3,418 research outputs found
A Physics-Based Approach to Unsupervised Discovery of Coherent Structures in Spatiotemporal Systems
Given that observational and numerical climate data are being produced at
ever more prodigious rates, increasingly sophisticated and automated analysis
techniques have become essential. Deep learning is quickly becoming a standard
approach for such analyses and, while great progress is being made, major
challenges remain. Unlike commercial applications in which deep learning has
led to surprising successes, scientific data is highly complex and typically
unlabeled. Moreover, interpretability and detecting new mechanisms are key to
scientific discovery. To enhance discovery we present a complementary
physics-based, data-driven approach that exploits the causal nature of
spatiotemporal data sets generated by local dynamics (e.g. hydrodynamic flows).
We illustrate how novel patterns and coherent structures can be discovered in
cellular automata and outline the path from them to climate data.Comment: 4 pages, 1 figure;
http://csc.ucdavis.edu/~cmg/compmech/pubs/ci2017_Rupe_et_al.ht
Discovering Functional Communities in Dynamical Networks
Many networks are important because they are substrates for dynamical
systems, and their pattern of functional connectivity can itself be dynamic --
they can functionally reorganize, even if their underlying anatomical structure
remains fixed. However, the recent rapid progress in discovering the community
structure of networks has overwhelmingly focused on that constant anatomical
connectivity. In this paper, we lay out the problem of discovering_functional
communities_, and describe an approach to doing so. This method combines recent
work on measuring information sharing across stochastic networks with an
existing and successful community-discovery algorithm for weighted networks. We
illustrate it with an application to a large biophysical model of the
transition from beta to gamma rhythms in the hippocampus.Comment: 18 pages, 4 figures, Springer "Lecture Notes in Computer Science"
style. Forthcoming in the proceedings of the workshop "Statistical Network
Analysis: Models, Issues and New Directions", at ICML 2006. Version 2: small
clarifications, typo corrections, added referenc
Impact of local information in growing networks
We present a new model of the evolutionary dynamics and the growth of on-line
social networks. The model emulates people's strategies for acquiring
information in social networks, emphasising the local subjective view of an
individual and what kind of information the individual can acquire when
arriving in a new social context. The model proceeds through two phases: (a) a
discovery phase, in which the individual becomes aware of the surrounding world
and (b) an elaboration phase, in which the individual elaborates locally the
information trough a cognitive-inspired algorithm. Model generated networks
reproduce main features of both theoretical and real-world networks, such as
high clustering coefficient, low characteristic path length, strong division in
communities, and variability of degree distributions.Comment: In Proceedings Wivace 2013, arXiv:1309.712
CURIE: a cellular automaton for concept drift detection
Data stream mining extracts information from large quantities of data flowing fast and continuously (data streams). They are usually affected by changes in the data distribution, giving rise to a phenomenon referred to as concept drift. Thus, learning models must detect and adapt to such changes, so as to exhibit a good predictive performance after a drift has occurred. In this regard, the development of effective drift detection algorithms becomes a key factor in data stream mining. In this work we propose CURIECURIE, a drift detector relying on cellular automata. Specifically, in CURIECURIE the distribution of the data stream is represented in the grid of a cellular automata, whose neighborhood rule can then be utilized to detect possible distribution changes over the stream. Computer simulations are presented and discussed to show that CURIECURIE, when hybridized with other base learners, renders a competitive behavior in terms of detection metrics and classification accuracy. CURIECURIE is compared with well-established drift detectors over synthetic datasets with varying drift characteristics.This work has received funding support from the ECSEL Joint Undertaking (JU) under grant agreement No 783163 (iDev40 project). The JU receives support from the European Union’s Horizon 2020 research and innovation programme, national grants from Austria, Belgium, Germany, Italy, Spain and Romania, as well as the European Structural and Investment Funds. Authors would like to also thank the ELKARTEK and EMAITEK funding programmes of the Basque Government (Spain
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