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317 results for “Multi modal”
Data Repository for the article: "Control of multi-modal scattering in a microwave frequency comb"
<p><strong>Abstract:</strong></p> <p>Control over the coupling between multiple modes of a frequency comb is an important step toward measurement-based quantum computation with a continuous-variable system.<br>We demonstrate the creation of square-ladder correlation graphs in a microwave comb with 95 modes.<br>The graphs are engineered through precise control of the relative phase of three pumps applied to a Josephson parametric oscillator. <br>Experimental measurement of the mode scattering matrix is in good agreement with theoretical predictions based on a linearized equation of motion of the parametric oscillator. <br>The digital methods used to create and measure the correlations are easily scaled to more modes and more pumps, with the potential to tailor a specific correlation graph topology.</p> <p> </p> <p>Article DOI:</p> <p>ArXiV: <a href="https://doi.org/10.48550/arXiv.2402.09068">arXiv.2402.09068</a></p> <p> </p> <p><strong>Content:</strong></p> <p>This repository contains the datased used in the article "Control of multi-modal scattering in a microwave frequency comb". Scripts to generate the figures are also included.</p>
VHAKG: Multi-modal Knowledge Graphs with Multi-view Videos of Daily Activities
<h2>Outline</h2> <ul> <li>This dataset is a multimodal knowledge graph (MMKG) of daily activity videos.</li> <li>This dataset integrates a KG with embedded multi-view videos created by <a href="https://github.com/aistairc/virtualhome_aist">VirtualHome-AIST</a>, an extended version of the VirtualHome simulator, and an event-centric KG generated by <a href="https://github.com/aistairc/virtualhome2kg">VirtualHome2KG</a>.</li> <li>We named this dataset <strong>VHAKG</strong> (VirtualHome-AIST-KG).</li> </ul> <h2>Details</h2> <ul> <li>VHAKG describes 2D bounding boxes of objects every five frames, compositional activities, primitive actions, target objects, object states, 3D bounding boxes, and their time-series changes.</li> <li>The videos are encoded in base64 and embedded as a literal value.</li> <li>VHAKG consists of 706 daily activity scenarios (e.g., clean desk, cook fried bread, and relax on sofa) and 3,530 videos captured by five synchronized cameras per scenario.</li> <li>The file format is <a href="https://www.w3.org/RDF/">RDF</a> (<a href="https://en.wikipedia.org/wiki/Turtle_(syntax)">Turtle</a>), which can be loaded into various <a href="https://en.wikipedia.org/wiki/Triplestore">Triplestores</a>.</li> <li>VHAKG's vocabularies are defined as an ontology and can be found in vh2kg_schema_v2.0.0.ttl.</li> </ul> <h2>Contents</h2> <ul> <li>vh2kg_video_base64.tar.gz <ul> <li><strong>{activity name}{scene}_{camera}_2dbbox.ttl</strong>: KG with video embedded in base64 format, including 2D bounding box data every 5 frames. <ul> <li>To learn more about {scene}, check <a href="https://github.com/xavierpuigf/virtualhome/tree/v2.2.0/simulation#environment">here</a>.</li> <li>To learn more about {camera}, check <a href="https://github.com/aistairc/virtualhome_unity_aist?tab=readme-ov-file#addition-of-new-four-cameras">here</a>.</li> </ul> </li> </ul> </li> <li>vh2kg_event.tar.gz <ul> <li><strong>{activity name}_{scene}.ttl</strong>: Event-centric KGs representing video content as sequences of events.</li> <li><strong>vh2kg_schema_v2.0.0.ttl</strong>: The ontology file of this dataset.</li> <li><strong>affordance.ttl</strong>: The affordance data of objects that were created by crowdsourcing. <ul> <li>Please see Section III.B of <a href="https://doi.org/10.1109/ACCESS.2023.3253807" target="_blank" rel="noopener">this paper</a> for more information.</li> </ul> </li> <li><strong>add_places.ttl</strong>: Events in which agents moved from one room to another.</li> </ul> </li> </ul> <h2>Tools</h2> <ul> <li><a href="https://github.com/aistairc/vhakg-tools">A set of tools</a> for searching and extracting videos from VHAKG is available.</li> </ul>
Representation learning for multi-modal spatially resolved transcriptomics data
<p>This folder contains the already unified input used for the models. The raw data is referenced here:</p> <ul> <li>LIBD Human DLPFC dataset is available at <a href="https://github.com/LieberInstitute/HumanPilot">https://github.com/LieberInstitute/HumanPilot</a> and <a href="http://research.libd.org/spatialLIBD" rel="nofollow">http://research.libd.org/spatialLIBD</a>;</li> <li>Human Breast Cancer - Zenodo <a href="https://doi.org/10.5281/zenodo.4739739" rel="nofollow">https://doi.org/10.5281/zenodo.4739739</a>,</li> <li>Human Liver Normal and Cancer - <a href="https://nanostring.com/products/cosmx-spatial-molecular-imager/human-liver-rna-ffpe-dataset/" rel="nofollow">https://nanostring.com/products/cosmx-spatial-molecular-imager/human-liver-rna-ffpe-dataset/</a>.</li> </ul>
Leveraging diversity in computer-aided musical orchestration with an artificial immune system for multi-modal optimization
