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22 results for “Linearization Method”
A Method for Generating a Quasi-Linear Convective System Suitable for Observing System Simulation Experiments: Dataset
<p>This repository provides data files to quickly run the QLCS observing system simulation experiments. The data file includes assimilated observations prepared for the data assimilation research testbed system (./data/obs/). The file also contains a restart files to initialize the nature run simulation (./data/nature_run/) and the initial prior ensemble at the time of the first data assimilation cycle (./data/initial_fcst_ensemble/).</p>
Research Data supporting "Linear-Scaling Density Functional Theory using the Projector Augmented Wave Method"
<p>Research Data supporting "Linear-Scaling Density Functional Theory using the Projector Augmented Wave Method" by Nicholas D. M. Hine</p>
Data from: A method of separating linear internal wave and vortical mode energies using shipboard ADCP velocity measurements
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Data for "Calculation of permanent magnet arrangements for stellarators: A linear least-squares method"
<p>Data associated with the paper "Calculation of permanent magnet arrangements for stellarators: A linear least-squares method"</p>
Efficient weighting methods for genomic best linear unbiased prediction (BLUP) adaption to the genetic architectures of quantitative traits
<p><a name="_Hlk19877414"></a>Genomic best linear unbiased prediction (GBLUP) assumes equal variance for all marker effects, which is suitable for traits that conform to the infinitesimal model. For traits controlled by major genes, Bayesian methods with shrinkage priors or genome-wide association study (GWAS) methods can be used to identify <a name="_Hlk24974556">causal variants</a> effectively. The information from Bayesian/GWAS methods can be used to construct the weighted genomic relationship matrix (<b>G</b>). However, it remains unclear which methods perform best for traits varying in genetic architecture. Therefore, we developed several methods to <a name="_Hlk23592218">optimize</a> the performance of weighted GBLUP and compare them with other available methods using simulated and real datasets. First, two types of methods (marker effects with local-shrinkage or normal prior) were used to obtain test statistics and estimates for each marker effect. Second, three weighted <b>G</b> matrices were constructed based on the marker information from the first step: (1) the genomic-feature weighted <b>G</b> (GFWG), (2) the estimated marker-variance weighted <b>G</b> (EVWG), and (3) the absolute value of estimated marker-effect weighted <b>G</b> (AEWG). Following the above process, six different weighted GBLUP methods (local-shrinkage/normal prior GF/EV/AE-WGBLUP) were proposed for genomic prediction. Analyses with both simulated and real data demonstrated that these options offer flexibility for optimizing the weighted GBLUP for traits with a broad spectrum of genetic architectures. The advantage of weighting methods over GBLUP in terms of accuracy were trait dependent, ranging from 14.8% to marginal for simulated traits and from 44% to marginal for real traits. Local-shrinkage prior EVWGBLUP is superior for traits mainly controlled by loci of large effect. Normal prior AEWGBLUP performs well for traits mainly controlled by loci of moderate effect. For traits controlled by some loci with large effects (<a name="_Hlk49869847">explain 25%~50% genetic variance</a>) and a range of loci with small effects, GFWGBLUP has advantages. In conclusion, the optimal weighted GBLUP method for genomic selection should take both the genetic architecture and number of QTLs of traits into consideration carefully.</p>
Data from: Body mass estimates of an exceptionally complete Stegosaurus (Ornithischia: Thyreophora): comparing volumetric and linear bivariate mass estimation methods
Body mass is a key biological variable, but difficult to assess from fossils. Various techniques exist for estimating body mass from skeletal parameters, but few studies have compared outputs from different methods. Here, we apply several mass estimation methods to an exceptionally complete skeleton of the dinosaur Stegosaurus. Applying a volumetric convex-hulling technique to a digital model of Stegosaurus, we estimate a mass of 1560 kg (95% prediction interval 1082–2256 kg) for this individual. By contrast, bivariate equations based on limb dimensions predict values between 2355 and 3751 kg and require implausible amounts of soft tissue and/or high body densities. When corrected for ontogenetic scaling, however, volumetric and linear equations are brought into close agreement. Our results raise concerns regarding the application of predictive equations to extinct taxa with no living analogues in terms of overall morphology and highlight the sensitivity of bivariate predictive equations to the ontogenetic status of the specimen. We emphasize the significance of rare, complete fossil skeletons in validating widely applied mass estimation equations based on incomplete skeletal material and stress the importance of accurately determining specimen age prior to further analyses.
