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901 results for “Modality”
Dataset for: Gülmüs M. et al. "Photoluminescence modal splitting via strong coupling in hybrid Au/WS2/GaP nanoparticle-on-mirror cavities"
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STS-Tooth: A multi-modal dental dataset for semi-supervised deep learning image segmentation
<p>In response to the increasing prevalence of dental diseases, dental health, a vital aspect of human well-being, warrants greater attention. Panoramic X-ray images (PXI) and Cone Beam Computed Tomography (CBCT) are key tools for dentists in diagnosing and treating dental conditions. Additionally, deep learning for tooth segmentation can focus on relevant treatment information and localize lesions. However, the scarcity of publicly available PXI and CBCT datasets hampers their use in tooth segmentation tasks. Therefore, this paper presents a multimodal dataset for semi-supervised deep learning in dental PXI and CBCT, named STS-2D-Tooth and STS-3D-Tooth. STS-2D-Tooth includes 4,000 images and 900 masks, categorized by age into children and adults. Moreover, we have collected CBCTs providing more detailed and three-dimensional information, resulting in the STS-3D-Tooth dataset comprising 148,400 unlabeled scans and 8,800 masks. To our knowledge, this is the first multimodal dataset combining dental PXI and CBCT, and it is the largest tooth segmentation dataset, a significant step forward for the advancement of tooth segmentation.</p>
Using Multi-Modal Path-Specific Transit Trips in Transportation Social Sustainability Analysis: Case Study in Atlanta, GA
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ADSCAN: AI-Powered Multi-modal Framework for Enhanced Ad Detection and Web Safety
<p>We published codes and datasets (SITE-D, TEXT-D and part of IMG-D) for paper "ADSCAN: AI-Powered Multi-modal Framework for Enhanced Ad Detection and Web Safety."</p>
Data from: Foraging modality and plasticity in foraging traits determine the strength of competitive interactions among carnivorous plants, spiders, and toads
1. Foraging modalities (e.g., passive, sit-and-wait, active) and traits are plastic in some species, but the extent to which this plasticity affects interspecific competition remains unclear. 2. Using a long-term laboratory mesocosm experiment, we quantified competition strength and the plasticity of foraging traits in a guild of generalist predators of arthropods with a range of foraging modalities. 3. Each mesocosm contained eight passively foraging pink sundews, and we employed an experimental design where treatments were the presence or absence of a sit-and-wait foraging spider and actively foraging toad crossed with five levels of prey abundance. We hypothesized that actively foraging toads would outcompete the other species at low prey abundance, but that spiders and sundews would exhibit plasticity in foraging traits to compensate for strong competition when prey were limited. 4. Results generally supported our hypotheses. Toads had a greater effect on sundews at low prey abundances, and toad presence caused spiders to locate webs higher above the ground. Additionally, the closer large spider webs were to the ground, the greater the trichome densities produced by sundews. Also, spider webs were larger with than without toads and as sundew numbers increased, and these effects were more prominent as resources became limited. Finally, spiders negatively affected toad growth only at low prey abundance. 5. These findings highlight the long-term importance of foraging modality and plasticity of foraging traits in determining the strength of competition within and across taxonomic kingdoms. Future research should assess whether plasticity in foraging traits helps to maintain coexistence within this guild and whether foraging modality can be used as a trait to reliably predict the strength of competitive interactions.
Data from: Creating a multi-track classical music performance dataset for multi-modal music analysis: challenges, insights, and applications
We introduce a dataset for facilitating audio-visual analysis of musical performances. The dataset comprises 44 simple multi-instrument classical music pieces assembled from coordinated but separately recorded performances of individual tracks. For each piece, we provide the musical score in MIDI format, the audio recordings of the individual tracks, the audio and video recording of the assembled mixture, and ground- truth annotation files including frame-level and note-level tran- scriptions. We describe our methodology for the creation of the dataset, particularly highlighting our approaches for addressing the challenges involved in maintaining synchronization and ex- pressiveness. We demonstrate the high quality of synchronization achieved with our proposed approach by comparing the dataset against existing widely-used music audio datasets. We anticipate that the dataset will be useful for the devel- opment and evaluation of existing music information retrieval (MIR) tasks, as well as for novel multi-modal tasks. We bench- mark two existing MIR tasks (multi-pitch analysis and score- informed source separation) on the dataset and compare against other existing music audio datasets. Additionally, we consider two novel multi-modal MIR tasks (visually informed multi-pitch analysis and polyphonic vibrato analysis) enabled by the dataset and provide evaluation measures and baseline systems for future comparisons (from our recent work). Finally, we propose several emerging research directions that the dataset enables.
