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997 results for “Awareness”
The Neural Mechanism Underlying The Effect of Musical Training on Phonological Awareness of preschoolers : A Meta-Analysis
Open the record for dataset details and reuse information.
NASTRA: Accurate analysis of short tandem repeat markers by nanopore sequencing with repeat-structure-aware algorithm
<p><span>Forensic short-tandem repeats (STR) genetic markers are multi-allelic and widely utilized for individual identification, kinship testing, and cell-line authentication. Nanopore sequencing, known for its portability, is emerging as a promising approach for STR typing, facilitating real-time and in-field testing. However, its efficacy is often hampered by sequencing noise. Previous methods rely on alignment-based genotyping, necessitating known alleles, which limits their applicability to unknown alleles. Here, we introduced NASTRA, an innovative allele reference-free tool for precise germline analysis of STR genetic markers. NASTRA incorporates a recursive algorithm to infer repeat structures of allele sequences using only known repeat motifs. Our tests, conducted on 80 individual samples and 8 DNA standards, have demonstrated NASTRA's exceptional 100% accuracy in genotyping nearly all diploid STRs across various multiplex kits and flow cells. It surpasses alignment-based methods in accuracy and speed. In a paternity testing case study, NASTRA accurately identified three relationships among six individuals within an 18-minute sequencing duration. These results underscore NASTRA's ability to perform STR analysis on both NGS and nanopore sequencing platforms, significantly enhancing the utility of nanopore sequencing in relevant applications.</span></p>
Placement Aware Grasp Planning for Efficient Sequential Manipulation
<p>This is a summary and real-world experimental video of Placement Aware Grasp Planning for Efficient Sequential Manipulation. </p> <p>This work accepted in ECAI 2024. </p> <p>Paper link : <a href="https://ebooks.iospress.nl/doi/10.3233/FAIA241020">IOS Press Ebooks - Placement Aware Grasp Planning for Efficient Sequential Manipulation</a></p>
Splicing-Aware Patient Stratification Using Different Biclustering Methods
<div> <div> <div> <p>Patient stratification refers to the process of categorizing patients into specific groups based on whether they exhibit certain characteristics of a disease. In this research, we investigated the feasibility of splicing-aware measurements for patient stratification in complex diseases like breast cancer and dilated cardiomyopathy.</p> <p>Splicing-aware patient stratification integrates the intricate dynamics of RNA splicing with the stratification of patients in clinical settings. The significance of this approach lies in its potential to revolutionize personalized medicine by offering more precise and effective treatment strategies. To execute our research, we collected datasets at three levels: genomic, transcrip- tomic, and isoform-specific expressions for both breast cancer and dilated cardiomyopathy. We applied network-constrained (BiCoN) and unconstrained (MoSBi) biclustering algorithms to classify individuals into subtypes. Here, we show that working not just with transcripts but with dominant isoforms of transcripts and different measurements can unveil new information. We took the dominant isoform of transcripts by a median of expression into account to examine the splicing effect. Despite various factors, we included in our research, both BiCoN and MoSBi appeared more efficient on the gene level. We observed that different biclustering methods or approaches work differently in both diseases and also on different levels in each disease.</p> <p>With our results, we aim to shed light on the potential applications of alternative splicing analysis for disease subtyping.</p> </div> </div> </div>
Segmentation aware probabilistic phenotyping of single-cell spatial protein expression data
<p>This repository contains raw and processed data of the original datasets generated for Lee et al. "Segmentation aware probabilistic phenotyping of single-cell spatial protein expression data".</p> <p>The files Dilution_3_ROI02.ome.tiff, Dilution_3_ROI02_mask.ome.tiff, st_reclustered_expr_data.h5ad contain raw data (as ome tiff), a mesmer-generated segmentation mask, and single-cell quantification in anndata format of the cell pellet data generated for the study.</p> <p>The file tonsil-for-zenodo.tar.gz contains all raw and processed data of the IMC data of the 16 human tonsil ROIs:</p> <ul> <li>imc contains raw tiff stacks of the acquisition</li> <li>mask contains mesmer-generated masks</li> <li>channel.yml contains channel info</li> <li>exp_mat/dc/ contains single cell quantifications using mesmer segmentation</li> </ul>
Semantic audio-visual congruence modulates visual sensitivity to biological motion across awareness levels
<p>This repository contains all experimental scripts, analysis and data files for the manuscript "Semantic audio-visual congruence modulates visual sensitivity to biological motion across awareness levels".</p>
Are deep learning models in hydrology entity aware?
