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99 results for “Transfer Learning”
A Deep Learning Based Cardiac Cine Segmentation Framework for Clinicians - Transfer Learning Application to 7T - Additional Data
<p><strong>Automatically Generated Segmentation Masks for Data Science Bowl Cardiac Challenge Data</strong></p> <p>These segmentation masks have been automatically generated with the <a href="https://github.com/baiwenjia/ukbb_cardiac">ukbb_cardiac</a> network by Bai et al. (2018, <a href="https://doi.org/10.1186/s12968-018-0471-x">doi:10.1186/s12968-018-0471-x</a>). In order to reproduce the data cleaning and conversion: download the data from <a href="https://www.kaggle.com/c/second-annual-data-science-bowl/data">Kaggle</a> and follow the data curation and conversion steps outlined in: <a href="https://github.com/chfc-cmi/cmr-seg-tl">cmr-seg-tl</a> and the associated publication (Link to be added).</p> <p>As this is a derived dataset please abide by the data use rules of the original dataset at kaggle and provide proper citation to the original data:</p> <blockquote> <p>The data for the Data Science Bowl is available for research and academic pursuits. Please cite as ‘Data Science Bowl Cardiac Challenge Data’.</p> </blockquote> <p>Please also cite the Bai et al. article for the algorithm and our publication for the data curation.</p>
Data from: Sharpening coarse-to-fine stereo vision by perceptual learning: asymmetric transfer across the spatial frequency spectrum
Neurons in the early visual cortex are finely tuned to different low-level visual features, forming a multi-channel system analysing the visual image formed on the retina in a parallel manner. However, little is known about the potential 'cross-talk' among these channels. Here, we systematically investigated whether stereoacuity, over a large range of target spatial frequencies, can be enhanced by perceptual learning. Using narrow-band visual stimuli, we found that practice with coarse (low spatial frequency) targets substantially improves performance, and that the improvement spreads from coarse to fine (high spatial frequency) three-dimensional perception, generalizing broadly across untrained spatial frequencies and orientations. Notably, we observed an asymmetric transfer of learning across the spatial frequency spectrum. The bandwidth of transfer was broader when training was at a high spatial frequency than at a low spatial frequency. Stereoacuity training is most beneficial when trained with fine targets. This broad transfer of stereoacuity learning contrasts with the highly specific learning reported for other basic visual functions. We also revealed strategies to boost learning outcomes 'beyond-the-plateau'. Our investigations contribute to understanding the functional properties of the network subserving stereovision. The ability to generalize may provide a key principle for restoring impaired binocular vision in clinical situations.
Data from: Direct transfer of learned behaviour via cell fusion in non-neural organisms
Cell fusion is a fundamental phenomenon observed in all eukaryotes. Cells can exchange resources such as molecules or organelles during fusion. In this paper, we ask whether a cell can also transfer an adaptive response to a fusion partner. We addressed this question in the unicellular slime mould Physarum polycephalum, in which cell–cell fusion is extremely common. Slime moulds are capable of habituation, a simple form of learning, when repeatedly exposed to an innocuous repellent, despite lacking neurons and comprising only a single cell. In this paper, we present a set of experiments demonstrating that slime moulds habituated to a repellent can transfer this adaptive response by cell fusion to individuals that have never encountered the repellent. In addition, we show that a slime mould resulting from the fusion of a minority of habituated slime moulds and a majority of unhabituated ones still shows an adaptive response to the repellent. Finally, we further reveal that fusion must last a certain time to ensure an effective transfer of the behavioural adaptation between slime moulds. Our results provide strong experimental evidence that slime moulds exhibit transfer of learned behaviour during cell fusion and raise the possibility that similar phenomena may occur in other cell–cell fusion systems.
How do students respond to different gestures produced by animated pedagogical agents? An investigation of attention, narrative recall, and knowledge transfer within a personalized learning context
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Predicting pore structure and oil-bearing characteristics in saline-alkaline lacustrine shale strata via transfer learning
<p>玛湖凹陷凤城组样品的原始数据和吉木萨凹陷芦草沟组样品的原始数据</p>
Predicting pore structure and oil-bearing characteristics in saline-alkaline lacustrine shale strata via transfer learning
<p>玛湖凹陷凤城组和吉木萨尔凹陷芦草沟组样本的原始数据,以及核心 Python 代码。</p>
Transfer learning evaluation on precipitation datasets
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Advanced Iterative Model for Lumpy Skin Disease Prediction Using Fine-grained Feature Fusion and Adaptive Transfer Learning
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Probabilistic Emulation of the Community Radiative Transfer Model Using Machine Learning
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Code and Data for Transfer Learning for Multi-material Classification of Transition Metal Dichalcogenides with Atomic Force Microscopy
<p>The data consists of atomic force microscopy (AFM) images of metal organic chemical deposition (MOCVD) grown transition metal dichalcogenides (TMDs), MoS2, WS2, WSe2, MoSe2, and Mo-WSe2, used for the results reported in the manuscript "Transfer Learning for Multi-material Classification of Transition Metal Dichalcogenides with Atomic Force Microscopy". The TMDs are grown at the Penn State's 2D crystal consortium (2DCC). The raw data is also available on the LiST (https://data.<br>2dccmip.org/Rut1mMC8u25M). The file names have the format: imageSNo_TMD_sampleLabel_sampleId_set.tif, where SNo, TMD, sampleLabel, sampleId, and set are serial numbers (1, 2, 3, ...), class of TMD (e.g. MoS2, WS2, ...), sample label, sample id, and train or test set, as used in the manuscript. There could be multiple images from the same samples (taken from the center, edges, etc, of wafer). Images from the same sample have the same sample label and sample id. In using the data, it is recommended that the same sample is not present in more than one data set to avoid data leakage. Additionally, github_static consists of the codes used to generate the results reported in the manuscript.</p>
HSI maize sample data for transfer learning
<p>HSI maize sample data for transfer learning</p>
A federated learning framework based on transfer learning and knowledge distillation for targeted advertising-Ad Display/Click Data on Taobao.com dataset
<p>https://tianchi.aliyun.com/dataset/56</p>
Data from: Sharpening coarse-to-fine stereo vision by perceptual learning: asymmetric transfer across the spatial frequency spectrum
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Data from: Evidence for absence of bilateral transfer of olfactory learned information in Apis dorsata and Apis mellifera
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Data from: Direct transfer of learned behaviour via cell fusion in non-neural organisms
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DeepC: Predicting chromatin interactions using megabase scaled deep neural networks and transfer learning (NG Capture-C)
GEO Series GSE137435. Homo sapiens. 4 samples. Type: Other.
DeepC: Predicting chromatin interactions using megabase scaled deep neural networks and transfer learning (Tiled-C)
GEO Series GSE137436. Homo sapiens. 2 samples. Type: Other.
Transfer learning of an in vivo-derived senescence signature identifies conserved tissue-specific senescence across species and diverse pathologies [bulk RNA-seq]
GEO Series GSE199864. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Decomposing cell identity for transfer learning across cellular measurements, platforms, tissues, and species.
GEO Series GSE118880. Mus musculus. 25 samples. Type: Expression profiling by high throughput sequencing.
Transfer learning enables identification of multiple types of RNA modification using nanopore direct RNA sequencing
GEO Series GSE227087. Oryza sativa; synthetic construct. 8 samples. Type: Expression profiling by high throughput sequencing.
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.