DravidianMultiModality: A Dataset for Multi-modal Sentiment Analysis in Tamil and Malayalam
<p>@article{dravidian_multimodality,<br> title={DravidianMultiModality: A Dataset for Multi-modal Sentiment Analysis in Tamil and Malayalam},<br> author={Bharathi Raja Chakravarthi, Jishnu Parameswaran P.K, Premjith B, K.P Soman, Rahul Ponnusamy, Prasanna Kumar Kumaresan, Kingston Pal Thamburaj, John P. McCrae},<br> journal={arXiv.org},<br> publisher={2021}<br> }</p>
Sister-Species Diverge in Modality-Specific Courtship Signal Form and Function
<p>Understanding the relative importance of different sources of selection (e.g. the environment, social/sexual selection) on the divergence or convergence of reproductive communication can shed light on the origin, maintenance, or even disappearance of species boundaries. Using a multi-step approach, we tested the hypothesis that two presumed sister-species of wolf spider with overlapping ranges and microhabitat use, yet differing degrees of sexual dimorphism, have diverged in their reliance on modality-specific courtship signaling. We predicted that male <i>Schizocosa crassipalpata</i> (no ornamentation) rely predominantly on diet-dependent vibratory signaling for mating success. In contrast, we predicted that male <i>S. bilineata </i>(black foreleg brushes) rely on diet-dependent visual signaling. We first tested and corroborated the sister-species relationship between <i>S. crassipalpata </i>and <i>S. bilineata </i>using phylogenomic scale data. Next, we tested for species-specific, diet-dependent vibratory and visual signaling by manipulating subadult diet and subsequently quantifying adult morphology and mature male courtship signals. As predicted, vibratory signal form was diet-dependent in <i>S. crassipalpata</i> while visual ornamentation (brush area) was diet-dependent in <i>S. bilineata. </i>We then compared the species-specific reliance on vibratory and visual signaling by recording mating across artificially manipulated signaling environments (presence/absence of each modality in a 2 x 2 full factorial design). In accordance with our diet dependence results for <i>S. crassipalpata, </i>the presence of vibratory signaling was important for mating success<i>. </i>In contrast, the light and vibratory environment interacted to influence mating success in <i>S. bilineata, </i>with vibratory signaling being important only in the absence of light. We found no differences in overall activity patterns. Given that these species overlap in much of their range and microhabitat use, we suggest that competition for signaling space may have led to the divergence and differential use of sensory modalities between these sister-species.</p>
Variation in the modality of a yeast signaling pathway is mediated by a single regulator
<p>Bimodal gene expression by genetically identical cells is a pervasive feature of signaling networks, and has been suggested to allow organisms to hedge their "bets" in uncertain conditions. In the galactose-utilization (GAL) pathway of Saccharomyces cerevisiae, gene induction is unimodal or bimodal depending on natural genetic variation and pre-induction conditions. Here, we find that this variation in modality arises from regulation of two features of the pathway response: the fraction of cells that show induction, and their level of expression. GAL3, the galactose sensor, controls the fraction of induced cells, and titrating its expression is sufficient to control modality; moreover, all the observed differences in modality between different pre-induction conditions and amongst natural isolates can be explained by changes in GAL3's regulation and activity. The ability to switch modality by tuning the activity of a single protein may allow rapid adaptation of bet hedging to maximize fitness in complex environments.</p>
Different mechanisms of magnitude and spatial representation for tactile and auditory modalities
<p>Dataset for Different mechanisms of magnitude and spatial representation for tactile and auditory modalities</p>
Data used for the Master Thesis "Multi Modal Generative Learning utilizing Normalizing Flows"
<p>Dataset needed to reproduce the Master Thesis "Multi Modal Generative Learning utilizing Normalizing Flows".</p> <p>It contains trained models and their evaluation results.</p> <p>For more details see the <a href="https://github.com/Jimmy2027/MMNF_RepSeP">github repository</a>.</p>
A comprehensive video dataset for Multi-Modal Recognition Systems
<p>A fully-labelled video dataset will act as a unique resource for researchers and analysts in the fields such as machine learning, computer vision and deep learning. The videos contain similar text recited by 67 different subjects. The text contains digits from 1 to 20 recited by 67 different subjects within the same experimental setup.</p>
Source data of scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection
<p>The source data of the manuscript: scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection.</p>
GOhydro Multi-modal Sensor Kit - 3D Printing Files
<p>These are the files for 3D printing the housing of the GOhydro Multi-modal Sensor Kit</p>
