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199 results for “active learning”
REVOLUTIONIZING ENGLISH TEXT TRANSLATION: TASK-BASED ACTIVITIES FOR EFFECTIVE LANGUAGE LEARNING
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FOCAL Dataset: Ford-OLIVES Collaboration on Active Learning
<p>In this dataset, we introduce the FOCAL (Ford-OLIVES Collaboration on Active Learning) dataset which enables the study of the impact of annotation-cost within a video active learning setting. Annotation-cost refers to the time it takes an annotator to label and quality-assure a given video sequence. A practical motivation for active learning research is to minimize annotation-cost by selectively labeling informative samples that will maximize performance within a given budget constraint. However, previous work in video active learning lacks real-time annotation labels for accurately assessing cost minimization and instead operates under the assumption that annotation-cost scales linearly with the amount of data to annotate. This assumption does not take into account a variety of real-world confounding factors that contribute to a nonlinear cost such as the effect of an assistive labeling tool and the variety of interactions within a scene such as occluded objects, weather, and motion of objects. FOCAL addresses this discrepancy by providing real annotation-cost labels for 126 video sequences across 69 unique city scenes with a variety of weather, lighting, and seasonal conditions. These videos have a wide range of interactions that are at the intersection of infrastructure-assisted autonomy and autonomous vehicle communities. We show through a statistical analysis of the FOCAL dataset that cost is more correlated with a variety of factors beyond just the length of a video sequence. We also introduce a set of conformal active learning algorithms that take advantage of the sequential structure of video data in order to achieve a better trade-off between annotation-cost and performance while also reducing floating point operations (FLOPS) overhead by at least 77.67%. We show how these approaches better reflect how annotations on videos are done in practice through a sequence selection framework. We further demonstrate the advantage of these approaches by introducing two performance-cost metrics and show that the best conformal active learning method is cheaper than the best traditional active learning method by 113 hours. </p><p>This work took place at the <a href="https://ghassanalregib.info/">OLIVES Lab @ Georgia Tech. </a></p><p>The codebase associated with this work can be found at this <a href="https://github.com/olivesgatech/FOCAL_Dataset">Github</a>.</p><p>Please refer to our <a href="https://github.com/olivesgatech">lab-wide github</a> for more information regarding the code associated with our other papers.</p>
The impacts of active and self-supervised learning on efficient annotation of single-cell expression data - source data
<p>Source data used to create all figures in the manuscript.</p>
MOTIVATION IN THE STRUCTURE OF LEARNING ACTIVITY OF A HIGHER EDUCATIONAL INSTITUTION STUDENT
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Raw data and code for publication "Optimisation of surfactin yield in Bacillus using active learning and high-throughput mass spectrometry"
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A Comparison of Machine-Learning Assisted Optical and Thermal Camera Systems for Beehive Activity Counting
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Data and Scripts used in "Analysis of Relations Between Solar Activity, Cosmic Rays and Earth Climate Using Machine Learning Techniques"
<p>This archive contains the data in relation to the work:</p> <p>Analysis of relations between solar activity, cosmic rays and earth climate using machine learning techniques<br> B. Belen, U. M. Leloglu, and M. B. Demirkoz </p> <p>See README file for more details.</p>
Supernova search with active learning in ZTF DR3
<p>Data sources for results presented in Pruzhinskaya et al., 2022.</p> <p>Results from the Active Anomaly Discovery (AAD) algorithm and the feature data set extracted from <a href="https://www.ztf.caltech.edu/ztf-public-releases.html">ZTF DR3</a> light curves.</p> <p>"log/anomalies_feature_*.txt" files contain the list of OIDs classified by the expert as anomalies, i.e. supernova candidates, for each ZTF field.</p> <p>"log/answers_feature_*.csv" files contain answers to the AAD output given by the expert in order of their appearance.</p> <p>"log/fields.csv" contains supernova statistics for each ZTF field.</p> <p>"features/" directory represent the dataset we used for supernova search in ZTF photometric data with AAD.</p> <p>"feature_*.dat" files contain object-ordered light curve feature data, every object is built on 42 feature values, which are encoded as little endian single precision IEEE-754 float (32bit float) numbers. Feature code-names are the same for all three data sets and are listed in plain text files "feature_*.name", one code-name per line. "oid_*.dat" files contain ZTF DR object identifiers encoded as little endian 64-bit unsigned integer numbers.