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33 results for “replay”
Data from: The temporal signature of memories: identification of a general mechanism for dynamic memory replay in humans
Reinstatement of dynamic memories requires the replay of neural patterns that unfold over time in a similar manner as during perception. However, little is known about the mechanisms that guide such a temporally structured replay in humans, because previous studies used either unsuitable methods or paradigms to address this question. Here, we overcome these limitations by developing a new analysis method to detect the replay of temporal patterns in a paradigm that requires participants to mentally replay short sound or video clips. We show that memory reinstatement is accompanied by a decrease of low-frequency (8 Hz) power, which carries a temporal phase signature of the replayed stimulus. These replay effects were evident in the visual as well as in the auditory domain and were localized to sensory-specific regions. These results suggest low-frequency phase to be a domain-general mechanism that orchestrates dynamic memory replay in humans.
Data from: Replaying evolution to test the cause of extinction of one ecotype in an experimentally evolved population
In a long-term evolution experiment with Escherichia coli, bacteria in one of twelve populations evolved the ability to consume citrate, a previously unexploited resource in a glucose-limited medium. This innovation led to the frequency-dependent coexistence of citrate-consuming (Cit+) and non-consuming (Cit–) ecotypes, with Cit−bacteria persisting on the exogenously supplied glucose as well as other carbon molecules released by the Cit+ bacteria. After more than 10,000 generations of coexistence, however, the Cit−lineage went extinct; cells with the Cit−phenotype dropped to levels below detection, and the Cit−clade could not be detected by molecular assays based on its unique genotype. We hypothesized that this extinction was a deterministic outcome of evolutionary change within the population, specifically the appearance of a more-fit Cit+ ecotype that competitively excluded the Cit−ecotype. We tested this hypothesis by re-evolving the population from a frozen population sample taken within 500 generations of the extinction and from another sample taken several thousand generations earlier, in each case for 500 generations and with 20-fold replication. To our surprise, the Cit−type did not go extinct in any of these replays, and Cit−cells also persisted in a single replicate that was propagated for 2,500 generations. Even more unexpectedly, we showed that the Cit−ecotype could reinvade the Cit+ population after its extinction. Taken together, these results indicate that the extinction of the Cit−ecotype was not a deterministic outcome driven by competitive exclusion by the Cit+ ecotype. The extinction also cannot be explained by demographic stochasticity alone, as the population size of the Cit−ecotype should have been many thousands of cells even during the daily transfer events. Instead, we infer that the extinction must have been caused by a rare chance event in which some aspect of the experimental conditions was inadvertently perturbed.
Retrospective Cohort Study of Rebif® Use in Pediatric Multiple Sclerosis (MS) Subjects (REPLAY)
ClinicalTrials.gov study NCT01207648. IPD Sharing: Not stated. Countries: 8. Publications: 0.
Data from: The temporal signature of memories: identification of a general mechanism for dynamic memory replay in humans
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Data from: Theta-modulation drives the emergence of connectivity patterns underlying replay in a network model of place cells
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Data from: Replaying evolution to test the cause of extinction of one ecotype in an experimentally evolved population
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M2-SCREAM: 3d,3-Hourly,Instantaneous,Model-Level,Assimilation,Assimilated Constituent Fields,Replayed MERRA-2 Meteorological Fields
The MERRA-2 Stratospheric Composition Reanalysis of Aura MLS (M2-SCREAM) products produced at NASA’s Global Modeling and Assimilation Office (GMAO) are generated by assimilating MLS and OMI retrievals into the GEOS Constituent Data Assimilation System (CoDAS) driven by meteorological fields from MERRA-2. M2-SCREAM assimilates hydrochloric acid (HCl), nitric acid (HNO3), stratospheric water vapor (H2O), nitrous oxide (N2O) and ozone with a system equipped with a version of the GEOS general circulation model and a stratospheric chemistry model, StratChem. Assimilated fields are provided globally at 0.5° by 0.625° resolution at three-hourly frequencies from 2004/09/01 to 2024/09/30. Assimilation uncertainties for each of the assimilated constituents are calculated from the CoDAS statistical output (Wargan et al., 2022) and provided as global full-resolution three-dimensional monthly files.Data product updates in March 2024, as a result of Aura MLS “duty cycle” of 190-GHz measurements, include reduced availability of H2O, N2O and HNO3 retrievals resulting in expected M2-SCREAM data quality degradation. However, preliminary analysis shows that the GEOS CoDAS handles the reduced temporal data coverage well, indicating that the GEOS model accurately propagates information from past observations. Data product updates in June 2024 resulting from MLS version upgrade to v5.0 include discontinuities in assimilated H2O (throughout the stratosphere) and N2O (in the lower stratosphere). To note: MLS water vapor is about 0.5 ppmv lower in v5.0, and the vertical range of assimilated N2O data is 100 hPa, extended down from 68 hPa. GMAO is not aware of discontinuities in HCl, HNO3, and ozone related to the version switch.