Estimating Modal Mineralogy using Raman Spectroscopy: Multivariate Analysis Models and Raman Cross-Section Proxies - Chapter 4 Data
<p>I provide supplementary data for Chapter 4 of my dissertation including electron microprobe analysis data as well as the Raman spectra and associated metadata of the mineral end-members, the mineral-mineral mixtures, and the diamond-mineral mixtures.</p>
EarSet: A Multi-Modal In-Ear Dataset
<p>EarSet aims at providing the research community with a novel, multi-modal, dataset, which, for the first time, will allow studying of the impact of body and head/face movements on both the morphology of the PPG wave captured at the ear, as well as on the vital signs estimation. To accurately collect in-ear PPG data, coupled with a 6 degrees-of-freedom (DoF) motion signature, we prototyped and built a flexible research platform for in-the-ear data collection. The platform is centered around a novel ear-tip design which includes a 3-channel PPG (green, red, infrared) and a 6-axis (accelerometer, gyroscope) motion sensor (IMU) co-located on the same ear-tip. This allows the simultaneous collection of spatially distant (i.e., one tip in the left and one in the right ear) PPG data at multiple wavelengths and the corresponding motion signature, for a total of 18 data streams. <br> Inspired by the Facial Action Coding Systems (FACS), we consider a set of potential sources of motion artifact (MA) caused by natural facial and head movements. Specifically, we gather data on 16 different head and facial motions - head movements (nodding, shaking, tilting), eyes movements (vertical eyes movements, horizontal eyes movements, brow raiser, brow lowerer, right eye wink, left eye wink), and mouth movements (lip puller, chin raiser, mouth stretch, speaking, chewing).<br> We also collect motion and PPG data under activities, of different intensities, which entail the movement of the entire body (walking and running). Together with in-ear PPG and IMU data, we collect several vital signs including, heart rate, heart rate variability, breathing rate, and raw ECG, from a medical-grade chest device.</p> <p>With approximately 17 hours of data from 30 participants of mixed gender and ethnicity (mean age: 28.9 years, standard deviation: 6.11 years), our dataset empowers the research community to analyze the morphological characteristics of in-ear PPG signals with respect to motion, device positioning (left ear, right ear), as well as a set of configuration parameters and their corresponding data quality/power consumption trade-off. We envision such a dataset could open the door to innovative filtering techniques to mitigate, and eventually eliminate, the impact of MA on in-ear PPG. We ran a set of preliminary analyses on the data, considering both handcrafted features, as well as a DNN (Deep Neural Network) approach. Ultimately, we observe statistically significant morphological differences in the PPG signal across different types of motions when compared to a situation where there is no motion. We also discuss a 3-classes classification task and show how full-body motions and head/face motions can be discriminated from a still baseline (and among themselves). These preliminary results represent the first step towards the detection of corrupted PPG segments and show the importance of studying how head/face movements impact PPG signals in the ear. </p> <p>To the best of our knowledge, this is the first in-ear PPG dataset that covers a wide range of full-body and head/facial motion artifacts. Being able to study the signal quality and motion artifacts under such circumstances will serve as a reference for future research in the field, acting as a stepping stone to fully enable PPG-equipped earables.</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>
Integration of imaging modalities with lipidomic characterization to investigate single cell MSCs potency metrics
<p>This dataset contains images used in the analysis for the publication mentioned above.</p>
Multi-modal Dataset of Human Activities of Daily Living with Ambient Audio, Vibration and Environmental Data
<pre>This dataset provides over 43000 samples of 25 different human activities (e.g. walking, opening/closing a door, sitting down, vacuum cleaning). Each sample is recorded by 5 sensor devices with multiple types of sensors. The main part is audio and vibration. Further metrics were recorded at a low frequency, and are: infrared array, light color, temperature, relative humidity, atmospheric pressure, air quality measure, volatile organic compounds and CO2 equivalent. The data was recorded in supervised sessions to label each sample. The recording environment consisted of a kitchen and dining room. Flawed samples were removed and the different metrics were synchronized, but no further processing or filtering of the data was performed. </pre>
The Comparison of Conventional and Advanced Bipolar Energy Modalities During Laparoscopic Staging Surgery of Gynecologic Cancers
ClinicalTrials.gov study NCT02822820. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.