50,614 research outputs found
Motility at the origin of life: Its characterization and a model
Due to recent advances in synthetic biology and artificial life, the origin
of life is currently a hot topic of research. We review the literature and
argue that the two traditionally competing "replicator-first" and
"metabolism-first" approaches are merging into one integrated theory of
individuation and evolution. We contribute to the maturation of this more
inclusive approach by highlighting some problematic assumptions that still lead
to an impoverished conception of the phenomenon of life. In particular, we
argue that the new consensus has so far failed to consider the relevance of
intermediate timescales. We propose that an adequate theory of life must
account for the fact that all living beings are situated in at least four
distinct timescales, which are typically associated with metabolism, motility,
development, and evolution. On this view, self-movement, adaptive behavior and
morphological changes could have already been present at the origin of life. In
order to illustrate this possibility we analyze a minimal model of life-like
phenomena, namely of precarious, individuated, dissipative structures that can
be found in simple reaction-diffusion systems. Based on our analysis we suggest
that processes in intermediate timescales could have already been operative in
prebiotic systems. They may have facilitated and constrained changes occurring
in the faster- and slower-paced timescales of chemical self-individuation and
evolution by natural selection, respectively.Comment: 29 pages, 5 figures, Artificial Lif
Multifunctionality in embodied agents: Three levels of neural reuse
The brain in conjunction with the body is able to adapt to new environments
and perform multiple behaviors through reuse of neural resources and transfer
of existing behavioral traits. Although mechanisms that underlie this ability
are not well understood, they are largely attributed to neuromodulation. In
this work, we demonstrate that an agent can be multifunctional using the same
sensory and motor systems across behaviors, in the absence of modulatory
mechanisms. Further, we lay out the different levels at which neural reuse can
occur through a dynamical filtering of the brain-body-environment system's
operation: structural network, autonomous dynamics, and transient dynamics.
Notably, transient dynamics reuse could only be explained by studying the
brain-body-environment system as a whole and not just the brain. The
multifunctional agent we present here demonstrates neural reuse at all three
levels.Comment: Accepted at Cognitive Science Conference, 201
A general learning co-evolution method to generalize autonomous robot navigation behavior
Congress on Evolutionary Computation. La Jolla, CA, 16-19 July 2000.A new coevolutive method, called Uniform Coevolution, is introduced, to learn weights for a neural network controller in autonomous robots. An evolutionary strategy is used to learn high-performance reactive behavior for navigation and collision avoidance. The coevolutive method allows the evolution of the environment, to learn a general behavior able to solve the problem in different environments. Using a traditional evolutionary strategy method without coevolution, the learning process obtains a specialized behavior. All the behaviors obtained, with or without coevolution have been tested in a set of environments and the capability for generalization has been shown for each learned behavior. A simulator based on the mini-robot Khepera has been used to learn each behavior. The results show that Uniform Coevolution obtains better generalized solutions to example-based problems
Neural network controller against environment: A coevolutive approach to generalize robot navigation behavior
In this paper, a new coevolutive method, called Uniform Coevolution, is introduced to learn weights of a neural network controller in autonomous robots. An evolutionary strategy is used to learn high-performance reactive behavior for navigation and collisions avoidance. The introduction of coevolutive over evolutionary strategies allows evolving the environment, to learn a general behavior able to solve the problem in different environments. Using a traditional evolutionary strategy method, without coevolution, the learning process obtains a specialized behavior. All the behaviors obtained, with/without coevolution have been tested in a set of environments and the capability of generalization is shown for each learned behavior. A simulator based on a mini-robot Khepera has been used to learn each behavior. The results show that Uniform Coevolution obtains better generalized solutions to examples-based problems.Publicad
Robust sound event detection in bioacoustic sensor networks
Bioacoustic sensors, sometimes known as autonomous recording units (ARUs),
can record sounds of wildlife over long periods of time in scalable and
minimally invasive ways. Deriving per-species abundance estimates from these
sensors requires detection, classification, and quantification of animal
vocalizations as individual acoustic events. Yet, variability in ambient noise,
both over time and across sensors, hinders the reliability of current automated
systems for sound event detection (SED), such as convolutional neural networks
(CNN) in the time-frequency domain. In this article, we develop, benchmark, and
combine several machine listening techniques to improve the generalizability of
SED models across heterogeneous acoustic environments. As a case study, we
consider the problem of detecting avian flight calls from a ten-hour recording
of nocturnal bird migration, recorded by a network of six ARUs in the presence
of heterogeneous background noise. Starting from a CNN yielding
state-of-the-art accuracy on this task, we introduce two noise adaptation
techniques, respectively integrating short-term (60 milliseconds) and long-term
(30 minutes) context. First, we apply per-channel energy normalization (PCEN)
in the time-frequency domain, which applies short-term automatic gain control
to every subband in the mel-frequency spectrogram. Secondly, we replace the
last dense layer in the network by a context-adaptive neural network (CA-NN)
layer. Combining them yields state-of-the-art results that are unmatched by
artificial data augmentation alone. We release a pre-trained version of our
best performing system under the name of BirdVoxDetect, a ready-to-use detector
of avian flight calls in field recordings.Comment: 32 pages, in English. Submitted to PLOS ONE journal in February 2019;
revised August 2019; published October 201
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Advances in Kriging-Based Autonomous X-Ray Scattering Experiments.
Autonomous experimentation is an emerging paradigm for scientific discovery, wherein measurement instruments are augmented with decision-making algorithms, allowing them to autonomously explore parameter spaces of interest. We have recently demonstrated a generalized approach to autonomous experimental control, based on generating a surrogate model to interpolate experimental data, and a corresponding uncertainty model, which are computed using a Gaussian process regression known as ordinary Kriging (OK). We demonstrated the successful application of this method to exploring materials science problems using x-ray scattering measurements at a synchrotron beamline. Here, we report several improvements to this methodology that overcome limitations of traditional Kriging methods. The variogram underlying OK is global and thus insensitive to local data variation. We augment the Kriging variance with model-based measures, for instance providing local sensitivity by including the gradient of the surrogate model. As with most statistical regression methods, OK minimizes the number of measurements required to achieve a particular model quality. However, in practice this may not be the most stringent experimental constraint; e.g. the goal may instead be to minimize experiment duration or material usage. We define an adaptive cost function, allowing the autonomous method to balance information gain against measured experimental cost. We provide synthetic and experimental demonstrations, validating that this improved algorithm yields more efficient autonomous data collection
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