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768 results for “reinforcement”
Data from: Behavioral isolation due to cascade reinforcement in Lucania killifish
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Dynamic robotic tracking of underwater targets using reinforcement learning
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Data from: Culling-induced perturbation of social networks of wild geese reinforces rather than disrupts associations among survivors
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Warmer temperatures reinforce negative land-use impacts on bees, but not on higher insect trophic levels
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Learning contact-rich whole-body manipulation with example-guided reinforcement learning
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Reinforcement-based processes actively regulate motor exploration along redundant solution manifolds
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Conflict over fertilization underlies the transient evolution of reinforcement
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Integration of environmental DNA metabarcoding technique to reinforce fish biodiversity assessments in seagrass ecosystems: A case study of Gazi Bay Seagrass meadows
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Data from: Sociality of a threatened ‘solitary’ marsupial before and after population reinforcement: associations and links to breeding success
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Wolbachia endosymbionts manipulate the self-renewal and differentiation of germline stem cells to reinforce fertility of their fruit fly host
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Offline replay supports planning in human reinforcement learning
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Data for: Mechanisms of root-reinforcement in soils: an experimental methodology using four-dimensional X-ray computed tomography and digital volume correlation
<p>Collection of data files used in the paper titled: Mechanisms of root-reinforcement in soils: an experimental methodology using four-dimensional X-ray computed tomography and digital volume correlation.</p> <p>Additional dataset which covers the noise study CT scans and digital volume correlation noise studies can be found in DOI: <a href="http://www.doi.org/10.5281/zenodo.3352268">10.5281/zenodo.3361832</a></p> <p><strong>Data contains the following:</strong></p> <ul> <li>X-ray CT data for the interrupted direct shear tests of a soil sample containing a Willow plant. The first file is the specimen scanned unloaded, followed by seven incremental shear load steps to 20 mm shear displacement. Files are 8-bit unsigned and 1800x1800x1600px. Voxel resolution is 0.04642 mm. <ol> <li><strong>20180430_HUTCH_1839_DJB_willow_C_8-bit_1800x1800x1600.raw</strong></li> <li><strong>20180430_HUTCH_1839_DJB_willow_C_Load_A_8-bit_1800x1800x1600.raw</strong></li> <li><strong>20180430_HUTCH_1839_DJB_willow_C_Load_B_8-bit_1800x1800x1600.raw</strong></li> <li><strong>20180430_HUTCH_1839_DJB_willow_C_Load_C_8-bit_1800x1800x1600.raw</strong></li> <li><strong>20180430_HUTCH_1839_DJB_willow_C_Load_D_8-bit_1800x1800x1600.raw</strong></li> <li><strong>20180430_HUTCH_1839_DJB_willow_C_Load_E_8-bit_1800x1800x1600.raw</strong></li> <li><strong>20180430_HUTCH_1839_DJB_willow_C_Load_F_8-bit_1800x1800x1600.raw</strong></li> <li><strong>20180430_HUTCH_1839_DJB_willow_C_Load_G_8-bit_1800x1800x1600.raw</strong></li> </ol> </li> <li>Direct shear vs. displacement data is presented in an Excel spreadsheet: <ul> <li><strong>All_load_data_with_reducing_area.xlsx</strong></li> </ul> </li> <li>Normal and shear strain DVC data which has been averaged and plotted against specimen depth is presented in an Excel spreadsheet: <ul> <li><strong>Average_slice_vs_depth_data_all_samples_all_loads3.xlsx</strong></li> </ul> </li> <li>CT scan metadata giving information to the scan settings, voxel resolution, etc. is contained in a .zip file which consists of .xtekct and .XML files generated from the Nikon CT scanner. <ul> <li><strong>CT_Scan_Metadata.zip</strong></li> </ul> </li> <li>Drawings and Solidworks CAD files of the direct shear test rig are contained within the .zip file. There are a number of parts to the assembly. The file SSSB1003-5.SLDASM contains the complete assembly of the direct shear rig and will help identify the part names. The folder CAD_Drawings contains pdf documents of the part drawings. <ul> <li><strong>Direct_Shear_Drawings_Solidworks_Files.zip</strong></li> </ul> </li> <li>Tabulated DVC data taken at 32x32x32px subset size is contained inside a .zip file. These consist of tab separated .dat files from unloaded (data_1.dat) through incremental load steps Load A, Load B... Load G, (data_2.dat, data_3.dat... data_8.dat). The structure of the .dat file contains columns of data with each column number corresponding to the following: (1) x, (2) y (3) z, (4) vx, (5) vy, (6) vz, (7) exx, (8) eyy, (9) ezz, (10) exy, (11) exz, (12) eyz, (13) volumetric strain, (14) is valid. Columns 1-3 are subset positions in mm, 4-6 are displacements in mm, 7-9 are normal strains, 10-12 are shear strains, 13 is volumetric strain and 14 is a binary value indicating if the subset is valid. <ul> <li><strong>DVC_Load_Data_Willow_C.zip</strong></li> </ul> </li> <li>Tabulated DVC data applied to the load step CT data at 32, 48, 64, 96, 128 pixels is presented in the .zip files containing .dat tab separated files. The structure of the .dat files is described in the previous bullet