The Experts below are selected from a list of 22299 Experts worldwide ranked by ideXlab platform
Brody Sandel - One of the best experts on this subject based on the ideXlab platform.
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Computing the skewness of the phylogenetic mean Pairwise Distance in linear time
Algorithms for molecular biology : AMB, 2014Co-Authors: Constantinos Tsirogiannis, Brody SandelAbstract:Background: The phylogenetic Mean Pairwise Distance (MPD) is one of the most popular measures for computing the phylogenetic Distance between a given group of species. More specifically, for a phylogenetic tree T and for a set of species R represented by a subset of the leaf nodes of T ,t he MPD ofR is equal to the average cost of all possible simple paths in T that connect pairs of nodes in R. Among other phylogenetic measures, the MPD is used as a tool for deciding if the species of a given group R are closely related. To do this, it is important to compute not only the value of the MPD for this group but also the expectation, the variance, and the skewness of this metric. Although efficient algorithms have been developed for computing the expectation and the variance the MPD, there has been no approach so far for computing the skewness of this measure. Results: In the present work we describe how to compute the skewness of the MPD on a tree T optimally, in �( n) time; here n is the size of the tree T . So far this is the first result that leads to an exact, let alone efficient, computation of the skewness for any popular phylogenetic Distance measure. Moreover, we show how we can compute in �( n) time several interesting quantities in T , that can be possibly used as building blocks for computing efficiently the skewness of other phylogenetic measures. Conclusions: The optimal computation of the skewness of the MPD that is outlined in this work provides one more tool for studying the phylogenetic relatedness of species in large phylogenetic trees. Until now this has been infeasible, given that traditional techniques for computing the skewness are inefficient and based on inexact resampling.
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computing the skewness of the phylogenetic mean Pairwise Distance in linear time
Workshop on Algorithms in Bioinformatics, 2013Co-Authors: Constantinos Tsirogiannis, Brody SandelAbstract:The phylogenetic Mean Pairwise Distance (MPD) is one of the most popular measures for computing the phylogenetic Distance between a given group of species. More specifically, for a phylogenetic tree \(\mathcal{T}\) and for a set of species R represented by a subset of the leaf nodes of \(\mathcal{T}\), the MPD of R is equal to the average cost of all possible simple paths in \(\mathcal{T}\) that connect pairs of nodes in R.
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Computing the Skewness of the Phylogenetic Mean Pairwise Distance in Linear Time
arXiv: Quantitative Methods, 2013Co-Authors: Constantinos Tsirogiannis, Brody SandelAbstract:The phylogenetic Mean Pairwise Distance (MPD) is one of the most popular measures for computing the phylogenetic Distance between a given group of species. More specifically, for a phylogenetic tree T and for a set of species R represented by a subset of the leaf nodes of T, the MPD of R is equal to the average cost of all possible simple paths in T that connect pairs of nodes in R. Among other phylogenetic measures, the MPD is used as a tool for deciding if the species of a given group R are closely related. To do this, it is important to compute not only the value of the MPD for this group but also the expectation, the variance, and the skewness of this metric. Although efficient algorithms have been developed for computing the expectation and the variance the MPD, there has been no approach so far for computing the skewness of this measure. In the present work we describe how to compute the skewness of the MPD on a tree T optimally, in Theta(n) time; here n is the size of the tree T. So far this is the first result that leads to an exact, let alone efficient, computation of the skewness for any popular phylogenetic Distance measure. Moreover, we show how we can compute in Theta(n) time several interesting quantities in T that can be possibly used as building blocks for computing efficiently the skewness of other phylogenetic measures.
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WABI - Computing the Skewness of the Phylogenetic Mean Pairwise Distance in Linear Time
Lecture Notes in Computer Science, 2013Co-Authors: Constantinos Tsirogiannis, Brody SandelAbstract:The phylogenetic Mean Pairwise Distance (MPD) is one of the most popular measures for computing the phylogenetic Distance between a given group of species. More specifically, for a phylogenetic tree \(\mathcal{T}\) and for a set of species R represented by a subset of the leaf nodes of \(\mathcal{T}\), the MPD of R is equal to the average cost of all possible simple paths in \(\mathcal{T}\) that connect pairs of nodes in R.
Kotagiri Ramamohanarao - One of the best experts on this subject based on the ideXlab platform.
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IJCNN - Segmented Pairwise Distance for Time Series with Large Discontinuities
2020 International Joint Conference on Neural Networks (IJCNN), 2020Co-Authors: Sarah M. Erfani, Sudanthi Wijewickrema, Stephen O'leary, Kotagiri RamamohanaraoAbstract:Time series with large discontinuities are common in many scenarios. However, existing Distance-based algorithms (e.g., DTW and its derivative algorithms) may perform poorly in measuring Distances between these time series pairs. In this paper, we propose the segmented Pairwise Distance (SPD) algorithm to measure Distances between time series with large discontinuities. SPD is orthogonal to Distance-based algorithms and can be embedded in them. We validate advantages of SPD-embedded algorithms over corresponding Distance-based ones on both open datasets and a proprietary dataset of surgical time series (of surgeons performing a temporal bone surgery in a virtual reality surgery simulator). Experimental results demonstrate that SPD-embedded algorithms outperform corresponding Distance-based ones in Distance measurement between time series with large discontinuities, measured by the Silhouette index (SI).
