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1,506 results for “objects”
Our processed LoveDA dataset for "LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images"
<p>Our processed LoveDA dataset is used for the paper "<a href="https://doi.org/10.7717/peerj-cs.1467">LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images</a>"</p>
Our processed CITY_OSM dataset for "LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images"
<p>Our processed CITY_OSM dataset is used for the paper "<a href="https://doi.org/10.7717/peerj-cs.1467">LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images</a>".</p>
A dataset of aerial images taken by UAV that we collected for "LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images"
<p>Our private dataset of UAV aerial imagery for paper "<a href="https://doi.org/10.7717/peerj-cs.1467">LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images</a>".</p>
The Twenty Questions of Bioimage Object Analysis
<p>A proposed 20 question schema for creating object analyses in bioimages. </p>
Data Sets for the study "What Performance Indicators to Use for Self-Adaptation in Multi-Objective Evolutionary Algorithms"
<p>This is the dataset of the paper "What Performance Indicators to Use for Self-Adaptation in Multi-Objective Evolutionary Algorithms"</p> <ul> <li>figures.ipynb provides postprocessing codes for using the attached data to generate tables and figures in the paper.</li> <li>the csv folder consists of the raw data of function evaluations that algorithms used to hit each solution the first time.</li> <li>the metric folder consists of the processed data, which records the convergence process of two single objectives (y1 and y2), Hypervolume, and the number of obtained Pareto solutions.</li> <li>the gif folder consists of the gif files plotting the convergence process of solving LOTZ.</li> </ul> <p> </p> <p>The code used to generate this data is available at https://github.com/FurongYe/GSEMO</p>
Figures and Data Set of "The Effelsberg survey of FU Orionis and EX Lupi objects II. - H2O maser observations"
<p>This is the full version of the fits files for each source with H2O maser detection for the paper "The Effelsberg survey of FU Orionis and EX Lupi objects II. - H2O maser observations”.<br>We also provide the figures from the paper. </p> <p>The paper is published in Astronomy & Astrophysics, and can be found at: https://doi.org/10.1051/0004-6361/202245032</p> <p>The fits files are given for each source and for each observing date. For the data behind Figure 4, we also provide the multiplied data for V512 Per. The units are in Jy in all cases.</p> <p> </p>
Observational predictions for Thorne–Zytkow objects
<p>Model data, input files, and plotting scripts for the paper : Observational predictions for Thorne–Zytkow objects</p><p> </p><p>Abstract</p><p>Thorne–Zytkow objects (TZO) are potential end products of the merger of a neutron star with a non-degenerate star. In this work, we have computed the first grid of evolutionary models of TZOs with the MESA stellar evolution code. With these models, we predict several observational properties of TZOs, including their surface temperatures and luminosities, pulsation periods, and nucleosynthetic products. We expand the range of possible TZO solutions to cover $3.45\( \lesssim\) log (T/Teff) \( \lesssim\) 3.65 and 4.85 \( \lesssim\)log (L/L_sun) \( \lesssim\)5.5. Due to the much higher densities our TZOs reach compared to previous models, if TZOs form we expect them to be stable over a larger mass range than previously predicted, without exhibiting a gap in their mass distribution. Using the GYRE stellar pulsation code we show that TZOs should have fundamental pulsation periods of 1000--2000 days, and period ratios of ~0.2--0.3. Models computed with a large 399 isotope fully-coupled nuclear network show a nucleosynthetic signal that is different to previously predicted. We propose a new nucleosynthetic signal to determine a star's status as a TZO: the isotopologues \(^{44}\rm{Ti O}\) and \(^{44}\rm{Ti O_2}\), which will have a shift in their spectral features as compared to stable titanium-containing molecules. We find that in the local Universe (~SMC metallicities and above) TZOs show little heavy metal enrichment, potentially explaining the difficulty in finding TZOs to-date.</p><p> </p><p>MESA version: r22.11.1</p><p> </p>
Heatmap of global collection units Any collection object in any museum can be categorized into only one of the 304 cells (19 collection types by 16 geographic regions).A "collection unit" is a single museum's holdings within a single cell. For 73 museums, there are 22,192 possible collection units. The heatmap shows the 1957 collection units with more than 10,000 objects. See supplementary materials for details and for a heatmap of the 242 collection units with more than 1 million objects. in A global approach for natural history museum collections
Heatmap of global collection units Any collection object in any museum can be categorized into only one of the 304 cells (19 collection types by 16 geographic regions).A "collection unit" is a single museum's holdings within a single cell. For 73 museums, there are 22,192 possible collection units. The heatmap shows the 1957 collection units with more than 10,000 objects. See supplementary materials for details and for a heatmap of the 242 collection units with more than 1 million objects.
