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2,139 results for “recognition”
mmWave Radar and RGB-D Camera Sensor Data for Human Activity Recognition
<p>This is a human activity recognition dataset with measurements from both mmWave radar and camera sensor. Meanwhile, we set multiple people scenario to mimic more realistic scenes. The other dataset collected in non-LOS(line-of-sight) environment, you can visit https://zenodo.org/record/7096889#.YynBvuhBwQ8 to get it. The mmWave radar sensors used in our experiments are composed of TI IWR6843ISK-ODS, eradar ESRR(corner radar), eradar EMRR(front radar). We appreciate the support of the eradar company, that provides corner radars and front radars for us, you can visit http://en.eradartech.com/ to get more information. </p>
Fig. 1 in Recognition of a second species in the Homalium involucratum complex (Salicaceae)
Fig. 1. – Homalium involucratum (DC.) Hoffm.: inflorescence
IMU-based Human Activity Recognition and Payload Estimation for Low-Back Exoskeletons
<p>This repository contains the data for a payload classification algorithm. Data are already split into train, validation and test sets. 12 subjects are included in this dataset (6M+6F).</p> <table> <tbody> <tr> <td><strong>Sensor ID</strong></td> <td><strong>Position</strong></td> </tr> <tr> <td>B6</td> <td>Chest</td> </tr> <tr> <td>3B</td> <td>Right Leg</td> </tr> <tr> <td>BB</td> <td>Right Wrist</td> </tr> <tr> <td>C9</td> <td>Left Leg</td> </tr> <tr> <td>B5</td> <td>Left Wrist</td> </tr> </tbody> </table> <p> </p> <p>This dataset was collected as part of a research project at the Politecnico di Milano and used to train deep-learning algorithms.</p>
Tracking the path from learning to innate predator recognition in Lymnaea stagnalis
<p>Organisms evolve adaptive strategies to adjust to the rapidly changing environmental stressors. Predation pressure is one of the strongest selective forces and organisms respond to predatory threats via innate and learned responses. We utilized a natural, experimental set-up, where two lakes –Stoney and Margo in Saskatchewan, Canada containing natural populations of the prey Lymnaea stagnalis differed in the presence and absence of an invasive, predatory Northern crayfish, Faxonius virilis. We exploited the contrast in the predation backgrounds of the snail populations from the two lakes to test, a) if predator-naïve snails learn to detect a novel invasive predator, b) predator recognition in predator-experienced snails is innate, and, c) if learning about a novel predator gets transmitted to the successive generations. We quantified predator fear memory formation using a higher-order learning paradigm called configural learning. We found that a) predator-naïve snails learned to recognize the novel predator even after a brief exposure to predator cues highlighting the role of learning in combating invasive predators, b) predator recognition in predator-experienced snails is innate, and, c) the learning and predator detection mechanisms is not transmitted to successive generations. The population variation observed in the predator-detection mechanism may be due to the activation and deactivation of a predatory template as a function of predator exposure in the environment. We find an interesting study system to address how fear learning occurs and the possible mechanism of the formation of innate fear recognition from a learned fear recognition.</p>
Cross-Camera View-Overlap Recognition
<p>Data accompanying the paper titled <em>Cross-Camera View-Overlap Recognition</em>, published in the proceedings of the European Conference on Computer Vision Workshop and presented used for the evaluation of the framework presented in the publication.</p> <p>The dataset consists of image sequence pairs from four scenarios: two scenarios that were collected with both hand-held and chest-mounted cameras – <em>gate</em> and <em>backyard</em> of four sequences each – and two publicly available datasets – TUM-RGB-D SLAM and <em>courtyard</em> from <a href="https://ieeexplore.ieee.org/document/6193110">CoSLAM</a> – for a total of ∼28,000 frames (∼25 minutes).</p> <p>The data consisting of images, annotations, and scripts to process existing public sequences.</p> <p>Image sequences are provided for the collected scenarios <em>gate</em> and <em>backyard</em>. We sub-sampled <em>backyard</em> from 30 to 10 fps for annotation purposes.</p> <p>Image sequences for the scenario <em>office</em> can be found at <a href="https://vision.in.tum.de/data/datasets/rgbd-dataset/download">TUM RGB-D SLAM</a> (fr1_desk, fr1_desk2, fr1_room). Scripts to process these sequences as used in the work are provided.</p> <p>The <em>courtyard</em> scenario consists of four sequences. We sub-sampled courtyard from 50 to 25 fps for annotation purposes. Original sequences are available at CoSLAM project <a href="http://drone.sjtu.edu.cn/dpzou/dataset/CoSLAM">website</a>.</p> <p>For all scenarios, we provide i) the annotation of angular distances, Euclidean distances, and overlap ratio of each view pair across camera sequences; ii) the annotation of the calibration (intrinsic) parameters; and iii) the annotation of the camera poses over time for each camera sequence, as automatically reconstructed with the structure-from-motion pipeline, <a href="https://colmap.github.io/">COLMAP</a>, or exploiting the depth data for the <em>office</em> scenario.