<p>This is the dataset and code accompanying the publication "Are deep learning models in hydrology entity aware?" of Benedikt Heudorfer, Hoshin V. Gupta, and Ralf Loritz to allow reproducible results. </p>
Data and models for "Center-fixing of tropical cyclones using uncertainty-aware deep learning applied to high-temporal-resolution geostationary satellite imagery" by Lagerquist et al.
<p><span><span><span>The file geocenter_models.tar contains all models comprising the GeoCenter ensemble: 3 convolutional neural networks (CNN), 3 isotonic-regression files (one for correcting each CNN’s mean estimate), and 3 more isotonic-regression files (one for correcting each CNN’s ensemble spread). Every model is found in a subdirectory whose names indicate which infrared (IR) wavelengths are used as input to the CNN. For example:</span></span></span></p> <ul> <li> <p><span><span><span>wavelengths-microns=3.900-7.340-13.300/model.weights.h5: An HDF5 file containing the trained CNN that uses data from bands 7, 10, 16 (corresponding to 3.9, 7.34, and 13.3 microns on the GOES ABI imager). The trained CNN can always be read by neural_net_utils.read_model() in the ml4tccf library (https://doi.org/10.5281/zenodo.15116854).</span></span></span></p> </li> <li> <p><span><span><span>wavelengths-microns=3.900-7.340-13.300/model_metadata.p: A Pickle file containing metadata for the trained CNN. This file is needed to read the CNN itself with neural_net_utils.read_model(). Otherwise, you will probably never need to access this metafile directly.</span></span></span></p> </li> <li> <p><span><span><span>wavelengths-microns=3.900-7.340-13.300/isotonic_regression/isotonic_regression.dill: A Dill file </span></span></span><span><span><span>containing isotonic-regression models used to bias-correct the ensemble mean from the same CNN. </span></span></span><span><span><span> The trained isotonic-regression models can always be read by scalar_isotonic_regression.read_file() in the ml4tccf library. Note that there are technically two isotonic-regression models for every CNN’</span></span></span><span><span><span>s ensemble mean</span></span></span><span><span><span>: one that bias-corrects the </span></span></span><em><span><span><span>x</span></span></span></em><span><span><span>-coordinate of the TC-center, another that bias-corrects the </span></span></span><em><span><span><span>y</span></span></span></em><span><span><span>-coordinate.</span></span></span></p> </li> <li> <p><span><span><span>wavelengths-microns=3.900-7.340-13.300/</span></span></span><span><span><span>uncertainty_calibration</span></span></span><span><span><span>/</span></span></span><span><span><span>uncertainty_calibration.dill: A Dill file containing isotonic-regression models used to bias-correct the ensemble spread from the same CNN. In the ml4tccf code, I make a distinction between “isotonic_regression” (correcting the ensemble mean) and “uncertainty_calibration” (correcting the ensemble spread), but note that both models are isotonic regression and use the sklearn.isotonic.IsotonicRegression class. The trained uncertainty-calibration models can always be read by scalar_uncertainty_calibration.read_file() in the ml4tccf library. Again, note that there are technically two uncertainty-calibration models per CNN: one for spread in the </span></span></span><span><span><span><em>x</em></span></span></span><span><span><span>-coordinate, one for spread in the </span></span></span><span><span><span><em>y</em></span></span></span><span><span><span>-coordinate.</span></span></span></p> </li> </ul> <p><span> </span></p> <p><span><span><span>As mentioned above, every trained CNN can be read by neural_net_utils.read_model(). Also, every trained CNN can be applied to new data (inference mode) by neural_net_utils.apply_model(). The input argument model_object should be the object returned by neural_net_utils.read_model(), and I suggest setting num_examples_per_batch = 10 to avoid out-of-memory errors. The only other input argument is predictor_matrices, which is a list of two numpy arrays. The first numpy array contains IR imagery centered at the first-guess TC center, and the second numpy array contains ATCF scalars. The first numpy array should have dimensions S (number of TC samples) x </span></span></span><span><span><span>3</span></span></span><span><span><span>00 (grid rows) x </span></span></span><span><span><span>3</span></span></span><span><span><span>00 (grid columns) x </span></span></span><span><span><span>9</span></span></span><span><span><span> (lag