Locations of participating natural history museums These 73 museums () collectively hold more than 1.1 billion objects. in A global approach for natural history museum collections
Locations of participating natural history museums These 73 museums () collectively hold more than 1.1 billion objects.
FIGURE 2 in Solving nomenclatural problems of genus-group names of the cuckoo-wasps (Hymenoptera, Chrysididae): objectively invalid and unavailable names, new type-species designations, new names, a new genus and new synonymies
FIGURE 2. Morphochrysis gen. nov., third metasomal tergum, postero-lateral view. A. M. personata, ³; B. M. pulchella, ♀; C. M. diadema, ³; D. M. atechka, ♀; E. M. asahinai, ♀; F. M. urakensis, ³; G. M. mosulensis, ♀; H. M. tedshensis, ♀;
FIGURE 1 in Solving nomenclatural problems of genus-group names of the cuckoo-wasps (Hymenoptera, Chrysididae): objectively invalid and unavailable names, new type-species designations, new names, a new genus and new synonymies
FIGURE 1. Morphochrysis gen. nov., habitus, dorsal view. A. M. asahinai, ♀; B. M. andradei, ♀; C. M. atechka, ♀; D. M. atechka, ³; E. M. pulchella, ³; F. M. pulchella, ♀; G. M. dives, ³; H. M. dives, ♀; I. M. personata, ³; J. M. rubicunda, ♀; K. M. trisinuata, ♀; L. M. turceyana, ♀;
FIGURE 3 in Solving nomenclatural problems of genus-group names of the cuckoo-wasps (Hymenoptera, Chrysididae): objectively invalid and unavailable names, new type-species designations, new names, a new genus and new synonymies
FIGURE 3. Morphochrysis gen. nov., fifth and sixth female metasomal terga. A–B. M. pulchella; C–D. M. calimorpha; E–F. M. goetheana.
DATASET - Automated grain sizing from UAV imagery of a gravel-bed river: benchmarking of three object-based methods and analysis of particle-size clustering
<p>Dataset used to compute the grain size distributions from in-field line sampling and digitally on orthoimages with automated methodologies and by manual labelling. It also contains the data used to produce spatial statistics.</p>
"Trust Your Neighbours: Handling Noise in Multi-Objective Optimisation Using kNN-Averaging" - Supporting Data
<p>Extracted data used to create the figures supporting the publication "Trust Your Neighbours: Handling Noise in Multi-Objective Optimisation Using kNN-Averaging", submitted to Applied Soft Computing.</p> <p> </p> <p> </p>
Object detection for graphical user interface: old fashioned or deep learning or a combination? - Model&Datasets
<p>This repo contains the datasets, trained models, and data splitting in ESEC/FSE 2020 "Object detection for graphical user interface: old fashioned or deep learning or a combination?" paper.</p>
Data for Objective identification of pressure wave events from networks of 1-Hz, high-precision sensors
<p>Data from pressure sensors assembled by Matthew Miller which consist of either a Bosch BMP388 or Bosch BME280 Adafruit breakout board connected to a Raspberry Pi Zero W single-board computer used to log the data. Data are recorded at 1-second intervals. The data are stored in .csv files: one for each day for each sensor. Sensors were placed in networks in the Toronto, ON, Canada, New York, NY, USA, and Raleigh, NC, USA metro areas. Code for processing these data can be found at https://doi.org/10.5281/zenodo.8087843.</p>
Constructing an Indicator System for Cultural Sustainability in Chinese Cities under the Objective of Urban Renewal and Capability Measurement
<p>This paper adopts the top-down approach based on the interpretation and connotation of the seven first-level and 22 second-level indicators formulated above. At the same time, we are designing the tertiary indicator database in strict accordance with the principles of scientific, systematic, and adaptive construction of the indicator system. After deliberation and modification according to the actual situation, 179 three-level indicators are formulated in Appendix 1 for details. </p>
VISIONE Feature Repository for VBS: Multi-Modal Features and Detected Objects from V3C1+V3C2 Dataset
