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20 results for “one-shot”
MatSim Dataset and benchmark for one-shot visual materials and textures recognition
<p><strong>The MatSim Dataset and benchmark</strong></p> <p>Synthetic dataset and real images benchmark for visual similarity recognition of materials and textures.</p> <p>MatSim: a synthetic dataset, a benchmark, and a method for computer vision-based recognition of similarities and transitions between materials and textures focusing on identifying any material under any conditions using one or a few examples (one-shot learning).</p> <p>Based on the paper: <a href="https://arxiv.org/pdf/2212.00648.pdf">One-shot recognition of any material anywhere using contrastive learning with physics-based rendering</a></p> <p> </p> <p><strong>Benchmark_MATSIM.zip: </strong>contain the benchmark made of real-world images as described in the paper</p> <p><strong>Dataset Generation Scripts.zip: </strong>Contain the Blender (4.1) Python scripts used for generating the dataset<br><br><a href="https://zenodo.org/record/7390166/files/MatSim_object_train_split_1.zip?download=1"><strong>MatSim_object_train_split_1,2,3....zip:</strong> </a>Contain a subset of the synthetics dataset for images of CGI images materials on random objects as described in the paper.</p> <p><strong>MatSimTrainObjectsNearField_.zip </strong>Contain train sets with near fieldlight sources</p> <p><strong><a href="https://zenodo.org/record/7390166/files/MatSim_Vessels_Train_1.zip?download=1">MatSim_Vessels_Train_1,2,3....zip </a></strong><a href="https://zenodo.org/api/files/020f90b2-7c41-44ad-86e3-69257884a569/MatSim_object_train_split_1.zip"><strong>:</strong> </a>Contain a subset of the synthetics dataset for images of CGI images materials inside transparent containers as described in the paper.<br><br><strong>*Note: these are subsets of the dataset; the full dataset can be found at:</strong><br><a href="https://e1.pcloud.link/publink/show?code=kZIiSQZCYU5M4HOvnQykql9jxF4h0KiC5MX">https://e1.pcloud.link/publink/show?code=kZIiSQZCYU5M4HOvnQykql9jxF4h0KiC5MX</a></p> <p>or<br><a href="https://icedrive.net/s/A13FWzZ8V2aP9T4ufGQ1N3fBZxDF">https://icedrive.net/s/A13FWzZ8V2aP9T4ufGQ1N3fBZxDF</a></p> <p> </p>
Supplementary material for the paper: "One-shot procedures for efficient minimum compliance topology optimization"
<p>MATLAB codes and results used in the article: "One-shot procedures for efficient minimum compliance topology optimization", published in Structural and Multidisciplinary Optimization, 2024.</p>
A dataset for evaluating one-shot categorization of novel object classes
<p>From just a single example, we can derive quite precise intuitions about what other class members look like. This stands in stark contrast to machine learning algorithms, which typically require tens or even hundreds of thousands of examples to learn a new category. One of the most important open questions in our field is: How do humans achieve this? The stimuli and data provided here (in MATLAB format) are from thousands of crowd-sourced human responses to novel objects. The data can be used to test machine learning generalization as compared to human and also can be used as a test bed for various kinds of category learning models. </p>
Dataset: The TCR repertoire reconstitution in multiple sclerosis: comparing one-shot and continuous immunosuppressive therapies
<p>This dataset, containing TCRbeta-chain data, is the basis for the following publication in Frontiers in Immunology: The TCR repertoire reconstitution in multiple sclerosis: comparing one-shot and continuous immunosuppressive therapies. The file key can be found in the file: file_key.xlsx. Relevant methodological details maybe found in the corresponding publication.</p>
Intact protein barcoding enables one-shot identification of CRISPRi strains and their metabolic state
<p>Raw data of "Intact protein barcoding enables one-shot identification of CRISPRi strains and their metabolic state". Agilent .d files were converted to mzml files with MSconvert. The control strain is labelled H2, the CRISPRi strains are indicated by target gene names. The numbers indicate replicate 1,2 and 3.</p> <p> </p>
Videos For: One-shot recognition of any material anywhere using contrastive learning with physics-based renderin
<p>Demonstration Video:</p> <p>Image Recognition of Materials States And Types From Single Example Identifying the state of the material in the main video by matching it to a few reference images (top). Each image contains a specific material state or type. The best match image is marked green. Based on the net for identifying similarity between any material state and type. The code for the net and the trained model used for this is available at: https://github.com/sagieppel/Contrastive-learning-for-one-shot-materials-and-textures-similarity-recognition-from-images Paper : One-shot recognition of any material anywhere using contrastive learning with physics-based rendering. https://arxiv.org/abs/2212.00648</p>
Data from: One-shot learning and behavioral eligibility traces in sequential decision making
In many daily tasks we make multiple decisions before reaching a goal. In order to learn such sequences of decisions, a mechanism to link earlier actions to later reward is necessary. Reinforcement learning theory suggests two classes of algorithms solving this credit assignment problem: In classic temporal-difference learning, earlier actions receive reward information only after multiple repetitions of the task, whereas models with eligibility traces reinforce entire sequences of actions from a single experience (one-shot). Here we show one-shot learning of sequences. We developed a novel paradigm to directly observe which actions and states along a multi-step sequence are reinforced after a single reward. By focusing our analysis on those states for which RL with and without eligibility trace make qualitatively distinct predictions, we find direct behavioral (choice probability) and physiological (pupil dilation) signatures of reinforcement learning with eligibility trace across multiple sensory modalities.
