scholarly journals The Shape of Trees – Limits of Current Diversification Models

2021 ◽  
Author(s):  
Orlando Schwery ◽  
Brian C. O’Meara
Keyword(s):  
Large Set ◽  
New Approach ◽  
Tree Shape ◽  
Model Adequacy ◽  
Model Based ◽  
Multiple Hypotheses ◽  
Test Models ◽  
Standard Procedures ◽  
Lineage Accumulation ◽  
Best Fitting

AbstractTo investigate how biodiversity arose, the field of macroevolution largely relies on model-based approaches to estimate rates of diversification and what factors influence them. The number of available models is rising steadily, facilitating the modeling of an increasing number of possible diversification dynamics, and multiple hypotheses relating to what fueled or stifled lineage accumulation within groups of organisms. However, growing concerns about unchecked biases and limitations in the employed models suggest the need for rigorous validation of methods used to infer. Here, we address two points: the practical use of model adequacy testing, and what model adequacy can tell us about the overall state of diversification models. Using a large set of empirical phylogenies, and a new approach to test models using aspects of tree shape, we test how a set of staple models performs with regards to adequacy. Patterns of adequacy are described across trees and models and causes for inadequacy – particularly if all models are inadequate – are explored. The findings make clear that overall, only few empirical phylogenies cannot be described by at least one model. However, finding that the best fitting of a set of models might not necessarily be adequate makes clear that adequacy testing should become a step in the standard procedures for diversification studies.

scholarly journals BoskR – Testing Adequacy of Diversification Models Using Tree Shape

2020 ◽  
Author(s):  
Orlando Schwery ◽  
Brian C. O’Meara
Keyword(s):  
Phylogenetic Trees ◽  
Graph Laplacian ◽  
R Package ◽  
Laplacian Spectrum ◽  
Tree Shape ◽  
Model Adequacy ◽  
Model Based ◽  
Absolute Sense ◽  
Best Fit

AbstractThe study of diversification largely relies on model-based approaches, estimating rates of speciation and extinction from phylogenetic trees. While a plethora of different models exist – all with different features, strengths and weaknesses – there is increasing concern about the reliability of the inference we gain from them. Apart from simply finding the model with the best fit for the data, we should find ways to assess a model’s suitability to describe the data in an absolute sense. The R package BoskR implements a simple way of judging a model’s adequacy for a given phylogeny using metrics for tree shape, assuming that a model is inadequate for a phylogeny if it produces trees that are consistently dissimilar in shape from the tree that should be analyzed. Tree shape is assessed via metrics derived from the tree’s modified graph Laplacian spectrum, as provided by RPANDA. We exemplify the use of the method using simulated and empirical example phylogenies. BoskR was mostly able to correctly distinguish trees simulated under clearly different models and revealed that not all models are adequate for the empirical example trees. We believe the metrics of tree shape to be an intuitive and relevant means of assessing diversification model adequacy. Furthermore, by implementing the approach in an openly available R package, we enable and encourage researchers to adopt adequacy testing into their workflow.

A New Approach in Model-Based Testing: Designing Test Models in TTCN-3

2011 ◽  
pp. 90-105 ◽  
Author(s):  
Antal Wu-Hen-Chang ◽  
Gusztáv Adamis ◽  
Levente Erős ◽  
Gábor Kovács ◽  
Tibor Csöndes
Keyword(s):  
New Approach ◽  
Model Based ◽  
Test Models ◽  
Model Based Testing

scholarly journals A Probabilistic Model for Real-Time Semantic Prediction of Human Motion Intentions from RGBD-Data

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4141
Author(s):  
Wouter Houtman ◽  
Gosse Bijlenga ◽  
Elena Torta ◽  
René van de Molengraft
Keyword(s):  
Real Time ◽  
Collision Avoidance ◽  
Probabilistic Model ◽  
Real Life ◽  
Human Motion ◽  
Model Based ◽  
Multiple Hypotheses ◽  
Navigation Algorithms ◽  
The Right ◽  
Motion Behavior

For robots to execute their navigation tasks both fast and safely in the presence of humans, it is necessary to make predictions about the route those humans intend to follow. Within this work, a model-based method is proposed that relates human motion behavior perceived from RGBD input to the constraints imposed by the environment by considering typical human routing alternatives. Multiple hypotheses about routing options of a human towards local semantic goal locations are created and validated, including explicit collision avoidance routes. It is demonstrated, with real-time, real-life experiments, that a coarse discretization based on the semantics of the environment suffices to make a proper distinction between a person going, for example, to the left or the right on an intersection. As such, a scalable and explainable solution is presented, which is suitable for incorporation within navigation algorithms.

