79,357 research outputs found
A Decentralized Mobile Computing Network for Multi-Robot Systems Operations
Collective animal behaviors are paradigmatic examples of fully decentralized
operations involving complex collective computations such as collective turns
in flocks of birds or collective harvesting by ants. These systems offer a
unique source of inspiration for the development of fault-tolerant and
self-healing multi-robot systems capable of operating in dynamic environments.
Specifically, swarm robotics emerged and is significantly growing on these
premises. However, to date, most swarm robotics systems reported in the
literature involve basic computational tasks---averages and other algebraic
operations. In this paper, we introduce a novel Collective computing framework
based on the swarming paradigm, which exhibits the key innate features of
swarms: robustness, scalability and flexibility. Unlike Edge computing, the
proposed Collective computing framework is truly decentralized and does not
require user intervention or additional servers to sustain its operations. This
Collective computing framework is applied to the complex task of collective
mapping, in which multiple robots aim at cooperatively map a large area. Our
results confirm the effectiveness of the cooperative strategy, its robustness
to the loss of multiple units, as well as its scalability. Furthermore, the
topology of the interconnecting network is found to greatly influence the
performance of the collective action.Comment: Accepted for Publication in Proc. 9th IEEE Annual Ubiquitous
Computing, Electronics & Mobile Communication Conferenc
Multiscale Information Decomposition: Exact Computation for Multivariate Gaussian Processes
Exploiting the theory of state space models, we derive the exact expressions
of the information transfer, as well as redundant and synergistic transfer, for
coupled Gaussian processes observed at multiple temporal scales. All of the
terms, constituting the frameworks known as interaction information
decomposition and partial information decomposition, can thus be analytically
obtained for different time scales from the parameters of the VAR model that
fits the processes. We report the application of the proposed methodology
firstly to benchmark Gaussian systems, showing that this class of systems may
generate patterns of information decomposition characterized by mainly
redundant or synergistic information transfer persisting across multiple time
scales or even by the alternating prevalence of redundant and synergistic
source interaction depending on the time scale. Then, we apply our method to an
important topic in neuroscience, i.e., the detection of causal interactions in
human epilepsy networks, for which we show the relevance of partial information
decomposition to the detection of multiscale information transfer spreading
from the seizure onset zone
MOLNs: A cloud platform for interactive, reproducible and scalable spatial stochastic computational experiments in systems biology using PyURDME
Computational experiments using spatial stochastic simulations have led to
important new biological insights, but they require specialized tools, a
complex software stack, as well as large and scalable compute and data analysis
resources due to the large computational cost associated with Monte Carlo
computational workflows. The complexity of setting up and managing a
large-scale distributed computation environment to support productive and
reproducible modeling can be prohibitive for practitioners in systems biology.
This results in a barrier to the adoption of spatial stochastic simulation
tools, effectively limiting the type of biological questions addressed by
quantitative modeling. In this paper, we present PyURDME, a new, user-friendly
spatial modeling and simulation package, and MOLNs, a cloud computing appliance
for distributed simulation of stochastic reaction-diffusion models. MOLNs is
based on IPython and provides an interactive programming platform for
development of sharable and reproducible distributed parallel computational
experiments
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