</p> <p> </p> <p>Camera poses are saved as .txt file for each sequence using the <a href="https://www.cvlibs.net/datasets/kitti/eval_odometry.php">KITTI</a> format. The pose of each frame is represented as a 3x4 matrix (12 parameters) that is converted into a vector by horizontally concatenating the rows of the matrix:<br> [r11 r12 r13 tx<br> r21 r22 r23 ty => [r11 r12 r13 tx r21 r22 r23 ty r31 r32 r33 tz]<br> r31 r32 r33 tz]</p> <p>Values of the parameters are saved in 6 digit floating point numbers as exponential notation.</p> <p> </p> <p>Along with the dataset, we also provide the global features computed by using <a href="https://doi.org/10.1109/TPAMI.2018.2833865">DeepBit</a> [<a href="https://github.com/kevinlin311tw/cvpr16-deepbit">code</a>] and <a href="https://doi.org/10.1109/TPAMI.2017.2711011">NetVLAD</a> [<a href="https://github.com/Relja/netvlad">code</a>] for each image of all camera sequences.</p> <p> </p> <p>If you use the data, please cite:<br> <br> A. Xompero and A. Cavallaro, <a href="http://www.eecs.qmul.ac.uk/~ax300/xview/"><strong>Cross-camera view-overlap recognition</strong></a>, International Workshop on Distributed Smart Cameras (IWDSC), European Conference on Computer Vision Workshops, 24 October 2022.</p> <p>ArXiv: <a href="https://arxiv.org/abs/2208.11661">https://arxiv.org/abs/2208.11661</a><br> Webpage: <a href="http://www.eecs.qmul.ac.uk/~ax300/xview/">http://www.eecs.qmul.ac.uk/~ax300/xview/</a></p>
Data for: Evolution Reinforces Cooperation with the Emergence of Self-Recognition Mechanisms: an empirical study of the Moran process for the iterated Prisoner's dilemma using reinforcement learning
<p>This contains data used for a paper titled: Evolution Reinforces Cooperation with the Emergence of Self-Recognition Mechanisms: an empirical study of the Moran process for the iterated Prisoner's dilemma using reinforcement learning.</p> <p>Numerous data sets are included, the main being `main.csv` which includes the fixation counts for a number of Moran processes between pairs of players from the Axelrod library.</p> <p>The source code and explanation of the data is here: https://github.com/Axelrod-Python/axelrod-moran. </p> <p> </p>
ICDAR 2015 Competition HTRtS: Handwritten Text Recognition on the tranScriptorium Dataset Rerelease
<p>A new release of the dataset used in the ICDAR 2015 HTR competition in which all Page XML files are based on the same 2013-07-15 schema. It only contains page level images, Page XML files for train and test (including the ground truth transcripts for the test and train batch 1) and plain text files for train batch 2 that have the page level ground truth transcripts. The original version of this dataset can be found at http://doi.org/10.5281/zenodo.248733<br> </p>
Sexual selection and species recognition promote complex male courtship displays in ungulates
<p>Identifying the evolutionary drivers of sexual signal complexity is a key challenge in the study of animal communication. Among mammals, male bovids and cervids often perform elaborate gestural displays during courtship, consisting of ritualized movements of various parts of the body but the causes underlying interspecific variation in complexity of such displays remain poorly understood. Here we apply the comparative method to investigate which factors may have either promoted or constrained gestural repertoire size. </p> <p>We found that sexual selection was a strong predictor of gestural display complexity in male bovids and cervids. Repertoire size was positively correlated with breeding group size, an indicator of the intensity of sexual selection on males. Moreover, repertoires were larger in species adopting non-territorial and lek breeding mating systems than in species adopting resource-defence territoriality, a finding that can be explained by more emphasis on direct benefits than indirect benefits in resource-defence systems, where male mating success may also be less skewed due to difficulty in monopolising mates.</p> <p>The results also indicate that gestural repertoire size was positively correlated with the number of closely-related species occurring in sympatry. This is consistent with display complexity being selected to facilitate species recognition during courtship and thereby avoid interspecific hybridization. At the same time, repertoire size was negatively associated with male body mass, possibly due to the energetic and mechanical constraints imposed on movements in very large species. By contrast, we found no evidence that the habitat drives selection for complex gestural courtship displays.</p>
Molecular Dynamics (MD) Simulation Data for Dynamics Underlie the Drug Recognition Mechanism by the Efflux Transporter EmrE
<p>MD simulations on the proton bound (PDB 8UWU), deprotonated on E14A (PDB 8UWU), TPP Bound (PDB 8UWU) on our NMR derived structures.</p> <p> </p> <p>MD simulations on the proton bound (7MH6) and deprotonated on E14A (7MH6) on X-ray structures. </p> <p> </p> <p>Total raw simulation data would be too large for uploading to repositories. To reduce size of file, starting structure and tpr files are uploaded. Final structure at 2.5 μs are also uploaded. </p>
FIGURE 8. Fielding B in The First Recognition of Cretaceous Deposits in California
FIGURE 8. Fielding B. Meek (Courtesy Smithsonian Institution Archives, Image # AI-1329)
DB3V: A Dialect Dominated Dataset of Bird Vocalisation for Cross-corpus Bird Species Recognition
<p>The first cross-corpus dataset that focuses on dialects in bird vocalisations. The DB3V comprises more than 25 hours of audio recordings from 10 bird species distributed across three distinct regions in the contiguous United States (CONUS).</p>
Molecular dynamics trajectories of pYEEI:SH2 recognition, unbiased, at all-atom resolution.
