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8 results for “Multitask learning”
Data from: RockNet: Rockfall and earthquake detection and association via multitask learning and transfer learning
<p>Seismological data can provide timely information for slope failure hazard assessments, among which rockfall waveform identification is challenging for its high waveform variations across different events and stations. A rockfall waveform does not have typical body waves as earthquakes do, so researchers have made enormous efforts to explore characteristic function parameters for automatic rockfall waveform detection. With recent advances in deep learning, algorithms can learn to automatically map the input data to target functions. We develop RockNet via multitask and transfer learning; the network consists of a single-station detection model and an association model. The former discriminates rockfall and earthquake waveforms. The latter determines the local occurrences of rockfall and earthquake events by assembling the single-station detection model representations with multiple station recordings. RockNet achieves macro F1 scores of 0.990 and 0.981 in terms of discriminating earthquakes and rockfalls from other events with the single-station detection and association models, respectively.</p>
Data from: RockNet: Rockfall and earthquake detection and association via multitask learning and transfer learning
Open the record for dataset details and reuse information.
Multitask Learning for Estimating Power Plant Greenhouse Gas Emissions from Satellite Imagery
<p><strong>Power Generation Data Set</strong></p> <p>This data set contains imaging data acquired by ESA's Sentinel-2<br> Earth-observing satellite constellation [1] for a sample of power stations that were picked using geographic coordinates <br> provided by the European Pollutant Release and Transfer Register [2]. The images<br> contain scenes of power stations, some of which are actively<br> emitting smoke plumes.</p> <p>This data set was created with the goal to automatically segment plumes, predict the type of fired fuel, predict the rate of power generation and estimate the amount of CO2 emissions, directly from remote sensing images.</p> <p><br> <strong>Description</strong><br> </p> <p>Each image is provided in the GeoTIFF file format, contains a total of 13 bands. Images have either a shape of 120x120 or 300x300 pixels (corresponding to a square area with an edge length of respectively 1.2 km and 3.0 km on the ground)<br> .</p> <p>This repository contains a total of 2131 images. This<br> repository contains a collection of JSON files that hold manual segmentation labels for plumes. Segmentation<br> labels were generated using label-studio [3]. Please note that polygon edge coordinates have to be scaled to fit the images.</p> <p><br> <strong>Content</strong></p> <p>The following files are contained in this repository:</p> <ul> <li>README.md - this file</li> <li>images.zip [2.0GB] - contains 2131 GeoTIFF images</li> <li>segmentation_labels.zip [1.5MB] - contains 2131 JSON files</li> <li>labels.csv [310KB] - contains additional labels for each image: <ul> <li>Generation output rate [4],[5]</li> <li>Country</li> <li>Type of fired fuel</li> <li>Latitude and longitude of the power plant</li> <li>Concurrent weather information (temperature, humidity and wind vector)</li> </ul> </li> </ul> <p> </p> <p><strong>Acknowledgement</strong></p> <p>If you use this data set, please cite our publication:</p> <p> Hanna, J., Mommert, M., Scheibenreif, L., Borth, D.,<br> "Multitask Learning for Estimating Power Plant Greenhouse Gas Emissions from Satellite Imagery",<br> Tackling Climate Change with Machine Learning workshop at NeurIPS 2021.</p> <p>Please refer to this publication for additional information on the data set.</p> <p>The code used for this publication is available at https://github.com/HSG-AIML/RemoteSensingCO2Estimation.</p> <p> </p> <p><br> <strong>Author</strong></p> <p>Joëlle Hanna</p> <p>University of St. Gallen, AIML Lab, School of Computer Science joelle.hanna@unisg.ch</p> <p><br> <strong>References</strong><br> </p> <p>[1]: https://earth.esa.int/web/sentinel/missions/sentinel-2<br> [2]: https://www.eea.europa.eu/data-and-maps/data/industrial-reporting-under-the-industrial<br> [3]: https://labelstud.io/<br> [4]: https://transparency.entsoe.eu/generation/r2/actualGenerationPerGenerationUnit/show<br> [5]: https://doi.org/10.5281/zenodo.3574566</p>
Datasets for "Accurate and efficient structure elucidation from routine one-dimensional NMR spectra using multitask machine learning"
<p>This upload contains the datasets used for the experiments in:</p> <p>Accurate and efficient structure elucidation from routine one-dimensional NMR spectra using multitask machine learning</p> <p>Frank Hu, Michael S. Chen, Grant M. Rotskoff, Matthew W. Kanan, and Thomas E. Markland</p> <p>https://arxiv.org/abs/2408.08284</p> <p> </p> <p>For file descriptions and usage, please refer to the supplied README.md file.</p> <p> </p> <p> </p>
MLM: A Benchmark Dataset for Multitask Learning with Multiple Languages and Modalities
<p><strong>Abstract:</strong></p> <p>We introduce the <strong>MLM (Multiple Languages and Modalities)</strong> dataset - a new resource to train and evaluate multitask systems on samples in multiple modalities and three languages. The generation process and inclusion of semantic data provide a resource that further tests the ability for multitask systems to learn relationships between entities. The dataset is designed for researchers and developers who build applications that perform multiple tasks on data encountered on the web and in digital archives. The second version of MLM provides a geo-representative subset of the data with weighted samples for countries of the European Union. We demonstrate the value of the resource in developing novel applications in the digital humanities with a motivating use case and specify a benchmark set of tasks to retrieve modalities and locate entities in the dataset. Evaluation