Testing Colour Appearance Model Based UCS Using HDR, WCG and COMBVD Datasets

2021 ◽  
Vol 2021 (29) ◽  
pp. 381-386
Author(s):  
Xu Qiang ◽  
Muhammad Safdar ◽  
Ming Ronnier Luo
Keyword(s):  
Correlation Coefficient ◽  
Power Factor ◽  
Marked Improvement ◽  
Appearance Model ◽  
Colour Difference ◽  
Similar Performance ◽  
Model Based ◽  
Test Models ◽  
Appearance Models ◽  
Colour Appearance

Two colour appearance models based UCSs, CAM16-UCS and ZCAM-QMh, were tested using HDR, WCG and COMBVD datasets. As a comparison, two widely used UCSs, CIELAB and ICTCP, were tested. Metrics of the STRESS and correlation coefficient between predicted colour differences and visual differences, together with local and global uniformity based on their chromatic discrimination ellipses, were applied to test models' performance. The two UCSs give similar performance. The luminance parametric factor kL, and power factor γ, were introduced to optimize colour-difference models. Factors kL and γ of 0.75 and 0.5, gave marked improvement to predict the HDR dataset. Factor kL of 0.3 gave significant improvement in the test of WCG dataset. In the test of COMBVD dataset, optimization provide very limited improvement.

scholarly journals A New Approach to Multiple Fault Diagnosis: A Combination of Diagnostic Matrices, Graphs, Algebraic and Rule-Based Models. The Case of Two-Layer Models

2008 ◽  
Vol 18 (4) ◽  
pp. 465-476 ◽  
Author(s):  
Antoni Ligęza ◽  
Jan Kościelny
Keyword(s):  
Fault Diagnosis ◽  
Simultaneous Occurrence ◽  
Diagnostic Model ◽  
Rule Based ◽  
New Approach ◽  
Multiple Fault ◽  
Multiple Faults ◽  
Model Based ◽  
Multiple Fault Diagnosis ◽  
System Variables

A New Approach to Multiple Fault Diagnosis: A Combination of Diagnostic Matrices, Graphs, Algebraic and Rule-Based Models. The Case of Two-Layer ModelsThe diagnosis of multiple faults is significantly more difficult than singular fault diagnosis. However, in realistic industrial systems the possibility of simultaneous occurrence of multiple faults must be taken into account. This paper investigates some of the limitations of the diagnostic model based on the simple binary diagnostic matrix in the case of multiple faults. Several possible interpretations of the diagnostic matrix with rule-based systems are provided and analyzed. A proposal of an extension of the basic, single-level model based on diagnostic matrices to a two-level one, founded on causal analysis and incorporating an OR and an AND matrix is put forward. An approach to the diagnosis of multiple faults based on inconsistency analysis is outlined, and a refinement procedure using a qualitative model of dependencies among system variables is sketched out.

scholarly journals Tip rates, phylogenies, and diversification: what are we estimating, and how good are the estimates?

2018 ◽  
Author(s):  
Pascal O. Title ◽  
Daniel L. Rabosky
Keyword(s):  
Diversification Rate ◽  
Rate Variation ◽  
Terminal Branch ◽  
List Type ◽  
Large Set ◽  
Extinction Rate ◽  
Diversification Rates ◽  
Speciation Rate ◽  
Model Based ◽  
Model Free

AbstractSpecies-specific diversification rates, or “tip rates”, can be computed quickly from phylogenies and are widely used to study diversification rate variation in relation to geography, ecology, and phenotypes. These tip rates provide a number of theoretical and practical advantages, such as the relaxation of assumptions of rate homogeneity in trait-dependent diversification studies. However, there is substantial confusion in the literature regarding whether these metrics estimate speciation or net diversification rates. Additionally, no study has yet compared the relative performance and accuracy of tip rate metrics across simulated diversification scenarios.We compared the statistical performance of three model-free rate metrics (inverse terminal branch lengths; node density metric; DR statistic) and a model-based approach (BAMM). We applied each method to a large set of simulated phylogenies that had been generated under different diversification processes; scenarios included multi-regime time-constant and diversity-dependent trees, as well as trees where the rate of speciation evolves under a diffusion process. We summarized performance in relation to the type of rate variation, the magnitude of rate heterogeneity and rate regime size. We also compared the ability of the metrics to estimate both speciation and net diversification rates.We show decisively that model-free tip rate metrics provide a better estimate of the rate of speciation than of net diversification. Error in net diversification rate estimates increases as a function of the relative extinction rate. In contrast, error in speciation rate estimates is low and relatively insensitive to extinction. Overall, and in particular when relative extinction was high, BAMM inferred the most accurate tip rates and exhibited lower error than non-model-based approaches. DR was highly correlated with true speciation rates but exhibited high error variance, and was the best metric for very small rate regimes.We found that, of the metrics tested, DR and BAMM are the most useful metrics for studying speciation rate dynamics and trait-dependent diversification. Although BAMM was more accurate than DR overall, the two approaches have complementary strengths. Because tip rate metrics are more reliable estimators of speciation rate, we recommend that empirical studies using these metrics exercise caution when drawing biological interpretations in any situation where the distinction between speciation and net diversification is important.