<p>Data resulting from the experiments described in "Leveraging diversity in computer-aided musical orchestration with an artificial immune system for multi-modal optimization" (https://doi.org/10.1016/j.swevo.2018.12.010). The contents of the files is the following:</p> <ul> <li>CAMO-AIS_SWEVO.zip: All of the data below in a single file</li> <li>CAMO_Iowa.zip: Audio and Data for the orchestrations with the Iowa sound database found at http://theremin.music.uiowa.edu/MIS.html</li> <li>CAMO_Phil.zip: Audio and Data for the orchestrations with the Philharmonia sound database found at https://www.philharmonia.co.uk/explore/sound_samples</li> <li>CAMO_RWC.zip: Audio and Data for the orchestrations with the RWC sound database found at https://staff.aist.go.jp/m.goto/RWC-MDB/rwc-mdb-i.html</li> <li>CAMO_SOL.zip: Audio and Data for the orchestrations with the Studio Online sound database available with Orchids http://forumnet.ircam.fr/product/orchids-en/</li> <li>Listening_Test.zip: Raw data (i.e., perceptual similarity ratings) from the listening test found at http://http://camo.inesctec.pt/. This data is anonymous (each participant is assigned a reference number) so the participants cannot be identified.</li> </ul> <p>See the README.txt file for a detailed description of the contents of each file.</p>
A multi-modal sensor dataset for continuous stress detection of nurses in a hospital
<p>Advances in wearable technologies provide the opportunity to monitor many physiological variables continuously. Stress detection has gained increased attention in recent years, especially because early stress detection can help individuals better manage health to minimize the negative impacts of long-term stress exposure. This paper provides a unique stress detection dataset created in a natural working environment in a hospital. This dataset is a collection of biometric data of nurses during the COVID-19 outbreak. Studying stress in a work environment is complex due to the influence of many social, cultural, and individuals experience in dealing with stressful conditions. In order to address these concerns, we captured both the physiological data and associated context pertaining to the stress events. We monitored specific physiological variables, including electrodermal activity, heart rate, skin temperature, and accelerometer data of the nurse subjects. A periodic smartphone-administered survey also captured the contributing factors for the detected stress events. A database containing the signals, stress events, and survey responses is available upon request.</p>
Concurrent tDCS with multi-modal MRI - Experiment 2
<p>The provided dataset includes 9 subjects (7 female) with DTI and pASL imaging before (run 1), during (run 2, run 3), and after (run 4, run 5) transcranial direct-current stimulation (tDCS). Subjects were recruited to CUNY IRB standards and provided informed consent on the publication of their data. The two sessions, Active and Sham, indicate whether subjects were stimulated with 4mA anodal tDCS (AF7,F7 - Anode, AF8, F8 - Cathode) or Sham (30 sec ramp-up and ramp-down) during the stimulation (run 2, run 3) time points. </p> <p>Raw DTI and pASL files are provided in Nifti (.nii) format with an associated T1w anatomical image. All data has been de-identified to preserve subject anonymity. Data is organized to BIDS standards for ease of processing. It is anticipated that MRI data with concurrent tDCS will benefit the field and allow others to analyze, validate tools, or compare with non-stimulated control measurements. </p>
Concurrent tDCS with multi-modal MRI - Experiment 1
<p>The provided dataset includes 20 subjects (6 female) with Arterial Transit Time (ATT), water exchange across the BBB (Kw) and cerebral blood flow (CBF) imaging before (run 1), during (run 2, run 3), and after (run 4, run 5) transcranial direct-current stimulation (tDCS). Subjects were recruited to CUNY IRB standards and provided informed consent on the publication of their data. The three sessions, (ses-1, ses-2, and ses-3), indicate whether subjects were stimulated Sham current (30 sec ramp-up and ramp-down), 2mA anodal tDCS (AF7 - Anode, AF8 - cathode) or 4mA anodal tDCS (AF7,F7 - Anode, AF8, F8 - Cathode), respectively.</p> <p>Pre-processed files are provided in Nifti (.nii) format with an associated T1w anatomical image. All data has been de-identified to preserve subject anonymity. Data is organized to BIDS standards for ease of processing. It is anticipated that MRI data with concurrent tDCS will benefit the field and allow others to analyze, validate tools, or compare with non-stimulated control measurements. </p>
National High-Resolution Cropland Classification of Japan with Agricultural Census Information and Multi-temporal Multi-modality datasets
