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49 results for “feature identification”
1QIsaa data collection (binarized images, feature files, and plotting scripts) for writer identification test using artificial intelligence and image-based pattern recognition techniques
<p><strong>The Great Isaiah Scroll (1QIsa<sup>a</sup>) data set for writer identification</strong></p> <p>This data set is collected for the ERC project:<br> The Hands that Wrote the Bible: Digital Palaeography and Scribal Culture of the Dead Sea Scrolls<br> PI: Mladen Popović<br> Grant agreement ID: 640497</p> <p>Project website: <a href="https://cordis.europa.eu/project/id/640497">https://cordis.europa.eu/project/id/640497</a><br> <br> <strong>Copyright (c) </strong> University of Groningen, 2021. All rights reserved.<br> <strong>Disclaimer and copyright notice for all data contained on this .tar.gz file:</strong></p> <p><strong>1)</strong> permission is hereby granted to use the data for research purposes. It is not allowed to distribute this data for commercial purposes.</p> <p><strong>2) </strong>provider gives no express or implied warranty of any kind, and any implied warranties of merchantability and fitness for purpose are disclaimed.</p> <p><strong>3) </strong>provider shall not be liable for any direct, indirect, special, incidental, or consequential damages arising out of any use of this data.</p> <p><strong>4) </strong>the user should refer to the first public article on this data set:<br> <br> <em>Popović, M., Dhali, M. A., & Schomaker, L. (2020). Artificial intelligence-based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsa<sup>a</sup>). arXiv preprint arXiv:2010.14476.</em><br> <br> BibTeX:</p> <pre>@article{popovic2020artificial, title={Artificial intelligence based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsaa)}, author={Popovi{\'c}, Mladen and Dhali, Maruf A and Schomaker, Lambert}, journal={arXiv preprint arXiv:2010.14476}, year={2020} }</pre> <p><strong>5) </strong>the recipient should refrain from proliferating the data set to third parties external to his/her local research group. Please refer interested researchers to this site for obtaining their own copy.</p> <p><strong>Organisation of the data:</strong></p> <p>The .tar.gz file contains three directories: images, features, and plots. The included 'README' file contains all the instructions.</p> <p>The 'images' directory contains NetPBM images of the columns of 1QIsa<sup>a</sup>. The NetPBM format is chosen because of its simplicity. Additionally, there is no doubt about lossy compression in the processing chain. There are two images for each of the Great Isaiah Scroll columns: one is the direct binarized output from the BiNet (<em>arxiv.org/abs/1911.07930</em>) system, and the other one is the manually cleaned version of the binarized output. The file names for the direct binarized output are of the format '1QIsaa_col<columnnr>.pbm', for example, '1QIsaa_col15.pbm'. And, for the cleaned version, the format is '1QIsaa_col<columnnr>_cleaned.pbm', for example, '1QIsaa_col15_cleaned.pbm'. Note: the image files are not in a separate directory; they will be extracted in the same place. However, due to the unique naming, there is no problem extracting them in one single directory.</p> <p>The 'features' directory contains feature files computed for each of the column images. There are two types of feature files: Hinge and Adjoined. They are distinguishable by their extension, for example, '1QIsaa_col15_cleaned.hinge' and '1QIsaa_col15_cleaned.adjoined'. They are also arranged in separate directories for ease of use.</p> <p>The 'plots' directory contains a simple python script to perform PCA on the feature files and then visualize them in a 3D plot. The file takes the location of feature files as an input. The 'README_plot' file contains examples of how-to-run in the terminal.</p> <p><strong>Brief description:</strong><br> According to ImageMagick's' identify' tool, the original images are in grayscale (.jpg) from Brill collection, in '8-bit Gray 256c'. These images pass through multiple preprocessing measures to become suitable for pattern recognition-based techniques. The first step in preprocessing is the image-binarization technique. In order to prevent any classification of the text-column images based on irrelevant background patterns, a specific binarization technique (BiNet) was applied, keeping the original ink traces intact. After performing the binarization, the images were cleaned further by removing the adjacent columns that partially appear on the target columns' images. Finally, few minor affine transformations and stretching corrections were performed in a restrictive manner. These corrections are also targeted for aligning the texts where the text lines get twisted due to the leather writing surface's degradation. Hence, the clean images are there in the directory along with the direct binarized images. No effort has been made to obtain a balanced set in any way.