scholarly journals Application of Model-Based Tests for Analysing the Consequences of Mine Fires

2018 ◽  
Vol 1 (1) ◽  
pp. 767-774
Author(s):  
Magdalena Tutak
Keyword(s):  
Fire Hazard ◽  
Physical Parameters ◽  
Gas Mixture ◽  
New Approach ◽  
Model Based ◽  
Underground Coal Mines ◽  
Mine Fires ◽  
Underground Mine Workings ◽  
The Subject ◽  
Chemical Products

Abstract One of the most common and most dangerous hazards in underground coal mines is fire hazard. Mine fires can be exogenous or endogenous in nature. In the case of the former, a particular hazard is posed by methane fires that occur in dog headings and longwalls. Endogenous and exogenous fires are large hazard for working crew in mining headings and cause economics losses for mining plants. Mine fires result in emission of harmful chemical products and have a crucial impact on the physical parameters of the airflow. The subject of the article concerns the analysis of the consequences of methane fires in dog headings. These consequences were identified by means of model-based tests. For this purpose, a model was developed and boundary conditions were adopted to reflect the actual layout of the headings and the condition of the atmosphere in the area under analysis. The objective of the test was to determine the effects of methane fires on the chemical composition of the atmosphere and the physical parameters of the gas mixture generated in the process. The results obtained clearly indicate that fires have a significant impact on the above-mentioned values. The paper presents the distributions for the physical parameters of the resulting gas mixture and the concentration of fire gases. Moreover, it shows the distributions of temperature and oxygen concentration levels in the headings under analysis. The methodology developed for the application of model-based tests to analyse fire events in mine headings represents a new approach to the problem of investigating the consequences of such fires. It is also suitable for variant analyses of the processes related to the ventilation of underground mine workings as well as for analyses of emergency states. Model-based tests should support the assessment of the methane hazard levels and, subsequently, lead to an improvement of work safety in mines.

Model-Based Testing of Embedded Systems Exemplified for the Automotive Domain

2010 ◽  
pp. 377-413 ◽  
Author(s):  
Justyna Zander ◽  
Ina Schieferdecker
Keyword(s):  
Embedded Systems ◽  
Embedded System ◽  
Reference Signal ◽  
Test Methods ◽  
Domain Model ◽  
Test Method ◽  
Model Based ◽  
Test Models ◽  
Black Box Testing ◽  
Weak Points

The purpose of this chapter is to introduce the test methods applied for embedded systems addressing selected problems in the automotive domain. Model-based test approaches are reviewed and categorized. Weak points are identified and a novel test method is proposed. It is called model-in-the-loop for embedded system test (MiLEST) and is realized in MATLAB®/Simulink®/Stateflow® environment. Its main contribution refers to functional black-box testing based on the system and test models. It is contrasted with the test methods currently applied in the industry that form dedicated solutions, usually specialized in a concrete testing context. The developed signal-feature-oriented paradigm developed herewith allows the abstract description of signals and their properties. It addresses the problem of missing reference signal flows and allows for a systematic and automatic test data selection. Processing of both discrete and continuous signals is possible, so that the hybrid behavior of embedded systems can be handled.

scholarly journals Improving Model-Based Genetic Programming for Symbolic Regression of Small Expressions

2020 ◽  
pp. 1-27 ◽  
Author(s):  
M. Virgolin ◽  
T. Alderliesten ◽  
C. Witteveen ◽  
P. A. N. Bosman
Keyword(s):  
Genetic Programming ◽  
Real World ◽  
Symbolic Regression ◽  
New Approach ◽  
Model Based ◽  
Real World Datasets ◽  
Linkage Learning ◽  
Optimal Mixing ◽  
Crucial Parameter

The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a model-based EA framework that has been shown to perform well in several domains, including Genetic Programming (GP). Differently from traditional EAs where variation acts blindly, GOMEA learns a model of interdependencies within the genotype, that is, the linkage, to estimate what patterns to propagate. In this article, we study the role of Linkage Learning (LL) performed by GOMEA in Symbolic Regression (SR). We show that the non-uniformity in the distribution of the genotype in GP populations negatively biases LL, and propose a method to correct for this. We also propose approaches to improve LL when ephemeral random constants are used. Furthermore, we adapt a scheme of interleaving runs to alleviate the burden of tuning the population size, a crucial parameter for LL, to SR. We run experiments on 10 real-world datasets, enforcing a strict limitation on solution size, to enable interpretability. We find that the new LL method outperforms the standard one, and that GOMEA outperforms both traditional and semantic GP. We also find that the small solutions evolved by GOMEA are competitive with tuned decision trees, making GOMEA a promising new approach to SR.

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