11,017 research outputs found
Entanglement dynamics and quasi-periodicity in discrete quantum walks
We study the entanglement dynamics of discrete time quantum walks acting on
bounded finite sized graphs. We demonstrate that, depending on system
parameters, the dynamics may be monotonic, oscillatory but highly regular, or
quasi-periodic. While the dynamics of the system are not chaotic since the
system comprises linear evolution, the dynamics often exhibit some features
similar to chaos such as high sensitivity to the system's parameters,
irregularity and infinite periodicity. Our observations are of interest for
entanglement generation, which is one primary use for the quantum walk
formalism. Furthermore, we show that the systems we model can easily be mapped
to optical beamsplitter networks, rendering experimental observation of
quasi-periodic dynamics within reach.Comment: 9 pages, 8 figure
Enhancing urban analysis through lacunarity multiscale measurement
Urban spatial configurations in most part of the developing countries showparticular urban forms associated with the more informal urban development ofthese areas. Latin American cities are prime examples of this sort, butinvestigation of these urban forms using up to date computational and analyticaltechniques are still scarce. The purpose of this paper is to examine and extendthe methodology of multiscale analysis for urban spatial patterns evaluation. Weexplain and explore the use of Lacunarity based measurements to follow a lineof research that might make more use of new satellite imagery information inurban planning contexts. A set of binary classifications is performed at differentthresholds on selected neighbourhoods of a small Brazilian town. Theclassifications are appraised and lacunarity measurements are compared in faceof the different geographic referenced information for the same neighbourhoodareas. It was found that even with the simple image classification procedure, animportant amount of spatial configuration characteristics could be extracted withthe analytical procedure that, in turn, may be used in planning and other urbanstudies purposes
Spectral Graph Convolutions for Population-based Disease Prediction
Exploiting the wealth of imaging and non-imaging information for disease
prediction tasks requires models capable of representing, at the same time,
individual features as well as data associations between subjects from
potentially large populations. Graphs provide a natural framework for such
tasks, yet previous graph-based approaches focus on pairwise similarities
without modelling the subjects' individual characteristics and features. On the
other hand, relying solely on subject-specific imaging feature vectors fails to
model the interaction and similarity between subjects, which can reduce
performance. In this paper, we introduce the novel concept of Graph
Convolutional Networks (GCN) for brain analysis in populations, combining
imaging and non-imaging data. We represent populations as a sparse graph where
its vertices are associated with image-based feature vectors and the edges
encode phenotypic information. This structure was used to train a GCN model on
partially labelled graphs, aiming to infer the classes of unlabelled nodes from
the node features and pairwise associations between subjects. We demonstrate
the potential of the method on the challenging ADNI and ABIDE databases, as a
proof of concept of the benefit from integrating contextual information in
classification tasks. This has a clear impact on the quality of the
predictions, leading to 69.5% accuracy for ABIDE (outperforming the current
state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion,
significantly outperforming standard linear classifiers where only individual
features are considered.Comment: International Conference on Medical Image Computing and
Computer-Assisted Interventions (MICCAI) 201
Visibility graphs and landscape visibility analysis
Visibility analysis based on viewsheds is one of the most frequently used GIS analysis tools. In this paper we present an approach to visibility analysis based on the visibility graph. A visibility graph records the pattern of mutual visibility relations in a landscape, and provides a convenient way of storing and further analysing the results of multiple viewshed analyses for a particular landscape region. We describe how a visibility graph may be calculated for a landscape. We then give examples, which include the interactive exploration ofa landscape, and the calculation of new measures of a landscape?s visual properties based on graph metrics ? in particular, neighbourhood clustering coefficient and path length analysis. These analyses suggest that measures derived from the visibility graph may be of particular relevance to the growing interest in quantifying the perceptual characteristics of landscapes
Spectral Theory of Sparse Non-Hermitian Random Matrices
Sparse non-Hermitian random matrices arise in the study of disordered
physical systems with asymmetric local interactions, and have applications
ranging from neural networks to ecosystem dynamics. The spectral
characteristics of these matrices provide crucial information on system
stability and susceptibility, however, their study is greatly complicated by
the twin challenges of a lack of symmetry and a sparse interaction structure.
In this review we provide a concise and systematic introduction to the main
tools and results in this field. We show how the spectra of sparse
non-Hermitian matrices can be computed via an analogy with infinite dimensional
operators obeying certain recursion relations. With reference to three
illustrative examples --- adjacency matrices of regular oriented graphs,
adjacency matrices of oriented Erd\H{o}s-R\'{e}nyi graphs, and adjacency
matrices of weighted oriented Erd\H{o}s-R\'{e}nyi graphs --- we demonstrate the
use of these methods to obtain both analytic and numerical results for the
spectrum, the spectral distribution, the location of outlier eigenvalues, and
the statistical properties of eigenvectors.Comment: 60 pages, 10 figure
Statistical Physics of Irregular Low-Density Parity-Check Codes
Low-density parity-check codes with irregular constructions have been
recently shown to outperform the most advanced error-correcting codes to date.
In this paper we apply methods of statistical physics to study the typical
properties of simple irregular codes.
We use the replica method to find a phase transition which coincides with
Shannon's coding bound when appropriate parameters are chosen.
The decoding by belief propagation is also studied using statistical physics
arguments; the theoretical solutions obtained are in good agreement with
simulations. We compare the performance of irregular with that of regular codes
and discuss the factors that contribute to the improvement in performance.Comment: 20 pages, 9 figures, revised version submitted to JP
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