5,421 research outputs found
Characterizing time series : when Granger causality triggers complex networks
In this paper, we propose a new approach to characterize time series with noise perturbations in both the time and frequency domains by combining Granger causality and complex networks. We construct directed and weighted complex networks from time series and use representative network measures to describe their physical and topological properties. Through analyzing the typical dynamical behaviors of some physical models and the MIT-BIH* human electrocardiogram data sets, we show that the proposed approach is able to capture and characterize various dynamics and has much potential for analyzing real-world time series of rather short length
Consciousness as a State of Matter
We examine the hypothesis that consciousness can be understood as a state of
matter, "perceptronium", with distinctive information processing abilities. We
explore five basic principles that may distinguish conscious matter from other
physical systems such as solids, liquids and gases: the information,
integration, independence, dynamics and utility principles. If such principles
can identify conscious entities, then they can help solve the quantum
factorization problem: why do conscious observers like us perceive the
particular Hilbert space factorization corresponding to classical space (rather
than Fourier space, say), and more generally, why do we perceive the world
around us as a dynamic hierarchy of objects that are strongly integrated and
relatively independent? Tensor factorization of matrices is found to play a
central role, and our technical results include a theorem about Hamiltonian
separability (defined using Hilbert-Schmidt superoperators) being maximized in
the energy eigenbasis. Our approach generalizes Giulio Tononi's integrated
information framework for neural-network-based consciousness to arbitrary
quantum systems, and we find interesting links to error-correcting codes,
condensed matter criticality, and the Quantum Darwinism program, as well as an
interesting connection between the emergence of consciousness and the emergence
of time.Comment: Replaced to match accepted CSF version; discussion improved, typos
corrected. 36 pages, 15 fig
Stochasticity in pandemic spread over the World Airline Network explained by local flight connections
Massive growth in human mobility has dramatically increased the risk and rate
of pandemic spread. Macro-level descriptors of the topology of the World
Airline Network (WAN) explains middle and late stage dynamics of pandemic
spread mediated by this network, but necessarily regard early stage variation
as stochastic. We propose that much of early stage variation can be explained
by appropriately characterizing the local topology surrounding the debut
location of an outbreak. We measure for each airport the expected force of
infection (AEF) which a pandemic originating at that airport would generate. We
observe, for a subset of world airports, the minimum transmission rate at which
a disease becomes pandemically competent at each airport. We also observe, for
a larger subset, the time until a pandemically competent outbreak achieves
pandemic status given its debut location. Observations are generated using a
highly sophisticated metapopulation reaction-diffusion simulator under a
disease model known to well replicate the 2009 influenza pandemic. The
robustness of the AEF measure to model misspecification is examined by
degrading the network model. AEF powerfully explains pandemic risk, showing
correlation of 0.90 to the transmission level needed to give a disease pandemic
competence, and correlation of 0.85 to the delay until an outbreak becomes a
pandemic. The AEF is robust to model misspecification. For 97% of airports,
removing 15% of airports from the model changes their AEF metric by less than
1%. Appropriately summarizing the size, shape, and diversity of an airport's
local neighborhood in the WAN accurately explains much of the macro-level
stochasticity in pandemic outcomes.Comment: article text: 6 pages, 5 figures, 28 reference
Beyond ranking nodes: Predicting epidemic outbreak sizes by network centralities
Identifying important nodes for disease spreading is a central topic in
network epidemiology. We investigate how well the position of a node,
characterized by standard network measures, can predict its epidemiological
importance in any graph of a given number of nodes. This is in contrast to
other studies that deal with the easier prediction problem of ranking nodes by
their epidemic importance in given graphs. As a benchmark for epidemic
importance, we calculate the exact expected outbreak size given a node as the
source. We study exhaustively all graphs of a given size, so do not restrict
ourselves to certain generative models for graphs, nor to graph data sets. Due
to the large number of possible nonisomorphic graphs of a fixed size, we are
limited to 10-node graphs. We find that combinations of two or more
centralities are predictive ( scores of 0.91 or higher) even for the most
difficult parameter values of the epidemic simulation. Typically, these
successful combinations include one normalized spectral centralities (such as
PageRank or Katz centrality) and one measure that is sensitive to the number of
edges in the graph
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