103 research outputs found
High-Dimensional Similarity Search with Quantum-Assisted Variational Autoencoder
Recent progress in quantum algorithms and hardware indicates the potential
importance of quantum computing in the near future. However, finding suitable
application areas remains an active area of research. Quantum machine learning
is touted as a potential approach to demonstrate quantum advantage within both
the gate-model and the adiabatic schemes. For instance, the Quantum-assisted
Variational Autoencoder has been proposed as a quantum enhancement to the
discrete VAE. We extend on previous work and study the real-world applicability
of a QVAE by presenting a proof-of-concept for similarity search in large-scale
high-dimensional datasets. While exact and fast similarity search algorithms
are available for low dimensional datasets, scaling to high-dimensional data is
non-trivial. We show how to construct a space-efficient search index based on
the latent space representation of a QVAE. Our experiments show a correlation
between the Hamming distance in the embedded space and the Euclidean distance
in the original space on the Moderate Resolution Imaging Spectroradiometer
(MODIS) dataset. Further, we find real-world speedups compared to linear search
and demonstrate memory-efficient scaling to half a billion data points
Azimuthal Correlations in the Target Fragmentation Region of High Energy Nuclear Collisions
Results on the target mass dependence of proton and pion pseudorapidity
distributions and of their azimuthal correlations in the target rapidity range
are presented. The data have been taken with the
Plastic-Ball detector set-up for 4.9 GeV p + Au collisions at the Berkeley
BEVALAC and for 200 GeV/ p-, O-, and S-induced reactions on
different nuclei at the CERN-SPS. The yield of protons at backward rapidities
is found to be proportional to the target mass. Although protons show a typical
``back-to-back'' correlations, a ``side-by-side'' correlation is observed for
positive pions, which increases both with target mass and with impact parameter
of a collision. The data can consistently be described by assuming strong
rescattering phenomena including pion absorption effects in the entire excited
target nucleus.Comment: 7 pages, figures included, complete postscript available at
ftp://qgp.uni-muenster.de/pub/paper/azi-correlations.ps submitted to Phys.
Lett.
Local Difference Measures between Complex Networks for Dynamical System Model Evaluation
Acknowledgments We thank Reik V. Donner for inspiring suggestions that initialized the work presented herein. Jan H. Feldhoff is credited for providing us with the STARS simulation data and for his contributions to fruitful discussions. Comments by the anonymous reviewers are gratefully acknowledged as they led to substantial improvements of the manuscript.Peer reviewedPublisher PD
Computing Vertex-Vertex Dissimilarities Using Random Trees: Application to Clustering in Graphs
International audienceA current challenge in graph clustering is to tackle the issue of complex networks, i.e, graphs with attributed vertices and/or edges. In this paper, we present GraphTrees, a novel method that relies on random decision trees to compute pairwise dissimilarities between vertices in a graph. We show that using different types of trees, it is possible to extend this framework to graphs where the vertices have attributes. While many existing methods that tackle the problem of clustering vertices in an attributed graph are limited to categorical attributes, GraphTrees can handle heterogeneous types of vertex attributes. Moreover, unlike other approaches, the attributes do not need to be preprocessed. We also show that our approach is competitive with well-known methods in the case of non-attributed graphs in terms of quality of clustering, and provides promising results in the case of vertex-attributed graphs. By extending the use of an already well established approach-the random trees-to graphs, our proposed approach opens new research directions, by lever-aging decades of research on this topic
Finding and testing network communities by lumped Markov chains
Identifying communities (or clusters), namely groups of nodes with
comparatively strong internal connectivity, is a fundamental task for deeply
understanding the structure and function of a network. Yet, there is a lack of
formal criteria for defining communities and for testing their significance. We
propose a sharp definition which is based on a significance threshold. By means
of a lumped Markov chain model of a random walker, a quality measure called
"persistence probability" is associated to a cluster. Then the cluster is
defined as an "-community" if such a probability is not smaller than
. Consistently, a partition composed of -communities is an
"-partition". These definitions turn out to be very effective for
finding and testing communities. If a set of candidate partitions is available,
setting the desired -level allows one to immediately select the
-partition with the finest decomposition. Simultaneously, the
persistence probabilities quantify the significance of each single community.
Given its ability in individually assessing the quality of each cluster, this
approach can also disclose single well-defined communities even in networks
which overall do not possess a definite clusterized structure
Azimuthal anisotropy in S+Au reactions at 200 A GeV
Azimuthal correlations of photons produced at mid-rapidity in 200 A GeV S + Au collisions have been studied using a preshower photon multiplicity detector in the WA93 experiment. The Fourier expansion method has been employed to estimate the event plane via the anisotropy of the event as a function of centrality. The event plane correlation technique has been used to determine the true event anisotropy, beyond the anisotropy which arises due to finite multiplicity. The VENUS event generator with rescattering and proper simulation of the detector response can explain only a portion of the observed anisotropy. The residual anisotropy is found to be of the order of 5% for semi-central collisions. This suggests that directed collective flow of the produced particles is present at SPS energies. (C) 1997 Published by Elsevier Science B.V
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