times) x 3 (wavelengths). Lag times should be in the following order: </span></span></span><span><span><span>240, 210, </span></span></span><span><span><span>180, 150, 120, 90, 60, 30, 0 min ago. Wavelengths should be in the order indicated by the subdirectory name. The numpy array itself should contain </span></span></span><em><span><span><span>normalized</span></span></span></em><span><span><span> brightness temperatures at the given lag times and wavelengths, following the grid specifications laid out in the journal paper (a </span></span></span><em><span><span><span>plate carrée</span></span></span></em><span><span><span> grid with 2-km spacing). The original IR data (brightness temperatures) must be normalized to </span></span></span><em><span><span><span>z</span></span></span></em><span><span><span>-scores using the same normalization parameters as in the journal paper, </span></span></span><em><span><span><span>i.e.,</span></span></span></em><span><span><span> those based on the training data. See details below. The second numpy array in predictor_matrices should have dimensions S (number of TC samples) x 9 (variables). The variables must in the order: absolute latitude, cosine of longitude, sine of longitude, TC intensity, minimum central pressure, tropical flag, subtropical flag, extratropical flag, disturbance flag. The journal paper contains details on all these variables in one table. These variables must come from A-deck files at the </span></span></span><span><span><span>second-</span></span></span><span><span><span>most recent synoptic time. Like the IR data, these ATCF scalars must be normalized to </span></span></span><em><span><span><span>z</span></span></span></em><span><span><span>-scores using the same normalization parameters as in the journal paper. See details below.</span></span></span></p> <p> </p> <p><span><span><span>Once you have predictions (estimated TC-center locations) from a CNN, you can bias-correct these predictions. To read the isotonic-regression model for the given CNN’s ensemble mean, use scalar_isotonic_regression.read_file() in the ml4tccf library. To apply the same model, use scalar_isotonic_regression.apply_models(). For the CNN’s ensemble spread, use scalar_uncertainty_calibration.read_file() and scalar_uncertainty_calibration.apply_models().</span></span></span></p> <p> </p> <p><span><span><span>To normalize the IR data, you will need the file ir_satellite_normalization_params.tar included with this dataset. Within the tar file is a single zarr file. You can read the zarr file with normalization.read_file() in the ml4tccf library; then you can normalize new data with normalization.normalize_data().</span></span></span></p> <p> </p> <p><span><span><span>To normalize the ATCF data, you will need the file a_deck_normalization_params.nc included with this dataset. This is a NetCDF file, containing the full set of training values for all 5 ATCF variables that are normalized (the binary storm-type flags are not normalized). You can read this file using any of the standard Python methods for reading NetCDF files, such as xarray.open_dataset(). To normalize new ATCF data, you can use the method normalization._normalize_one_variable(), where the argument actual_values_training is the list of training values from a_deck_normalization_params.nc for the given variable, while actual_values_new is the list of values to be normalized (currently in physical units, to be converted to </span></span></span><em><span><span><span>z</span></span></span></em><span><span><span>-score units).</span></span></span></p>
Demonstrating a Bayesian Online Learning forEnergy-Aware Resource Orchestration in vRANs
<p>Radio Access Network Virtualization (vRAN) will spearhead the quest towards supple radio stacks that adapt to heterogeneous infrastructure: from energy-constrained platforms deploying cells-on-wheels (e.g., drones) or battery-powered cells to green edge clouds. We demonstrate a novel machine learning approach to solve resource orchestration problems in energy-constrained vRANs. Specifically, we demonstrate two algorithms: (i) BP-vRAN, which uses Bayesian online learning to balance performance and energy consumption, and (ii) SBP-vRAN, which augments our Bayesian optimization approach with safe controls that maximize performance while respecting hard power constraints. We show that our approaches are data-efficient, converge an order of magnitude faster than other machine learning methods-and have provably performance, which is paramount for carrier-grade vRANs. We demonstrate the advantages of our approach in a testbed comprised of fully-fledged LTE stacks and a power meter, and implemented our approach into O-RAN's non-real-time RAN Intelligent Controller (RIC).</p>
