scholarly journals Development and assessment of fully automated and globally transitive geometric morphometric methods, with application to a biological comparative dataset with high interspecific variation

2016 ◽  
Author(s):  
Tingran Gao ◽  
Gabriel S. Yapuncich ◽  
Ingrid Daubechies ◽  
Sayan Mukherjee ◽  
Doug M. Boyer
Keyword(s):  
Ground Truth ◽  
Interspecific Variation ◽  
Shape Space ◽  
Substantial Fraction ◽  
Geometric Morphometric ◽  
Full Sample ◽  
Morphometric Methods ◽  
Automated Methods ◽  
Variance Structure

AbstractAutomated geometric morphometric methods are promising tools for shape analysis in comparative biology: they improve researchers’ abilities to quantify biological variation extensively (by permitting more specimens to be analyzed) and intensively (by characterizing shapes with greater fidelity). Although use of these methods has increased, automated methods have some notable limitations: pairwise correspondences are frequently inaccurate or lack transitivity (i.e., they are not defined with reference to the full sample). In this study, we reassess the accuracy of two previously published automated methods, cPDist [1] and auto3Dgm [2], and evaluate several modifications to these methods. We show that a substantial fraction of alignments and pairwise maps between specimens of highly dissimilar geometries were inaccurate in the study of Boyer et al. [1], despite a taxonomically sensitive variance structure of continuous Procrustes distances. We also show these inaccuracies can be remedied by utilizing a globally informed methodology within a collection of shapes, instead of only comparing shapes in a pairwise manner (c.f. [2]). Unfortunately, while global information generally enhances maps between dissimilar objects, it can degrade the quality of correspondences between similar objects due to the accumulation of numerical error. We explore a number of approaches to mitigate this degradation, quantify the performance of these approaches, and compare the generated pairwise maps (as well as the shape space characterized by these maps) to a “ground truth” obtained from landmarks manually collected by geometric morphometricians. Novel methods both improve the quality of the pairwise correspondences relative to cPDist, and achieve a taxonomic distinctiveness comparable to auto3Dgm.

Model and Method for Contributor’s Quality Assessment in Community Image Tagging Systems

2018 ◽  
pp. 45-51
Author(s):  
A. V. Ponomarev
Keyword(s):  
Large Scale ◽  
Wide Spectrum ◽  
Preference Relation ◽  
Pairwise Comparison ◽  
Ground Truth ◽  
Comparison Method ◽  
Characteristic Matrix ◽  
Image Tagging ◽  
Proposed Model

Introduction: Large-scale human-computer systems involving people of various skills and motivation into the information processing process are currently used in a wide spectrum of applications. An acute problem in such systems is assessing the expected quality of each contributor; for example, in order to penalize incompetent or inaccurate ones and to promote diligent ones.Purpose: To develop a method of assessing the expected contributor’s quality in community tagging systems. This method should only use generally unreliable and incomplete information provided by contributors (with ground truth tags unknown).Results:A mathematical model is proposed for community image tagging (including the model of a contributor), along with a method of assessing the expected contributor’s quality. The method is based on comparing tag sets provided by different contributors for the same images, being a modification of pairwise comparison method with preference relation replaced by a special domination characteristic. Expected contributors’ quality is evaluated as a positive eigenvector of a pairwise domination characteristic matrix. Community tagging simulation has confirmed that the proposed method allows you to adequately estimate the expected quality of community tagging system contributors (provided that the contributors' behavior fits the proposed model).Practical relevance: The obtained results can be used in the development of systems based on coordinated efforts of community (primarily, community tagging systems). 

scholarly journals Arothron: An R package for geometric morphometric methods and virtual anthropology applications

2021 ◽  
Author(s):  
Profico Antonio ◽  
Buzi Costantino ◽  
Castiglione Silvia ◽  
Melchionna Marina ◽  
Piras Paolo ◽  
...  
Keyword(s):  
R Package ◽  
Geometric Morphometric ◽  
Morphometric Methods ◽  
Virtual Anthropology

scholarly journals DeepMAsED: evaluating the quality of metagenomic assemblies

2020 ◽  
Vol 36 (10) ◽  
pp. 3011-3017 ◽  
Author(s):  
Olga Mineeva ◽  
Mateo Rojas-Carulla ◽  
Ruth E Ley ◽  
Bernhard Schölkopf ◽  
Nicholas D Youngblut
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Ground Truth ◽  
Supplementary Information ◽  
Learning Approach ◽  
Wide Range ◽  
Metagenome Assembly ◽  
Model Training ◽  
Reference Genomes

