5,242 research outputs found
A polynomial time algorithm for calculating the probability of a ranked gene tree given a species tree
In this paper, we provide a polynomial time algorithm to calculate the
probability of a {\it ranked} gene tree topology for a given species tree,
where a ranked tree topology is a tree topology with the internal vertices
being ordered. The probability of a gene tree topology can thus be calculated
in polynomial time if the number of orderings of the internal vertices is a
polynomial number. However, the complexity of calculating the probability of a
gene tree topology with an exponential number of rankings for a given species
tree remains unknown
Inferring Species Trees from Incongruent Multi-Copy Gene Trees Using the Robinson-Foulds Distance
We present a new method for inferring species trees from multi-copy gene
trees. Our method is based on a generalization of the Robinson-Foulds (RF)
distance to multi-labeled trees (mul-trees), i.e., gene trees in which multiple
leaves can have the same label. Unlike most previous phylogenetic methods using
gene trees, this method does not assume that gene tree incongruence is caused
by a single, specific biological process, such as gene duplication and loss,
deep coalescence, or lateral gene transfer. We prove that it is NP-hard to
compute the RF distance between two mul-trees, but it is easy to calculate the
generalized RF distance between a mul-tree and a singly-labeled tree. Motivated
by this observation, we formulate the RF supertree problem for mul-trees
(MulRF), which takes a collection of mul-trees and constructs a species tree
that minimizes the total RF distance from the input mul-trees. We present a
fast heuristic algorithm for the MulRF supertree problem. Simulation
experiments demonstrate that the MulRF method produces more accurate species
trees than gene tree parsimony methods when incongruence is caused by gene tree
error, duplications and losses, and/or lateral gene transfer. Furthermore, the
MulRF heuristic runs quickly on data sets containing hundreds of trees with up
to a hundred taxa.Comment: 16 pages, 11 figure
Consistency Properties of Species Tree Inference by Minimizing Deep Coalescences
Methods for inferring species trees from sets of gene trees need to account for the possibility of discordance among the gene trees. Assuming that discordance is caused by incomplete lineage sorting, species tree estimates can be obtained by finding those species trees that minimize the number of -deep- coalescence events required for a given collection of gene trees. Efficient algorithms now exist for applying the minimizing-deep-coalescence (MDC) criterion, and simulation experiments have demonstrated its promising performance. However, it has also been noted from simulation results that the MDC criterion is not always guaranteed to infer the correct species tree estimate. In this article, we investigate the consistency of the MDC criterion. Using the multipscies coalescent model, we show that there are indeed anomaly zones for the MDC criterion for asymmetric four-taxon species tree topologies, and for all species tree topologies with five or more taxa.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90434/1/cmb-2E2010-2E0102.pd
Image-based deep learning for classification of noise transients in gravitational wave detectors
The detection of gravitational waves has inaugurated the era of gravitational
astronomy and opened new avenues for the multimessenger study of cosmic
sources. Thanks to their sensitivity, the Advanced LIGO and Advanced Virgo
interferometers will probe a much larger volume of space and expand the
capability of discovering new gravitational wave emitters. The characterization
of these detectors is a primary task in order to recognize the main sources of
noise and optimize the sensitivity of interferometers. Glitches are transient
noise events that can impact the data quality of the interferometers and their
classification is an important task for detector characterization. Deep
learning techniques are a promising tool for the recognition and classification
of glitches. We present a classification pipeline that exploits convolutional
neural networks to classify glitches starting from their time-frequency
evolution represented as images. We evaluated the classification accuracy on
simulated glitches, showing that the proposed algorithm can automatically
classify glitches on very fast timescales and with high accuracy, thus
providing a promising tool for online detector characterization.Comment: 25 pages, 8 figures, accepted for publication in Classical and
Quantum Gravit
Consensus properties for the deep coalescence problem and their application for scalable tree search
Convolutional neural networks: a magic bullet for gravitational-wave detection?
In the last few years, machine learning techniques, in particular
convolutional neural networks, have been investigated as a method to replace or
complement traditional matched filtering techniques that are used to detect the
gravitational-wave signature of merging black holes. However, to date, these
methods have not yet been successfully applied to the analysis of long
stretches of data recorded by the Advanced LIGO and Virgo gravitational-wave
observatories. In this work, we critically examine the use of convolutional
neural networks as a tool to search for merging black holes. We identify the
strengths and limitations of this approach, highlight some common pitfalls in
translating between machine learning and gravitational-wave astronomy, and
discuss the interdisciplinary challenges. In particular, we explain in detail
why convolutional neural networks alone cannot be used to claim a statistically
significant gravitational-wave detection. However, we demonstrate how they can
still be used to rapidly flag the times of potential signals in the data for a
more detailed follow-up. Our convolutional neural network architecture as well
as the proposed performance metrics are better suited for this task than a
standard binary classifications scheme. A detailed evaluation of our approach
on Advanced LIGO data demonstrates the potential of such systems as trigger
generators. Finally, we sound a note of caution by constructing adversarial
examples, which showcase interesting "failure modes" of our model, where inputs
with no visible resemblance to real gravitational-wave signals are identified
as such by the network with high confidence.Comment: First two authors contributed equally; appeared at Phys. Rev.
Seedless clustering in all-sky searches for gravitational-wave transients
The problem of searching for unmodeled gravitational-wave bursts can be
thought of as a pattern recognition problem: how to find statistically
significant clusters in spectrograms of strain power when the precise signal
morphology is unknown. In a previous publication, we showed how "seedless
clustering" can be used to dramatically improve the sensitivity of searches for
long-lived gravitational-wave transients. In order to manage the computational
costs, this initial analysis focused on externally triggered searches where the
source location and emission time are both known to some degree of precision.
In this paper, we show how the principle of seedless clustering can be extended
to facilitate computationally-feasible, all-sky searches where the direction
and emission time of the source are entirely unknown. We further demonstrate
that it is possible to achieve a considerable reduction in computation time by
using graphical processor units (GPUs), thereby facilitating more sensitive
searches.Comment: 9 pages, 2 figure
A robust method for calculating interface curvature and normal vectors using an extracted local level set
The level-set method is a popular interface tracking method in two-phase flow
simulations. An often-cited reason for using it is that the method naturally
handles topological changes in the interface, e.g. merging drops, due to the
implicit formulation. It is also said that the interface curvature and normal
vectors are easily calculated. This last point is not, however, the case in the
moments during a topological change, as several authors have already pointed
out. Various methods have been employed to circumvent the problem. In this
paper, we present a new such method which retains the implicit level-set
representation of the surface and handles general interface configurations. It
is demonstrated that the method extends easily to 3D. The method is validated
on static interface configurations, and then applied to two-phase flow
simulations where the method outperforms the standard method and the results
agree well with experiments.Comment: 31 pages, 18 figure
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