<p>Multi-modality datasets offer advantages for processing frameworks with complementary information, particularly for large-scale cropland mapping. Extensive training datasets are required to train machine learning algorithms, which can be challenging to obtain. To alleviate the limitations, we extract the training samples from the agricultural census information. We focus on Japan and demonstrate how agricultural census data in 2015 can map different crop types for the entire country. Due to the lack of Sentinel-2 datasets in 2015, this study utilized Sentinel-1 and Landsat-8 collected across Japan and combined observations into composites for different prefecture periods (monthly, bimonthly, seasonal). Recent deep learning techniques have been investigated the performance of the samples from agricultural census information.<br> Finally, we obtain nine crop types on a countrywide scale (around 31 million parcels) and compare our results to those obtained from agricultural census testing samples as well as those obtained from recent land cover products in Japan. The generated map accurately represents the distribution of crop types across Japan and achieves an overall accuracy of 87% for nine classes in 47 prefectures.</p>
MMpedia: A Large-scale Multi-modal Knowledge Graph
<p><strong>List of files:</strong></p> <ul> <li>entity2image.json: The entity to map images file</li> <li>MMpedia_triples.ttl: The triples file</li> <li>EntlistXX.tar: The image file</li> </ul> <p>MMpedia dataset is split into 132 subsets and each subset is compressed into a <code>.tar</code> file. After unziping files under the folder "MMpedia", the data structure are as following:</p> <pre><code>|-MMpedia |-Entitylist1 |-Entity1 |-1.jpg |-2.jpg |-3.jpg ... |-Entity2 |-Entity3 ... |-Entitylist2 |-Entitylist3</code></pre> <p>For example, the path <code>MMpedia/Entlist141/Bart Tanski/Bart Tanski+1.jpg</code> means the image corresponding to the entity "Bart Tanski"</p> <p><strong>Other image files can be found in following URLs:</strong></p> <p><a href="https://zenodo.org/record/7854781#.ZEU7Uc7iu38">MMpedia2 | Zenodo</a></p> <p><a href="https://zenodo.org/record/7855010">MMpedia3 | Zenodo</a></p> <p><a href="https://zenodo.org/record/7855226">MMpedia4 | Zenodo</a></p> <p> <strong>For more details, please refer to </strong><a href="https://github.com/Delicate2000/MMpedia">Delicate2000/MMpedia (github.com)</a></p>
Imaging 3D Chemistry at 1 nm Resolution with Fused Multi-Modal Electron Tomography
<p>Measuring the three-dimensional (3D) distribution of chemistry in nanoscale matter is a longstanding challenge for metrological science. The inelastic scattering events required for 3D chemical imaging are too rare, requiring high beam exposure that destroys the specimen before an experiment completes. Even larger doses are required to achieve high resolution. Thus, chemical mapping in 3D has been unachievable except at lower resolution with the most radiation-hard materials. Here, high-resolution 3D chemical imaging is achieved near or below one nanometer resolution in a Au-Fe<sub>3</sub>O<sub>4</sub> metamaterial, Co<sub>3</sub>O<sub>4</sub> - Mn<sub>3</sub>O<sub>4</sub> core-shell nanocrystals, and ZnS-Cu<sub>0.64</sub>S<sub>0.36</sub> nanomaterial using fused multi-modal electron tomography. Multi-modal data fusion enables high-resolution chemical tomography often with 99% less dose by linking information encoded within both elastic (HAADF) and inelastic (EDX / EELS) signals. Now sub-nanometer 3D resolution of chemistry is measurable for a broad class of geometrically and compositionally complex materials.</p>
A Multi-modal, Physician-centered Intervention to Improve Guideline-concordant Prostate Cancer Imaging
ClinicalTrials.gov study NCT03445559. IPD Sharing: NO. Countries: 1. Publications: 19.
MAST Trial: Multi-modal Analgesic Strategies in Trauma
ClinicalTrials.gov study NCT03472469. IPD Sharing: NO. Countries: 1. Publications: 2.
Increasing Uptake of Evidence-Based Screening Services Through CHW-led Multi-modality Intervention
ClinicalTrials.gov study NCT02970136. IPD Sharing: NO. Countries: 1. Publications: 1.
Effect of Multi-modal Intervention Care on Cachexia in Patients With Advanced Cancer Compared to Conventional Management (MIRACLE)
ClinicalTrials.gov study NCT04907864. IPD Sharing: YES. Countries: 1. Publications: 1.
Multi-Modal Education to Improve Compliance, Knowledge Retention & Anxiety After Dental Extractions
ClinicalTrials.gov study NCT07191132. IPD Sharing: YES. Countries: 1. Publications: 13.
Emission rates of species-specific volatiles vary across communities of Clarkia species: evidence for multi-modal character displacement
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A multi-modal sensor dataset for continuous stress detection of nurses in a hospital
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Multi-modal dataset of a polycrystalline metallic material: 3D microstructure and deformation fields
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mRI: multi-modal 3d human pose estimation dataset using mmwave, rgb-d, and inertial sensors
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Multi-modal characterization of rodent dental development
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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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.