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9 results for “Visual languages”
lilGym: Natural Language Visual Reasoning with Reinforcement Learning, model files
<p>Baselines models for the paper <a href="https://lil.nlp.cornell.edu/lilgym"><em>lil</em>Gym: Natural Language Visual Reasoning with Reinforcement Learning</a>.</p>
Prediction and Visualization of Human Transmembrane Proteins using AlphaFold and Protein Language Models
<p><strong>Description:</strong> <strong>TMvis</strong> ("TMvis496.tar.gz") is a dataset containing 496 3D-structures of predicted human transmembrane proteins (TMP) and their predicted membrane embedding. The method TMbed [1], based on the protein language model ProtT5 [2] predicted 4.967 TMP for the human proteome (20,375 proteins, UniProt [3] version April 2022; excluding TITIN_HUMAN due to length). For these proteins, we obtained AlphaFold [4] structures from AlphaFoldDB [5] with an average per-residue confidence score (pLDDT) of more than 90%. This resulted in the 496 proteins of TMvis, as can be found in "TMvis496.fasta". The membrane embedding was predicted using the methods ANVIL [6], PPM3 [7], and per-residue TMbed predictions. As the three methods are based on different approaches, we decided to publish results for all. The figure “TMvis_project_overview.png” provides a graphical overview for each step described above.</p> <p><strong>TMvis Folder Structure:</strong> TMvis is separated into “alpha” containing predicted alpha-helical TMPs, and “beta” containing predicted beta-barrel TMPs. Within these folders, each protein is assigned one folder, identifiable by the respective unique UniProt ID. Each protein folder consists of:<br> - “UniprotID.fasta” with UniProt ID, sequence, TMbed per-residue prediction<br> - “AF-UniprotID-F1-model_v2.pdb” with the AlphaFold structure<br> - “AF-UniprotID-F1-model_v2.cif” with the AlphaFold structure<br> - “AF-UniprotID-F1-model_v2_ANVIL.pdb” with predicted ANVIL membrane embedding<br> - “AF-UniprotID-F1-model_v2_ppm.pdb” predicted PPM3 membrane embedding</p> <p>TMvis <br> | <br> ├── alpha <br> │ │ <br> │ ├── A0A087X1C5 <br> │ │ ├── A0A087X1C5.fasta <br> │ │ ├── AF-A0A087X1C5-F1-model_v2.pdb <br> │ │ ├── AF-A0A087X1C5-F1-model_v2.cif <br> │ │ ├── AF-A0A087X1C5-F1-model_v2_ANVIL.pdb <br> │ │ └── AF-A0A087X1C5-F1-model_v2_ppm.PDB <br> │ └── ... <br> └── beta <br> └── P45880</p> <p><strong>TMvis visualization:</strong> The 3D-visualization of every protein in the dataset TMvis can be easily accessed using the Jupyter Notebook “TMvis.ipynb”. It contains detailed descriptions the different membrane prediction tools ANVIL, PPM3, and TMbed as well as the respective code. Additionally, it allows to visualize the per-residue confidence scores (pLDDT) of AlphaFold.</p> <p>——————————————————————————————————————————————————————————————————————————</p> <p><strong>References:</strong></p> <p>[1] TMbed - TMbed Bernhofer, Michael, and Burkhard Rost. 2022. “TMbed – Transmembrane Proteins Predicted through Language Model Embeddings.” bioRxiv.</p> <p>[2] ProtT5 - A. Elnaggar et al., "ProtTrans: Towards Cracking the Language of Lifes Code Through Self-Supervised Deep Learning and High Performance Computing," in IEEE Transactions on Pattern Analysis and Machine Intelligence, doi: 10.1109/TPAMI.2021.3095381.</p> <p>[3] UniProt - UniProt Consortium (2021). UniProt: the universal protein knowledgebase in 2021. Nucleic acids research, 49(D1), D480–D489.</p> <p>[4] AlphaFold - AlphaFold Jumper, John, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, et al. 2021. “Highly Accurate Protein Structure Prediction with AlphaFold.” Nature 596 (7873): 583–89.</p> <p>[5] Alphafold DB - Varadi, Mihaly, Stephen Anyango, Mandar Deshpande, Sreenath Nair, Cindy Natassia, Galabina Yordanova, David Yuan, et al. 2022. “AlphaFold Protein Structure Database: Massively Expanding the Structural Coverage of Protein-Sequence Space with High-Accuracy Models.” Nucleic Acids Research 50 (D1): D439–44.</p> <p>[6] ANVIL - ANVIL Postic, Guillaume, Yassine Ghouzam, Vincent Guiraud, and Jean-Christophe Gelly. 2016. “Membrane Positioning for High- and Low-Resolution Protein Structures through a Binary Classification Approach.” Protein Engineering, Design & Selection: PEDS 29 (3): 87–91.</p> <p>[7] PPM3 - PPM3 Lomize, Mikhail A., Irina D. Pogozheva, Hyeon Joo, Henry I. Mosberg, and Andrei L. Lomize. 2012. “OPM Database and PPM Web Server: Resources for Positioning of Proteins in Membranes.” Nucleic Acids Research 40 (Database issue): D370–76.</p> <p>——————————————————————————————————————————————————————————————————————————</p> <p><strong>License:</strong></p> <p>This work is licensed under a Creative Commons Attribution 4.0 International License (CC-BY 4.0).</p> <p> </p>
