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615 results for “Tuning”
MAGICC7 SSP carbon cycle output using AR6 tuning
<p>Output from the MAGICC7 carbon cycle model under the SSP scenarios with the configuration used in IPCC AR6. For further details on MAGICC and expectations re use of data and the model, please see https://magicc.org/download/magicc7.</p>
Data and code for "Tuning the lattice thermal conductivity in van-der-Waals structures through rotational (dis)ordering"
<p>This record contains neuroevolution potential (NEP) models for C, BN, and MoS<sub>2</sub> that have been constructed to model the potential energy surfaces of these materials in the presence of interlayer rotations. It also contains databases with the results from density functional theory calculations that were used for constructing the NEP models.</p> <p><strong>Databases</strong><br> The <code>*.db</code> files are databases with the results from density functional theory (DFT) calculations. These are sqlite databases in ase format, see <a href="https://wiki.fysik.dtu.dk/ase/tutorials/tut06_database/database.html">here</a> for more information. The <code>demo-database-access.py</code> script illustrates the most basic access.</p> <p><strong>Models</strong><br> The neuroevolution potential (NEP) models described in the publication can be found in the <code>nep-*.txt</code> files. They can be used in conjunction with the <a href="https://gpumd.org">GPUMD package</a>. The <a href="https://calorine.materialsmodeling.org">calorine package</a> provides a Python interface to GPUMD.</p> <p><strong>Primitive structures</strong><br> Several primitive structures in extended xyz format can be found in the <code>*.xyz</code> files. These structures have been relaxed using the NEP models included here. The <code>demo-for-using-structures-and-models.py</code> script illustrates how to access the structures and models.</p>
Learning to Do or Learning While Doing: Reinforcement Learning and Bayesian Optimisation for Online Continuous Tuning
<p>Dataset of optimisation runs performed for a study comparing reinforcement learning and Bayesian optimisation for online continuous tuning at the example of a linear particle accelerator tuning task.</p> <p> </p> <p><strong>Abstract of the Paper on the Study</strong></p> <p>Online tuning of real-world plants is a complex optimisation problem that continues to require manual intervention by experienced human operators. Autonomous tuning is a rapidly expanding field of research, where learning-based methods, such as Reinforcement Learning-trained Optimisation (RLO) and Bayesian optimisation (BO), hold great promise for achieving outstanding plant performance and reducing tuning times. Which algorithm to choose in different scenarios, however, remains an open question. Here we present a comparative study at the example of a routine task on a real particle accelerator, showing that RLO generally outperforms BO, but is not always the best choice. Based on the study’s results, we provide a clear set of criteria to guide the choice of algorithm for a given tuning task. These can ease the adoption of learning-based autonomous tuning solutions to the operation of complex real-world plants, ultimately improving the availability and pushing the limits of operability of these facilities, thereby enabling scientific and engineering<br> advancements.</p>
Datasets and example code for "Tuning perception and decisions to temporal context"
<p>Here are the datasets and examples of code of the paper "Tuning perception and decisions to temporal context"</p> <p> </p> <p>The datasets of both experiment are stored in a .csv file, organized in a long format.<br> Each row corresponds to a trial, while columns are variables.</p> <p>See the README.txt document for further information.</p>
Code repository for: Base editing mutagenesis maps functional alleles to tune human T cell activity
<p>Jupyter notebook and supplemental datasets required to created critical figures for the publication.</p>
Data from: Cochlear tuning in early aging estimated with three methods
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Sun compass neurons are tuned to migratory orientation in monarch butterflies
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Data from: Signal integration and adaptive sensory diversity tuning in Escherichia coli chemotaxis
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Data from: Efficiency of using electric toothbrush as an alternative to tuning fork for artificial buzz pollination is independent of instrument buzzing frequency
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The role of population size in folk tune complexity
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Geometric latches enable tuning of ultrafast, spring-propelled movements
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Asymmetric retinal direction tuning predicts optokinetic eye movements across stimulus conditions
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Fine-tuned phenotypes: Tadpole plasticity under 16 combinations of predators and competitors.
