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66 results for “memory model”
Datasets from study: "Land surface observations boost temperature forecast skill: experiments using Long Short-Term Memory surrogate for physics-based models to assess potential predictability"
<p>This repository contains the datasets needed to reproduce the figures from manuscript: Land surface observations boost temperature forecast skill: experiments using Long Short-Term Memory surrogate for physics-based models to assess potential predictability.</p> <p>In this study, we examine the potential of land surface temperature and vegetation data, which are not routinely assimilated in NWP models, for enhancing temperature forecast skill. We build surrogate models for NWP using Long Short-Term Memory.</p>
Artifact for "Challenges in Empirically Testing Memory Persistency Models"
<p>Presented here are the litmus tests, auxiliary scripts, and output data constituting the artifact accompanying the paper titled 'Challenges in Empirically Testing Memory Persistency Models,' published in ICSE NIER'24. For detailed information, please refer to the README.</p>
Dataset of "Hysteresis, impedance and transients effects in halide perovskite solar cells and memory devices analysis by neuron-style models"
<p>This dataset supports the article published<em> </em>in the Advanced Energy Materials:</p> <p>"Hysteresis, impedance and transients effects in halide perovskite solar cells and memory devices analysis by neuron-style models"</p> <p> </p> <p>Raw data for the article "Hysteresis, impedance and transients effects in halide perovskite solar cells and memory devices analysis by neuron-style models". For further details see the readme.txt file.</p>
Finite strain continuum phenomenological model describing the shape-memory effects in multi-phase semi-crystalline networks
<p>This dataset comes from the following paper:</p> <p>Matteo Arricca, Nicoletta Inverardi, Stefano Pandini, Maurizio Toselli, Massimo Messori, Giulia Scalet, Finite strain continuum phenomenological model describing the shape-memory effects in multi-phase semi-crystalline networks, Journal of the Mechanics and Physics of Solids, 105955, 2024. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jmps.2024.105955" target="_blank" rel="noopener"><span><span>https://doi.org/10.1016/j.jmps.2024.105955</span></span></a></p> <p>It contains:</p> <ul> <li>"Notes.pdf" describing all the files uploaded</li> <li>. txt experimental data</li> </ul>
Figure 2. Decision Making Model based on Emotions, Drives, and Different Memory Types
<p>The first model that is briefly introduced is an approach inspired by research findings in<br> neuro-psychology and psychoanalysis (see figure 2). The model considers the environment, the<br> body, and the brain/mind of an artificial being. The environment and the body are perceived via<br> sensors. (Re-)actions can be carried out via actuators. The model focuses on the decision-making<br> process of how to (re-)act according to situations currently perceived. In this decision-making<br> process, there are involved concepts like emotions, desires, drives, and different types of memory as<br> well as concepts like the Ego-Id-Superego model of Sigmund Freud. There are considered fast<br> reactions in form of reflexes and slower reactions that need reflection, thinking, and planning. For a<br> detailed model description, see [14, 17, 18].</p>
Figures of the paper"A Conceptual Model of Chinese Oral Memory Based on Digital Humanities"
<p>Those are the figures used for the paper "A Conceptual Model of Chinese Oral Memory Based on Digital Humanities".</p>
from Memories of Cas Jeekel, friend, colleague, and role model - ZooKeys 156: 67-70 (20 December 2011) https://doi.org/10.3897/zookeys.156.2215
- C.A.W. Jeekel at home, 1979. Image courtesy L.A. Pereira.