Phonons from Density-Functional Perturbation Theory using the All-Electron Full-Potential Linearized Augmented Plane-Wave Method FLEUR
<p>The archive files contain the input and result files for the corresponding publication in IOP Electronic Structure - Technical Notes, as well as a short python script to plot them.</p>
Supporting data for "A method for non-linear inversion of the stellar structure applied to gravity-mode pulsators"
<p>These are the inlist and run_star_extras required to reproduce the stellar and asteroseismic models presented in 'A method for non-linear inversion of the stellar structure applied to gravity-mode pulsators', run with MESA r22.05.1.</p>
Research on UAV Autonomous Recognition and Approach Method for Linear Target Splicing Sleeves Based on Deep Learning and Stereo Vision
<p><span>Link to the video as supplementary material for the paper-《Research on UAV Autonomous Recognition and Approach Method for Linear Target Splicing Sleeves Based on Deep Learning and Stereo Vision》.</span></p>
Data of Rosenbrock method of "A unifying framework for ADI-like methods for linear matrix equations and beneficial consequences"
<p>This deposit contains the datasets generated by the Rosenbrock method for the paper:</p> <ul> <li>J. Schulze, J. Saak: "A unifying framework for ADI-like methods for linear matrix equations and beneficial consequences".</li> </ul> <p>Download the individual files and store them inside the directory <code>data/rosenbrock/</code>.</p> <p>The file names are structured as follows.</p> <ul> <li><code>Rail5177</code>: <a href="https://morwiki.mpi-magdeburg.mpg.de/morwiki/index.php/Steel_Profile">Steel Profile</a> benchmark problem of dimension 5177</li> <li><code>adi_initprev=true|false</code>: whether the initial ADI iterate was set to the solution at the previous time step (or zero)</li> <li><code>adi_kwargs=...</code>: keyword arguments passed to ADI method <ul> <li><code>maxiters=200</code>: maximum number of iterations</li> <li><code>reltol=1e-10</code>: relative tolerance to reason about convergence</li> <li><code>shifts=...</code>: shift strategy</li> </ul> </li> <li><code>nsteps=45|150</code>: number of Rosenbrock steps</li> <li><code>tspan=(4500.0, 0.0)</code>: global time span of DRE</li> <li><code>.jld2</code>: file suffix. All file have been generated with <a href="https://github.com/JuliaIO/JLD2.jl">JLD2.jl</a> version 0.4.38</li> </ul> <p>Load the dataset via <code>using JLD2</code> and <code>file = load(FILENAME)</code>. This will yield a dictionary having the following entries:</p> <ul> <li><code>file["rosenbrock_metrics"]</code>: data frame containing execution metrics of Rosenbrock iterations</li> <li><code>file["adi_metrics"]</code>: data frame containing execution metrocs of ADI iterations of all Rosenbrock iterations</li> <li><code>file["timer"]</code>: isolated runtime metrics generated with <a href="https://github.com/KristofferC/TimerOutputs.jl">TimerOutputs.jl</a> version 0.5.23</li> <li><code>file["timer_metrics"]</code>: runtime metrics of seperate run with additional data observers enabled</li> <li><code>file["config"]</code>: internal configuration object that led to this dataset (information also embedded in file name)</li> <li><code>file["failed"]</code>: Boolean on whether configuration has failed (always <code>false</code>) </li> </ul> <p>All data frames were generated with <a href="https://github.com/JuliaData/DataFrames.jl">DataFrames.jl</a> version 1.6.1 and have their columns documented <a href="https://dataframes.juliadata.org/stable/lib/metadata/">using metadata</a>.</p> <p>Generating this dataset took ~22h and consumed ~2.72kWh of electricity.</p>
Data of Newton method of "A unifying framework for ADI-like methods for linear matrix equations and beneficial consequences"