</p> <p><strong>Tools:</strong><br> <strong>Binarization:</strong><br> The BiNet tool is available for scientific use upon request (m.a.dhal(at)rug.nl)</p> <p><strong>Image Morphing:</strong><br> In the original article, data augmentation was performed using image morphing. The tool is available on GitHub:<br> https://github.com/GrHound/imagemorph.c</p> <p><strong>Features for writer identification:</strong><br> Lambert Schomaker<br> http://www.ai.rug.nl/~lambert/allographic-fraglet-codebooks/allographic-fraglet-codebooks.html<br> http://www.ai.rug.nl/~lambert/hinge/hinge-transform.html<br> <em><strong>1. </strong>L. Schomaker & M. Bulacu (2004). Automatic writer identification using connected-component contours and edge-based features of upper-case Western script. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 26(6), June 2004, pp. 787 - 798.<br> <strong>2. </strong>Bulacu, M. & Schomaker, L.R.B. (2007). Text-independent Writer Identification and Verification Using Textural and Allographic Features, IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), Special Issue - Biometrics: Progress and Directions, April, 29(4), p. 701-717.</em><br> <br> The features (hinge, fraglets) have been combined in a single MS Windows application, GIWIS, which is available for scientific use upon request (l.r.b.schomaker(at)rug.nl)</p> <p><strong>If you have any question, please contact us:</strong><br> Maruf A. Dhali <m.a.dhali(at)rug.nl><br> Lambert Schomaker <l.r.b.schomaker(at)rug.nl><br> Mladen Popović <m.popovic(at)rug.nl></p> <p><strong>Please cite our papers if you use this data set:</strong><br> <em><strong>1.</strong> Popović, M., Dhali, M. A., & Schomaker, L. (2020). Artificial intelligence based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsa<sup>a</sup>). arXiv preprint arXiv:2010.14476.<br> <strong>2. </strong>Dhali, M. A., de Wit, J. W., & Schomaker, L. (2019). Binet: Degraded-manuscript binarization in diverse document textures and layouts using deep encoder-decoder networks. arXiv preprint arXiv:1911.07930.</em></p>
Identification of high-wind features within extratropical cyclones using a probabilistic random forest - Part 2: Climatology - Dataset
<p>This dataset provides output of RAMEFI for the wind feature climatology presented in Eisenstein et al. (2023; 10.5194/wcd-2023-10) for the winter months October to March 2000-2019 using COSMO-REA6 (https://reanalysis.meteo.uni-bonn.de/?COSMO-REA6).</p> <p><strong>rf_crea_<yyyymm>.nc</strong> include the unfiltered probabilities for 'no feature' (p0), warm jet (p1), cold-frontal convection (p2), cold jet (p3) and cold-sector winds (p5) for each month.</p> <p>To filter for cyclone tracks, use <strong>cyclone_tracks.csv</strong>. The<strong> </strong>file includes interpolated ERA5 cyclone tracks for the investigated area and time period (see Section 2.4 of the paper).</p> <p><strong>mask.nc</strong> includes a land sea mask, height of surface level and a mask to exclude certain grid points as discussed in the manuscript (e.g., grid points with an altitude over 800m and the Balkans) for further filtering.</p>
Identification of high-wind features within extratropical cyclones using a probabilistic random forest - Part 2: Climatology - Video Supplement
<p>These videos provide examples of application of RAMEFI (RAndom-forest based MEsoscale wind Feature Identification) for winter storm cases between 2000 and 2019 and an animation of cyclone-relative occurrence (cf. Fig. 7 of 10.5194/wcd-2023-10).</p>
Objective identification of high-wind features within extratropical cyclones using a probabilistic random forest (RAMEFI). Part I: Method and illustrative case studies - Video Supplement
<p>These videos provide examples of application of RAMEFI (RAndom-forest based MEsoscale wind Feature Identification), a new objective identification of high-wind features within extratropical cyclones, for twelve selected case studies.</p>
Identification of genetic variants associated with clinical features of sickle cell disease
Open the record for dataset details and reuse information.
