12,320 research outputs found
Distance entropy cartography characterises centrality in complex networks
We introduce distance entropy as a measure of homogeneity in the distribution
of path lengths between a given node and its neighbours in a complex network.
Distance entropy defines a new centrality measure whose properties are
investigated for a variety of synthetic network models. By coupling distance
entropy information with closeness centrality, we introduce a network
cartography which allows one to reduce the degeneracy of ranking based on
closeness alone. We apply this methodology to the empirical multiplex lexical
network encoding the linguistic relationships known to English speaking
toddlers. We show that the distance entropy cartography better predicts how
children learn words compared to closeness centrality. Our results highlight
the importance of distance entropy for gaining insights from distance patterns
in complex networks.Comment: 11 page
Automatic acquisition of LFG resources for German - as good as it gets
We present data-driven methods for the acquisition of LFG resources from two German treebanks. We discuss problems specific to semi-free word order languages as well as problems arising fromthe data structures determined
by the design of the different treebanks. We compare two ways of encoding semi-free word order, as done in the two German treebanks, and argue that the design of the TiGer treebank is more adequate for the acquisition of LFG
resources. Furthermore, we describe an architecture for LFG grammar acquisition for German, based on the two German treebanks, and compare our results with a hand-crafted German LFG grammar
3D mesh processing using GAMer 2 to enable reaction-diffusion simulations in realistic cellular geometries
Recent advances in electron microscopy have enabled the imaging of single
cells in 3D at nanometer length scale resolutions. An uncharted frontier for in
silico biology is the ability to simulate cellular processes using these
observed geometries. Enabling such simulations requires watertight meshing of
electron micrograph images into 3D volume meshes, which can then form the basis
of computer simulations of such processes using numerical techniques such as
the Finite Element Method. In this paper, we describe the use of our recently
rewritten mesh processing software, GAMer 2, to bridge the gap between poorly
conditioned meshes generated from segmented micrographs and boundary marked
tetrahedral meshes which are compatible with simulation. We demonstrate the
application of a workflow using GAMer 2 to a series of electron micrographs of
neuronal dendrite morphology explored at three different length scales and show
that the resulting meshes are suitable for finite element simulations. This
work is an important step towards making physical simulations of biological
processes in realistic geometries routine. Innovations in algorithms to
reconstruct and simulate cellular length scale phenomena based on emerging
structural data will enable realistic physical models and advance discovery at
the interface of geometry and cellular processes. We posit that a new frontier
at the intersection of computational technologies and single cell biology is
now open.Comment: 39 pages, 14 figures. High resolution figures and supplemental movies
available upon reques
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