<p>This repository contains a diverse set of features extracted from the V3C1+V3C2 dataset, sourced from the Vimeo Creative Commons Collection. These features were utilized in the VISIONE system [Amato et al. 2023, Amato et al. 2022] during the latest editions of the Video Browser Showdown (VBS) competition (<a href="https://www.videobrowsershowdown.org/">https://www.videobrowsershowdown.org/</a>).</p> <p>The original V3C1+V3C2 dataset, provided by NIST, can be downloaded using the instructions provided at <a href="https://videobrowsershowdown.org/about-vbs/existing-data-and-tools/">https://videobrowsershowdown.org/about-vbs/existing-data-and-tools/</a>.</p> <p>It comprises 7,235 video files, amounting for 2,300h of video content and encompassing 2,508,113 predefined video segments.</p> <p>We subdivided the predefined video segments longer than 10 seconds into multiple segments, with each segment spanning no longer than 16 seconds. As a result, we obtained a total of 2,648,219 segments. For each segment, we extracted one frame, specifically the middle one, and computed several features, which are described in detail below.</p> <p>This repository is released under a Creative Commons Attribution license. If you use it in any form for your work, please cite the following paper:</p> <blockquote> <pre>@inproceedings{amato2023visione, title={VISIONE at Video Browser Showdown 2023}, author={Amato, Giuseppe and Bolettieri, Paolo and Carrara, Fabio and Falchi, Fabrizio and Gennaro, Claudio and Messina, Nicola and Vadicamo, Lucia and Vairo, Claudio}, booktitle={International Conference on Multimedia Modeling}, pages={615--621}, year={2023}, organization={Springer} }</pre> </blockquote> <p> </p> <p>This repository comprises the following files:</p> <ul> <li><strong><em>msb.tar.gz </em></strong> contains tab-separated files (.tsv) for each video. Each tsv file reports, for each video segment, the timestamp and frame number marking the start/end of the video segment, along with the timestamp of the extracted middle frame and the associated identifier ("id_visione"). </li> <li><em><strong>extract-keyframes-from-msb.tar.gz</strong></em> contains a Python script designed to extract the middle frame of each video segment from the MSB files. To run the script successfully, please ensure that you have the original V3C videos available.</li> <li><strong><em>features-aladin.tar.gz<sup>†</sup></em> </strong>contains <a href="https://github.com/mesnico/ALADIN">ALADIN</a> [Messina N. et al. 2022] features extracted for all the segment's middle frames. </li> <li><em><strong>features-clip-laion.tar.gz<sup>†</sup></strong></em> contains <a href="https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K">CLIP ViT-H/14 - LAION-2B </a>[Schuhmann et al. 2022] features extracted for all the segment's middle frames.</li> <li><em><strong>features-clip-openai.tar.gz<sup>†</sup> </strong></em>contains <a href="https://huggingface.co/openai/clip-vit-large-patch14">CLIP ViT-L/14</a> [Radford et al. 2021] features extracted for all the segment's middle frames. </li> <li><em><strong>features-clip2video.tar.gz<sup>†</sup> </strong></em>contains <a href="https://github.com/CryhanFang/CLIP2Video">CLIP2Video</a> [Fang H. et al. 2021] extracted for all the video segments. <strong> </strong>In particular 1) we concatenate consecutive short segments so to create segments at least 3 seconds long; 2) we downsample the obtained segments to 2.5 fps; 3) we feed the network with the first min(36, n) frames, where n is the number of frames of the segment. Notice that the minimum processed length consists of 7 frames, given that the segment is no shorter than 3s. </li> <li><em><strong>objects-frcnn-oiv4.tar.gz<sup>*</sup> </strong></em>contains the objects detected using <a href="http://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1">Faster R-CNN+Inception ResNet</a> (trained on the Open Images V4 [Kuznetsova et al. 2020]). </li> <li><em><strong>objects-mrcnn-lvis.tar.gz<sup>*</sup></strong></em> contains the objects detected using Mask R-CNN [He et al. 2017] (trained on LVIS).</li> <li><em><strong>objects-vfnet64-coco.tar.gz<sup>*</sup></strong></em> contains the objects detected using VfNet [Zhang et al. 2021] (trained on COCO dataset).