point. These files were used to construct the noise study applied to incrementally loaded CT data by sampling regions away from the shear zone where the strain signals are close to zero. <ul> <li><strong>DVC_Noise_Study_Data_Load_Steps_Willow_C.zip</strong></li> </ul> </li> <li>Processed noise study data which compares the effects of DVC subset size is presented in the .xlsx file. This file contains both the controlled noise experiment data (stationary, magnification and rigid body motion) and noise study data applied to incrementally loaded data using sampled regions away from the shear zone where the strain signals are close to zero. <ul> <li><strong>Willow_C_Processed_Noise_study2.xlsx</strong></li> </ul> </li> <li>Tabulated data which compares the local x displacement vs depth profile from DVC and direct measurement of root position is contained in the following Excel spreadsheet file: <ul> <li><strong>X_Displacement_Root_and_DVC_Comparison_Willow C.xlsx</strong></li> </ul> </li> </ul>
Neurogenomic divergence during speciation by reinforcement of mating behaviors in chorus frogs (Pseudacris) – De novo reference transcriptome raw data, contigs and gene annotations
<p>RNA-Seq raw data used in the assembly and annotation of a reference transcriptome for the Upland Chorus Frog, <em>Pseudacris feriarum</em>. Raw data were obtained by sequencing of four tissue types: Brain, eyes, testis, and somatic. Assembled contigs (Trinity) and gene annotations (Trinotate) are also provided.</p>
Training data from Reinforced SciNet
<p><strong>Summary:</strong></p> <p>The results from the training of neural networks in v2 of <a href="https://github.com/HendrikPN/reinforced_scinet">Reinforced SciNet</a>, published partially in v2 of the paper <a href="https://arxiv.org/abs/2001.00593">Operationally meaningful representations of physical systems in neural networks</a>.</p> <p> </p> <p><strong>File description:</strong></p> <p>results.txt - The results from the training during<em> reinforcement learning</em>.</p> <p>results_loss.txt - The loss from the training during <em>representation learning</em>.</p> <p>selection.txt - The noise level of latent neurons during <em>representation learning</em>.</p> <p> </p> <p><strong>Parameters: Reinforcement Learning</strong></p> <p>Server parameters</p> <ul> <li>21 workers, 2 predictors, 1 trainer each</li> <li>3M episodes</li> </ul> <p>Training parameters</p> <ul> <li>glow: 0.1</li> <li>gamma: 0.01</li> <li>softmax: 0.5</li> <li>learning rate: 0.00005</li> <li>reward clipping: 1.0e-7</li> </ul> <p>Network parameters</p> <ul> <li>DPS model:<br> {'env1': [128, 128, 128, 128, 64, 32],<br> 'env2': [128, 128, 128, 128, 64, 32],<br> 'env3': [128, 128, 128, 128, 64, 32]}</li> </ul> <p> </p> <p><strong>Parameters: Representation Learning</strong></p> <p>Server parameters</p> <ul> <li>21 workers, 2 predictors, 1 trainer each</li> <li>5M episodes</li> </ul> <p>Training parameters</p> <ul> <li>learning rate: 0.0001</li> <li>reward clipping: 1.0e-7</li> <li>selection discount: 0.04</li> <li>minimization discount: 0.02</li> <li>ae discount: 10.0</li> <li>agent discount: 1.</li> <li>reward rescaling: 10</li> <li>predicted actions: 1</li> <li>training data: 200K</li> </ul> <p>Network parameters</p> <ul> <li>Prediction model:<br> {'env1': [64, 128, 128, 128, 128, 64, 32],<br> 'env2': [64, 128, 128, 128, 128, 64, 32],<br> 'env3': [64, 128, 128, 128, 128, 64, 32]}</li> <li>Encoder model: [128, 128, 64, 32]</li> <li>Decoder model: [32, 64, 128, 128, 128]</li> </ul>
Reinforcement in the banded darter Etheostoma zonale: the effect of sex and sympatry on preferences
<p class="BodyA">Reinforcement occurs when selection against hybrid offspring strengthens behavioral isolation between parental species and may be an important factor in speciation. Theoretical models and experimental evidence indicate that both female and male preferences can be strengthened upon secondary contact via reinforcement. However, the question remains whether this process is more likely to affect the preferences of one sex or the other. Males of polygynous species are often predicted to exhibit weaker preferences than females, potentially limiting the ability for reinforcement to shape male preferences. Yet, in darters (Percidae: <i>Etheostoma</i>), male preference for conspecific mates appears to arise before female preferences during the early stages of allopatric speciation, and research suggests that male, but not female, preferences become reinforced upon secondary contact. In the current study, we aimed to determine whether the geographically widespread darter species <i>Etheostoma zonale </i>exhibits a signature of reinforcement,<i> </i>by comparing the strength of preference for conspecific mates between populations that are sympatric and allopatric with respect to a close congener, <i>E. barrenense</i>. We examined the strength of preference for conspecifics for males and females separately to determine if the preferences of one or both sexes have been strengthened by reinforcement. Our results show that both sexes of <i>E. zonale </i>from sympatric populations exhibit stronger conspecific preferences than <i>E. zonale </i>from allopatric populations, but that female preferences appear to be more strongly reinforced than male preferences. Results therefore suggest that reinforcement of female preferences may promote behavioral isolation upon secondary contact, even in a genus that is characterized by pervasive male mate choice.</p>
Deep Reinforcement Learning for END-To-END Local Motion Planning of Autonomous Aerial Robots in Unknown Outdoor Environments: Real-Time Flight Experiments
