Sampling (signal processing) ≈ Sampling (signal processing)
View article: MEGA11: Molecular Evolutionary Genetics Analysis Version 11
MEGA11: Molecular Evolutionary Genetics Analysis Version 11 Open
The Molecular Evolutionary Genetics Analysis (MEGA) software has matured to contain a large collection of methods and tools of computational molecular evolution. Here, we describe new additions that make MEGA a more comprehensive tool for …
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Comparison of Convenience Sampling and Purposive Sampling Open
This article studied and compared the two nonprobability sampling techniques namely, Convenience Sampling and Purposive Sampling. Convenience Sampling and Purposive Sampling are Nonprobability Sampling Techniques that a researcher uses to …
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iNEXT: an R package for rarefaction and extrapolation of species diversity (<span>H</span>ill numbers) Open
Summary Hill numbers (or the effective number of species) have been increasingly used to quantify the species/taxonomic diversity of an assemblage. The sample‐size‐ and coverage‐based integrations of rarefaction (interpolation) and extrapo…
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Purposive sampling: complex or simple? Research case examples Open
Background Purposive sampling has a long developmental history and there are as many views that it is simple and straightforward as there are about its complexity. The reason for purposive sampling is the better matching of the sample to t…
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Exploring the temporal structure of heterochronous sequences using TempEst (formerly Path-O-Gen) Open
Gene sequences sampled at different points in time can be used to infer molecular phylogenies on a natural timescale of months or years, provided that the sequences in question undergo measurable amounts of evolutionary change between samp…
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dynesty: a dynamic nested sampling package for estimating Bayesian posteriors and evidences Open
We present dynesty, a public, open-source, python package to estimate Bayesian posteriors and evidences (marginal likelihoods) using the dynamic nested sampling methods developed by Higson et al. By adaptively allocating samples based on p…
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Snowball Sampling: A Purposeful Method of Sampling in Qualitative Research Open
Background and Objectives Snowball sampling is applied when samples with the target characteristics are not easily accessible. This research describes snowball sampling as a purposeful method of data collection in qualitative research. Met…
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Sampling and Definitions of Placental Lesions: Amsterdam Placental Workshop Group Consensus Statement Open
-The group agreed on sets of uniform sampling criteria, placental gross descriptors, pathologic terminologies, and diagnostic criteria. The terminology and microscopic descriptions for maternal vascular malperfusion, fetal vascular malperf…
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Recruiting the ABCD sample: Design considerations and procedures Open
The ABCD study is a new and ongoing project of very substantial size and scale involving 21 data acquisition sites. It aims to recruit 11,500 children and follow them for ten years with extensive assessments at multiple timepoints. To deli…
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Atmospheric microplastics: A review on current status and perspectives Open
Microplastics have recently been detected in the atmosphere of urban, suburban, and even remote areas far away from source regions of microplastics, suggesting the potential long-distance atmospheric transport for microplastics. There stil…
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<i>landscapemetrics</i> : an open‐source <i>R</i> tool to calculate landscape metrics Open
Quantifying landscape characteristics and linking them to ecological processes is one of the central goals of landscape ecology. Landscape metrics are a widely used tool for the analysis of patch‐based, discrete land‐cover classes. Existin…
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Normalizing Flows: An Introduction and Review of Current Methods Open
Normalizing Flows are generative models which produce tractable distributions where both sampling and density evaluation can be efficient and exact. The goal of this survey article is to give a coherent and comprehensive review of the lite…
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Sampling and Sampling Methods Open
This article is on representation of basis and the basis selection of techniques.The representation of this two is performed either by the method of probability random sampling or by the method of non-probability random sampling.The select…
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DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction Open
Compressed sensing magnetic resonance imaging (CS-MRI) enables fast acquisition, which is highly desirable for numerous clinical applications. This can not only reduce the scanning cost and ease patient burden, but also potentially reduce …
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Population Research: Convenience Sampling Strategies Open
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Understanding Back-Translation at Scale Open
An effective method to improve neural machine translation with monolingual data is to augment the parallel training corpus with back-translations of target language sentences. This work broadens the understanding of back-translation and in…
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Local light field fusion Open
We present a practical and robust deep learning solution for capturing and rendering novel views of complex real world scenes for virtual exploration. Previous approaches either require intractably dense view sampling or provide little to …
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Generative Modeling by Estimating Gradients of the Data Distribution Open
We introduce a new generative model where samples are produced via Langevin dynamics using gradients of the data distribution estimated with score matching. Because gradients can be ill-defined and hard to estimate when the data resides on…
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Critical considerations for the application of environmental <span>DNA</span> methods to detect aquatic species Open
Summary Species detection using environmental DNA ( eDNA ) has tremendous potential for contributing to the understanding of the ecology and conservation of aquatic species. Detecting species using eDNA methods, rather than directly sampli…
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Sampling, isolating and identifying microplastics ingested by fish and invertebrates Open
Microplastic debris (<5 mm) is a prolific environmental pollutant, found worldwide in marine, freshwater and terrestrial ecosystems. This review assesses the numerous different methods used to identify microplastics ingested by marine orga…
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Importance Nested Sampling and the MultiNest Algorithm Open
Bayesian inference involves two main computational challenges. First, in\nestimating the parameters of some model for the data, the posterior\ndistribution may well be highly multi-modal: a regime in which the convergence\nto stationarity …
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Multi-rate Poisson tree processes for single-locus species delimitation under maximum likelihood and Markov chain Monte Carlo Open
Motivation In recent years, molecular species delimitation has become a routine approach for quantifying and classifying biodiversity. Barcoding methods are of particular importance in large-scale surveys as they promote fast species disco…
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Research Design and Methods: A Systematic Review of Research Paradigms, Sampling Issues and Instruments Development Open
This study is aimed at to contribute a detailed systematic review on research paradigms, sampling and instrument development issues in the field of business research. This study has reconnoitered the levels of theory and their implications…
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Improved estimates of ocean heat content from 1960 to 2015 Open
A new assessment of how much heat Earth has accumulated since 1960 is made by examining ocean heat content changes.
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The Inconvenient Truth About Convenience and Purposive Samples Open
Most research is conducted on convenience and purposive samples that may be randomly or nonrandomly drawn. A convenience sample is the one that is drawn from a source that is conveniently accessible to the researcher. A purposive sample is…
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Correlation detection strategies in microbial data sets vary widely in sensitivity and precision Open
Disruption of healthy microbial communities has been linked to numerous diseases, yet microbial interactions are little understood. This is due in part to the large number of bacteria, and the much larger number of interactions (easily in …
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Less is more: Sampling chemical space with active learning Open
The development of accurate and transferable machine learning (ML) potentials for predicting molecular energetics is a challenging task. The process of data generation to train such ML potentials is a task neither well understood nor resea…
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Soft Robotic Grippers for Biological Sampling on Deep Reefs Open
This article presents the development of an underwater gripper that utilizes soft robotics technology to delicately manipulate and sample fragile species on the deep reef. Existing solutions for deep sea robotic manipulation have historica…
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Density Estimation Using Real NVP Open
Unsupervised learning of probabilistic models is a central yet challenging problem in machine learning. Specifically, designing models with tractable learning, sampling, inference and evaluation is crucial in solving this task. We extend t…
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A Late Pleistocene sea level stack Open
Late Pleistocene sea level has been reconstructed from ocean sediment core data using a wide variety of proxies and models. However, the accuracy of individual reconstructions is limited by measurement error, local variations in salinity a…