Supplementary information to "Rainbow in the dark. The identification of diagnostic projectile impact features on rock crystal"
<p>Images of the experimental projectiles (pre-hafting, hafted before and after shooting), Hirox (Low magnification) pictures of pre-hafting and post-shooting projectiles and selection of fractures smoked with magnesium.</p>
Identification of system-level features in HIV migration within a host
<p><strong>Objective</strong>: Identify system-level features in HIV migration within a host across body tissues. Evaluate heterogeneity in the presence and magnitude of these features across hosts. </p> <p><strong>Method</strong>: Using HIV DNA deep sequencing data generated across multiple tissues from 8 people with HIV, we represent the complex dependencies of HIV migration among tissues as a network and model these networks using the family of exponential random graph models (ERGMs). ERGMs allow for the statistical assessment of whether network features occur more (or less) frequently in viral migration than might be expected by chance. The analysis investigates ve potential features of the viral migration network: (1) bi-directional ow between tissues; (2) preferential migration among tissues in the same biological system; (3) heterogeneity in the level of viral migration related to HIV reservoir size; (4) hierarchical structure of migration; and (5) cyclical migration among several tissues. We calculate the Cohran's Q statistic to assess heterogeneity in the magnitude of the presence of these features across hosts. The analysis adjusts for missing data on body tissues. </p> <p><strong>Results</strong>: We observe strong evidence for bi-directional ow between tissues; migration among tissues in the same biological system; and hierarchical structure of the viral migration network. This analysis shows no evidence for differential level of viral migration with respect to the HIV reservoir size of a tissue. There is evidence that cyclical migration among three tissues occurs less frequently than expected given the amount of viral migration. The analysis also provides evidence for heterogeneity in the magnitude that these features are present across hosts. Adjusting for missing tissue data identifies system-level features within a host as well as heterogeneity in the presence of these features across hosts that are not detected when the analysis only considers the observed data. </p> <p><strong>Discussion</strong>: Identification of common features in viral migration may increase the efficiency of HIV cure efforts as it enables targeting specific processes.</p>
Data from: Machine learning identification of microhabitat features associated with occupancy of artificial nestboxes by hazel dormice (Muscardinus avellanarius) in a UK woodland site
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Identification of system-level features in HIV migration within a host
Open the record for dataset details and reuse information.
Figures 3–12. Crematogaster worker features useful for identification. 3 in Taxonomy in the phylogenomic era: species boundaries and phylogenetic relationships among North American ants of the Crematogaster scutellaris group (Formicidae: Hymenoptera)
Figures 3–12. Crematogaster worker features useful for identification. 3, petiole shape, dorsal view, C. lineolata (CASENT0863235) (3A) and C. torosa (CASENT0795540) (3B); 4, sculpture on declivitous face of propodeum, C. dentinodis (CASENT0863070) (4A) and C. depilis (CASENT0863478) (4B); 5, mesosomal pilosity, C. punctulata (CASENT0863277) (5A) and C. emeryana (CASENT0863099) (5B); 6, position and shape of propodeal spines, dorsal view, C. isolata (CASENT0863072) (6A) and C. pinicola (CASENT0882129) (6B); 7, petiole shape, dorsal view, C. mutans (CASENT0862488); 8, subpetiolar tooth, lateral view, C. mutans (CASENT0862488); 9, promesonotal sculpture, C. californica (CASENT0221085) (9A) and C. coarctata (CASENT0221962) (9B); 10, postpetiolar seta, C. browni (CASENT0863144) (10A) and C. cerasi (CASENT0795541) (10B); 11, cephalic pilosity, C. torosa (CASENT0795540) (11A) and C. missouriensis (CASENT0221041) (11B); 12, mesosomal profile, C. crinosa (CASENT0795543) (12A) and C. torosa (CASENT0863226) (12B).
Supplementary materials for "Testing Software's Changing Features with Environment-Driven Abstraction Identification"
<p><strong>Supplementary materials of the paper entitled:</strong></p> <p><strong>“Testing Software’s Changing Features with Environment-Driven Abstraction Identification”</strong></p>
Fig. 5 in Identification and localization of morphological feature-specific metabolites in Reynoutria multiflora roots
Fig. 5. MS/MS-based identification of representative specialised metabolites detected in the DESI-MSI analysis: A, 2,3,5,4′–Tetrahydroxystilbene; B, 3′-O-Methylmyricetin; C, Torachrysone; D, Hypaphorine, and E, LPC.
Fig. 1 in Identification and localization of morphological feature-specific metabolites in Reynoutria multiflora roots
Fig. 1. Schematic diagram of tissue features of R. multiflora roots and their preferred solvent systems applied in DESI-MSI.
Fig. 2 in Identification and localization of morphological feature-specific metabolites in Reynoutria multiflora roots
Fig. 2. Major specialised metabolites detected at AVB by DESI-MSI (A-I, detected at PAB; J-L, detected at XAB).
Fig. 3 in Identification and localization of morphological feature-specific metabolites in Reynoutria multiflora roots
Fig. 3. Major specialised metabolites detected at cork and cortex by DESI-MSI (A, B and D-F, detected at cork and cortex; C, detected at cortex).