Abstract Motivation Methodological advances in metagenome assembly are rapidly increasing in the number of published metagenome assemblies. However, identifying misassemblies is challenging due to a lack of closely related reference genomes that can act as pseudo ground truth. Existing reference-free methods are no longer maintained, can make strong assumptions that may not hold across a diversity of research projects, and have not been validated on large-scale metagenome assemblies. Results We present DeepMAsED, a deep learning approach for identifying misassembled contigs without the need for reference genomes. Moreover, we provide an in silico pipeline for generating large-scale, realistic metagenome assemblies for comprehensive model training and testing. DeepMAsED accuracy substantially exceeds the state-of-the-art when applied to large and complex metagenome assemblies. Our model estimates a 1% contig misassembly rate in two recent large-scale metagenome assembly publications. Conclusions DeepMAsED accurately identifies misassemblies in metagenome-assembled contigs from a broad diversity of bacteria and archaea without the need for reference genomes or strong modeling assumptions. Running DeepMAsED is straight-forward, as well as is model re-training with our dataset generation pipeline. Therefore, DeepMAsED is a flexible misassembly classifier that can be applied to a wide range of metagenome assembly projects. Availability and implementation DeepMAsED is available from GitHub at https://github.com/leylabmpi/DeepMAsED. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Comparing skull formation of the Hungarian (Hortobágy) Zackel sheep breed by geometrics morphometrics

2015 ◽  
pp. 29-33
Author(s):  
Zsuzsanna Csóri ◽  
András Gáspárdy ◽  
András Jávor
Keyword(s):  
Morphological Changes ◽  
Time Change ◽  
Carpathian Basin ◽  
Sheep Breed ◽  
Geometric Morphometric ◽  
The Past ◽  
Morphometric Methods ◽  
Skull Formation ◽  
Over Time

This work seeks to explore the morphological changes of the Hungarian (Hortobágy) Zackel sheep's skull, which occurred in the past 50–70 years. In this study, we compared individuals skull forms by geometric morphometric methods. The origin of the breed is not known, we do not know when entering the Carpathian Basin. Therefore, the comparison involved the only known early archaeological findings. We have shown that there is no difference between each period colour variations, but over time change has occurred in the skull formation of the breed.

scholarly journals DeepLMS: a deep learning predictive model for supporting online learning in the Covid-19 era

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Sofia B. Dias ◽  
Sofia J. Hadjileontiadou ◽  
José Diniz ◽  
Leontios J. Hadjileontiadis
Keyword(s):  
Online Learning ◽  
Deep Learning ◽  
Predictive Model ◽  
Short Term Memory ◽  
Ground Truth ◽  
Face To Face ◽  
Learning Techniques ◽  
Learning Plans ◽  
First Time

AbstractCoronavirus (Covid-19) pandemic has imposed a complete shut-down of face-to-face teaching to universities and schools, forcing a crash course for online learning plans and technology for students and faculty. In the midst of this unprecedented crisis, video conferencing platforms (e.g., Zoom, WebEx, MS Teams) and learning management systems (LMSs), like Moodle, Blackboard and Google Classroom, are being adopted and heavily used as online learning environments (OLEs). However, as such media solely provide the platform for e-interaction, effective methods that can be used to predict the learner’s behavior in the OLEs, which should be available as supportive tools to educators and metacognitive triggers to learners. Here we show, for the first time, that Deep Learning techniques can be used to handle LMS users’ interaction data and form a novel predictive model, namely DeepLMS, that can forecast the quality of interaction (QoI) with LMS. Using Long Short-Term Memory (LSTM) networks, DeepLMS results in average testing Root Mean Square Error (RMSE) $$<0.009$$ < 0.009 , and average correlation coefficient between ground truth and predicted QoI values $$r\ge 0.97$$ r ≥ 0.97 $$(p<0.05)$$ ( p < 0.05 ) , when tested on QoI data from one database pre- and two ones during-Covid-19 pandemic. DeepLMS personalized QoI forecasting scaffolds user’s online learning engagement and provides educators with an evaluation path, additionally to the content-related assessment, enriching the overall view on the learners’ motivation and participation in the learning process.