Data and code for training and testing a ResMLP model with experience replay for machine-learning physics parameterization
<p>This directory contains the training data and code for training and testing a ResMLP with experience replay for creating a machine-learning physics parameterization for the Community Atmospheric Model. </p> <p>The directory is structured as follows:</p> <p>1. Download training and testing data: https://portal.nersc.gov/archive/home/z/zhangtao/www/hybird_GCM_ML</p> <p>2. Unzip nncam_training.zip</p> <p>nncam_training</p> <p> - models</p> <p> model definition of ResMLP and other models for comparison purposes</p> <p> - dataloader </p> <p> utility scripts to load data into pytorch dataset</p> <p> - training_scripts</p> <p> scripts to train ResMLP model with/without experience replay</p> <p> - offline_test</p> <p> scripts to perform offline test (Table 2, Figure 2)</p> <p>3. Unzip nncam_coupling.zip</p> <p>nncam_srcmods</p> <p> - SourceMods</p> <p> SourceMods to be used with CAM modules for coupling with neural network</p> <p> - otherfiles</p> <p> additional configuration files to setup and run SPCAM with neural network</p> <p> - pythonfiles</p> <p> python scripts to run neural network and couple with CAM</p> <p> - ClimAnalysis</p> <p> - paper_plots.ipynb</p> <p> scripts to produce online evaluation figures (Figure 1, Figure 3-10)</p> <p> </p>
IL-17 Blockade to Decrease irAEs (REPLAY)
ClinicalTrials.gov study NCT07237594. IPD Sharing: NO. Countries: 0. Publications: 0.
Diagnostic and Prognostic Value of Reticulated Platelet Fraction in Ventilator-Associated Pneumonia (REPLAY-VAP)
ClinicalTrials.gov study NCT07013032. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Technology Development for Physical Rehabilitation of Patients With Upper Extremity Motor Deficits (RePlay)
ClinicalTrials.gov study NCT03621969. IPD Sharing: NO. Countries: 1. Publications: 0.
UTKPAD: Replay Attack Database for Face Age Verification
<p><strong>Description</strong></p> <p>UTKPAD is a replay attack database prepared for face age verification as part of the paper "Vulnerability of Face Age Verification to Replay Attacks" published in ICASSP 2024 conference. This database is originated from the well-known large dataset, UTKFace of face images with age labels ranging from 1 to 100 plus years old. Replay attack are recorded with three different mobiles phones: Apple iPhone 12, Samsung Galaxy S9 and Huawei Mate 30. With UTKPAD database, we also provide file lists that can be used for training and testing of replay attacks detection and for vulnerability assessment of age verification systems.</p> <p>The face images in the <a href="https://susanqq.github.io/UTKFace/">UTKFace</a> are first enhanced with a face restoration CodeFormer method. Each enhanced image is converted into a video clip with a subsequent concatenation process to have one video clip including all image-to-video converted files. The final video clip is then replayed on Apple iPad Pro in order to record it with three different mobiles phones: Apple iPhone 12, Samsung Galaxy S9 and Huawei Mate 30. And, finally, the recordings are de-concatenated/segmented into sub video clips and each video clip is sampled by taking the middle frame to construct the database of replay attack images.</p> <p>To avoid breaching the copyright, we release the dataset in the form of deltas that are not actual images. The attack images can be recovered only if the user also obtains and downloads the original UTKFace dataset. When UTKFace is downloaded, however, the attack images of UTKPAD can be easily computed using the script we provide. </p> <p> </p> <p><strong>Reference</strong></p> <p>If you're using this dataset, please cite the following publication</p> <p>@INPROCEEDINGS{Korshunov_ICASSP_2024,<br> author = {Korshunov, Pavel and George, Anjith and {\"O}zbulak, G{\"o}khan and Marcel, S{\'{e}}bastien},<br> projects = {Idiap, Biometrics Center},<br> title = {Vulnerablity of Face Age Verification to Replay Attacks},<br> booktitle = {ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},<br> year = {2024},<br>}</p>
Replay-triggered Brain-wide Activation in Humans
<p>Main data and results of 'Replay-triggered Brain-wide Activation in Humans'.</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.