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19 results for “flexibility of learning”
Backyard Beetles and Pollinators Dataset - EREN/NEON Flexible Learning Project
<p>This dataset comes from the EREN-NEON flexible learning project 'Backyard Beetles and Pollinators.' It can be used for teaching field, computational, or hybrid courses. The dataset is standardized, visual observations of insect plant visitors, identified to standard functional groups, to indirectly assess pollination and construct plant-pollinator interaction networks. Insects were identified to morphospecies in the field using reference images. We also collected information about the flowers the insects were observed on - including functional type information about the color, size, and type of flower - as well as cover. This information is part of an ongoing course-based undergraduate research project to both teach about plants, insects, and functional biodiversity in a flexible and inclusive way - while collaboratively assessing interaction networks across landscapes and time. </p> <p>To use the flexible lesson materials or join the collaboration, get more information here: https://erenweb.org/eren-neon-flexible-learning-projects/ Or contact the project lead, Dr. Stack Whitney, directly at kxwsbi [at] RIT [dot] edu. </p>
Training dataset used in the magazine paper entitled "A Flexible Machine Learning-Aware Architecture for Future WLANs"
<p><a href="https://arxiv.org/pdf/1910.03510.pdf"><strong>A Flexible Machine Learning-Aware Architecture for Future WLANs</strong></a></p> <p><strong>Authors: </strong>Francesc Wilhelmi, Sergio Barrachina-Muñoz, Boris Bellalta, Cristina Cano, Anders Jonsson & Vishnu Ram.</p> <p><strong>Abstract: </strong>Lots of hopes have been placed in Machine Learning (ML) as a key enabler of future wireless networks. By taking advantage of the large volumes of data generated by networks, ML is expected to deal with the ever-increasing complexity of networking problems. Unfortunately, current networking systems are not yet prepared for supporting the ensuing requirements of ML-based applications, especially for enabling procedures related to data collection, processing, and output distribution. This article points out the architectural requirements that are needed to pervasively include ML as part of future wireless networks operation. To this aim, we propose to adopt the International Telecommunications Union (ITU) unified architecture for 5G and beyond. Specifically, we look into Wireless Local Area Networks (WLANs), which, due to their nature, can be found in multiple forms, ranging from cloud-based to edge-computing-like deployments. Based on ITU's architecture, we provide insights on the main requirements and the major challenges of introducing ML to the multiple modalities of WLANs.</p> <p><strong>Dataset description: </strong>This is the dataset generated for training a Neural Network (NN) in the Access Point (AP) (re)association problem in IEEE 802.11 Wireless Local Area Networks (WLANs). </p> <p>In particular, the NN is meant to output a prediction function of the throughput that a given station (STA) can obtain from a given Access Point (AP) after association. The features included in the dataset are:</p> <ol> <li>Identifier of the AP to which the STA has been associated.</li> <li>RSSI obtained from the AP to which the STA has been associated.</li> <li>Data rate in bits per second (bps) that the STA is allowed to use for the selected AP.</li> <li>Load in packets per second (pkt/s) that the STA generates.</li> <li>Percentage of data that the AP is able to serve before the user association is done.</li> <li>Amount of traffic load in pkt/s handled by the AP before the user association is done.</li> <li>Airtime in % that the AP enjoys before the user association is done.</li> <li>Throughput in pkt/s that the STA receives after the user association is done.</li> </ol> <p>The dataset has been generated through random simulations, based on the model provided in <a href="https://github.com/toniadame/WiFi_AP_Selection_Framework">https://github.com/toniadame/WiFi_AP_Selection_Framework</a>. More details regarding the dataset generation have been provided in <a href="https://github.com/fwilhelmi/machine_learning_aware_architecture_wlans">https://github.com/fwilhelmi/machine_learning_aware_architecture_wlans</a>.</p>
Great tits (Parus major) flexibly learn that herbivore-induced plant volatiles indicate prey location – an experimental evidence with two tree species
<p>1. When searching for food, great tits (Parus major) can use herbivore-induced plant volatiles (HIPVs) as an indicator of arthropod presence. Their ability to detect HIPVs was shown to be learned, and not innate, yet the flexibility and generalization of learning remains unclear. 2. We studied if, and if so how, naïve and trained great tits (Parus major) discriminate between herbivore-induced and non-induced saplings of Scotch elm (Ulmus glabra) and cattley guava (Psidium cattleyanum). We chemically analysed the used plants and showed that their HIPVs differed significantly and overlapped only in a few compounds. 3. Birds trained to discriminate between herbivore-induced and non-induced saplings preferred the herbivore-induced saplings of the plant species they were trained to. Naïve birds did not show any preferences. Our results indicate that the attraction of great tits to herbivore-induced plants is not innate, rather it is a skill that can be acquired through learning, one tree species at a time. 4. We demonstrate that the ability to learn to associate HIPVs with food reward is flexible, expressed to both tested plant species, even if the plant species has not coevolved with the bird species (i.e. guava). Our results imply that the birds are not capable of generalising HIPVs among tree species but suggest that they either learn to detect individual compounds or associate whole bouquets with food rewards.</p>
Adult-level learning and behavioural flexibility in T. scincoides
<p>Raw data files and R code</p> <p> </p>
Great tits (Parus major) flexibly learn that herbivore-induced plant volatiles indicate prey location – an experimental evidence with two tree species
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Cognitive flexibility supports the development of cumulative cultural learning in children
<p><span>The scale of cumulative cultural evolution (CCE), the improvement of cultural traits over generations via social transmission, is widely believed to be one of humans' most defining characteristics. Our capacity to build upon others' knowledge, skills, and technologies has produced the most diverse and complex technological repertoire on the planet. Despite growing interest in the field of CCE, the cognitive underpinnings supporting its development remain relatively understudied. In this study, we examined the role that cognitive flexibility plays in supporting cumulative cultural learning by studying U.S. children's (</span><span>N</span><span> = 167, 3-5-year-olds) propensity to relinquish an inefficient solution to a problem in favor of a more efficient alternative. We also examined whether children would resist revertin</span><span>g</span><span> back to earlier versions and omit redundant actions from previous behaviors. In contrast to previous work with chimpanzees, most children who first learned to solve a puzzlebox in a highly inefficient way switched to an observed, more efficient alternative. However, over multiple task interactions, 85% of children who did switch also reverted back to the original, inefficient method. Moreover, almost all children in a control condition (who first learned the efficient method before observing the inefficient method) switched to the inefficient method. This suggests that </span><span>children were keen to explore an alternative modeled solution but were overall conservative in reverting to their first-learned method across subsequent task interactions. We discuss these findings in the context of their implications for the cognitive ontogeny of CCE.</span></p>
Cognitive flexibility supports the development of cumulative cultural learning in children
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Food discovery is associated with different reliance on social learning and lower cognitive flexibility across environments in a food caching bird
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Structure Learning Training and Cognitive Flexibility
ClinicalTrials.gov study NCT05611788. IPD Sharing: YES. Countries: 1. Publications: 14.