Hybrid dynamic model for shape memory alloy linear and unimorph actuators
<p>Shape memory alloy morphing actuators are a type of soft actuator with many attractive properties. These actuators exhibit large deformation, small form factor, self-sense ability, and physical reservoir computing potential, while also being inexpensive. These morphing actuators are composed of active shape memory alloy wires and a passive base layer that is used to magnify the overall deflection. Although morphing actuators have great potential, the modeling of shape memory alloy actuators is difficult due to both shape memory alloy characteristics and the nonlinearity of the passive layer. Here, a hybrid dynamical model is proposed that couples the phase kinetics & thermal modeling for the shape memory alloy with a dynamic Cosserat nonlinear beam model. This hybrid model is benchmarked against linear and morphing experimental actuators. The model resulted in a root mean squared error of 1.48 mm and 1.63 mm for the morphing actuator configuration for two different actuators. This model can expand the capability and design of novel morphing actuators for a designed deformation profile for use in soft robotics.</p>
Building a realistic, scalable memory model with independent engrams using a homeostatic mechanism
<p>Code and data for our paper:</p> <p><a href="doi.org/10.3389/fninf.2024.1323203">Building a realistic, scalable memory model with independent engrams using a homeostatic mechanism</a><br>Marvin Kaster, Fabian Czappa, Markus Butz-Ostendorf, Felix Wolf</p>
Models & Unity Task for Shape of U: The non-monotonic relationship between object-location memory and expectedness
<p>These are the fitted models as well as the Unity task for <em>Shape of U: The non-monotonic relationship between object-location memory and expectedness</em> to appear <em>Psychological Science</em>.</p> <p>For more information please see <a href="https://github.com/JAQuent/schemaVR">here</a>. Non-registered versions of these files can also be downloaded via <a href="https://github.com/JAQuent/schemaVR/blob/master/downloads_and_moves_files_from_osf.R">this script</a>.</p>
SESMG scenario-files of the study "Model-based run-time and memory reduction for a mixed-use multi-energy system model with high spatial resolution"
<p>This dataset contains model scenario-files belonging to the publication "Model-based run-time and memory reduction for a mixed-use multi-energy system model with high spatial resolution".</p> <p>The individual scenarios can be executed and evaluated with the "Spreadsheet Energy System Model Generator" (<a href="https://github.com/chrklemm/SESMG">SESMG</a>) <a href="https://github.com/chrklemm/SESMG/tree/v0.4.0rc1">v0.4.0rc1</a></p> <p>The respective file names indicate to which model run mentioned in the main study the scenario-files belong. For model runs for which no sepparate scenario file exists, the scenario "reference.xlsx" with adjusted SESMG settings was used.</p> <p> </p>
[PLDI'23] Compound Memory Models: Artifact
<p>This is the artifact for the PLDI'23 Artifact Evaluation for the submission "Compound Memory Models". See README.md for more details.</p>
Modeling of pressure induced magnetic and magnetocaloric effects in dissipative magnetic shape memory alloy systems
<p>This article presents a coupled magneto-thermo-mechanical model of pressure-dependent Magneto-caloric Effect (MCE) and magnetization responses for polycrystalline Magnetic Shape Memory Alloys (MSMA). Coupled constitutive equations are derived from a Helmholtz free energy function in a consistent thermodynamic way. The hysteretic and dissipative characteristics of phase transformations in MSMAs are captured by the internal state variables approach with their evolution equations. The model is calibrated and validated with the existing experimental data. The validated constitutive model is then exploited to predict MCEs at different pressures and magnetic field levels. Some predicted results are compared with the available experimental data.</p>
Modeling of pressure induced magnetic and magnetocaloric effects in dissipative magnetic shape memory alloy systems
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Hybrid dynamic model for shape memory alloy linear and unimorph actuators
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Accommodating the role of site memory in dynamic species distribution models
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Tables of the paper"A Conceptual Model of Chinese Oral Memory Based on Digital Humanities"
<p>Those are the tables used for the paper "A Conceptual Model of Chinese Oral Memory Based on Digital Humanities".</p>
Models and Predictions for "Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network"
<p><strong>Models and Predictions for the paper "Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network"</strong></p> <p>GitHub: <a href="https://github.com/gauchm/mts-lstm">https://github.com/gauchm/mts-lstm</a></p> <p><strong>Results</strong></p> <p>The file `results.tar.gz` contains:</p> <ul> <li>ensembled predictions for all models (generated from the models in `models/` using the <a href="https://neuralhydrology.readthedocs.io/en/latest/api/neuralhydrology.utils.nh_results_ensemble.html">`nh-results-ensemble` command</a>). These predictions were used in the `results-analysis.ipynb` and `odelstm-analysis.ipynb` notebooks on the GitHub repository for the paper.</li> <li>the NWM predictions <ul> <li>`nwm_chrt_v2_1h.p` contains hourly NWM predictions for the CAMELS basins between 1993 and 2007. The file is derived from the reanalysis on <a href="https://docs.opendata.aws/nwm-archive/readme.html">aws</a>.</li> <li>`nwm_results.p` is derived from `nwm_chrt_v2_1h.p` and contains hourly and day-aggregated results and performance metrics for the test period of our paper.</li> </ul> </li> <li>a file `signatures.p` with hydrologic signatures that were calculated from the models' predictions. These signatures were used in the `results-analysis.ipynb` notebook on the GitHub repository for the paper.</li> </ul> <p><strong>Models</strong></p> <p>The tar.gz files prefixed with `models-` contain the trained MTS-LSTM, sMTS-LSTM, and ODE-LSTM models from our experiments. For each experiment, there exist 10 model setups (one for each random seed).<br> Besides the trained models, each model's tar.gz also contains the predictions on the test or validation perod and the configuration file used to train the model.