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199 results for “Active Learning”
Libraries generated in: Using Machine Learning to Predict the Antibacterial Activity of Ruthenium Complexes
<p>Libraries generated in the manuscript: "<strong>Using Machine Learning to Predict the Antibacterial Activity of Ruthenium Complexes"</strong>. The libraries can be generated locally by running the code provided on <a href="https://github.com/TheFreiLab/RutheniumML">GitHub</a>, but are also provided here free to download.</p>
User preference optimization for control of ankle exoskeletons using sample efficient active learning
<p>A major challenge to the widespread success of augmentative exoskeletons is accurately adjusting the controller to provide cooperative assistance with their wearer. Often, the controller parameters are ``tuned'' to optimize a physiological or biomechanical objective. However, these approaches are resource-intensive, while typically only enabling optimization of a single objective. In reality, the exoskeleton user experience is derived from many factors, including comfort and stability, among others. This work introduces an approach to conveniently tune four parameters of the exoskeleton controller that maximize user preference. We use an evolutionary algorithm to recommend potential parameters, which are ranked by a neural network that is pre-trained with previously collected preference data. The controller parameters that have the highest preference ranking are provided to the exoskeleton, and the wearer provides feedback as forced-choice comparisons. Our approach was able to converge on controller parameters preferred by the wearer compared to randomized parameters with an accuracy of 88% on average. The result indicates that the proposed algorithm was able to identify users' preferences while requiring less than 50 queries to users. This work demonstrates user preference can be used to tune high-dimensional controller spaces easily and accurately, which shows the potential of translating lower-limb wearable technologies into our daily lives.</p>
Data from: Dynamic changes in chloride homeostasis coordinate midbrain inhibitory network activity during reward learning
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Data from: Leveraging data mining, active learning, and domain adaptation for efficient discovery of advanced oxygen evolution electrocatalysts
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User preference optimization for control of ankle exoskeletons using sample efficient active learning
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Vocalization data and scripts to model reindeer rut activity using on-animal acoustic recorders and machine learning
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Supplementary Material for the paper: Automatic Document Screening of Medical Literature Using Word and Text Embeddings in an Active Learning Setting
<p>This is the dataset used in the paper: Automatic Document Screening of Medical Literature Using Word and Text Embeddings in an Active Learning Setting. </p> <p>It is composed of: </p> <p>- Pre-trained models using active learning for document screening on HealthCLEF and Epistemonikos datasets. </p> <p>- Epistemonikos and HealthCLEF datasets containing medical questions and relevant/non relevant articles. </p> <p>- Embeddings and Document Representations used for experiments on both datasets. </p> <p>Scripts to run experiments can be found at: <a href="https://github.com/afcarvallo/active_learning_document_screening">https://github.com/afcarvallo/active_learning_document_screening</a></p> <p> </p> <p><strong>Paper abstract:</strong></p> <p>Document screening is a fundamental task within Evidence-based Medicine (EBM), a practice that provides scientific evidence to support medical decisions. Several approaches have tried to reduce physicians' workload of screening and labeling vast amounts of documents to answer clinical questions. Previous works tried to semi-automate document screening, reporting promising results, but their evaluation was conducted on small datasets, which hinders generalization. Moreover, recent works in natural language processing have introduced neural language models, but none have compared their performance in EBM. In this paper, we evaluate the impact of several document representations such as TF-IDF along with neural language models (BioBERT, BERT, Word2vec, and GloVe) on an active learning-based setting for document screening in EBM. Our goal is to reduce the number of documents that