MeDeMo - a dependency model for DNA methylation-aware transcription factor binding predictions
<p>The uploaded <em>fasta </em>files contain extended reference genomes for three cell lines HepG2, GM12878, K562 (ENCODE) and two primary liver hepatocyte samples from the german epigenomics consortium (DEEP). The extended reference genomes contain information on DNA methylation in a CpG context. They can be used as input for <em>MeDeMo</em>, a tool to infer transcription factor binding sites incorporating not only sequence specificity but also DNA methylation. <em>MeDeMo </em>is available online at: <a href="http://www.jstacs.de/index.php/MeDeMo">http://www.jstacs.de/index.php/MeDeMo</a>.</p> <p>We considered the files ENCFF279HCL and ENCFF835NTC for GM12878, ENCFF867JRG and ENCFF721JMB for K562 as well as ENCFF064GJQ and ENCFF369YQW for HepG2. From DEEP, we considered samples 41_Hf01 and 41_Hf03 which are available through the International Human Epigenomics Consortium (IHEC).</p> <p>In addition, we provide all models trained using the mentioned data sets as well models for and motifs from genome wide predictions.</p>
Survey of Generation Z's Social Media Privacy Awareness
<p>This dataset is the result of a survey regarding "Social Media Privacy Awareness of Generation Z" to 127 students in Institut Teknologi Sepuluh Nopember (ITS) Surabaya</p>
Presentation scripts of "Predictability awareness rather than mere predictability enhances the perceptual benefits for targets in auditory rhythms over targets following temporal cues"
<p>These are the presentation scripts accompanying the publication </p> <p>"Predictability awareness rather than mere predictability enhances the perceptual benefits for targets in auditory rhythms over targets following temporal cues". </p> <p>Check the readMe for an instruction.</p>
Survey security awareness - pretest data
<p>Pretest data an survey of security awareness of smart home users.</p>
Survey security awareness - maintest data
<p>Maintest data for survey of security awareness of smart home users.</p>
Companion data for Communication-Aware Load Balancing of the LU Factorization over Heterogeneous Clusters
<p>This is the companion data repository for the paper entitled <strong>Communication-Aware Load Balancing of the LU Factorization over Heterogeneous Clusters</strong> by Lucas Leandro Nesi, Lucas Mello Schnorr, and Arnaud Legrand. The manuscript has been accepted in the <a href="https://icpads2020.comp.polyu.edu.hk/">ICPADS 2020</a>.</p>
Mobility-aware Dataset
<p>If you use this dataset, please cite the following paper:</p> <p> </p> <table> <tbody> <tr> <td> <p>John Violos, Theodoros Theodoropoulos, Angelos-Christos Maroudis, Aris Leivadeas, and Konstantinos. Tserpes, “Self-Attention based Encoder-Decoder for Multistep Human Density Prediction,” Elsevier in Journal of Urban Mobility, vol. 2, p. 100022, Dec. 2022, doi: 10.1016/j.urbmob.2022.100022.</p> </td> </tr> </tbody> </table> <p><br> </p> <p>The simulation took place from 2021-07-16 to 2021-07-23 in the New York Central park with SUMO (Simulation of Urban MObility) [2]. The New York Central park is divided into six Regions Of Interest (ROI) and every time step we estimated the number of people gathered into every ROI as we can see in Fig. 1. We used a time step of 5 mins. In the file Final_regions_timestep.csv the 1st column represents the number of the step and the following six steps represent the six ROIs. In the files 5min_timestep_Day_i.csv (with i from 1 to 7) we can see the SUMO generated data for the i day of the simulation. In every row we can see the id of the pedestrian, the date, the timestamp, the latitude, the longitude. The sixth and the seventh columns are not taken into consideration in our experiments. The sixth column is a constant of SUMO and the seventh is the velocity of the pedestrians.</p> <p>More info can be found in the Dataset Description</p>
Data for "Haplotype-aware pantranscriptome analyses using spliced pangenome graphs"
<p>This archive contains mapping benchmark tables and haplotype-specific expression estimates from the paper "Haplotype-aware pantranscriptome analyses using spliced pangenome graphs".</p> <p> </p>
Energy consumption, execution time and fail requests rate of a proactive energy-aware auto-scaling solution for edge-based infrastructures applied to real-world workload.