scholarly journals Multi-View Partial Multi-Label Learning with Graph-Based Disambiguation

2020 ◽  
Vol 34 (04) ◽  
pp. 3553-3560 ◽  
Author(s):  
Ze-Sen Chen ◽  
Xuan Wu ◽  
Qing-Guo Chen ◽  
Yao Hu ◽  
Min-Ling Zhang
Keyword(s):  
Predictive Model ◽  
Clustering Analysis ◽  
Experimental Studies ◽  
Ground Truth ◽  
Two Stage ◽  
Feature Vectors ◽  
Similarity Graph ◽  
Training Examples ◽  
Guided Clustering

In multi-view multi-label learning (MVML), each training example is represented by different feature vectors and associated with multiple labels simultaneously. Nonetheless, the labeling quality of training examples is tend to be affected by annotation noises. In this paper, the problem of multi-view partial multi-label learning (MVPML) is studied, where the set of associated labels are assumed to be candidate ones and only partially valid. To solve the MVPML problem, a two-stage graph-based disambiguation approach is proposed. Firstly, the ground-truth labels of each training example are estimated by disambiguating the candidate labels with fused similarity graph. After that, the predictive model for each label is learned from embedding features generated from disambiguation-guided clustering analysis. Extensive experimental studies clearly validate the effectiveness of the proposed approach in solving the MVPML problem.

scholarly journals Geometric Morphometric Analyses Support Incorporating the Goshen Point Type into Plainview

2019 ◽  
Vol 85 (1) ◽  
pp. 171-181 ◽  
Author(s):  
Briggs Buchanan ◽  
Mark Collard ◽  
Michael J. O'Brien
Keyword(s):  
Recent Work ◽  
Social Dynamics ◽  
Spatiotemporal Variation ◽  
Single Type ◽  
Geometric Morphometric ◽  
Morphometric Analyses ◽  
Morphometric Methods ◽  
Point Type

Recent work has demonstrated that Goshen points overlap in time with another group of unfluted lanceolate points from the Plains, Plainview points. This has raised the question of whether the two types should be kept separate or consolidated into a single type. We sought to resolve this issue by applying geometric morphometric methods to a sample of points from well-documented Goshen and Plainview assemblages. We found that their shapes were statistically indistinguishable, which indicates that Goshen and Plainview points should be assigned to the same type. Because Plainview points were recognized before Goshen points, it is the latter type name that should be dropped. Sinking Goshen into Plainview allows us to move beyond taxonomic issues and toward understanding both the spatiotemporal variation that exists among Plainview assemblages and what it can tell us about the adaptations and social dynamics of Plainview groups.

scholarly journals Core–periphery structure in directed networks

2020 ◽  
Vol 476 (2241) ◽  
pp. 20190783
Author(s):  
Andrew Elliott ◽  
Angus Chiu ◽  
Marya Bazzi ◽  
Gesine Reinert ◽  
Mihai Cucuringu
Keyword(s):  
Statistical Significance ◽  
Ground Truth ◽  
Directed Networks ◽  
Political Blogs ◽  
Novel Structure ◽  
Two Measures ◽  
Likelihood Approach ◽  
Core Sets ◽  
Empirical Networks

Empirical networks often exhibit different meso-scale structures, such as community and core–periphery structures. Core–periphery structure typically consists of a well-connected core and a periphery that is well connected to the core but sparsely connected internally. Most core–periphery studies focus on undirected networks. We propose a generalization of core–periphery structure to directed networks. Our approach yields a family of core–periphery block model formulations in which, contrary to many existing approaches, core and periphery sets are edge-direction dependent. We focus on a particular structure consisting of two core sets and two periphery sets, which we motivate empirically. We propose two measures to assess the statistical significance and quality of our novel structure in empirical data, where one often has no ground truth. To detect core–periphery structure in directed networks, we propose three methods adapted from two approaches in the literature, each with a different trade-off between computational complexity and accuracy. We assess the methods on benchmark networks where our methods match or outperform standard methods from the literature, with a likelihood approach achieving the highest accuracy. Applying our methods to three empirical networks—faculty hiring, a world trade dataset and political blogs—illustrates that our proposed structure provides novel insights in empirical networks.

Sign in / Sign up

Export Citation Format

Share Document