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667 results for “distinguishability”
Using machine learning to distinguish between authentic and imitation Jackson Pollock poured paintings: Art images
<p>Jackson Pollock's abstract poured paintings are celebrated for their striking aesthetic qualities. They are also among the most financially valued and imitated artworks, making them vulnerable to high-profile controversies involving Pollock-like paintings of unknown origin. Given the increased employment of artificial intelligence applications across society, we investigate whether established machine learning techniques can be adopted by the art world to help detect imitation Pollocks. The low number of images compared to typical artificial intelligence projects presents a potential limitation for art-related applications. To address this limitation, we develop a machine learning strategy involving a novel image ingestion method which decomposes the images into sets of multi-scaled tiles. Leveraging the power of transfer learning, this approach distinguishes between authentic and imitation poured artworks with an accuracy of 98.9%. The machine also uses the multi-scaled tiles to generate novel visual aids and interpretational parameters which together facilitate comparisons between the machine's results and traditional investigations of Pollock's artistic style.</p>
Raw coordinates of 3D landmarks related to the article 'A new zooarchaeological application for geometric morphometric methods: Distinguishing Ovis aries morphotypes to address connectivity and mobility of prehistoric Central Asian pastoralists' by Haruda et al.
<p>Raw coordinates from 3D landmarks of <em>Ovis aries </em>astragali. These bones originate from Final Bronze Age archaeological contexts from central and southeastern Kazakhstan. These relate to the article 'A new zooarchaeological application for geometric morphometric methods: Distinguishing <em>Ovis aries</em> morphotypes to address connectivity and mobility of prehistoric Central Asian pastoralists' by Haruda et al. </p>
Input Data for "Distinguishing attributes using ConceptNet Numberbatch"
<p>In a post on blog.conceptnet.io, we're showing how to use ConceptNet Numberbatch alone to create a good solution to SemEval-2018 Task 10, Capturing Discriminative Attributes. This is an alternative, simplified implementation of a result presented in the SemEval paper <a href="http://aclweb.org/anthology/S18-1162">Distinguishing Attributes Using Text Corpora and Relational Knowledge</a>, by Robyn Speer and Joanna Lowry-Duda.</p> <p>This data repository contains the data necessary to make the simplified implementation work.</p>
Distinguishing mutations and null alleles from genotyping errors using mother progeny comparisons in Brazilian pine (Araucaria angustifolia)
The use of microsatellite markers provides a window into the evolutionary processes of a given species. As such, these markers are widely used in scientific and applied research and are praised for their practicality and ease of use, however, the unavoidable incidence of genotyping deviations has been broadly neglected in the literature. Therefore, the present study aimed to estimate the rate of null alleles, mutations and genotyping errors in microsatellite loci, using Araucaria angustifolia, a threatened species, as a case study. We estimated the rates of the different types of genotyping deviations using mother-progeny genotype comparison from 50 seed-trees and their respective progeny (seeds). A total of 2336 A. angustifolia samples were genotyped, and we found that the rate of null alleles was 0.045. From the 1972 mother-progeny comparisons, the overall genotype deviation rate was 1.58%, consisting of 145 inconsistences (mutations), 339 null alleles and 210 genotyping errors. In terms of seed numbers, 128 (6.5%) showed inconsistencies in at least one locus, 118 (6.0%) null alleles, and 321 (16.3%) genotyping errors. This is the first study to describe the inconsistences (mutations) between mother-progeny genotypes for A. angustifolia, and the outcome makes it clear that an understanding of these genotyping deviations must be considered in assessing the accuracy of inferences made based on population genetics analyses.
Distinguishing mutations and null alleles from genotyping errors using mother progeny comparisons in Brazilian pine (Araucaria angustifolia)
the rate of null alleles, mutations and genotyping errors in microsatellite loci, using Araucaria angustifolia, a threatened species, as a case study. We estimated the rates of the different types of genotyping deviations using mother-progeny genotype comparison from 50 seed-trees and their respective progeny (seeds). A total of 2336 A. angustifolia samples were genotyped, and we found that the rate of null alleles was 0.045. From the 1972 mother-progeny comparisons, the overall genotype deviation rate was 1.58%, consisting of 145 inconsistences (mutations), 339 null alleles and 210 genotyping errors. In terms of seed numbers, 128 (6.5%) showed inconsistencies in at least one locus, 118 (6.0%) null alleles, and 321 (16.3%) genotyping errors. This is the first study to describe the inconsistences (mutations) between mother-progeny genotypes for A. angustifolia, and the outcome makes it clear that an understanding of these genotyping deviations must be considered in assessing the accuracy of inferences made based on population genetics analyses.
