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38 results for “multitask”
Figure 2 in Nitrogen-fixing Cyanothece sp. as a mixotroph and silver nanoparticle synthesizer: a multitasking exceptional cyanobacterium
Figure 2. Disc inhibition zone against MRSA Staphylococcus aureus using (a) gold nanoparticles, (b) silver nanoparticles, and (c) both silver nanoparticles and gold nanoparticles.
Multitask Carnatic Music Dataset
<p>This dataset contains the multitask annotation for the Ragas in the Carnatic style of Indian classical music. The multitasks present in the dataset are the Swaras set, Melakarta set, Aaroh set, Avroh set, Janak/janya, and Raag Id. The music for extracting the mel-spectrogram feature is taken from the Dunya corpus [1]. The dataset contains 40 Ragas, and each contains 12 music recordings. </p> <p>[1] Porter, Alastair, Mohamed Sordo, and Xavier Serra. "Dunya: A system for browsing audio music collections exploiting cultural context." Britto A, Gouyon F, Dixon S. 14th International Society for Music Information Retrieval Conference (ISMIR); 2013 Nov 4-8; Curitiba, Brazil.[place unknown]: ISMIR; 2013. p. 101-6.. International Society for Music Information Retrieval (ISMIR), 2013.</p> <p> </p> <p>_________________________________________________________________________________________________________<br> This project was funded under the grant number: ECR/2018/000204 by the Science & Engineering Research Board (SERB).</p>
Multitask Hindustani Music Dataset
<p>This dataset contains the multitask annotation for the Raags in the Carnatic style of Indian classical music. The multitasks present in the dataset are the Swaras set, Jati, Thaat, Vadi, Samvadi, Aaroh set, Avroh set, and Raag Id. The music for extracting the mel-spectrogram feature is taken from the Dunya corpus [1]. The dataset contains 30 Ragas, and each contains 10 music recordings. </p> <p>[1] Porter, Alastair, Mohamed Sordo, and Xavier Serra. "Dunya: A system for browsing audio music collections exploiting cultural context." Britto A, Gouyon F, Dixon S. 14th International Society for Music Information Retrieval Conference (ISMIR); 2013 Nov 4-8; Curitiba, Brazil.[place unknown]: ISMIR; 2013. p. 101-6. International Society for Music Information Retrieval (ISMIR), 2013.</p> <p> </p> <p>_________________________________________________________________________________________________________<br> This project was funded under grant number: ECR/2018/000204 by the Science & Engineering Research Board (SERB).</p>
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 for "Rapid mapping of alloy surface phase diagrams via Bayesian evolutionary multitasking"
<p>For the ORR study, the final datasets of the DFT-relaxed adsorbate-alloy configurations for the Pd-Ag(111) surface are stored in <strong>ads_PdAg_111_dft.db. </strong>For the SMR study, the final datasets of the DFT-relaxed adsorbate-alloy configurations for the Pt-Ni(111), (100) and (311) surfaces are stored in <strong>ads_PtNi_111_dft.db</strong>, <strong>ads_PtNi_100_dft.db</strong> and <strong>ads_PtNi_311_dft.db</strong>, respectively.</p> <p>The 76,265 tasks (combining 15,253 SMR conditions with 5 exploration parameters) used for the BEM runs in the SMR study can be found in <strong>bem_smr_tasks.csv</strong>.</p> <p>All the input files and scripts for BEM high-throughput screening (for both ORR and SMR studies), DFT calculations, EMT benchmarks, SGCMC simulations, structure generation and plotting (e.g. surface free energy diagrams and 2D phase diagrams) are all provided in <strong>inputs_and_scripts.zip</strong>.</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 Human Navigation in VR with Motion Tracking
<p>Data from human subjects in virtual reality performing some combination of collecting targets, avoiding obstacles, and following a path. Raw data has been parsed into 300 ms samples for use in machine learning algorithms. The data includes object positions in the virtual environment, human position tracking, and task instructions. </p> <p> </p>
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>
Data from: Behavioral trade-offs and multitasking by elk in relation to predation risk from Mexican gray wolves
<p>Non-consumptive effects of predation can alter foraging time, stress levels, and habitat use by prey, potentially resulting in reduced fitness. However, prey can mitigate the non-consumptive effects of predation by increasing vigilance, chewing and vigilance synchronization (i.e., multitasking), and spatiotemporal avoidance of predators. We quantified the effects of the Mexican wolf (<em>Canis lupus baileyi</em>) predation risk on elk (<em>Cervus canadensis</em>) behavior in the southwestern United States. We conducted behavioral observations on adult female elk and developed predation risk indices using Mexican wolf GPS collar data, locations of elk killed by Mexican wolves, and landscape covariates. We compared a priori models to determine the best predictors of adult female behavior and multitasking, separately. Metrics that quantified both spatial and temporal predation risk were top predictors in both datasets. Adult female vigilance was positively associated with increased predation risk. Increased predation risk had little effect on the probability of foraging, but resulted in decreased time spent resting. In a post hoc analysis, the effect of predation risk on foraging and resting differed across diurnal periods. During midday when wolf activity was relatively low, the probability of foraging increased while resting decreased, in areas with high spatial predation risk. During crepuscular periods when elk and wolves were most active, increased predation risk was associated with increased vigilance and slight decreases in foraging. Our results suggest elk are temporally avoiding predation risk from Mexican wolves by trading resting for foraging, a trade-off often not evaluated in behavioral studies. The probability of multitasking increased with predation risk, suggesting that adult female elk may be offsetting the non-consumptive effects of risk on feeding time. These results highlight potentially important but often excluded behaviors and trade-offs prey species may use to reduce the indirect effects of predation and contribute additional context to our understanding of predator-prey dynamics.</p>
20230505-MTOA: Multitasking agents improve their average and maximum accuracy when tasks overlap.