<p>This deposit contains the datasets generated by the Newton method for the paper:</p> <ul> <li>J. Schulze, J. Saak: "A unifying framework for ADI-like methods for linear matrix equations and beneficial consequences".</li> </ul> <p>Download the individual files and store them inside the directory <code>data/newton-adi/</code>.</p> <p>The file names are structured as follows.</p> <ul> <li><code>Rail5177</code>: <a href="https://morwiki.mpi-magdeburg.mpg.de/morwiki/index.php/Steel_Profile">Steel Profile</a> benchmark problem of dimension 5177</li> <li><code>adi_initprev=true|false</code>: whether the initial ADI iterate was set to the solution of the previous Newton step (or zero)</li> <li><code>adi_kwargs=...</code>: keyword arguments passed to ADI method <ul> <li><code>maxiters=1000</code>: maximum number of iterations</li> <li><code>shifts=...</code>: shift strategy</li> </ul> </li> <li><code>newton_kwargs=...</code>: keyword arguments passed to Newton method <ul> <li><code>inexact=true|false</code>: whether to use inexact Newton method</li> <li><code>inexact_hybrid=true|false</code>: whether to switch back to classical Newton method in later iterations (only present if <code>inexact=true</code>)</li> <li><code>linesearch=true|false</code>: whether to employ line search</li> <li><code>reltol=1e-10</code>: relative tolerance to reason about convergence</li> </ul> </li> <li><code>β=1000</code>: scaling of the quadratic term in the ARE</li> <li><code>.jld2</code>: file suffix. All file have been generated with <a href="https://github.com/JuliaIO/JLD2.jl">JLD2.jl</a> version 0.4.38</li> </ul> <p>Load the dataset via <code>using JLD2</code> and <code>file = load(FILENAME)</code>. This will yield a dictionary having the following entries:</p> <ul> <li><code>file["newton_metrics"]</code>: data frame containing execution metrics of Newton iterations</li> <li><code>file["adi_metrics"]</code>: data frame containing execution metrocs of ADI iterations of all Newton iterations</li> <li><code>file["timer"]</code>: isolated runtime metrics generated with <a href="https://github.com/KristofferC/TimerOutputs.jl">TimerOutputs.jl</a> version 0.5.23</li> <li><code>file["timer_metrics"]</code>: runtime metrics of seperate run with additional data observers enabled</li> <li><code>file["config"]</code>: internal configuration object that led to this dataset (information also embedded in file name)</li> <li><code>file["failed"]</code>: Boolean on whether configuration has failed</li> </ul> <p>All data frames were generated with <a href="https://github.com/JuliaData/DataFrames.jl">DataFrames.jl</a> version 1.6.1 and have their columns documented <a href="https://dataframes.juliadata.org/stable/lib/metadata/">using metadata</a>.</p> <p>Generating this dataset took ~3h and consumed ~0.25kWh of electricity.</p>
Fig. 1. Linear monochrome 16 in A new method of staining larvae of Lepidoptera using carbol fuchsin
Fig. 1. Linear monochrome 16-bit TIFF files: a — general view of larvae, b — enlarged view, yellow rectangles correspond to areas where brightness for the seta (1) and cuticle near the seta (2) were determined. Рис. 1. Линейный монохромный TIFF файл: a — обЩий вид, b — увеличенный вид, желтыми прЯмоугольниками покаЗаны области иЗмерениЯ Яркости Щетинки (1) и фона рЯдом с Щетинкой (2).
Developing a Method to Automatically Extract Road Boundary and Linear Road Markings from MMS Point Cloud using OBB Collision Detection Techniques
<p>This video demonstrates the application of our method in a software tool for constructing road boundaries and lane marking data.</p>
Data from: Body mass estimates of an exceptionally complete Stegosaurus (Ornithischia: Thyreophora): comparing volumetric and linear bivariate mass estimation methods
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Efficient weighting methods for genomic best linear unbiased prediction (BLUP) adaption to the genetic architectures of quantitative traits
Open the record for dataset details and reuse information.
Data for "Quantum algorithm for the variational optimization of correlated electronic states with the linear method"
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Data from: A simulation–based evaluation of methods for inferring linear barriers to gene flow
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Generalization of the sci-L3 method to achieve high-throughput linear amplification for replication template strand sequencing, genome conformation capture, and the joint profiling of RNA and chromati
GEO Series GSE281238. Homo sapiens. 7 samples. Type: Other.
Voice Analysis Using the LPC (Linear Predictive Coding)Method for the Prediction of Aspiration
ClinicalTrials.gov study NCT01981239. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Use of an Isothermal Linear Amplification Method with Small Samples on DNA Microarrays
GEO Series GSE2252. Mus musculus; unidentified; Homo sapiens. 15 samples. Type: Expression profiling by array.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.