Clinical and Biochemical Features for the Identification of Dominant Calpainopathies
ClinicalTrials.gov study NCT05956132. IPD Sharing: NO. Countries: 1. Publications: 1.
Investigating Non-Usually Employed Features in the Identification of Architectural Smells: A Machine Learning-Based Approach
<p>Architectural smells (ASs) negatively affect the maintenance and evolution of software at the architectural level. Most of the current approaches for ASs identification rely on the same small and well-known set of usually employed metrics (UE-Ms) with fixed thresholds. Machine learning (ML) is a promising technique for smell identification as algorithms can learn from a rich set of metrics/features, covering several characteristics of the software and incorporating a certain degree of subjectivity. This has been explored by building datasets with a robust and rich set of features, including not only the UE-Ms but also other non-usually employed metrics (NUE-Ms). However, usually the UE-Ms determine the output of the algorithms, obfuscating other metrics that have the potential to improve the classification. This also leads to inflated and difficult to maintain datasets. <br> In this paper, we investigate the accuracy of some ML algorithms employing only NUE-Ms. We scoped our study in the classification of two smells: God Component and Unstable Dependency. This investigation revealed a set of NUE-Ms that can be also used to identify these smells and the contribution of each one for the classification. Based on this information, software engineers can then build a final dataset just with the potential features. We also briefly present our tool, called InSet, that was used by academics and practitioners to identify smells in their systems. The feedback of them was used as the oracle to compare our tool to other approaches and good results were reached. </p>
Data from:Identification of landscape features influencing gene flow: how useful are habitat selection models?
Understanding how dispersal patterns are influenced by landscape heterogeneity is critical for modelling species connectivity. Resource selection function (RSF) models are increasingly used in landscape genetics approaches. However, because the ecological factors that drive habitat selection may be different from those influencing dispersal and gene flow, it is important to consider their explicit assumptions. We calculated pairwise genetic distances among 301 Alaskan Dall's sheep (Ovis dalli dalli) using an intensive sampling effort and 15 microsatellite loci. We used multiple regression of distance matrices to assess the correlation of pairwise genetic distance and landscape resistance derived from an RSF, and combinations of landscape features hypothesized to influence dispersal. Dall's sheep gene flow was positively correlated with steep slopes, moderate peak normalized difference vegetation indices (NDVI), and open land cover. Whereas RSF covariates were significant in predicting genetic distance, the RSF model itself was not significantly correlated with Dall's sheep gene flow, suggesting that certain habitat features important seasonally (rugged terrain, mid-range elevation) were not influential to breeding dispersal. This work underscores that consideration of both habitat selection and landscape genetics models in developing conservation strategies will ensure resources are managed to meet both the immediate survival needs of a species and allow for long-term genetic connectivity.
Figure 13-16 from: Gabriel R, Longhorn SJ (2015) Revised generic placement of Brachypelma embrithes (Chamberlin & Ivie, 1936) and Brachypelma angustum Valerio, 1980, with definition of the taxonomic features for identification of female Sericopelma Ausserer, 1875 (Araneae, Theraphosidae). ZooKeys 526: 75-104. https://doi.org/10.3897/zookeys.526.6315
Figure 13-16 - Selected taxa with similar spermathecae to Sericopelma. 13 Brachypelma emilia, type species of the genus from México, specimen EME10 in SJLC 14 Brachypelma verdezi from México, PAL4 in SJLC 15 Megaphobema robustum type species of the genus from Colombia, OUMNH 2008 072 (ROB3); and 16 Megaphobema mesomelas from Costa Rica as MES4 in SJLC.
Figure 17-18 from: Gabriel R, Longhorn SJ (2015) Revised generic placement of Brachypelma embrithes (Chamberlin & Ivie, 1936) and Brachypelma angustum Valerio, 1980, with definition of the taxonomic features for identification of female Sericopelma Ausserer, 1875 (Araneae, Theraphosidae). ZooKeys 526: 75-104. https://doi.org/10.3897/zookeys.526.6315
Figure 17-18 - 17 Leg IV tarsus and metatarsus of Sericopelma immensum, allotype female (Naranjal de Guarumal, Cantón Puriscal, San José, Costa Rica), showing most extensive metatarsal 'trace' scopula 18 Nymphal (pre-dispersal) young misidentified by Petrunkevitch as Brachypelma vagans Panama, and inset, older yet smaller (post-dispersal) young of Brachypelma cf. vagans (pettrade, from Mexico).
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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
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.