This archive contains the results of a multi-agent simulation experiment [1] carried out with Lazy lavender [2] environment.<br><br>Experiment Label: 20230505-MTOA<br><br>Experiment design: Agents specialize by accepting or not to play a task.<br><br>Experiment setting: Agents are trained with respect to different tasks and then coordinate upon acting on them. Each time they disagree, one agent adapts its knowledge with respect to the current task.<br><br>Hypotheses: Agents will improve their accuracy more on tasks they choose to play.<br><br>Detailed information can be found in index.html or notebook.ipynb.<br><br>[1] <a href="https://sake.re/20230505-MTOA">https://sake.re/20230505-MTOA</a><br>[2] <a href="https://gitlab.inria.fr/moex/lazylav/">https://gitlab.inria.fr/moex/lazylav/</a><br><br>
20231220-MTOA: Multitasking agent populations can achieve equitable task exploration to the detriment of their agents' average and maximum accuracy.
<p>This archive contains the results of a multi-agent simulation experiment [1] carried out with Lazy lavender [2] environment.<br><br>Experiment Label: 20231220-MTOA<br><br>Experiment design: Agents improve task equitability by favoring the reproduction of agents with rare and conflicting knowledge.<br><br>Experiment setting: Each agent is initially trained on all tasks. The agents then carry all tasks. When they disagree the following take place:</p> <p>(a) The agent with the lower score will adapt its knowledge accordingly. If its memory limit is attained, the agent will try to forget knowledge.</p> <p>(b) The agent with the highest score will decide for both agents. If this agent's decision is correct, the agent will receive the points corresponding to both agents.</p> <p>Every 20000 games, 9 new agents are born and 9 agents are removed. Each new agent will be trained with examples provided by two parents.The first parent is selected either randomly, or based on their collected points/success rate (low success rate, high success rate, low points, high points).</p> <p>The second parent is selected based on its success rate with respect to the first parent (parent 2 is the the agent that agrees more often with parent 1).Agents undertake 3 tasks having a limited memory, enough for learning 1/3 tasks accurately (4 and 12 classes respectively)</p> <p><br>Hypotheses: Favoring the reproduction of agents with the lowest success rate (agents that agree less with their peers) will allow agent populations to equally explore all tasks. Hence, agent populations will become equally accurate on all tasks and thus improve their efficiency.<br><br>Detailed information can be found in index.html or notebook.ipynb.<br><br>[1] <a href="https://sake.re/20231220-MTOA">https://sake.re/20231220-MTOA</a><br>[2] <a href="https://gitlab.inria.fr/moex/lazylav/">https://gitlab.inria.fr/moex/lazylav/</a><br><br></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>
Data from: Behavioral trade-offs and multitasking by elk in relation to predation risk from Mexican gray wolves
Open the record for dataset details and reuse information.
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>
multitask_MHA_esm2_t30_150M_UR50D_neg_ratio_8+8_shift_30_mask_0.2_2023-03-25_90
<p>data need for phos-ST run</p>
Improving Executive Control in Cognitively Healthy Older Adults: the MUltitasking STrategy (MUST) Study
ClinicalTrials.gov study NCT06995638. IPD Sharing: YES. Countries: 1. Publications: 3.
The Multitasking Rehabilitation She Enhanced Walking Speed Compared to the Simple Post Stroke Rehabilitation Task (AVC)?
ClinicalTrials.gov study NCT03009773. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effects of a Music-Based Multitask Exercises Program on Gait, Balance and Fall Risk in the Elderly
ClinicalTrials.gov study NCT01107288. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Further development of the Assessment of Military Multitasking Performance: iterative reliability testing
The Assessment of Military Multitasking Performance (AMMP) is a battery of functional dual-tasks and multitasks based on military activities that target known sensorimotor, cognitive, and exertional vulnerabilities after concussion/mild traumatic brain injury (mTBI). The AMMP was developed to help address known limitations in post concussive return to duty assessment and decision making. Once validated, the AMMP is intended for use in combination with other metrics to inform duty-readiness decisions in Active Duty Service Members following concussion. This study used an iterative process of repeated interrater reliability testing and feasibility feedback to drive modifications to the 9 tasks of the original AMMP which resulted in a final version of 6 tasks with metrics that demonstrated clinically acceptable ICCs of > 0.92 (range of 0.92–1.0) for the 3 dual tasks and > 0.87 (range 0.87–1.0) for the metrics of the 3 multitasks. Three metrics involved in recording subject errors across 2 tasks did not achieve ICCs above 0.85 set apriori for multitasks (0.64) and above 0.90 set for dual-tasks (0.77 and 0.86) and were not used for further analysis. This iterative process involved 3 phases of testing with between 13 and 26 subjects, ages 18–42 years, tested in each phase from a combined cohort of healthy controls and Service Members with mTBI. Study findings support continued validation of this assessment tool to provide rehabilitation clinicians further return to duty assessment methods robust to ceiling effects with strong face validity to injured Warriors and their leaders.
Figure 1 in Nitrogen-fixing Cyanothece sp. as a mixotroph and silver nanoparticle synthesizer: a multitasking exceptional cyanobacterium
Figure 1. FTIR spectrum of biogenic silver nanoparticles from unfiltered external solution of Cyanothece sp.
ScienceDex guides
Understand access before you commit
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.