66,637 research outputs found
A modified broadcast strategy for distributed signal estimation in a wireless sensor network with a tree topology
We envisage a wireless sensor network (WSN) where each node is tasked with estimating a set of node-specific desired signals that has been corrupted by additive noise. The nodes accomplish this estimation by means of the distributed adaptive node-specific estimation (DANSE) algorithm in a tree topology (T-DANSE). In this paper, we consider a network where there is at least one node with a large (virtually infinite) energy budget, which we select as the root node. We propose a modification to the signal flow of the T-DANSE algorithm where instead of each node having two-way signal communication, there is a single signal flow toward the root node of the tree topology which then broadcasts a single signal to all other nodes. We demonstrate that the modified algorithm is equivalent to the original T-DANSE algorithm in terms of the signal estimation performance, shifts a large part of the communication burden toward the high-power root node to reduce the energy consumption in the low-power nodes and reduces the input-output delay
Twisted trees and inconsistency of tree estimation when gaps are treated as missing data -- the impact of model mis-specification in distance corrections
Statistically consistent estimation of phylogenetic trees or gene trees is
possible if pairwise sequence dissimilarities can be converted to a set of
distances that are proportional to the true evolutionary distances. Susko et
al. (2004) reported some strikingly broad results about the forms of
inconsistency in tree estimation that can arise if corrected distances are not
proportional to the true distances. They showed that if the corrected distance
is a concave function of the true distance, then inconsistency due to long
branch attraction will occur. If these functions are convex, then two "long
branch repulsion" trees will be preferred over the true tree -- though these
two incorrect trees are expected to be tied as the preferred true. Here we
extend their results, and demonstrate the existence of a tree shape (which we
refer to as a "twisted Farris-zone" tree) for which a single incorrect tree
topology will be guaranteed to be preferred if the corrected distance function
is convex. We also report that the standard practice of treating gaps in
sequence alignments as missing data is sufficient to produce non-linear
corrected distance functions if the substitution process is not independent of
the insertion/deletion process. Taken together, these results imply
inconsistent tree inference under mild conditions. For example, if some
positions in a sequence are constrained to be free of substitutions and
insertion/deletion events while the remaining sites evolve with independent
substitutions and insertion/deletion events, then the distances obtained by
treating gaps as missing data can support an incorrect tree topology even given
an unlimited amount of data.Comment: 29 pages, 3 figure
Estimating Infection Sources in Networks Using Partial Timestamps
We study the problem of identifying infection sources in a network based on
the network topology, and a subset of infection timestamps. In the case of a
single infection source in a tree network, we derive the maximum likelihood
estimator of the source and the unknown diffusion parameters. We then introduce
a new heuristic involving an optimization over a parametrized family of Gromov
matrices to develop a single source estimation algorithm for general graphs.
Compared with the breadth-first search tree heuristic commonly adopted in the
literature, simulations demonstrate that our approach achieves better
estimation accuracy than several other benchmark algorithms, even though these
require more information like the diffusion parameters. We next develop a
multiple sources estimation algorithm for general graphs, which first
partitions the graph into source candidate clusters, and then applies our
single source estimation algorithm to each cluster. We show that if the graph
is a tree, then each source candidate cluster contains at least one source.
Simulations using synthetic and real networks, and experiments using real-world
data suggest that our proposed algorithms are able to estimate the true
infection source(s) to within a small number of hops with a small portion of
the infection timestamps being observed.Comment: 15 pages, 15 figures, accepted by IEEE Transactions on Information
Forensics and Securit
Detection of recombination in DNA multiple alignments with hidden markov models
CConventional phylogenetic tree estimation methods assume that all sites in a DNA multiple alignment have the same evolutionary history. This assumption is violated in data sets from certain bacteria and viruses due to recombination, a process that leads to the creation of mosaic sequences from different strains and, if undetected, causes systematic errors in phylogenetic tree estimation. In the current work, a hidden Markov model (HMM) is employed to detect recombination events in multiple alignments of DNA sequences. The emission probabilities in a given state are determined by the branching order (topology) and the branch lengths of the respective phylogenetic tree, while the transition probabilities depend on the global recombination probability. The present study improves on an earlier heuristic parameter optimization scheme and shows how the branch lengths and the recombination probability can be optimized in a maximum likelihood sense by applying the expectation maximization (EM) algorithm. The novel algorithm is tested on a synthetic benchmark problem and is found to clearly outperform the earlier heuristic approach. The paper concludes with an application of this scheme to a DNA sequence alignment of the argF gene from four Neisseria strains, where a likely recombination event is clearly detected
Tree Topology Estimation
<p>Tree-like structures are fundamental in nature. A wide variety of two-dimensional imaging techniques allow us to image trees. However, an image of a tree typically includes spurious branch crossings and the original relationships of ancestry among edges may be lost. We present a methodology for estimating the most likely topology of a rooted, directed, three-dimensional tree given a single two-dimensional image of it. We regularize this inverse problem via a prior parametric tree-growth model that realistically captures the morphology of a wide variety of trees. We show that the problem of estimating the optimal tree has linear complexity if ancestry is known, but is NP-hard if it is lost. For the latter case, we present both a greedy approximation algorithm and a heuristic search algorithm that effectively explore the space of possible trees. Experimental results on retinal vessel, plant root, and synthetic tree datasets show that our methodology is both accurate and efficient.</p>Dissertatio
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