It is now well appreciated that most organisms can alter their phenotypes when faced with environmental variation. Decades of empirical investigations have documented hundreds of examples of phenotypic plasticity, yet most studies have focused on the presence or absence of a single environmental factor. As a result, we know little about how organisms respond to gradients of environmental factors (i.e., threshold responses vs. continuous responses), nor do we understand how organisms respond to combinations of environmental variables. I examined how larval wood frogs (Rana sylvatica) altered their behavior, morphology, and growth in response to combined gradients of predation and competition. Increased predation risk induced lower activity, deeper tails, and shorter bodies, which collectively caused slower growth. Increased competition caused slower growth which induced higher activity, shallower tails, and longer bodies. For both environmental gradients, the responses were frequently continuous rather than threshold responses. Moreover, predation and competition had interactive effects. Responses to predators were always larger under low competition than under high competition. Responses to competition were larger under low predation risk when predation and competition induced traits in the same direction, but larger under high predation risk when predation and competition induced traits in opposite directions. The results demonstrate that responses to phenotypically plastic traits can be fine-tuned to a wide variety of environmental combinations.
Dataset for: Quantum cascade lasers with discrete and non equidistant extended tuning tailored by simulated annealing
<p>Dataset used for article 10.1364/OE.27.026701.</p>
SATune: An Auto-tuning Approach for Configurable Static Analysis Tools
<p>Version 2.0.0 updates: Renamed tool to SATune.</p> <p>-------</p> <p>This contains both the executables and the data for our ICST 2021 submission, "SATune: An Auto-tuning Approach for Configurable Static Analysis Tools." The results are in results.tar.xz, and the SATune source code and experimental environment is is experiments.tar.xz.</p>
IPSL-CM6A-LR tuning , datasets for figures 5, 6 and 7
<p>Pre-processed data sets to re-do Fig. 5, 6 and 7 of the manuscript entitled "The tuning strategy of IPSL-CM6A-LR" by Mignot et al in revision for JAMES</p>
Data associated to the article "A semiclassical Thomas–Fermi model to tune the metallicity of electrodes in molecular simulations"
<p>Contains input files and data used to generate the figures of the article:</p> <p>A semiclassical Thomas–Fermi model to tune the metallicity of electrodes in molecular simulations</p> <p>Laura Scalfi, Thomas Dufils, Kyle G. Reeves, Benjamin Rotenberg and Mathieu Salanne, J. Chem. Phys. 153, 174704 (2020)</p> <p>https://doi.org/10.1063/5.0028232</p> <p>The folder typical_input_files contains typical MetalWalls input files used to perform the simulations.</p> <p>The folder DATA_FIGURES contains the processed data used to plot all the figures of the paper.</p>
Data set for ``Why is Differential Evolution Better than Grid Search for Tuning Defect Predictors?''
<p>One of the black arts of data mining is learning the magic parameters that control the learners. In software analytics, at least for defect prediction, several methods, like grid search and differential evolution(DE), have been proposed to learn those parameters. They’ve been proved to be able to improve learner performance.</p> <p>We want to evaluate which method can find better parameters in terms of performance score and runtime. This paper compares grid search to differential evolution, which is an evolutionary algorithm that makes extensive use of stochastic jumps around the search space. We find that the seemingly complete approach of grid search does no better, and sometimes worse, than the stochastic search. Yet, when repeated 20 times to check for conclusion validity, DE was over 210 times faster (6.2 hours for DE vs 54 days for grid search when both tuning Random Forest over 17 test data sets with F-measure as optimization objective).</p> <p>These results are puzzling: why does a quick partial search be just as effective as a much slower, and much more, extensive search? To answer that question, we turned to the theoretical optimization literature. Bergstra and Bengio conjecture that grid search is not more effective than more randomized searchers if the underlying search space is inherently low dimensional. This is significant since recent results show that defect prediction exhibits very low intrinsic dimensionality– an observation that explains why a fast method like DE may work as well as a seemingly more thorough grid search. This suggests, as a future research direction, that it might be possible to peek at data sets before doing any optimization in order to match the optimization algorithm to the problem at hand.</p>
Tuning CH3NH3Pb(I1-xBrx)3 Perovskite Oxygen Stability in Thin Films and Solar Cells