physicians need to label to answer clinical questions. We evaluate these methods using both a small challenging dataset (HealthCLEF 2017) as well as a larger one but easier to rank (Epistemonikos). Our results indicate that word as well as textual neural embeddings always outperform the traditional TF-IDF representation. When comparing among neural and textual embeddings, in the HealthCLEF dataset the models BERT and BioBERT yielded the best results. On the larger dataset, Epistemonikos, Word2Vec and BERT were the most competitive, showing that BERT was the most consistent model across different corpuses. In term of active learning, an uncertainty sampling strategy combined with logistic regression achieved the best performance overall, above other methods under evaluation, and in fewer iterations.</p>
Learning Active Constraints to Efficiently Solve Bilevel Problems
<p>Datafiles for the test cases in the article <a href="https://arxiv.org/abs/2010.06344">"Learning Active Constraints to Efficiently Solve Bilevel Problems"</a>. The corresponding code is available on <a href="https://github.com/eleaprat/Bilevel---Active-Constraints">GitHub</a>.</p>
Active Learning with RESSPECT: Data Set
<p>This folder contains pre-processed simulated data first made available by Rick Kessler for the <br> <a href="https://arxiv.org/abs/1008.1024">Supernova Photometric Classification Challenge (SNPCC)</a>.</p> <p>All data were feature extracted using the <a href="https://arxiv.org/pdf/0904.1066.pdf">Bazin parametric function</a>.</p> <p>This version of the data set was used to obtain the results reported in <a href="https://arxiv.org/pdf/2010.05941.pdf">Kennamer et al., 2020 - <em>Active learning with RESSPECT: resource allocation for extragalactic astronomical transients</em>.</a> Published during the <a href="http://www.ieeessci2020.org/symposiums/ciastro.html">2020 IEEE Symposium Series on Computational Intelligence</a>. The code used to obtain the results shown in the paper is available in the <a href="https://github.com/COINtoolbox/RESSPECT">COINtoolbox</a> (github). <br> <br> This work was developed under the <a href="https://cosmostatistics-initiative.org/resspect/">RESSPECT project</a>, an inter-collaboration agreement established between the <a href="https://lsstdesc.org/">LSST Dark Energy Science Collaboration (LSST-DESC)</a> and the <a href="https://cosmostatistics-initiative.org/">Cosmostatistics Initiative (COIN)</a> in order to develop an active learning pipeline to advise the allocation of telescope resources.</p>
Experimental Data for the Paper 'Hierarchical Fusion and Divergent Activation Based Weakly Supervised Learning for Object Detection from Remote Sensing Images'
<p><strong>Experimental Data for the Paper 'Hierarchical Fusion and Divergent Activation Based Weakly Supervised Learning for Object Detection from Remote Sensing Images'</strong></p> <p>In this repository, we provide the implementation of the algorithms developed in the paper 'Hierarchical Fusion and Divergent Activation Based Weakly Supervised Learning for Object Detection from Remote Sensing Images' along with the experimental results, and the methods used for comparison.<br> The goal is to provide the elements needed to validate and reproduce our research work as well as all the tools needed to reach the same conclusions as we did.<br> The data used in our experiments that we have the copyright of [<a href="http://doi.org/10.1109/ACCESS.2020.3019956">A</a>],[<a href="http://doi.org/10.5281/zenodo.3843229">B</a>] is already published <a href="http://doi.org/10.5281/zenodo.3843229">on zenodo</a>.<br> The licences valid for the elements of this repository are discussed under point "3. Licenses" below.</p> <p><strong>1. Structure</strong></p> <p>The repository contains the following items:</p> <ol> <li>"CODE_AND_RESULTS.zip" with the source codes and results of our method and the comparison methods,</li> <li>"README" - this text here.</li> <li>"LICENSE" - the <a href="https://mit-license.org/">MIT License</a></li> </ol> <p>We now focus on the structure of the file CODE_AND_RESULTS.zip.<br> It contains the following items:</p> <ol> <li>The directory "new_methods" contains the source code and results of the new methods proposed in our paper.</li> <li>The directory "comparison" contains the source code of the two approaches used for comparison: ACoL [<a href="https://doi.org/10.1109/CVPR.2018.00144">A</a>] and DANet [<a href="http://doi.org/10.1109/ICCV.2019.00669">B</a>].</li> <li>The folder "tools_and_metrics" holds additional libraries, software tools, and metrics using in our experiments. </li> <li>"README" - this text here.