<p> </p> <p>Videos for the real flight tests and the simulation experiments </p>
Data from: Hybridization and postzygotic isolation promote reinforcement of male mating preferences in a diverse group of fishes with traditional sex roles
Behavioral isolation is thought to arise early in speciation due to differential sexual and/or natural selection favoring different preferences and traits in different lineages. Alternatively, behavioral isolation can arise due to reinforcement favoring traits and preferences that prevent maladaptive hybridization. In darters, female preference for male coloration has been hypothesized to drive speciation, because behavioral isolation evolves before F1 inviability. However, as with many long-lived organisms, the fitness of second generation hybrids has not been assessed because raising animals to adulthood in the lab is challenging. Recently, reinforcement of male preferences has been implicated in darters because male preference for conspecific females is high in sympatry but absent in allopatry in multiple species pairs. The hypothesis that reinforcement accounts for behavioral isolation in sympatry assumes that hybridization and postzygotic isolation are present. Here, we used genomic and morphological data to demonstrate that hybridization is ongoing between orangethroat and rainbow darters and used hybrids collected from nature to measure postzygotic barriers across two hybrid generations. We observed sex ratio distortion in adult F1s and a dramatic reduction in backcross survival. Our findings indicate that selection to avoid hybridization promotes the evolution of male-driven behavioral isolation via reinforcement in this system.
Data from: Multi-generational genetic consequences of reinforcement in a bird metapopulation
<p>Translocation of conspecific individuals to reduce extinction risk of small, isolated populations and prevent genetic depletion is a powerful tool in conservation biology. An important question is how the translocated individuals influence the long-term genetic composition of the recipient population. Here, we experimentally reinforced a house sparrow (<i>Passer domesticus</i>) population, and examined the impact of this translocation on allele frequencies, levels of heterozygosity and genetic differentiation over six cohorts. We found no permanent increase in the mean number of alleles across loci or levels of observed heterozygosity, but a few alleles private to the translocated individuals remained in the population and we found a short-term increase in heterozygosity. Consequently, genetic differentiation of the recipient population compared to the genetic composition prior to reinforcement was small. The limited genetic impact was due to combined effects of a small probability of establishment and low mating success for the translocated individuals, together with increased genetic drift in the recipient population. Our findings emphasize the importance of selection and genetic drift as forces that may decrease the genetic contribution of reinforcement, especially in small populations. Conservation managers should aim to improve habitat quality in the recipient population to reduce genetic drift following translocation and thereby avoid the need for continued reinforcement. Furthermore, by facilitating establishment success and selecting individuals expected to have high mating success, possibly indicated by sexually selected traits, genetic contribution of released individuals is increased which in turn will decrease reproductive skew and genetic drift.</p>
Quasi-Static Cyclic Tests of Two U-Shaped Reinforced Concrete Walls
<p>U-shaped or channel-shaped walls are frequently used as lateral strength providing members in reinforced concrete (RC) buildings since their form does not only provide strength and stiffness in any horizontal direction but is also well suited to accommodate elevator shafts or staircases. Despite this popularity, experimental results on the seismic behavior of U-shaped walls are scarce. For this reason a research program with the objective to provide additional experimental evidence for such walls under seismic loading was developed. It included quasi-static cyclic testing of two U-shaped walls at the structural engineering laboratories of the ETH Zurich. The walls were built at half-scale and designed for high ductility. The main difference between the two walls was their wall thickness. The project was chiefly focusing on the bending behavior in different directions and therefore the walls were subjected to a bi-directional loading regime. This article discusses the design of the test units, the test setup and the test predictions. Finally the main results are summarized in terms of failure mechanisms and force-displacement hystereses.</p>
Tests on Thin Reinforced Concrete Walls Subjected to In-plane and Out-of-plane Cyclic Loading - General
<p>The entire dataset describes an experimental campaign on five thin T-shaped reinforced concrete walls, including: details on the test units, materials, test setup, loading protocol, instrumentation, main features of each unit’s response, organization of the provided test data, and examples of derived data. The tests aimed at assessing the influence of wall thickness on member stability, the role of lap splices on damage distribution and displacement ductility, and the effects of the simultaneous application of out-of-plane loading on the member response.</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.