Donato Traversa - One of the best experts on this subject based on the ideXlab platform.
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Phylogenetic relationships of Habronema microstoma and Habronema muscae (Spirurida: Habronematidae) within the order Spirurida inferred using mitochondrial cytochrome c oxidase subunit 1 (cox1) gene analysis
Parasitology Research, 2008Co-Authors: Raffaella Iorio, Jan Šlapeta, Domenico Otranto, Barbara Paoletti, Annunziata Giangaspero, Donato TraversaAbstract:The present study investigated genetic variability within a population of Habronema microstoma and Habronema muscae (Spirurida: Habronematidae) affecting horses in an endemic area of central Italy using polymerase chain reaction (PCR)-coupled sequencing of the mitochondrial cytochrome c oxidase subunit 1 gene ( cox1 ). No different cox1 sequences were detected in any of the H. muscae individual, while two haplotypes representing H. microstoma individuals differed for one substitution. The Pairwise Distance between the H. muscae and H. microstoma was 11%, coding for five amino acid changes. The sequence of an informative region within the cox1 gene of H. microstoma and H. muscae was analyzed by Maximum Likelihood and Bayesian phylogenetic methods using available mitochondrial sequences spirurid taxa belonging to Filarioidea, Thelazioidea, and Habronematoidea. Phylogenetic analysis supported the split of the tree into two sister spirurid groups, Habronematoidea and Filarioidea + Thelazioidea. The phylogenetic and evolutionary implications of Habronema with Filaroidea and Thelazioidea are discussed.
Herve Bourlard - One of the best experts on this subject based on the ideXlab platform.
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Automatic dysarthric speech detection exploiting Pairwise Distance-based convolutional neural networks
arXiv: Audio and Speech Processing, 2020Co-Authors: Parvaneh Janbakhshi, Ina Kodrasi, Herve BourlardAbstract:Automatic dysarthric speech detection can provide reliable and cost-effective computer-aided tools to assist the clinical diagnosis and management of dysarthria. In this paper we propose a novel automatic dysarthric speech detection approach based on analyses of Pairwise Distance matrices using convolutional neural networks (CNNs). We represent utterances through articulatory posteriors and consider pairs of phonetically-balanced representations, with one representation from a healthy speaker (i.e., the reference representation) and the other representation from the test speaker (i.e., test representation). Given such pairs of reference and test representations, features are first extracted using a feature extraction front-end, a frame-level Distance matrix is computed, and the obtained Distance matrix is considered as an image by a CNN-based binary classifier. The feature extraction, Distance matrix computation, and CNN-based classifier are jointly optimized in an end-to-end framework. Experimental results on two databases of healthy and dysarthric speakers for different languages and pathologies show that the proposed approach yields a high dysarthric speech detection performance, outperforming other CNN-based baseline approaches.
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novel gcc phat model in diffuse sound field for microphone array Pairwise Distance based calibration
International Conference on Acoustics Speech and Signal Processing, 2015Co-Authors: Jose Velasco, Mohammad J Taghizadeh, Herve Bourlard, Afsaneh Asaei, Carlos Julian Martinarguedas, Javier Maciasguarasa, Daniel PizarroAbstract:We propose a novel formulation of the generalized cross correlation with phase transform (GCC-PHAT) for a pair of microphones in diffuse sound field. This formulation elucidates the links between the microphone Distances and the GCC-PHAT output. Hence, it leads to a new model that enables estimation of the Pairwise Distances by optimizing over the Distances best matching the GCC-PHAT observations. Furthermore, the relation of this model to the coherence function is elaborated along with the dependency on the signal bandwidth. The experiments conducted on real data recordings demonstrate the theories and support the effectiveness of the proposed method.
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ICASSP - Novel GCC-PHAT model in diffuse sound field for microphone array Pairwise Distance based calibration
2015 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2015Co-Authors: Jose Velasco, Mohammad J Taghizadeh, Herve Bourlard, Afsaneh Asaei, Carlos Julián Martín-arguedas, Javier Macias-guarasa, Daniel PizarroAbstract:We propose a novel formulation of the generalized cross correlation with phase transform (GCC-PHAT) for a pair of microphones in diffuse sound field. This formulation elucidates the links between the microphone Distances and the GCC-PHAT output. Hence, it leads to a new model that enables estimation of the Pairwise Distances by optimizing over the Distances best matching the GCC-PHAT observations. Furthermore, the relation of this model to the coherence function is elaborated along with the dependency on the signal bandwidth. The experiments conducted on real data recordings demonstrate the theories and support the effectiveness of the proposed method.
