scholarly journals Neural Networks for self-adjusting Mutation Rate Estimation when the Recombination Rate is unknown

2021 ◽  
Author(s):  
Klara Elisabeth Burger ◽  
Peter Pfaffelhuber ◽  
Franz Baumdicker
Keyword(s):  
Neural Networks ◽  
Population Genetics ◽  
Mutation Rate ◽  
Estimation Method ◽  
Feedforward Neural Networks ◽  
Estimation Methods ◽  
Learning Tools ◽  
Effective Population ◽  
Recombination Rates ◽  
Optimal Estimators

Estimating the mutation rate, or equivalently effective population size, is a common task in population genetics. If recombination is low or high, the optimal linear estimation methods, namely Fu’s and Watterson’s estimator, are known and well understood. For intermediate recombination rates, the calculation of optimal estimators is more involved. As an alternative to model-based estimation, neural networks and other machine learning tools could help to develop good estimators in these involved scenarios. However, if no benchmark is available it is difficult to assess how well suited these tools are for different applications in population genetics.Here we investigate feedforward neural networks for the estimation of the mutation rate and compare their performance with the frequently used optimal estimators introduced by Fu and Watterson. We find that neural networks can reproduce the optimal estimators if provided with the appropriate features and training sets. Remarkably, only one hidden layer is necessary to obtain a single estimator that performs almost as well as the optimal estimators for both, low and high recombination rates and provides a superior estimation method for intermediate recombination rates at the same time.

scholarly journals Online Capacity Estimation for Lithium-Ion Batteries Based on Semi-Supervised Convolutional Neural Network

2021 ◽  
Vol 12 (4) ◽  
pp. 256
Author(s):  
Yi Wu ◽  
Wei Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Lithium Ion Batteries ◽  
Estimation Method ◽  
Lithium Ion ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Capacity Estimation ◽  
Practical Applications

Accurate capacity estimation can ensure the safe and reliable operation of lithium-ion batteries in practical applications. Recently, deep learning-based capacity estimation methods have demonstrated impressive advances. However, such methods suffer from limited labeled data for training, i.e., the capacity ground-truth of lithium-ion batteries. A capacity estimation method is proposed based on a semi-supervised convolutional neural network (SS-CNN). This method can automatically extract features from battery partial-charge information for capacity estimation. Furthermore, a semi-supervised training strategy is developed to take advantage of the extra unlabeled sample, which can improve the generalization of the model and the accuracy of capacity estimation even in the presence of limited labeled data. Compared with artificial neural networks and convolutional neural networks, the proposed method is demonstrated to improve capacity estimation accuracy.

scholarly journals Integration of Functional Link Neural Networks into a Parameter Estimation Methodology

2021 ◽  
Vol 11 (19) ◽  
pp. 9178
Author(s):  
Tuan-Ho Le ◽  
Mengyuan Tang ◽  
Jun Hyuk Jang ◽  
Hyeonae Jang ◽  
Sangmun Shin
Keyword(s):  
Neural Networks ◽  
Robust Design ◽  
Least Squares Method ◽  
Estimation Method ◽  
Information Criterion ◽  
Primary Objective ◽  
Estimation Methods ◽  
Estimation Model ◽  
Functional Link ◽  
Dual Response

In the field of robust design, most estimation methods for output responses of input factors are based on the response surface methodology (RSM), which makes several assumptions regarding the input data. However, these assumptions may not consistently hold in real-world industrial problems. Recent studies using artificial neural networks (ANNs) indicate that input–output relationships can be effectively estimated without the assumptions mentioned above. The primary objective of this research is to generate a new, robust design dual-response estimation method based on ANNs. First, a second-order functional-link-NN-based robust design estimation approach has been proposed for the process mean and standard deviation (i.e., the dual-response model). Second, the optimal structure of the proposed network is defined based on the Bayesian information criterion. Finally, the estimated response functions of the proposed functional-link-NN-based estimation method are applied and compared with that obtained using the conventional least squares method (LSM)-based RSM. The numerical example results imply that the proposed functional-link-NN-based dual-response robust design estimation model can provide more effective optimal solutions than the LSM-based RSM, according to the expected quality loss criteria.

