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4,210 results for “memory”
Ecological memory effects on plants and soils in early post-fire steppe, Barton Ecological Research Area, Pocatello, Idaho, 2021
In many regions of the world, wildfires are becoming more frequent due to the invasion of exotic grasses that are highly flammable and often replace native plants as burned landscapes regrow. To prevent invasive species from dominating post-burn landscapes, land managers are increasingly applying seeds of native plants to suppress invasive plants and encourage ecosystem recovery. However, there is still much to learn about the ability of seeded species to establish and suppress flammable invaders. It is also unclear how previous human-caused landscape changes, such as nitrogen pollution or the removal of shrubs (a common practice in western USA rangelands), affect the success of native seed additions and plant recovery from fire. This study addresses these issues by building on a long-term experiment investigating the legacy effects of past nitrogen pollution and shrub removal in a highly invaded sagebrush steppe ecosystem at Idaho State University’s Barton Ecological Research Area in Pocatello, ID. This experiment burned in a wildfire in August, 2020, providing a unique opportunity to evaluate how a history of nitrogen pollution and shrub removal influences plant recovery from wildfire. We developed three native seed mixes intended to suppress invasive plants, particularly flammable annual grasses, and in April, 2021, we sowed the experimental mixes into research plots within the original experiment. To measure the initial effects of the experimental seed additions and the legacy effects of previous nitrogen pollution and shrub removal, we collected the data provided here during the summer of 2021, the first growing season following the wildfire. We established 240 monitoring quadrats (1 m²) within the original experiment, dividing the quadrats between areas where shrubs had formerly been (evidenced by stumps) and intershrub areas. At a microhabitat scale, the presence of shrubs alters soil properties and can create legacy effects after shrub death, and we were int
Neural Overlap in Item Representations Across Episodes Impairs Context Memory
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Human hippocampal replay during rest prioritizes weakly learned information and predicts memory performance
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Perceptual decisions result from the continuous accumulation of memory and sensory evidence
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Visual working memory: Study one Task fMRI and Behavioural response
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Visual working memory: Study two Task fMRI and Behavioural response
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Briefly cueing memories leads to suppression of their neural representations
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Multiple-object tracking as atool for parametrically modulating memory reactivation
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Multiple interactive memory representations underlie the induction of false memory
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Adaptive memory distortions are predicted by feature representations in parietal cortex
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Longitudinal high-throughput TCR repertoire profiling reveals the dynamics of T cell memory formation after mild COVID-19 infection
<p>Processed TCRbeta and TCRalpha repertoires after mild COVID-19 (Version 2.0: day 85 timepoints added) infection, see preprint: <a href="https://www.biorxiv.org/content/10.1101/2020.05.18.100545v3">https://www.biorxiv.org/content/10.1101/2020.05.18.100545v3</a></p> <p>and GitHub repository: <a href="https://github.com/pogorely/Minervina_COVID">https://github.com/pogorely/Minervina_COVID</a></p> <p>Two donors (M and W), two biological replicates of PBMC (F1 and F2), CD4+, CD8+, and Memory subpopulations for each post-infection time points (day 15, 30, 37, 45, 85 post-infection), and pre-infection PBMC repertoires sampled in 2019 and 2018. </p>
NoSyms: A neural network approach to detecting data structures in raw memory
<p>This data was used for a experiments with graph convolutional neural networks for memory forensics as part of a bachelor thesis (included as pdf).<br> <br> Abstract:<br> <br> This work presents a neural network based approach for data structure detection in raw memory that does not require an entirely matching description of the target data structure. Instead, it’s merely necessary to provide multiple descriptions of data structures similar to the target as training data in the form of debugging symbols. The core contribution of this work is a formal description and implementation of encoding data structure definitions as well as raw memory contents such that they can be processed by graph convolutional neural networks. A description and implementation of a neural network meant to detect data structures in the memory contents of a Linux Kernel demonstrates the practical applicability of the described approach.<br> <br> The Code is available on GitHub <a href="https://github.com/NiklasBeierl/nosyms">https://github.com/NiklasBeierl/nosyms</a>.<br> <br> nokaslr_dump is the qemu memory snapshot used to test the model.<br> nokaslr.raw is the "raw" form of the snapshot as produced by Volatility 3's layerwriter plugin.<br> symbols-training-data contains the Volatility symbol JSON files from which training data was derived.<br> nokaslr_pointers.csv lists the kernel space pointers in the snapshot and<br> nokaslr_tasks.csv lists task structs in the snapshot. Both were extracted via a Volatility plugins that are included in the GitHub Repo.<br> vmlinux-5.4.0-58-generic.json is the symbol file for the kernel the snapshot was taken from.<br> other-symbols.zip contains symbol files I generated vor various other kernels but did not end up using, use at your own discretion.</p>
Storing single photons emitted by a quantum memory on a highly excited Rydberg state - Figure data
<p>The files contain the data associated with the paper Distante, Farrera et al. "Mapping single photons emitted by a quantum memory on a highly excited Rydberg state".</p> <p>The name of each file corresponds to the figure number (e.g. figure2a, figure3b, etc.) and the first element in each file (the first row) is the date in which the data has been taken. The data is the result of a pre-processing of histogram and time-stamping files in the experiment. On reasonable request, we can provide the raw data.</p> <p>The fits in the figures are referenced throughout the open-access paper, and can be found in the Supplementary Information of the publication.</p>
Raw experimental data for `Tailoring the Rotational Memory Effect in Multimode Fibers`
<p>Raw data for the article [**Tailoring the Rotational Memory Effect in Multimode Fibers**](https://arxiv.org/abs/2310.19337)</p><p>Measurement of transmission matrices and rotational memory effect for 4 segments of 50 micron core graded index multimode fibers with a numerical aperture of 0.2.</p>
Resistive switching in benzylammonium-based Ruddlesden–Popper layered hybrid perovskites for non-volatile memory and neuromorphic computing
<p><span>Structural, optoelectronic, and supplementary characterisation data for “</span><span>Resistive Switching in Benzylammonium-Based Ruddlesden-Popper Layered Hybrid Perovskites for Non-Volatile Memory and Neuromorphic Computing ”</span><span>, DOI:</span><span>10.1039/d3ma00618b</span><span>.</span></p>
cgiotis/palimpsest_memories: Dataset and Code Material for "Palimpsest Memories Stored in Memristive Synapses" Manuscript."