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enhanced diffuse field model for ad hoc microphone array calibration
Signal Processing, 2014Co-Authors: Mohammad J Taghizadeh, Philip N Garner, Herve BourlardAbstract:In this paper, we investigate the diffuse field coherence model for microphone array Pairwise Distance estimation. We study the fundamental constraints and assumptions underlying this approach and propose evaluation methodologies to measure the adequacy of diffuseness for microphone array calibration. In addition, an enhanced schemebased on coherence averaging and histogramming, is presented to improve the robustness and performance of the Pairwise Distance estimation approach. The proposed theories and algorithms are evaluated on simulated and real data recordings for calibration of microphone array geometry in an ad hoc set-up. HighlightsAveraging and histogramming improve the diffuse field coherence model for calibration.A novel approach for assessment of the adequacy of diffuseness is formulated.The relation between Distance, enclosure dimension and diffuseness is characterized.A methodology for augmenting the diffuse sound field is proposed.Fundamental limitation of calibration based on the coherence model is analyzed.
Constantinos Tsirogiannis - One of the best experts on this subject based on the ideXlab platform.
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Computing the skewness of the phylogenetic mean Pairwise Distance in linear time
Algorithms for molecular biology : AMB, 2014Co-Authors: Constantinos Tsirogiannis, Brody SandelAbstract:Background: The phylogenetic Mean Pairwise Distance (MPD) is one of the most popular measures for computing the phylogenetic Distance between a given group of species. More specifically, for a phylogenetic tree T and for a set of species R represented by a subset of the leaf nodes of T ,t he MPD ofR is equal to the average cost of all possible simple paths in T that connect pairs of nodes in R. Among other phylogenetic measures, the MPD is used as a tool for deciding if the species of a given group R are closely related. To do this, it is important to compute not only the value of the MPD for this group but also the expectation, the variance, and the skewness of this metric. Although efficient algorithms have been developed for computing the expectation and the variance the MPD, there has been no approach so far for computing the skewness of this measure. Results: In the present work we describe how to compute the skewness of the MPD on a tree T optimally, in �( n) time; here n is the size of the tree T . So far this is the first result that leads to an exact, let alone efficient, computation of the skewness for any popular phylogenetic Distance measure. Moreover, we show how we can compute in �( n) time several interesting quantities in T , that can be possibly used as building blocks for computing efficiently the skewness of other phylogenetic measures. Conclusions: The optimal computation of the skewness of the MPD that is outlined in this work provides one more tool for studying the phylogenetic relatedness of species in large phylogenetic trees. Until now this has been infeasible, given that traditional techniques for computing the skewness are inefficient and based on inexact resampling.
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computing the skewness of the phylogenetic mean Pairwise Distance in linear time
Workshop on Algorithms in Bioinformatics, 2013Co-Authors: Constantinos Tsirogiannis, Brody SandelAbstract:The phylogenetic Mean Pairwise Distance (MPD) is one of the most popular measures for computing the phylogenetic Distance between a given group of species. More specifically, for a phylogenetic tree \(\mathcal{T}\) and for a set of species R represented by a subset of the leaf nodes of \(\mathcal{T}\), the MPD of R is equal to the average cost of all possible simple paths in \(\mathcal{T}\) that connect pairs of nodes in R.
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Computing the Skewness of the Phylogenetic Mean Pairwise Distance in Linear Time
arXiv: Quantitative Methods, 2013Co-Authors: Constantinos Tsirogiannis, Brody SandelAbstract:The phylogenetic Mean Pairwise Distance (MPD) is one of the most popular measures for computing the phylogenetic Distance between a given group of species. More specifically, for a phylogenetic tree T and for a set of species R represented by a subset of the leaf nodes of T, the MPD of R is equal to the average cost of all possible simple paths in T that connect pairs of nodes in R. Among other phylogenetic measures, the MPD is used as a tool for deciding if the species of a given group R are closely related. To do this, it is important to compute not only the value of the MPD for this group but also the expectation, the variance, and the skewness of this metric. Although efficient algorithms have been developed for computing the expectation and the variance the MPD, there has been no approach so far for computing the skewness of this measure. In the present work we describe how to compute the skewness of the MPD on a tree T optimally, in Theta(n) time; here n is the size of the tree T. So far this is the first result that leads to an exact, let alone efficient, computation of the skewness for any popular phylogenetic Distance measure. Moreover, we show how we can compute in Theta(n) time several interesting quantities in T that can be possibly used as building blocks for computing efficiently the skewness of other phylogenetic measures.
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WABI - Computing the Skewness of the Phylogenetic Mean Pairwise Distance in Linear Time
Lecture Notes in Computer Science, 2013Co-Authors: Constantinos Tsirogiannis, Brody SandelAbstract:The phylogenetic Mean Pairwise Distance (MPD) is one of the most popular measures for computing the phylogenetic Distance between a given group of species. More specifically, for a phylogenetic tree \(\mathcal{T}\) and for a set of species R represented by a subset of the leaf nodes of \(\mathcal{T}\), the MPD of R is equal to the average cost of all possible simple paths in \(\mathcal{T}\) that connect pairs of nodes in R.