Using The Fully Modified M Method In Estimating The Effect Of Cultivated Area On Barley Production In Iraq For The Period 1970-2018

2020 ◽  
Vol 2020 (66) ◽  
pp. 101-110
Author(s):  
. Azhar Kadhim Jbarah ◽  
Prof Dr. Ahmed Shaker Mohammed
Keyword(s):  
Regression Model ◽  
Moving Average ◽  
Estimation Method ◽  
Estimation Methods ◽  
Autoregressive Moving Average ◽  
Moving Average Models ◽  
Error Terms ◽  
Relationship Of ◽  
The Relationship ◽  
Barley Crop

The research is concerned with estimating the effect of the cultivated area of barley crop on the production of that crop by estimating the regression model representing the relationship of these two variables. The results of the tests indicated that the time series of the response variable values is stationary and the series of values of the explanatory variable were nonstationary and that they were integrated of order one ( I(1) ), these tests also indicate that the random error terms are auto correlated and can be modeled according to the mixed autoregressive-moving average models ARMA(p,q), for these results we cannot use the classical estimation method to estimate our regression model, therefore, a fully modified M method was adopted, which is a robust estimation methods, The estimated results indicate a positive significant relation between the production of barley crop and cultivated area.

scholarly journals Deep neural networks using a single neuron: folded-in-time architecture using feedback-modulated delay loops

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Florian Stelzer ◽  
André Röhm ◽  
Raul Vicente ◽  
Ingo Fischer ◽  
Serhiy Yanchuk
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Network ◽  
Single Neuron ◽  
Deep Neural Networks ◽  
Back Propagation ◽  
Local Network ◽  
Multiple Time ◽  
Learning Tools ◽  
Back Propagation Algorithm

AbstractDeep neural networks are among the most widely applied machine learning tools showing outstanding performance in a broad range of tasks. We present a method for folding a deep neural network of arbitrary size into a single neuron with multiple time-delayed feedback loops. This single-neuron deep neural network comprises only a single nonlinearity and appropriately adjusted modulations of the feedback signals. The network states emerge in time as a temporal unfolding of the neuron’s dynamics. By adjusting the feedback-modulation within the loops, we adapt the network’s connection weights. These connection weights are determined via a back-propagation algorithm, where both the delay-induced and local network connections must be taken into account. Our approach can fully represent standard Deep Neural Networks (DNN), encompasses sparse DNNs, and extends the DNN concept toward dynamical systems implementations. The new method, which we call Folded-in-time DNN (Fit-DNN), exhibits promising performance in a set of benchmark tasks.

scholarly journals Comprehensive genotyping of a Brazilian cassava (Manihot esculenta Crantz) germplasm bank: insights into diversification and domestication

2021 ◽  
Vol 134 (5) ◽  
pp. 1343-1362
Author(s):  
Alex C. Ogbonna ◽  
Luciano Rogerio Braatz de Andrade ◽  
Lukas A. Mueller ◽  
Eder Jorge de Oliveira ◽  
Guillaume J. Bauchet
Keyword(s):  
Population Genetics ◽  
Population Structure ◽  
Manihot Esculenta ◽  
Ex Situ ◽  
Germplasm Bank ◽  
Effective Population ◽  
Manihot Esculenta Crantz ◽  
Genetic Groups ◽  
Population Structure Analysis ◽  
Modern Breeding

Abstract Key message Brazilian cassava diversity was characterized through population genetics and clustering approaches, highlighting contrasted genetic groups and spatial genetic differentiation. Abstract Cassava (Manihot esculenta Crantz) is a major staple root crop of the tropics, originating from the Amazonian region. In this study, 3354 cassava landraces and modern breeding lines from the Embrapa Cassava Germplasm Bank (CGB) were characterized. All individuals were subjected to genotyping-by-sequencing (GBS), identifying 27,045 single-nucleotide polymorphisms (SNPs). Identity-by-state and population structure analyses revealed a unique set of 1536 individuals and 10 distinct genetic groups with heterogeneous linkage disequilibrium (LD). On this basis, a density of 1300–4700 SNP markers were selected for large-effect quantitative trait loci (QTL) detection. Identified genetic groups were further characterized for population genetics parameters including minor allele frequency (MAF), observed heterozygosity $$({H}_{o})$$ ( H o ) , effective population size estimate $$\widehat{{(N}_{e}}$$ ( N e ^ ) and polymorphism information content (PIC). Selection footprints and introgressions of M. glaziovii were detected. Spatial population structure analysis revealed five ancestral populations related to distinct Brazilian ecoregions. Estimation of historical relationships among identified populations suggests an early population split from Amazonian to Atlantic forest and Caatinga ecoregions and active gene flows. This study provides a thorough genetic characterization of ex situ germplasm resources from cassava’s center of origin, South America, with results shedding light on Brazilian cassava characteristics and its biogeographical landscape. These findings support and facilitate the use of genetic resources in modern breeding programs including implementation of association mapping and genomic selection strategies.