<br> "oid_*.dat" and "feature_*.dat" have same object order, for example the first 8 bytes of "oid_796.dat" files contain the OID of the ZTF DR3 light curve which feature are presented in the first 168 bytes of "feature_796.dat" file. Note that only observations between 58194 ≤ MJD ≤ 58483 are used, see <a href="https://doi.org/10.1093/mnras/stab316">Malanchev et al. 2021</a> for features details.</p> <p>The sample Python code to access the data as Numpy arrays:</p> <pre><code class="language-python">import numpy as np oid = np.memmap('oid_796.dat', mode='r', dtype=np.uint64) with open('feature_796.name') as f: names = f.read().split() dtype = [(name, np.float32) for name in names] feature = np.memmap('feature_796.dat', mode='r', dtype=dtype, shape=oid.shape) idx = np.argmax(feature['amplitude']) print('Object {} has maximum amplitude {:.3f}'.format(oid[idx], feature['amplitude'][idx]))</code></pre> <p>It should print "Object 796206400001779 has maximum amplitude 3.739"</p>
Computational Chemistry and Machine Learning-assisted Screening of Supported Amorphous Metal Oxide Nanoclusters for Methane Activation
<p>Gaussian input and output files for reproducibility of the results.</p>
Few-Shot Meta-Learning Applied to Whole Brain Activity Maps Improves Systems Neuropharmacology and Drug Discovery
<p>Data and Code are all included</p>
The interdependence of critical thinking and active participation in constructivist learning for public secondary students
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Data from: Evaluating active learning methods for annotating semantic predications
Objectives: This study evaluated and compared a variety of active learning strategies, including a novel strategy we proposed, as applied to the task of filtering incorrect SemRep semantic predications. Materials and Methods: We evaluated three types of active learning strategies – uncertainty, representative, and combined– on two datasets of semantic predications from SemMedDB covering the domains of substance interactions and clinical medicine, respectively. We also designed a novel combined strategy with dynamic β without hand-tuned hyperparameters. Each strategy was assessed by the Area under the Learning Curve (ALC) and the number of training examples required to achieve a target Area Under the ROC curve (AUC). We also visualized and compared the query patterns of the query strategies. Results: Combined strategies outperformed all other methods in terms of ALC, outperforming the baseline by over 0.05 ALC for both datasets and reducing 58% annotation efforts in the best case. While representative strategies performed well, their performance was matched or outperformed by the combined methods. All the uncertainty sampling methods beat the baseline but they were the worst performing methods overall. Our proposed AL method with dynamic β shows promising ability to achieve near-optimal performance across two datasets. Discussion: Our visual analysis of query patterns indicates that strategies which efficiently obtain a representative subsample perform better on this task. Conclusion: Active learning is shown to be effective at reducing annotation costs for filtering incorrect semantic predications from SemRep. Our proposed AL method demonstrated promising performance.
Dataset and Code for the Paper "Reinforced Active Learning for CVD-Grown Two-Dimensional Materials Characterization"
<p>This repository contains the original data and code for the paper titled as "Reinforced Active Learning for CVD-Grown Two-Dimensional Materials Characterization" published on <em>IISE Transactions</em>.</p>
Instrumentation Neutron Activation Analysis & Proton Induced X-RAY Emission techniques supported with Machine learning analysis for rare earth/macro/micro elements correlation from O. Sativa Rice varieties in Senegal River valley
<p>data sheet INAA;results</p>
Assessing Impact of Active Learning on Student Outcomes: Texas Initiatives for Children's Activity and Nutrition (ICAN)
ClinicalTrials.gov study NCT03087279. IPD Sharing: NO. Countries: 0. Publications: 1.
Deep Learning Based Early Warning Score in Rapid Response Team Activation
ClinicalTrials.gov study NCT04951973. IPD Sharing: NO. Countries: 0. Publications: 3.
Women Exercising, Active, and Learning Together 2.0
ClinicalTrials.gov study NCT06785025. IPD Sharing: NO. Countries: 1. Publications: 0.
A Study to Learn About the Safety and Immune Activity of RSVpreF in Children 2 to <18 Years of Age
ClinicalTrials.gov study NCT05900154. IPD Sharing: NO. Countries: 1. Publications: 0.
A Study to Learn About the Safety of Vedolizumab and How Well it Works in Children and Teenagers With Active Chronic Pouchitis
ClinicalTrials.gov study NCT06443502. IPD Sharing: YES. Countries: 8. Publications: 0.
Data from: Individual learning performance and exploratory activity are linked to colony foraging success in a mass-recruiting ant
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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.