Figure 1 in Using otolith shape analysis to distinguish barracudas Sphyraena sphyraena and Sphyraena viridensis from the Algerian coast
Figure 1. - Location of studied site in the South-Western Mediterranean Sea.
Three-point contact data for "Multi-contact statistics distinguish models of chromosome organization"
<p>Three-point contact data for the publication "Multi-contact statistics distinguish models of chromosome organization".</p>
SomaScan dataset used to identify protein biomarkers for distinguishing between bacterial and viral infections in febrile children
<p>Protein profiles of children with confirmed bacterial infections (DB), confirmed viral infections (DV) in addition to healthy controls (HC). Protein profiles generated through the SomaScan 1.3k assay (SomaLogic, Colorado, USA).</p> <p>Data has been normalised already, including batch effect correction using COCONUT (https://cran.r-project.org/web/packages/COCONUT/COCONUT.pdf) and log2 transformed. </p> <p>Accompanying the protein abundance values is a separate .csv file containing information about the proteins, including UniProt ID and Entrez gene IDs associated with the proteins.</p>
Shape data ferrets and polecat (from paper: Gruwier, B.: A geometric morphometric approach to distinguish ferret from polecat and its application to an archaeological specimen from Mechelen (Belgium))
<p>Shape data ferrets and polecat (used in paper: Gruwier, B.: A geometric morphometric approach to distinguish ferret from polecat and its application to an archaeological specimen from Mechelen (Belgium))</p>
Genomic data resolve long-standing uncertainty by distinguishing white marlin (Kajikia albida) and striped marlin (K. audax) as separate species
<p>Large pelagic fishes are often broadly and continuously distributed and capable of long-distance movements. These factors can promote gene flow that makes it difficult to disentangle intra- vs. inter-specific levels of genetic differentiation. Here, we assess the relationship of two istiophorid billfishes, white marlin (<em>Kajikia</em> <em>albida</em>) and striped marlin (<em>K</em>. <em>audax</em>), presently considered sister species inhabiting separate ocean basins. Previous studies report levels of genetic differentiation between white marlin and striped marlin that are <a>smaller</a> than those observed among populations of other istiophorid species. To determine whether white marlin and striped marlin comprise separate species or populations of a single globally distributed species, we surveyed 2<a>520</a> single nucleotide polymorphisms (SNPs) in 62 white marlin and 242 striped marlin sampled across the Atlantic, Pacific, and Indian oceans. Multivariate analyses resolved white marlin and striped marlin as distinct groups, and a species tree composed of separate lineages was strongly supported over a single lineage tree. Genetic differentiation between white marlin and striped marlin (<em>F</em><sub>ST</sub> = 0.5384) was also substantially larger than between populations of striped marlin (<em>F</em><sub>ST</sub> = 0.0192–0.0840), and we identified SNPs that allow unambiguous species identification. Our findings indicate that white marlin and striped marlin comprise separate species, which we estimate diverged at approximately 2.38 Mya.</p>
Data from: Distinguishing distribution dynamics from temporary emigration using dynamic occupancy models
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Distinguishing Impatiens capensis from Impatiens pallida (Balsaminaceae) using leaf traits
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Genomic data resolve long-standing uncertainty by distinguishing white marlin (Kajikia albida) and striped marlin (K. audax) as separate species
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Pinellia discolor: A new cryptic species distinguished from P. cordata in Mainland China
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Dataset for: Smith et al., Phylogenomic analysis of the parrots of the world distinguishes artifactual from biological sources of gene tree discordance
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Using machine learning to distinguish between authentic and imitation Jackson Pollock poured paintings: Art images
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Data and R analysis code: Asian elephants distinguish sexual status and identity of unfamiliar elephants using urinary odours
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Using geometric wing morphometrics to distinguish Aedes japonicus japonicus and Aedes koreicus
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Data for: Distinguishing externally- from saccade-induced motion in visual cortex
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FIGURE 20 in Taxonomic evaluation of the Grallaria rufula (Rufous Antpitta) complex (Aves: Passeriformes: Grallariidae) distinguishes sixteen species
FIGURE 20. Map showing the taxonomy and distribution of the Grallaria rufula complex as revised herein, Part 2: G. rufocinerea and populations formerly ascribed to G. blakei. Solid circles represent sectors (Isler 1997) that contain locations of genetic samples used in a companion paper (Chesser et al. 2020) and stars are sectors containing locations where specimens have been collected or vocalizations recorded. Sectors may contain more than one genetic or vocal sample or specimen. Lines encompass sectors attributed to the same taxon and are not projections of geographic range which can be found in the companion paper (Chesser et al. 2020).
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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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.