175,649 research outputs found
Efficient algorithm to study interconnected networks
Interconnected networks have been shown to be much more vulnerable to random
and targeted failures than isolated ones, raising several interesting questions
regarding the identification and mitigation of their risk. The paradigm to
address these questions is the percolation model, where the resilience of the
system is quantified by the dependence of the size of the largest cluster on
the number of failures. Numerically, the major challenge is the identification
of this cluster and the calculation of its size. Here, we propose an efficient
algorithm to tackle this problem. We show that the algorithm scales as O(N log
N), where N is the number of nodes in the network, a significant improvement
compared to O(N^2) for a greedy algorithm, what permits studying much larger
networks. Our new strategy can be applied to any network topology and
distribution of interdependencies, as well as any sequence of failures.Comment: 5 pages, 6 figure
Detection of Complex Networks Modularity by Dynamical Clustering
Based on cluster de-synchronization properties of phase oscillators, we
introduce an efficient method for the detection and identification of modules
in complex networks. The performance of the algorithm is tested on computer
generated and real-world networks whose modular structure is already known or
has been studied by means of other methods. The algorithm attains a high level
of precision, especially when the modular units are very mixed and hardly
detectable by the other methods, with a computational effort on
a generic graph with nodes and links.Comment: 5 pages, 2 figures. Version accepted for publication on PRE Rapid
Communications: figures changed and text adde
An Efficient Algorithm for Clustering of Large-Scale Mass Spectrometry Data
High-throughput spectrometers are capable of producing data sets containing
thousands of spectra for a single biological sample. These data sets contain a
substantial amount of redundancy from peptides that may get selected multiple
times in a LC-MS/MS experiment. In this paper, we present an efficient
algorithm, CAMS (Clustering Algorithm for Mass Spectra) for clustering mass
spectrometry data which increases both the sensitivity and confidence of
spectral assignment. CAMS utilizes a novel metric, called F-set, that allows
accurate identification of the spectra that are similar. A graph theoretic
framework is defined that allows the use of F-set metric efficiently for
accurate cluster identifications. The accuracy of the algorithm is tested on
real HCD and CID data sets with varying amounts of peptides. Our experiments
show that the proposed algorithm is able to cluster spectra with very high
accuracy in a reasonable amount of time for large spectral data sets. Thus, the
algorithm is able to decrease the computational time by compressing the data
sets while increasing the throughput of the data by interpreting low S/N
spectra.Comment: 4 pages, 4 figures, Bioinformatics and Biomedicine (BIBM), 2012 IEEE
International Conference o
Learning Personalized Models with Clustered System Identification
We address the problem of learning linear system models from observing
multiple trajectories from different system dynamics. This framework
encompasses a collaborative scenario where several systems seeking to estimate
their dynamics are partitioned into clusters according to their system
similarity. Thus, the systems within the same cluster can benefit from the
observations made by the others. Considering this framework, we present an
algorithm where each system alternately estimates its cluster identity and
performs an estimation of its dynamics. This is then aggregated to update the
model of each cluster. We show that under mild assumptions, our algorithm
correctly estimates the cluster identities and achieves an approximate sample
complexity that scales inversely with the number of systems in the cluster,
thus facilitating a more efficient and personalized system identification
process
A Holistic Methodology for Improved RFID Network Lifetime by Advanced Cluster Head Selection using Dragonfly Algorithm
Radio Frequency Identification (RFID) networks usually require many tags along with readers and computation facilities. Those networks have limitations with respect to computing power and energy consumption. Thus, for saving energy and to make the best use of the resources, networks should operate and be able to recover in an efficient way. This will also reduce the energy expenditure of RFID readers. In this work, the RFID network life span will be enlarged through an energy-efficient cluster-based protocol used together with the Dragonfly algorithm. There are two stages in the processing of the clustering system: the cluster formation from the whole structure and the election of a cluster leader. After completing those procedures, the cluster leader controls the other nodes that are not leaders. The system works with a large energy node that provides an amount of energy while transmitting aggregated data near a base station
Particle identification with the cluster counting technique for the IDEA drift chamber
IDEA (Innovative Detector for an Electron-positron Accelerator) is a
general-purpose detector concept, designed to study electron-positron
collisions in a wide energy range from a very large circular leptonic collider.