Replication package for The Use of Domain-Specific Languages for Visual Analytics: A Systematic Literature Review
<p>In order to provide reproducibility, we have made all the data collected in the study titled: "The Use of Domain-Specific Languages for Visual Analytics: A Systematic Literature Review" as a replication package. This package includes the following files:</p> <ol> <li>A zip file containing the codes used for this Systematic Literature Review from NVIVO software. One separate file for each of the codes in the Zip file. (Code Summary.zip)</li> <li>Data collection form for different rounds of study. (Data_Collection_Form_Final.xlsx)</li> <li>Summary of number of papers retrieved in each round. (Number_of_Retrieved_studies.pdf)</li> </ol>
Data from: Speakers of different languages remember visual scenes differently
<p>Language can have a powerful effect on how people experience events. Here, we examine how the languages people speak guide attention and influence what they remember from a visual scene. When hearing a word, listeners activate other similar-sounding words before settling on the correct target. We tested whether this linguistic co-activation during a visual search task changes memory for objects. Bilinguals and monolinguals remembered English competitor words that overlapped phonologically with a spoken English target better than control objects without name overlap. High Spanish proficiency also enhanced memory for Spanish competitors that overlapped across languages. We conclude that linguistic diversity partly accounts for differences in higher cognitive functions like memory, with multilinguals providing a fertile ground for studying the interaction between language and cognition.</p>
Data from: Speakers of different languages remember visual scenes differently
Open the record for dataset details and reuse information.
Representations of language in a model of visually grounded speech signal: Data
<p>The set of datafiles to reproduce results from:</p> <ul> <li>Chrupała, G., Gelderloos, L., & Alishahi, A. (2017). Representations of language in a model of visually grounded speech signal. ACL. arXiv preprint: https://arxiv.org/abs/1702.01991</li> </ul>
AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments - Supplementary Data
<p><strong>Introduction</strong></p> <p>In this zip, we release the auxiliary data that is beneficial to execute the implementation of AVLEN described in our paper AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments by Sudipta Paul, Amit K Roy-Chowdhury, and Anoop Cherian, NeurIPS, 2022.</p> <p><strong>At a Glance</strong></p> <ul> <li>The size of the unzipped data is 4.6G</li> <li>The unzipped folder contains: (i) a README.md file and (ii) ./AVLEN-data folder. The latter contains the following zip files. Please see the AVLEN code to see how to unzip these files into their respective folders. <ul> <li>ckpt.119.pth -- 61M </li> <li>connectivity.zip -- 1.4M </li> <li>pretrained_weights.zip -- 1.7G</li> <li>ResNet-152-imagenet.zip -- 2.9G</li> <li>semantic_audionav_dialog_approx.zip -- 2.7M</li> <li>soundspaces.zip -- 479K</li> <li>speaker_model_weights.zip -- 51M</li> </ul> </li> </ul> <p><strong>Other Resources</strong></p> <p>For the implementation of AVLEN that uses the data shared here, please visit <a href="https://www.merl.com/publications/TR2022-131">MERL TR2022-131</a>.</p> <p><strong>Citation</strong></p> <p>If you use AVLEN in your research, please cite our paper:</p> <pre><code>@InProceedings{paul2022avlen, title={AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments}, booktitle={Advances in Neural Information Processing Systems}, author={Paul, Sudipta and Roy-Chowdhury, Amit and Cherian, Anoop}, volume={35}, pages={6236--6249}, year={2022} }</code></pre> <p><strong>Copyright and License</strong></p> <p>The AVLEN dataset is released under CC-BY-SA-4.0 license.</p> <p>All data:</p> <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2023 SPDX-License-Identifier: CC-BY-SA-4.0</code></pre> <p> </p>
Visual Gender Biases in Wikipedia: A Systematic Evaluation across the Ten MostSpoken Languages
<p>We collect a large dataset of Wikipedia biographies and their images, and make it publicly available for research use.</p> <p>Reference: Visual Gender Biases in Wikipedia: A Systematic Evaluation across the Ten Most Spoken Languages, AAAI ICWSM 2022.</p>
Effectiveness of a Large Language Model-Based Educational Tool on Visual Field Test Reliability in Glaucoma Patients
ClinicalTrials.gov study NCT07327242. IPD Sharing: NO. Countries: 1. Publications: 0.
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.