</li> <li>"LICENSE" - the <a href="https://mit-license.org/">MIT License</a></li> </ol> <p>Inside the folder "new_methods," the following sub-folders are provided:</p> <ol> <li>"data" includes data loading code and code for how organizing the input data of the neural network.</li> <li>"expr" includes training code.</li> <li>"model" includes neural network model, basic network and additional modules, depending on the file name, including improved network, and comparison model.</li> <li>"utils" includes some used library functions and test codes when testing, including image segmentation, searching for the largest connected area and data visualization, etc. Verification on the WSADD dataset is done via test_airplane.py and on the DIOR dataset via val_model.py.</li> </ol> <p>In our experiments, we used two datasets:</p> <p>"WSADD" [<a href="http://doi.org/10.1109/ACCESS.2020.3019956">A</a>],[<a href="http://doi.org/10.5281/zenodo.3843229">B</a>], which is already published <a href="http://doi.org/10.5281/zenodo.3843229">on zenodo</a> under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a> license.<br> The "<a href="https://doi.org/10.1109/CVPR.2018.00144">DIOR</a>" proposed in [<a href="http://doi.org/10.1016/j.isprsjprs.2019.11.023">C</a>].</p> <p><strong>2. References</strong></p> <p>[<a href="http://doi.org/10.1109/ACCESS.2020.3019956">A</a>] Z.-Z. Wu, T. Weise, Y. Wang, Y. Wang, Convolutional neural network based weakly supervised learning for aircraft detection from remote sensing image, <em>IEEE Access</em> 8 (2020) 158097-158106. doi:<a href="http://doi.org/10.1109/ACCESS.2020.3019956">10.1109/ACCESS.2020.3019956</a>. <br> [<a href="http://doi.org/10.5281/zenodo.3843229">B</a>] Z.-Z. Wu. Weakly Supervised Airplane Detection Dataset: WSADD. May 2020. zenodo.org. doi:<a href="http://doi.org/10.5281/zenodo.3843229">10.5281/zenodo.3843229</a>.<br> [<a href="http://doi.org/10.1016/j.isprsjprs.2019.11.023">C</a>] K. Li, G. Wan, G. Cheng, L. Meng, J. Han, Object detection in optical remote sensing images: A survey and a new benchmark, <em>ISPRS Journal of Photogrammetry and Remote Sensing</em> 159 (2020) 296-307. doi:<a href="http://doi.org/10.1016/j.isprsjprs.2019.11.023">10.1016/j.isprsjprs.2019.11.023</a>. <br> [<a href="https://doi.org/10.1109/CVPR.2018.00144">D</a>] X. Zhang, Y. Wei, J. Feng, Y. Yang, T. S. Huang, Adversarial complementary learning for weakly supervised object localization, in: <em>Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition</em> (CVPR'18), Jun. 18-22, 2018, Salt Lake City, UT, USA, IEEE Computer Society, 2018, pp. 1325-1334. doi:<a href="https://doi.org/10.1109/CVPR.2018.00144">10.1109/CVPR.2018.00144</a>. <br> [<a href="http://doi.org/10.1109/ICCV.2019.00669">E</a>] H. Xue, C. Liu, F. Wan, J. Jiao, X. Ji, Q. Ye, DANet: Divergent activation for weakly supervised object localization, in: <em>Proceedings of the IEEE/CVF International Conference on Computer Vision</em> (ICCV'19), Oct. 27-Nov. 2, 2019, Seoul, Korea, IEEE, 2019, pp. 6588-6597. doi:<a href="http://doi.org/10.1109/ICCV.2019.00669">10.1109/ICCV.2019.00669</a>.</p> <p><strong>3. Licenses</strong></p> <p>The following licenses apply for the files and folders in the archive "CODE_AND_RESULTS.zip":</p> <ul> <li>The files in the folder `new_methods` are under the <a href="https://mit-license.org/">MIT License</a>.</li> <li>The files in the folder `comparison/ACoL` have been obtained from https://github.com/xiaomengyc/ACoL, which is under the <a href="https://mit-license.org/">MIT License</a>.</li> <li>We put our code and data under the </li> <li>The files in the folder "comparison/DANet" have been obtained from <a href="https://github.com/xuehaolan/DANet">https://github.com/xuehaolan/DANet</a>, which is an open source project without associated license at the time of this writing. They will therefore remain under the copyright of the user <a href="https://github.com/xuehaolan/">https://github.com/xuehaolan/</a>.</li> <li>The files in the folder "tools_and_metrics/detections_DIOR" are related to the repository <a href="https://github.com/rafaelpadilla/Object-Detection-Metrics">https://github.com/rafaelpadilla/Object-Detection-Metrics</a>, which is under the <a href="https://mit-license.org/">MIT License</a>, and therefore are under the same license.</li> <li>The files in the folder "tools_and_metrics/Nest-pytorch" are based on the repository <a href="https://github.com/ZhouYanzhao/Nest">https://github.com/ZhouYanzhao/Nest</a>, which is under the <a href="https://mit-license.org/">MIT License</a>.