scholarly journals A phylogenetic estimator of effective population size or mutation rate.

Genetics ◽  
1994 ◽  
Vol 136 (2) ◽  
pp. 685-692 ◽  
Author(s):  
Y X Fu
Keyword(s):  
Population Size ◽  
Effective Population Size ◽  
Mutation Rate ◽  
Dna Sequences ◽  
High Efficiency ◽  
Minimum Variance ◽  
Population Subdivision ◽  
Effective Population ◽  
Fisher Model ◽  
Mitochondrial Sequences

Abstract A new estimator of the essential parameter theta = 4Ne mu from DNA polymorphism data is developed under the neutral Wright-Fisher model without recombination and population subdivision, where Ne is the effective population size and mu is the mutation rate per locus per generation. The new estimator has a variance only slightly larger than the minimum variance of all possible unbiased estimators of the parameter and is substantially smaller than that of any existing estimator. The high efficiency of the new estimator is achieved by making full use of phylogenetic information in a sample of DNA sequences from a population. An example of estimating theta by the new method is presented using the mitochondrial sequences from an American Indian population.

scholarly journals Comparative Analysis of Kinect-Based and Oculus-Based Gaze Region Estimation Methods in a Driving Simulator

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 26
Author(s):  
David González-Ortega ◽  
Francisco Javier Díaz-Pernas ◽  
Mario Martínez-Zarzuela ◽  
Míriam Antón-Rodríguez
Keyword(s):  
Virtual Reality ◽  
Comparative Analysis ◽  
Driving Simulator ◽  
Estimation Method ◽  
Estimation Methods ◽  
Gaze Estimation ◽  
Driving Simulators ◽  
Oculus Rift ◽  
Gaze Patterns ◽  
And Performance

Driver’s gaze information can be crucial in driving research because of its relation to driver attention. Particularly, the inclusion of gaze data in driving simulators broadens the scope of research studies as they can relate drivers’ gaze patterns to their features and performance. In this paper, we present two gaze region estimation modules integrated in a driving simulator. One uses the 3D Kinect device and another uses the virtual reality Oculus Rift device. The modules are able to detect the region, out of seven in which the driving scene was divided, where a driver is gazing at in every route processed frame. Four methods were implemented and compared for gaze estimation, which learn the relation between gaze displacement and head movement. Two are simpler and based on points that try to capture this relation and two are based on classifiers such as MLP and SVM. Experiments were carried out with 12 users that drove on the same scenario twice, each one with a different visualization display, first with a big screen and later with Oculus Rift. On the whole, Oculus Rift outperformed Kinect as the best hardware for gaze estimation. The Oculus-based gaze region estimation method with the highest performance achieved an accuracy of 97.94%. The information provided by the Oculus Rift module enriches the driving simulator data and makes it possible a multimodal driving performance analysis apart from the immersion and realism obtained with the virtual reality experience provided by Oculus.

scholarly journals Asymptotic Prediction Error Variance for Feedforward Neural Networks

2020 ◽  
Vol 53 (2) ◽  
pp. 1108-1113
Author(s):  
Magnus Malmström ◽  
Isaac Skog ◽  
Daniel Axehill ◽  
Fredrik Gustafsson
Keyword(s):  
Neural Networks ◽  
Prediction Error ◽  
Feedforward Neural Networks ◽  
Error Variance ◽  
Prediction Error Variance
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