Data from: Learning outdoors: Male lizards show flexible spatial learning under semi-natural conditions
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Data from: Unified pre- and postsynaptic long-term plasticity enables reliable and flexible learning
Although it is well known that long-term synaptic plasticity can be expressed both pre- and postsynaptically, the functional consequences of this arrangement have remained elusive. We show that spike-timing-dependent plasticity with both pre- and postsynaptic expression develops receptive fields with reduced variability and improved discriminability compared to postsynaptic plasticity alone. These long-term modifications in receptive field statistics match recent sensory perception experiments. Moreover, learning with this form of plasticity leaves a hidden postsynaptic memory trace that enables fast relearning of previously stored information, providing a cellular substrate for memory savings. Our results reveal essential roles for presynaptic plasticity that are missed when only postsynaptic expression of long-term plasticity is considered, and suggest an experience-dependent distribution of pre- and postsynaptic strength changes.
Data from: Hatching late in the season requires flexibility in the timing of song learning
Most songbirds learn their songs from adult tutors, who can be their father or other male conspecifics. However, the variables that control song learning in a natural social context are largely unknown. We investigated whether the time of hatching of male domesticated canaries has an impact on their song development and on the neuroendocrine parameters of the song control system. Average age difference between early- and late-hatched males was 50 days with a maximum of 90 days. Song activity of adult tutor males decreased significantly during the breeding season. While early-hatched males were exposed to tutor songs for on average the first 99 days, late-hatched peers heard adult song only during the first 48 days of life. Remarkably, although hatching late in the season negatively affected body condition, no differences between both groups of males were found in song characteristics either in autumn or in the following spring. Similarly, hatching date had no effect on song nucleus size and circulating testosterone levels. Our data suggest that late-hatched males must have undergone accelerated song development. Furthermore, the limited tutor song exposure did not affect adult song organization and song performance.
Data from: Generalization of learned preferences covaries with behavioral flexibility in red junglefowl chicks
The relationship between animal cognition and consistent among-individual behavioral differences (i.e. behavioral types, animal personality, or coping styles), has recently received increased research attention. Focus has mainly been on linking different behavioral types to performance in learning tasks. It has been suggested that behavioral differences could influence also how individuals use previously learnt information to generalize about new stimuli with similar properties. Nonetheless, this has rarely been empirically tested. Here we therefore explore the possibility that individual variation in generalization is related to variation in behavioral types in red junglefowl chicks (Gallus gallus). We show that more behaviorally flexible chicks have a stronger preference for a novel stimulus that is intermediate between two learnt positive stimuli compared to more inflexible chicks. Thus, more flexible and inflexible chicks differ in how they generalize. Further, behavioral flexibility correlates with fearfulness, suggesting a coping style, supporting that variation in generalization is related to variation in behavioral types. How individuals generalize affects decision making and responses to novel situations or objects, and can thus have a broad influence on the life of an individual. Our results add to the growing body of evidence linking cognition to consistent behavioral differences.
Data from: Discrimination reversal learning reveals greater female behavioural flexibility in guppies
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Data from: Unified pre- and postsynaptic long-term plasticity enables reliable and flexible learning
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Data from: Generalization of learned preferences covaries with behavioral flexibility in red junglefowl chicks
Open the record for dataset details and reuse information.
Data from: Hatching late in the season requires flexibility in the timing of song learning
Open the record for dataset details and reuse information.
An Observational Study, Called RegoFlex EU, to Learn More About the Use of Stivarga at Reduced Doses as Recommended (Flexible Dosing) to Treat People With Metastatic Colorectal Cancer in Real World Se
ClinicalTrials.gov study NCT05551039. IPD Sharing: NO. Countries: 1. Publications: 0.
Intervention-Induced Plasticity of Flexibility and Learning Mechanisms in ASD
ClinicalTrials.gov study NCT05131659. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
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.