</li> <li>The files in the folder "tools_and_metrics/PRM-pytorch" are based on the repository <a href="https://github.com/ZhouYanzhao/PRM">https://github.com/ZhouYanzhao/PRM</a>, which is an open source project without associated license at the time of this writing. They will therefore remain under the copyright of the user <a href="https://github.com/ZhouYanzhao/">https://github.com/ZhouYanzhao/</a>.</li> </ul> <p>The <a href="https://mit-license.org/">MIT License</a> is included here as file "LICENSE".</p> <p><strong>4. Contact</strong></p> <p>1. Dr. <a href="http://iao.hfuu.edu.cn/146">Zhize WU</a>, wuzz@hfuu.edu.cn<br> 2. Dr. <a href="http://iao.hfuu.edu.cn/5">Thomas WEISE</a>, tweise@hfuu.edu.cn, tweise@ustc.edu.cn</p> <p>Institute of Applied Optimization, <br> School of Artificial Intelligence and Big Data, <br> Hefei University, South Campus 2, Jinxiu Dadao 99, <br> Hefei Economic and Technological Development Area, <br> Shushan District, Hefei 230601, Anhui, China<br> </p>
Neural ensemble reactivation in REM and SWS coordinate with muscle activity to promote rapid motor skill learning
<p>Neural activity patterns of recent experiences are reactivated during sleep in structures critical for memory storage, including hippocampus and neocortex. This reactivation process is thought to aid memory consolidation. Although synaptic rearrangement dynamics following learning involve an interplay between slow-wave sleep (SWS) and rapid eye movement sleep (REM), most physiological evidence implicates SWS directly following experience as a preferred window for reactivation. Here we show that reactivation occurs in both REM and SWS, and that coordination of REM and SWS activation on the same day is associated with rapid learning of a motor skill. We performed 6-hour recordings from cells in rats' motor cortex as they were trained daily on a skilled reaching task. In addition to SWS following training, reactivation occurred in REM, primarily during the pre-task rest period, and REM and SWS reactivation occurred on the same day in rats that acquired the skill rapidly. Both pre-task REM and posttask SWS activation were coordinated with muscle activity during sleep, suggesting a functional role for reactivation in skill learning. Our results provide the first demonstration that reactivation in REM sleep occurs during motor skill learning, and that coordinated reactivation in both sleep states on the same day, although at different times, is beneficial for skill learning.</p>
Physical Activity is Associated with Reduced Implicit Learning but Enhanced Relational Memory and Executive Functioning in Young Adults
<p>This file contains all data reported in the main analyses of our paper, including age, gender, BMI, Stroop effect, implicit learning sessions 1 and 2, relational memory, and all physical activity variables</p>
Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments
<p>There are 16,930 sound instances in our database with durations ranging 242 from 1 to 10 seconds, which correspond to (approximately) 15 hours of environmental 243 sounds. All sound files were converted into raw 16 bit encoding, mono-channel, and 16 244 kHz sampling rate, as various formats and rates were used in the original versions 245 retrieved from the web.</p>
Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments
<p>There are 16,930 sound instances in our database with durations ranging 242 from 1 to 10 seconds, which correspond to (approximately) 15 hours of environmental 243 sounds. All sound files were converted into raw 16 bit encoding, mono-channel, and 16 244 kHz sampling rate, as various formats and rates were used in the original versions 245 retrieved from the web.</p>
IMBALANCED MACHINE LEARNING CLASSIFICATION MODELS FOR REMOVAL BIOSIMILAR DRUGS AND INCREASED ACTIVITY IN PATIENTS WITH RHEUMATIC DISEASES
<p>Objective: Predict long-term disease worsening and the removal of biosimilar medication in patients with rheumatic diseases.</p><p>Methodology: Observational, retrospective, and descriptive study. Review of a database of patients with immune-mediated inflammatory rheumatic diseases. Disease worsening and removing biosimilars are imbalanced variables, that require using imbalanced machine learning models selected based on their superior f1-scores and great accuracy. Previously, we selected the most important variables using mutual information tests.</p><p>Results: The best imbalanced machine learning models to predict disease worsening and the removal of the biosimilar obtained f1-scores of 0.52 and 0.63, respectively. Both models are decision trees. In the first one, two important factors are switching of biosimilar and age, and in the second, the relevant variables are optimization and the value of the initial CRP. </p><p>Conclusions: Biosimilar drugs do not always work well for rheumatic diseases. We obtained two imbalanced machine learning models to detect those cases, where the drug should be removed or where the activity of the disease increases from low to high. Our decision trees use variables, such as age or switching, not considered in previous studies.</p>