of baseline multitask and single-task systems on the full and geo-representative versions of MLM demonstrate the challenges of generalizing on diverse data. In addition to the digital humanities, we expect the resource to contribute to research in multimodal representation learning, location estimation, and scene understanding. </p> <p><strong>Introduction:</strong><br> Multiple Languages and Modalities comprises data points on 236k human settlements for evaluating and optimizing multitask learning systems. MLM presents a dataset with a high level of diversity in terms of modality and language. For each entity, we have extracted text summaries, images, coordinates, and their respective triple classes. Text summaries are available in three languages (English, French, and German) with each entity having between one and three language entries. </p> <p>Human settlements from all continents are provided in the overall dataset (MLM) with 72% located in Europe. Two further versions of the dataset - MLM-irle and MLM-irle-gr - were generated for use in the benchmark evaluation for multitask systems described in the paper (see above). MLM-irle-gr (ie geo-representative) was generated to serve organizations that focus on the European Union by providing a geographically balanced coverage of human settlements in this region. MLM-irle-gr contains data on 24k human settlements across the EU weighted in relation to the population count for each of the 28 countries.</p> <p>MLM contains the following fields:</p> <pre><code>---------------------------------------------------------------------- # field-label description ---------------------------------------------------------------------- 1. id a unique identifier 2. label textual label 3. coordinates longitude, latitude geo-location value 4. summaries list of textual summaries related to the entity 5. images list of images related to the entity 6. classes list of associated triple class ----------------------------------------------------------------------</code></pre> <p>MLM - Details by Dataset Version:</p> <pre><code>----------------------------------------------------------- Num. of MLM MLM-irle MLM-irle-gr ----------------------------------------------------------- Entities 236496 218681 22501 Images 412422 314533 31621 Summaries 497899 462328 47508 Triple classes 1685 1655 452 -----------------------------------------------------------</code></pre> <p><strong>Availability:</strong></p> <p>All three versions of MLM listed in the table directly above are available for direct download and use. To support findability and sustainability, the MLM dataset is published as an on-line resource at<em> <a href="https://doi.org/10.5281/zenodo.3885753">https://doi.org/10.5281/zenodo.3885753</a></em>. A separate page with detailed explanations and illustrations is available at <em><a href="http://cleopatra.ijs.si/goal-mlm/">http://cleopatra.ijs.si/goal-mlm/</a> </em>to promote ease-of-use. The project GitHub repository contains the complete source code for the system and the generation script is available at <em><a href="http://github.com/GOALCLEOPATRA/MLM">https://github.com/GOALCLEOPATRA/MLM</a></em>. Documentation adheres to the standards of <em>FAIR Data principles</em> with all relevant metadata specified to the research community and users. It is freely accessible under the Creative Commons Attribution 4.0 International license, which makes it reusable for almost any purpose. </p> <p><strong>Updating and Reusability:</strong><br> MLM is supported by a team of researchers from the University of Bonn, the Leibniz Information Center for Science and Technology, and Jožef Stefan Institute. The resource is already in use for individual projects and as a contribution to the project deliverables of the Marie Skłodowska-Curie CLEOPATRA Innovative Training Network. In addition to the steps above that make the resource available to the wider community, the usage of MLM will be promoted to the network of researchers in this project. Use among researchers and practitioners in digital humanities will be promoted by demonstrations and presentations at domain-related events. Activities are planned for the Digital Methods Summer School run by the University of Amsterdam. The range of modalities and languages present in the dataset also extend its application to research on multimodal representation learning, multilingual machine learning, information retrieval, location estimation, and the Semantic Web. MLM will be supported and maintained for three years in the first instance. A second release of the dataset is already scheduled and the generation process outlined above is designed to enable rapid scaling.</p>
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
<p>Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving. Researchers are usually constrained to study a small set of problems on one dataset, while real-world computer vision applications require performing tasks of various complexities. We construct BDD100K, the largest driving video dataset with 100K videos and 10 tasks to evaluate the exciting progress of image recognition algorithms on autonomous driving. The dataset possesses geographic, environmental, and weather diversity, which is useful for training models that are less likely to be surprised by new conditions. Based on this diverse dataset, we build a benchmark for heterogeneous multitask learning and study how to solve the tasks together. Our experiments show that special training strategies are needed for existing models to perform such heterogeneous tasks. BDD100K opens the door for future studies in this important venue.</p>
Validation of a Multitask Deep Learning System at Spine Metastasis CT
ClinicalTrials.gov study NCT05156567. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Application of Multitask Deep Learning Model in Grading the Severity of Spinal Facet Joint Degeneration
ClinicalTrials.gov study NCT05635006. IPD Sharing: NO. Countries: 1. Publications: 0.
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