<p>Spreadsheet of the results obtained with our horizontal auto-scaling proposal presented in "A proactive energy-aware auto-scaling solution for edge-based infrastructures". In that research, we present a proactive horizontal auto-scaling framework for edge infrastructures, which considers both the base (idle) and dynamic (due to application execution) energy consumption of edge nodes and the node scaling mechanism. Simulations were performed with the EdgeCloudSim simulator with a workload provided by Shanghai Telecom and the results show up to a 92.5% decrease in energy consumption, a failed request rate of up to 0%, and reasonable execution times of the auto-scaling process for different problem sizes.</p> <p>Proactive auto-scaling mechanisms in edge-based infrastructures can anticipate user service requests by allocating computing resources while supporting the quality of service needed by a vast range of applications requiring, e.g., a low latency or response time. </p> <p>This work is supported by the European Union's H2020 research and innovation program under grant agreement DAEMON 101017109 and by the projects co-financed by FEDER funds LEIA UMA18-FEDERJA-15, MEDEA RTI2018-099213-B-I00 (MCI/AEI) and RHEA P18-FR-1081.</p>
PAX: Partition-Aware Autoscaling for the Cassandra NoSQL Database
<p>Apache Cassandra has emerged as one of the most widely adopted NoSQL databases. However, there is still a limited understanding on how to optimally operate Cassandra in the cloud using autoscaling methods, by which resources can be scaled up or down to reduce operational costs and meet service-level objectives (SLOs).</p> <p>To address this limitation, we present PAX, a partition-aware elastic resource management system for Apache Cassandra. PAX uses low-overhead query sampling and knowledge of the data-partitioning across the nodes to automatically adapt capacity in Cassandra clusters. Differently from existing autoscaling methods for Cassandra, which incur large acquisition times for new nodes, PAX exploits Cassandra's hinted handoff mechanism and a shared hints storage to minimize the time needed to acquire a node into the cluster.</p> <p>We propose a reactive and a proactive implementation of PAX and compare their performance against different workloads with varying intensities and item popularity distributions, finding that the proactive version significantly reduces SLO violations. </p>
Dataset from: "Uncertainty-Aware Interpretable Prognosis for Wave Energy Converters with Recurrent Expansion"
<p>This dataset comprises run-to-failure sensor data derived from a mathematical model simulating wave energy converter behavior, particularly for Oscillating Water Column Turbines (OWCTs). The dataset includes vibration, temperature, pressure, acceleration, strain, flow, torque, rotation, and remaining useful life (RUL) readings for one life cycle. Through normalization, the data is scaled uniformly for robust analysis and interpretation. Researchers can leverage this dataset to develop and validate their prognostic models for OWCTs.</p> <p>To cite this dataset, please refer to:</p> <p>Berghout Tarek and Benbouzid Mohamed. (2024). Uncertainty-Aware Interpretable Prognosis for Wave Energy Converters with Recurrent Expansion. SSRN, 1–22. <span><a href="https://dx.doi.org/10.2139/ssrn.4825408" target="_blank" rel="noopener"><span>http://dx.doi.org/10.2139/ssrn.4825408</span></a> </span></p>
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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.