Contrary to previous assumptions that most mutations are deleterious, there is increasing evidence for persistence of large-effect mutations in natural populations. A possible explanation for these observations is that mutant phenotypes and fitness may depend upon the specific environmental conditions to which a mutant is exposed. Here, we tested this hypothesis by growing large-effect flowering time mutants of Arabidopsis thaliana in multiple field sites and seasons to quantify their fitness effects in realistic natural conditions. By constructing environment-specific fitness landscapes based on flowering time and branching architecture, we observed that a subset of mutations increased fitness, but only in specific environments. These mutations increased fitness via different paths: through shifting flowering time, branching, or both. Branching was under stronger selection, but flowering time was more genetically variable, pointing to the importance of indirect selection on mutations through their pleiotropic effects on multiple phenotypes. Finally, mutations in hub genes with greater connectedness in their regulatory networks had greater effects on both phenotypes and fitness. Together, these findings indicate that large-effect mutations may persist in populations because they influence traits that are adaptive only under specific environmental conditions. Understanding their evolutionary dynamics therefore requires measuring their effects in multiple natural environments
Redundant neural vision systems: competing for collision recognition roles
Ability to detect collisions is vital for future robots that interact with humans in complex visual environments. Lobula giant movement detectors (LGMD) and directional selective neurons (DSNs) are two types of identified neurons found in the visual pathways of insects such as locusts. Recent modelling studies showed that the LGMD or grouped DSNs could each be tuned for collision recognition. In both biological and artificial vision systems, however, which one should play the collision recognition role and the way the two types of specialized visual neurons could be functioning together are not clear. In this modeling study, we compared the competence of the LGMD and the DSNs, and also investigate the cooperation of the two neural vision systems for collision recognition via artificial evolution. We implemented three types of collision recognition neural subsystems – the LGMD, the DSNs and a hybrid system which combines the LGMD and the DSNs subsystems together, in each individual agent. A switch gene determines which of the three redundant neural subsystems plays the collision recognition role. We found that, in both robotics and driving environments, the LGMD was able to build up its ability for collision recognition quickly and robustly therefore reducing the chance of other types of neural networks to play the same role. The results suggest that the LGMD neural network could be the ideal model to be realized in hardware for collision recognition
Fast Cell Discovery in mm-wave 5G Networks with Context Information
The exploitation of mm-wave bands is one of the key-enabler for 5G mobile
radio networks. However, the introduction of mm-wave technologies in cellular
networks is not straightforward due to harsh propagation conditions that limit
the mm-wave access availability. Mm-wave technologies require high-gain antenna
systems to compensate for high path loss and limited power. As a consequence,
directional transmissions must be used for cell discovery and synchronization
processes: this can lead to a non-negligible access delay caused by the
exploration of the cell area with multiple transmissions along different
directions.
The integration of mm-wave technologies and conventional wireless access
networks with the objective of speeding up the cell search process requires new
5G network architectural solutions. Such architectures introduce a functional
split between C-plane and U-plane, thereby guaranteeing the availability of a
reliable signaling channel through conventional wireless technologies that
provides the opportunity to collect useful context information from the network
edge.
In this article, we leverage the context information related to user
positions to improve the directional cell discovery process. We investigate
fundamental trade-offs of this process and the effects of the context
information accuracy on the overall system performance. We also cope with
obstacle obstructions in the cell area and propose an approach based on a
geo-located context database where information gathered over time is stored to
guide future searches. Analytic models and numerical results are provided to
validate proposed strategies.Comment: 14 pages, submitted to IEEE Transaction on Mobile Computin
Sensor Deployment for Network-like Environments
This paper considers the problem of optimally deploying omnidirectional
sensors, with potentially limited sensing radius, in a network-like
environment. This model provides a compact and effective description of complex
environments as well as a proper representation of road or river networks. We
present a two-step procedure based on a discrete-time gradient ascent algorithm
to find a local optimum for this problem. The first step performs a coarse
optimization where sensors are allowed to move in the plane, to vary their
sensing radius and to make use of a reduced model of the environment called
collapsed network. It is made up of a finite discrete set of points,
barycenters, produced by collapsing network edges. Sensors can be also
clustered to reduce the complexity of this phase. The sensors' positions found
in the first step are then projected on the network and used in the second
finer optimization, where sensors are constrained to move only on the network.
The second step can be performed on-line, in a distributed fashion, by sensors
moving in the real environment, and can make use of the full network as well as
of the collapsed one. The adoption of a less constrained initial optimization
has the merit of reducing the negative impact of the presence of a large number
of local optima. The effectiveness of the presented procedure is illustrated by
a simulated deployment problem in an airport environment
Information content of colored motifs in complex networks
We study complex networks in which the nodes of the network are tagged with
different colors depending on the functionality of the nodes (colored graphs),
using information theory applied to the distribution of motifs in such
networks. We find that colored motifs can be viewed as the building blocks of
the networks (much more so than the uncolored structural motifs can be) and
that the relative frequency with which these motifs appear in the network can
be used to define the information content of the network. This information is
defined in such a way that a network with random coloration (but keeping the
relative number of nodes with different colors the same) has zero color
information content. Thus, colored motif information captures the
exceptionality of coloring in the motifs that is maintained via selection. We
study the motif information content of the C. elegans brain as well as the
evolution of colored motif information in networks that reflect the interaction
between instructions in genomes of digital life organisms. While we find that
colored motif information appears to capture essential functionality in the C.
elegans brain (where the color assignment of nodes is straightforward) it is
not obvious whether the colored motif information content always increases
during evolution, as would be expected from a measure that captures network
complexity. For a single choice of color assignment of instructions in the
digital life form Avida, we find rather that colored motif information content
increases or decreases during evolution, depending on how the genomes are
organized, and therefore could be an interesting tool to dissect genomic
rearrangements.Comment: 21 pages, 8 figures, to appear in Artificial Lif
Phototaxic foraging of the archaepaddler, a hypothetical deep-sea species
An autonomous agent (animat, hypothetical animal), called the (archae) paddler, is simulated in sufficient detail to regard its simulated aquatic locomotion (paddling) as physically possible. The paddler is supposed to be a model of an animal that might exist, although it is perfectly possible to view it as a model of a robot that might be built. The agent is assumed to navigate in a simulated deep-sea environment, where it hunts autoluminescent prey. It uses a biologically inspired phototaxic foraging-strategy, while paddling in a layer just above the bottom. The advantage of this living space is that the navigation problem is essentially two-dimensional. Moreover, the deep-sea environment is physically simple (and hence easier to simulate): no significant currents, constant temperature, completely dark. A foraging performance metric is developed that circumvents the necessity to solve the travelling salesman problem. A parametric simulation study then quantifies the influence of habitat factors, such as the density of prey, and the body-geometry (e.g. placement, direction and directional selectivity of the eyes) on foraging success. Adequate performance proves to require a specific body-% geometry adapted to the habitat characteristics. In general performance degrades smoothly for modest changes of the geometric and habitat parameters, indicating that we work in a stable region of 'design space'. The parameters have to strike a compromise between on the one hand the ability to 'fixate' an attractive target, and on the other hand to 'see' as many targets at the same time as possible. One important conclusion is that simple reflex-based navigation can be surprisingly efficient. In the second place, performance in a global task (foraging) depends strongly on local parameters like visual direction-tuning, position of the eyes and paddles, etc. Behaviour and habitat 'mould' the body, and the body-geometry strongly influences performance. The resulting platform enables further testing of foraging strategies, or vision and locomotion theories stemming either from biology or from robotics
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