</p> <p><em>MTS-LSTM</em></p> <ul> <li>`mtslstm_seed*` -- the MTS-LSTM from the benchmarking section of the paper (using one forcings product, trained on daily and hourly data)</li> <li>`mtslstm_multiforcing_seed*` -- the MTS-LSTM from the section on per-timescale input data, experiment "multi-forcing B" (using just NLDAS as hourly inputs)</li> <li>`mtslstm_multiforcing_dailyhourly_seed*` -- the MTS-LTSM from the section on per-timescale input data, experiment "multi-forcing A" (ingesting daily forcings into the hourly model)</li> <li>`mtsltsm_136H1D_seed*` -- the MTS-LTSM from the section on prediction at other timescales (1-, 3-, 6-hourly and daily predictions)</li> </ul> <p><em>sMTS-LSTM</em></p> <ul> <li>`smtslstm_seed*` -- the sMTS-LSTM from the benchmarking section of the paper (using one forcings product, trained on daily and hourly data)</li> <li>`smtslstm_noregularization_seed*` -- the sMTS-LSTM from the section on cross-timescale consistency (trained without regularization)</li> </ul> <p><em>Time-Continuous Experiments</em></p> <p>The file `models-timecontinuous.tar.gz` contains one sub-folder per basin on which we conducted our initial experiments.<br> Each basin directory contains:</p> <ul> <li>Experiment A (trained on daily and 12-hourly, evaluated on hourly): <ul> <li>`odelstm_a_seed*` -- the ODE-LSTM from experiment A</li> <li>`mtslstm_a_seed*` -- the MTS-LSTM from experiment A</li> </ul> </li> <li>Experiment B (trained on hourly and 3-hourly, evaluated on daily) <ul> <li>`odelstm_b_seed*` -- the ODE-LSTM from experiment B</li> <li>`mtslstm_b_seed*` -- the MTS-LSTM from experiment B</li> </ul> </li> </ul> <p><em>Related Datasets: </em><a href="https://doi.org/10.5281/zenodo.4072700">https://doi.org/10.5281/zenodo.4072700</a> contains the hourly NLDAS forcings and USGS streamflow required to use the models from this dataset.</p>
Data from: Can collective memories shape fish distributions? A test, linking space-time occurrence models and population demographics
Social learning can be fundamental to cohesive group living, and schooling fishes have proven ideal test subjects for recent work in this field. For many species, both demographic factors, and inter- (and intra-) generational information exchange are considered vital ingredients in how movement decisions are reached. Yet key information is often missing on the spatial outcomes of such decisions, and questions concerning how migratory traditions are influenced by collective memory, density-dependent and density-independent processes remain open. To explore these issues, we focused on Atlantic herring (Clupea harengus), a long-lived, dense-schooling species of high commercial importance, noted for its unpredictable shifts in winter distribution, and developed a series of Bayesian space-time occurrence models to investigate wintering dynamics over 23 years, using point-referenced fishery and survey records from Icelandic waters. We included covariates reflecting local-scale environmental factors, temporally-lagged prey biomass and recent fishing activity, and through an index capturing distributional persistence over time, derived two proxies for spatial memory of past wintering sites. The previous winter's occurrence pattern was a strong predictor of the present pattern, its influence increasing with adult population size. Although the mechanistic underpinnings of this result remain uncertain, we suggest that a 'wisdom of the crowd' dynamic may be at play, by which navigational accuracy towards traditional wintering sites improves in larger and/or denser, better synchronized schools. Wintering herring also preferred warmer, fresher, moderately stratified waters of lower velocity, close to hotspots of summer zooplankton biomass, our results indicative of heightened environmental sensitivity in younger cohorts. Incorporating spatiotemporal correlation structure and time-varying regression coefficients improved model performance, and validation tests on independent observations one-year ahead illustrate the potential of uniting demographic information and non-stationary models to quantify both the strength of collective memory in animal groups and its relevance for the spatial management of populations.
Data from: Energy benefits and emergent space use patterns of an empirically parameterized model of memory-based patch selection
Many species frequently return to previously visited foraging sites. This bias towards familiar areas suggests that remembering information from past experience is beneficial. Such a memory-based foraging strategy has also been hypothesized to give rise to restricted space use (i.e. a home range). Nonetheless, the benefits of empirically derived memory-based foraging tactics and the extent to which they give rise to restricted space use patterns are still relatively unknown. Using a combination of stochastic agent-based simulations and deterministic integro-difference equations, we developed an adaptive link (based on energy gains as a foraging currency) between memory-based patch selection and its resulting spatial distribution. We used a memory-based foraging model developed and parameterized with patch selection data of free-ranging bison Bison bison in Prince Albert National Park, Canada. Relative to random use of food patches, simulated foragers using both spatial and attribute memory are more efficient, particularly in landscapes with clumped resources. However, a certain amount of random patch use is necessary to avoid frequent returns to relatively poor-quality patches, or avoid being caught in a relatively poor quality area of the landscape. Notably, in landscapes with clumped resources, simulated foragers that kept a reference point of the quality of recently visited patches, and returned to previously visited patches when local patch quality was poorer than the reference point, experienced higher energy gains compared to random patch use. Furthermore, the model of memory-based foraging resulted in restricted space use in simulated landscapes and replicated the restricted space use observed in free-ranging bison reasonably well. Our work demonstrates the adaptive value of spatial and attribute memory in heterogeneous landscapes, and how home ranges can be a byproduct of non-omniscient foragers using past experience to minimize temporal variation in energy gains.
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.