<p>Dataset and code material for the "Palimpsest Memories Stored in Memristive Synapses" manuscript, currently available on ArXiv. This repository currently contains instructions and the necessary dataset to recreate Figures 1 and 2 from the paper, as well as parametric code to recreate the simulation results and generate the relevant plots (Figures 3 and 4).</p>
Prioritization of semantic over visuo- perceptual aspects in multi-item working memory
<p>All data and code supporting Prioritization of semantic over visuo- perceptual aspects in multi-item working memory</p>
Numerical data for "Black Hole Metamorphosis and Stabilization by Memory Burden [arXiv:2006.00011]"
<p>This is the numerical data that belongs to the paper</p> <p>G. Dvali, L. Eisemann, M. Michel, S. Zell, <em>Black Hole Metamorphosis and Stabilization by Memory Burden</em>, <a href="https://doi.org/10.1103/PhysRevD.102.103523">Phys. Rev. D <strong>102</strong> (2020) 103523</a>, <a href="https://arxiv.org/abs/2006.00011">arXiv:2006.00011</a>.</p> <p>The numerical data is generated using the computer program <em>TimeEvolver</em>, which was presented in</p> <p>M. Michel, S. Zell, <em>TimeEvolver: A Program for Time Evolution With Improved Error Bound, </em><a href="https://doi.org/10.1016/j.cpc.2022.108374">Comput. Phys. Commun. <strong>277</strong> (2022) 108374</a>, <a href="https://arxiv.org/abs/2205.15346">arXiv:2205.15346</a>.</p> <p>In the following, equation numbers refer to the latest arXiv-version of <a href="https://arxiv.org/abs/2006.00011">arXiv:2006.00011</a>, where also all relevant definitions can be found. In this paper, the procedure for generating the data, which we summarize in the following, is also described in more detail.</p> <p>First, among the five parameters N<sub>c</sub>; <sup><span class="math-tex">\(\epsilon\)</span></sup><sub>m</sub>; C<sub>0</sub>; ∆N<sub>c</sub>; K all but one are fixed (according to eq. (36)). Then for different values of the remaining unfixed parameter - subsequently called X - the following 2-step process is performed.</p> <ol> <li>Time evolution is computed (with the initial state shown in eq. (35)) for many different values of the parameter C<sub>m</sub> in the interval [0;1] (sampling step 10<sup>-3</sup>). The results for X are stored in five folders called "X", the subfolders of which contain data for different values of X. For example, the subfolder "01" of the folder "C0" consists of data for C<sub>0</sub>=0.01. Please note that the folders "Cgap", "N0" and "Q" correspond to X=<span class="math-tex">\(\epsilon\)</span><sub>m</sub>, X=N<sub>c</sub> and X=K, respectively. Subsequently, "rewriting values" of C<sub>m</sub> are selected as those for which the amplitude of n<sub>0</sub> is sufficiently large (1.2 times than in the case C<sub>m</sub>=0). This is done in Mathematica-notebooks "_New.nb". Finally, rewriting values for different values of X are collected using Mathematica-notebooks with names that start on "_Meta". These notebooks generate the 5 plots shown in figures 4(a), 5(a), 6(a), 7(a) and 8(a).</li> <li>Next finer scans are performed around some of the rewriting values determined in step 1 (new sampling step 5 10<sup>-5</sup>). The results are stored in five folders called "XRates", where again subfolders correspond to different values of X. For example, the subfolder "01" of the folder "C0Rates" consists of finer scans in C<sub>m</sub> around rewriting value of C<sub>m</sub> for C<sub>0</sub>=0.01. Next, Mathematica-notebooks with the names "_New.nb" or "_NewRates.nb" are used to select around each rewriting value the C<sub>m</sub> that leads to the largest rate (see definition in <a href="https://arxiv.org/abs/2006.00011">arXiv:2006.00011</a>). Finally, these maximal rates for different values of X are collected using Mathematica-notebooks with names that start on "_Meta" and end on "Rates.nb". These notebooks generate the 5 plots shown in figures 4(b), 5(b), 6(b), 7(b) and 8(b).</li> </ol>
Memory reactivation in slow wave sleep enhances relational learning
<p>EEG raw files in Brain Product format (3 files per participant). Sleep EEG recordings from healthy volunters under TMR stimulation. </p> <p>Two conditions: up-going slow oscillation stimulation / down-going oscillation stimulations</p> <p>Two type of sounds: experimental (they hear them before) and control (completely new)</p> <p>Triggers: 100-112 up experimental sounds / >112 up control sounds / 1-12 down experimetal sounds / 12-100 down control</p> <p>Please cite:</p> <p>"Memory reactivation in slow wave sleep enhances relational learning" ( https://doi.org/10.1101/2022.03.29.486197 )</p> <p> </p>
Wikipedia. Events and collective memory detection dataset
<p>This is the data accompanying the code (https://github.com/mizvol/WikiBrain), required to reproduce results of "Wikipedia graph mining: dynamic structure of collective memory" paper (https://arxiv.org/abs/1710.00398).</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.