710 research outputs found
Brain complexity born out of criticality
In this essay we elaborate on recent evidence demonstrating the presence of a
second order phase transition in human brain dynamics and discuss its
consequences for theoretical approaches to brain function. We review early
evidence of criticality in brain dynamics at different spatial and temporal
scales, and we stress how it was necessary to unify concepts and analysis
techniques across scales to introduce the adequate order and control parameters
which define the transition. A discussion on the relation between structural
vs. dynamical complexity exposes future steps to understand the dynamics of the
connectome (structure) from which emerges the cognitome (function).Comment: In Proceedings of the 12th Granada Seminar "Physics, Computation, and
the Mind - Advances and Challenges at Interfaces-". (J. Marro, P. L. Garrido
& J. J. Torres, Eds.) American Institute of Physics (2012, in press
Machine Learning for Fluid Mechanics
The field of fluid mechanics is rapidly advancing, driven by unprecedented
volumes of data from field measurements, experiments and large-scale
simulations at multiple spatiotemporal scales. Machine learning offers a wealth
of techniques to extract information from data that could be translated into
knowledge about the underlying fluid mechanics. Moreover, machine learning
algorithms can augment domain knowledge and automate tasks related to flow
control and optimization. This article presents an overview of past history,
current developments, and emerging opportunities of machine learning for fluid
mechanics. It outlines fundamental machine learning methodologies and discusses
their uses for understanding, modeling, optimizing, and controlling fluid
flows. The strengths and limitations of these methods are addressed from the
perspective of scientific inquiry that considers data as an inherent part of
modeling, experimentation, and simulation. Machine learning provides a powerful
information processing framework that can enrich, and possibly even transform,
current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202
Connections Between Adaptive Control and Optimization in Machine Learning
This paper demonstrates many immediate connections between adaptive control
and optimization methods commonly employed in machine learning. Starting from
common output error formulations, similarities in update law modifications are
examined. Concepts in stability, performance, and learning, common to both
fields are then discussed. Building on the similarities in update laws and
common concepts, new intersections and opportunities for improved algorithm
analysis are provided. In particular, a specific problem related to higher
order learning is solved through insights obtained from these intersections.Comment: 18 page
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