</li> </ul> <p>*Please be sure to use the <strong>v2 version </strong>of this repository, since v1 feature files may contain inconsistencies that have now been corrected</p> <p><em><strong>*Note on the object annotations:</strong></em> Within an object archive, there is a jsonl file for each video, where each row contains a record of a video segment (the <em>"_id"</em> corresponds to the <em>"id_visione"</em> used in the msb.tar.gz) . Additionally, there are three arrays representing the objects detected, the corresponding scores, and the bounding boxes. The format of these arrays is as follows:</p> <ul> <li><em>"object_class_names"</em>: vector with the class name of each detected object.</li> <li><em>"object_scores"</em>: scores corresponding to each detected object.</li> <li><em>"object_boxes_yxyx"</em>: bounding boxes of the detected objects in the format <em>(ymin, xmin, ymax, xmax).</em></li> </ul> <p> </p> <p><em><strong><sup>†</sup>Note on the cross-modal features: </strong></em>The extracted multi-modal features (ALADIN, CLIPs, CLIP2Video) enable internal searches within the V3C1+V3C2 dataset using the query-by-image approach (features can be compared with the dot product). However, to perform searches based on free text, the text needs to be transformed into the joint embedding space according to the specific network being used. Please be aware that t<strong>he service for transforming text into features is not provided within this repository and should be developed independently using the original feature repositories linked above.</strong></p> <p>We have plans to release the code in the future, allowing the reproduction of the VISIONE system, including the instantiation of all the services to transform text into cross-modal features. However, this work is still in progress, and the code is not currently available.</p> <p> </p> <p><strong>References:</strong></p> <p>[Amato et al. 2023] Amato, G.et al., 2023, January. VISIONE at Video Browser Showdown 2023. In International Conference on Multimedia Modeling (pp. 615-621). Cham: Springer International Publishing.</p> <p>[Amato et al. 2022] Amato, G. et al. (2022). VISIONE at Video Browser Showdown 2022. In: , et al. MultiMedia Modeling. MMM 2022. Lecture Notes in Computer Science, vol 13142. Springer, Cham. </p> <p>[Fang H. et al. 2021] Fang H. et al., 2021. Clip2video: Mastering video-text retrieval via image clip. arXiv preprint arXiv:2106.11097.</p> <p>[He et al. 2017] He, K., Gkioxari, G., Dollár, P. and Girshick, R., 2017. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 2961-2969).</p> <p>[Kuznetsova et al. 2020] Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A. and Duerig, T., 2020. The open images dataset v4. International Journal of Computer Vision, 128(7), pp.1956-1981.</p> <p>[Lin et al. 2014] Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P. and Zitnick, C.L., 2014, September. Microsoft coco: Common objects in context. In European conference on computer vision (pp. 740-755). Springer, Cham.</p> <p>[Messina et al. 2022] Messina N. et al., 2022, September. Aladin: distilling fine-grained alignment scores for efficient image-text matching and retrieval. In Proceedings of the 19th International Conference on Content-based Multimedia Indexing (pp. 64-70).</p> <p>[Radford et al. 2021] Radford A. et al., 2021, July. Learning transferable visual models from natural language supervision. In International conference on machine learning (pp. 8748-8763). PMLR.</p> <p>[Schuhmann et al. 2022] Schuhmann C. et al., 2022. Laion-5b: An open large-scale dataset for training next generation image-text models. Advances in Neural Information Processing Systems, 35, pp.25278-25294.</p> <p>[Zhang et al. 2021] Zhang, H., Wang, Y., Dayoub, F. and Sunderhauf, N., 2021. Varifocalnet: An iou-aware dense object detector. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 8514-8523).</p>
Collection of Object-Centric Event Logs (OCEL 1.0 format; JSON-OCEL specification)
<p>We provide some realistic examples of logs in the OCEL standard. The logs have originally been published at https://www.ocel-standard.org/ </p>
.RData objects needed to reproduce ST vignette
<p>BC.Rdata: Seurat Object</p> <p>ST_expr_smooth_out.RData: Pre-computed smooths spatial transcriptomics gene expression using the weighted mean of neighbouring spots in one compartment.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.