15,704 research outputs found
Feigenbaum graphs: a complex network perspective of chaos
The recently formulated theory of horizontal visibility graphs transforms
time series into graphs and allows the possibility of studying dynamical
systems through the characterization of their associated networks. This method
leads to a natural graph-theoretical description of nonlinear systems with
qualities in the spirit of symbolic dynamics. We support our claim via the case
study of the period-doubling and band-splitting attractor cascades that
characterize unimodal maps. We provide a universal analytical description of
this classic scenario in terms of the horizontal visibility graphs associated
with the dynamics within the attractors, that we call Feigenbaum graphs,
independent of map nonlinearity or other particulars. We derive exact results
for their degree distribution and related quantities, recast them in the
context of the renormalization group and find that its fixed points coincide
with those of network entropy optimization. Furthermore, we show that the
network entropy mimics the Lyapunov exponent of the map independently of its
sign, hinting at a Pesin-like relation equally valid out of chaos.Comment: Published in PLoS ONE (Sep 2011
Table Detection in Invoice Documents by Graph Neural Networks
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.Tabular structures in documents offer a complementary dimension to the raw textual data, representing logical
or quantitative relationships among pieces of information.
In digital mail room applications, where a large amount of
administrative documents must be processed with reasonable
accuracy, the detection and interpretation of tables is crucial.
Table recognition has gained interest in document image
analysis, in particular in unconstrained formats (absence of
rule lines, unknown information of rows and columns). In
this work, we propose a graph-based approach for detecting
tables in document images. Instead of using the raw content
(recognized text), we make use of the location, context and
content type, thus it is purely a structure perception approach,
not dependent on the language and the quality of the text
reading. Our framework makes use of Graph Neural Networks
(GNNs) in order to describe the local repetitive structural information of tables in invoice documents. Our proposed model
has been experimentally validated in two invoice datasets and
achieved encouraging results. Additionally, due to the scarcity
of benchmark datasets for this task, we have contributed to
the community a novel dataset derived from the RVL-CDIP
invoice data. It will be publicly released to facilitate future
research.European Unio
A combinatorial framework to quantify peak/pit asymmetries in complex dynamics
LL’s acknowledges funding from an EPSRC Early Career Fellowship EP/P01660X/1
Time reversibility from visibility graphs of nonstationary processes
Visibility algorithms are a family of methods to map time series into
networks, with the aim of describing the structure of time series and their
underlying dynamical properties in graph-theoretical terms. Here we explore
some properties of both natural and horizontal visibility graphs associated to
several non-stationary processes, and we pay particular attention to their
capacity to assess time irreversibility. Non-stationary signals are
(infinitely) irreversible by definition (independently of whether the process
is Markovian or producing entropy at a positive rate), and thus the link
between entropy production and time series irreversibility has only been
explored in non-equilibrium stationary states. Here we show that the visibility
formalism naturally induces a new working definition of time irreversibility,
which allows to quantify several degrees of irreversibility for stationary and
non-stationary series, yielding finite values that can be used to efficiently
assess the presence of memory and off-equilibrium dynamics in non-stationary
processes without needs to differentiate or detrend them. We provide rigorous
results complemented by extensive numerical simulations on several classes of
stochastic processes
Effectiveness of dismantling strategies on moderated vs. unmoderated online social platforms
Online social networks are the perfect test bed to better understand
large-scale human behavior in interacting contexts. Although they are broadly
used and studied, little is known about how their terms of service and posting
rules affect the way users interact and information spreads. Acknowledging the
relation between network connectivity and functionality, we compare the
robustness of two different online social platforms, Twitter and Gab, with
respect to dismantling strategies based on the recursive censor of users
characterized by social prominence (degree) or intensity of inflammatory
content (sentiment). We find that the moderated (Twitter) vs unmoderated (Gab)
character of the network is not a discriminating factor for intervention
effectiveness. We find, however, that more complex strategies based upon the
combination of topological and content features may be effective for network
dismantling. Our results provide useful indications to design better strategies
for countervailing the production and dissemination of anti-social content in
online social platforms
Post-OCR Paragraph Recognition by Graph Convolutional Networks
Paragraphs are an important class of document entities. We propose a new
approach for paragraph identification by spatial graph convolutional neural
networks (GCN) applied on OCR text boxes. Two steps, namely line splitting and
line clustering, are performed to extract paragraphs from the lines in OCR
results. Each step uses a beta-skeleton graph constructed from bounding boxes,
where the graph edges provide efficient support for graph convolution
operations. With only pure layout input features, the GCN model size is 3~4
orders of magnitude smaller compared to R-CNN based models, while achieving
comparable or better accuracies on PubLayNet and other datasets. Furthermore,
the GCN models show good generalization from synthetic training data to
real-world images, and good adaptivity for variable document styles
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