<p>The rapid development of organic-inorganic lead halide perovskites has resulted in high efficiency photovoltaic devices. However the susceptibility of these devices to degradation under environmental stress has so far hindered commercial development, requiring for example expensive device encapsulation. Herein, we have investigated the stability of CH3NH3Pb(I1-xBrx)3 [x = 0..1] thin film and solar cells under controlled humidity, light, and oxygen conditions. We show that higher bromide ratios increases tolerance to moisture, with x = 1 thin films being stable to 120 hr of moisture stress. Under light and dry air, partial bromide (x < 1) subsitution does not enhance film stability significantly, with the corresponding solar cells degrading within two hours. In contrast CH3NH3PbBr3 films show excellent stability, with device stability being limited by the organic interlayer. For these x = 1 films we show charge carriers are quenched in the presence of oxygen and form superoxide; however in contrast to perovskites containing iodide, this superoxide does not degrade the crystal. Our observations show that iodide limits the oxygen and light stability of CH3NH3Pb(I1-xBrx)3 perovskites, but that CH3NH3PbBr3 provides an opportunity to develop inherently stable high voltage photovoltaic devices and 4-terminal tandem solar cells.</p>
Fingerboard Markers 5f for Guitars that are Configured with All-Fourths Tuning
<p><strong>General Description:</strong></p><p>The files that are included in this archive ("Fingerboard_Markers_5f.zip", DOI: 10.5281/zenodo.10119514) describe a novel system of fingerboard markers that is intended for 6-string guitars that are configured with the following all-fourths tuning system: </p><ul><li>String number 6 is tuned to an open pitch of E2, which has a tuning frequency of 82.407 hertz (cycles per second).</li><li>String number 5 is tuned to an open pitch of A2, which has a tuning frequency of 110.000 hertz (cycles per second).</li><li>String number 4 is tuned to an open pitch of D3, which has a tuning frequency of 146.832 hertz (cycles per second).</li><li>String number 3 is tuned to an open pitch of G3, which has a tuning frequency of 195.998 hertz (cycles per second).</li><li>String number 2 is tuned to an open pitch of C4, which has a tuning frequency of 261.626 hertz (cycles per second).</li><li>String number 1 is tuned to an open pitch of F4, which has a tuning frequency of 349.228 hertz (cycles per second).</li></ul><p> </p><p>Furthermore, the fingerboard markers that are described by the files that are included in this archive ("Fingerboard_Markers_5f.zip", DOI: 10.5281/zenodo.10119514) were designed for guitars that exhibit the following specifications: </p><ul><li>Scale Length: 647.7 mm</li><li>String Spacing at the Nut: 7.04 mm</li><li>String Spacing at the Bridge: 10.5 mm</li><li>Number of frets: 22</li></ul><p> </p><p><strong>Definitions:</strong></p><p>If the fingerboard of a guitar is viewed while the longitudinal axis of the guitar neck is oriented vertically, with the nut at the top and the bridge at the bottom, then it is assumed herein that the strings of the guitar are numbered sequentially from string number 1 to string number 6, wherein string number 1 is positioned nearest to the right edge of the fingerboard and string number 6 is positioned nearest to the left edge of the fingerboard. </p><p>The term "String Spacing" herein denotes the distance between the centroidal axes of any two adjacent strings. </p><p> </p><p><strong>Contents of this Archive:</strong></p><ul><li>"Fingerboard_Markers_5f.DXF": Full-scale drawing of the complete system of fingerboard markers.</li><li>"Fingerboard_Markers_5f.pdf": Full-scale drawing of the complete system of fingerboard markers.</li><li>"Fingerboard_Markers_5f.svg": Full-scale drawing of the complete system of fingerboard markers.</li><li>"Fingerboard_Markers_5f_LICENSE.pdf": The license that applies to the system of fingerboard markers that is described by the contents of this archive ("Fingerboard_Markers_5f.zip", DOI: 10.5281/zenodo.10119514).</li><li>"Fingerboard_Markers_5f_Notes.pdf": Schematic diagram of the notes that surround each fingerboard marker.</li><li>"Fingerboard_Markers_5f_Notes.svg": Schematic diagram of the notes that surround each fingerboard marker.</li><li>"Fingerboard_Markers_5f_ReadMe.pdf": This document.</li></ul><p> </p><p><strong>Copyright and License:</strong></p><p>Copyright © 2023 Hart Honickman</p><p>Copyright in the system of fingerboard markers that is described by the contents of this archive ("Fingerboard_Markers_5f.zip", DOI: 10.5281/zenodo.10119514) is owned by Hart Honickman, and is licensed under Creative Commons Attribution 4.0 International. To view a copy of this license, view "Fingerboard_Markers_5f_LICENSE.pdf" or visit <a href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</a>. </p>
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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.