<div> </div> <p>Set of 772 all-atom trajectories simulated from an unbound (apo) configuration of the human p56 -lck tyrosine kinase SH2 domain with its high-specificity phosphopeptide recognition substrate pYEEI (initial structure based on PDB:<a href="https://www.rcsb.org/structure/1LKK">1LKK</a> ). Approximately 24 trajectories spontaneously reach a bound state with ligand RMSD < 2 Â from the crystal. System building and run details are described in [1].</p> <p>A preliminary version of this dataset have been analyzed and discussed in [1] (approx 200 ns per trajectory were available and used in [1]). </p> <p>The trajectories provided here are extended to ~800 ns each, for a total of ~640 μs sampled time. The full dataset is analyzed in [2] with a SOM-based technique.</p> <div> <h2>Notes</h2> </div> <div> <ul> <li>These are all-atom simulations (with TIP3P water). Water molecules have been stripped off from these files (filtered).</li> <li>Not all trajectories have the same length. Some are cut short due to the distributed computing setup.</li> <li>Frame-to-frame interval is 1 ns.</li> </ul> </div> <h2>Acknowledgments</h2> <p>We thank the volunteers of the GPUGRID.net project for donating computing time.</p> <p> </p> <h2>References</h2> <p>[1] T. Giorgino, I. Buch, and G. De Fabritiis. <a href="https://pubs.acs.org/doi/10.1021/ct300003f">Visualizing the Induced Binding of SH2-Phosphopeptide</a>, J. Chem. Theory Comput. 2012, 8, 4, 1171-1175. doi:10.1021/ct300003f</p> <p>[2] Lara Callea, Camilla Caprai, Laura Bonati, Toni Giorgino, Stefano Motta. Self-Organizing Maps of Unbiased Ligand-Target Binding Pathways and Kinetics. J. Chem. Phys, 2024. https://doi.org/10.1063/5.0225183 </p> <p> </p> <div> </div> <div> <p> </p> </div>
Optical Recognition of the English Alphabet Using Proteinoids
<p>This data set contains the potential in mV vs time in seconds for each letter of the English alphabet detected by proteinoids.</p>
Object Shape Recognition and Targeting Using 4x4 Ultrasonic Sensor Array
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Data from: Molecular phylogenetics of Distephanus supports the recognition of a new tribe, Distephaneae (Asteraceae)
<p>The genus <em>Distephanus</em> Cass. comprises 43 distinctive species of shrubs and small trees that have been placed historically within the ironweed tribe, Vernonieae (Asteraceae). Utilizing the most expansive sampling of <em>Distephanus </em>to date, this study aims to test the monophyly of this genus and facilitate its classification. Molecular phylogenetic analyses were conducted using four molecular markers from the nuclear and plastid genomes. These data also supported divergence dating analyses that were performed to understand the timing of diversification events within <em>Distephanus</em> and other related genera. Results from this study indicate that as currently circumscribed, Vernonieae is not monophyletic and that <em>Distephanus </em>is actually sister to a clade that comprises Vernonieae and another tribe, Moquinieae, which only includes two species restricted to Brazil. Based on these findings, <em>Distephanus </em>is recognized in a new tribe that we describe here, Distephaneae. This new tribe comprises 41 species of <em>Disptehanus</em> that are easily distinguished from Moquinieae and Vernonieae based on the presence of florets with yellow corollas and trinervate leaves.</p>
FIGURE 3 in The First Recognition of Cretaceous Deposits in California
FIGURE 3. "Ammonites Batesii" Gabb
FIGURE 7. John S in The First Recognition of Cretaceous Deposits in California
FIGURE 7. John S. Newberry (Courtesy Smithsonian Institution Archives. RU 7177, Box 8, Folder 25)
FIGURE 9. William P in The First Recognition of Cretaceous Deposits in California
FIGURE 9. William P. Blake (Courtesy Smithsonian Institution Archives, RU 7177, Box 1. Folder 43)
A Comprehensive Central Kurdish Sound Dataset for Robust Automatic Speech Recognition (Part 1).
<p>Exploring the intricacies of Speech Recognition Technology (SRT), our dataset encompasses a wide range of age demographics, spanning from adolescents to individuals in their fifties. This diverse dataset comprises a substantial collection of raw data, amounting to 1,739,089 entries. Within this dataset, a meticulous curation process has yielded a total of 1,683 hours of data, providing a thorough examination of language acquisition patterns across different age cohorts within the Central Kurdish linguistic domain.</p>
Automated recognition of RNA structure motifs by their SHAPE data signatures
<p>Datasets, code and results supporting the manuscript:</p> <p>Radecki P., Ledda M. & Aviran S., Automated recognition of RNA structure motifs by their SHAPE data signatures</p>
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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.