Its drift chamber is designed to provide an efficient tracking, a high
precision momentum measurement and an excellent particle identification by
exploiting the application of the cluster counting technique. To investigate
the potential of the cluster counting techniques on physics events, a
simulation of the ionization clusters generation is needed, therefore we
developed an algorithm which can use the energy deposit information provided by
Geant4 toolkit to reproduce, in a fast and convenient way, the clusters number
distribution and the cluster size distribution. The results obtained confirm
that the cluster counting technique allows to reach a resolution 2 times better
than the traditional dE/dx method. A beam test has been performed during
November 2021 at CERN on the H8 to validate the simulations results, to define
the limiting effects for a fully efficient cluster counting and to count the
number of electron clusters released by an ionizing track at a fixed
as a function of the track angle. The simulation and the beam
test results will be described briefly in this issue.Comment: 2 pages, 4 figures, Proceedings of: PM202
Connected component identification and cluster update on GPU
Cluster identification tasks occur in a multitude of contexts in physics and
engineering such as, for instance, cluster algorithms for simulating spin
models, percolation simulations, segmentation problems in image processing, or
network analysis. While it has been shown that graphics processing units (GPUs)
can result in speedups of two to three orders of magnitude as compared to
serial codes on CPUs for the case of local and thus naturally parallelized
problems such as single-spin flip update simulations of spin models, the
situation is considerably more complicated for the non-local problem of cluster
or connected component identification. I discuss the suitability of different
approaches of parallelization of cluster labeling and cluster update algorithms
for calculations on GPU and compare to the performance of serial
implementations.Comment: 15 pages, 14 figures, one table, submitted to PR
GARLIC: GAmma Reconstruction at a LInear Collider experiment
The precise measurement of hadronic jet energy is crucial to maximise the
physics reach of a future Linear Collider. An important ingredient required to
achieve this is the efficient identification of photons within hadronic
showers. One configuration of the ILD detector concept employs a highly
granular silicon-tungsten sampling calorimeter to identify and measure photons,
and the GARLIC algorithm described in this paper has been developed to identify
photons in such a calorimeter. We describe the algorithm and characterise its
performance using events fully simulated in a model of the ILD detector
Multimodal nested sampling: an efficient and robust alternative to MCMC methods for astronomical data analysis
In performing a Bayesian analysis of astronomical data, two difficult
problems often emerge. First, in estimating the parameters of some model for
the data, the resulting posterior distribution may be multimodal or exhibit
pronounced (curving) degeneracies, which can cause problems for traditional
MCMC sampling methods. Second, in selecting between a set of competing models,
calculation of the Bayesian evidence for each model is computationally
expensive. The nested sampling method introduced by Skilling (2004), has
greatly reduced the computational expense of calculating evidences and also
produces posterior inferences as a by-product. This method has been applied
successfully in cosmological applications by Mukherjee et al. (2006), but their
implementation was efficient only for unimodal distributions without pronounced
degeneracies. Shaw et al. (2007), recently introduced a clustered nested
sampling method which is significantly more efficient in sampling from
multimodal posteriors and also determines the expectation and variance of the
final evidence from a single run of the algorithm, hence providing a further
increase in efficiency. In this paper, we build on the work of Shaw et al. and
present three new methods for sampling and evidence evaluation from
distributions that may contain multiple modes and significant degeneracies; we
also present an even more efficient technique for estimating the uncertainty on
the evaluated evidence. These methods lead to a further substantial improvement
in sampling efficiency and robustness, and are applied to toy problems to
demonstrate the accuracy and economy of the evidence calculation and parameter
estimation. Finally, we discuss the use of these methods in performing Bayesian
object detection in astronomical datasets.Comment: 14 pages, 11 figures, submitted to MNRAS, some major additions to the
previous version in response to the referee's comment
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