Dataset/Models for: Active learning accelerated exploration of the single atom local environments in multimetallic systems for oxygen electrocatalysis
<p>This is the datasets and trained models for the work "Active learning accelerated exploration of the single atom local environments in multimetallic systems for oxygen electrocatalysis", by Hoje Chun, Jaclyn R. Lunger, Jeung Ku Kang, Rafael Gómez-Bombarelli, and Byungchan Han. Folder named Models contains the trained models of "m-PaiNN" and "per-site PaiNN". Folder named Dataset contains the torch dataset and Dataset_raw contains the raw Density Functional Theory (DFT) dataset parsed in format of pymatgen Structure. Some structures (868) in the search space are missing due to the lost track of the geometry optimization during the initial dataset curation.</p>
Datasets for Insights into prismatic loop formation in irradiated Fe-Cr alloys from hypothesis-driven active learning and causal analysis
<p>Datasets for irradiated Fe-Cr alloys are collected from the experimental reports on dislocation loop type and dislocation density. We have constructed a data set from experimental literature containing 182 data points. To address such challenges to predict dislocation density, we have implemented a three-step ML approach as listed in the following:</p> <div> <div> <div> <ul> <li> <p>impute dataset to fill in the missing data to construct a predictive model using the RF regression algorithm. </p> </li> <li> <p>generate functionalized features and evaluate feature importance using the predictive model</p> </li> <li> <p>use the physics-based important functionalized features as hypotheses (physics-augmented GP models) in a hypothesis-driven active learning scheme to learn and predict dislocation density for all alloys. </p> </li> </ul> </div> </div> </div>
Code & data of the paper "Data sampling via Active Learning in Cartesian Genetic Programming for Biomedical Data"
<p>Code & data of the paper "Data sampling via Active Learning in Cartesian Genetic Programming for Biomedical Data"</p> <p> </p>
APNet, an explainable sparse deep learning model to discover differentially active drivers of severe COVID-19
<p><i><strong>Motivation:</strong></i> Computational analyses of plasma proteomics provide translational insights into complex diseases such as COVID-19 by revealing molecules, cellular phenotypes, and signaling patterns that contribute to unfavorable clinical outcomes. Current in silico approaches dovetail differential expression, biostatistics, and machine learning, but often overlook nonlinear proteomic dynamics, like post-translational modifications, and provide limited biological interpretability beyond feature ranking.</p><p><i><strong>Results:</strong></i> We introduce APNet, a novel computational pipeline that combines differential activity analysis based on SJARACNe co-expression networks with PASNet, a biologically-informed sparse deep learning model to perform explainable predictions for COVID-19 severity. Co-expression and classification weights are ingested by the APNet driver-pathway network to aid result interpretation and hypothesis generation. APNet outperforms alternative models in patient classification across three COVID-19 proteomic datasets, identifying predictive drivers and pathways, including some confirmed by single-cell omics and highlighting under-explored biomarker circuitries in COVID-19.</p><p><i><strong>Availability and Implementation:</strong></i></p><p> APNet's R, Python scripts and Cytoscape methodologies are available at </p><p><a href="https://github.com/BiodataAnalysisGroup/APNet">https://github.com/BiodataAnalysisGroup/APNet</a></p>
Dataset for "Reinforcement Learning reveals fundamental limits on the mixing of active particles"
<p>Open access data set for manuscript "Reinforcement Learning reveals fundamental limits on the mixing of active particles" currently (2021) in preparation.</p>
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.