LOLA - Evaluation Tables - One-Shot
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Comparative Study Between One-Shot Dilatation Versus Serial Dilatation Techniques for Access in Percutaneous Nephrolithotomy in Adult Patients
ClinicalTrials.gov study NCT07279389. IPD Sharing: YES. Countries: 1. Publications: 1.
ONE-SHOT Trial - Ultra-hypofractionated Single-dose SBRT for Prostate Cancer
ClinicalTrials.gov study NCT03294889. IPD Sharing: NO. Countries: 1. Publications: 1.
A Study on Safety of tDCS One-shot in UCP
ClinicalTrials.gov study NCT03137940. IPD Sharing: NO. Countries: 1. Publications: 1.
One-shot Ovarian Stimulation Protocol (One-shot)
ClinicalTrials.gov study NCT06635538. IPD Sharing: UNDECIDED. Countries: 1. Publications: 10.
Data from: One-shot learning and behavioral eligibility traces in sequential decision making
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Data from: One-shot dilation versus serial dilation technique for access in percutaneous nephrolithotomy: a systematic review and meta-analysis
Objective: The purpose of this study was to systematically review the outcomes of the use of one-shot dilation (OSD) and serial tract dilation for percutaneous nephrolithotomy (PCNL). Methods: A systematic review and meta-analysis was conducted. The randomized controlled trials (RCTs) included in the study were identified from EMBASE, MEDLINE, and the Cochrane Central Register of Controlled Trials. The last search was performed on April 30, 2018. Summary effects were calculated as risk ratios (RRs) with 95% confidence intervals (CIs) or mean differences (MDs) with 95% CIs. The endpoints included access time, fluoroscopy time, successful dilation rate, stone-free rate, postoperative decrease in hemoglobin levels, transfusion rate, complication rate, and length of postoperative hospital stay. Results: A total of 7 RCTs were included in the study, with clinical data reported for 697 patients. The overall access time was approximately 110 seconds shorter in the OSD group than in the serial dilation group (MD, -110.14; 95% CI, -161.99 to -58.30; P<0.0001). The fluoroscopy time was shorter with OSD in all RCTs. In addition, the decrease in postoperative hemoglobin levels was approximately 0.23 g/dl less in patients in the OSD group than in those in the serial dilation group (MD, -0.23; 95% CI, -0.39 to -0.07; P=0.004). No relationship was found between the successful dilation rate, stone-free rate, transfusion rate, or complication rate and the method of tract dilation. Conclusion: OSD is a safe and efficacious tract dilation technique that can reduce the access time, fluoroscopy time, and postoperative decrease in hemoglobin level. No difference was found in the successful dilation rate, stone-free rate, transfusion rate, or rate of complications between the OSD and serial dilation groups. The difference in the length of postoperative hospital stay was uncertain. OSD may be a better method of tract creation for PCNL.
Data from: One-shot dilation versus serial dilation technique for access in percutaneous nephrolithotomy: a systematic review and meta-analysis
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Data from: Group size effect on cooperation in one-shot social dilemmas II: curvilinear effect
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One-shot analysis of translated mammalian lncRNAs with AHARIBO
GEO Series GSE167865. Mus musculus. 5 samples. Type: Other.
Assessment of One-shot Cardiac-cervical-intracranial MRI in the Etiological Work up of Ischemic Stroke and Transient Ischemic Attacks
ClinicalTrials.gov study NCT04790357. IPD Sharing: NO. Countries: 1. Publications: 0.
Platelet Rich Plasma (PRP) One-Shot Injection For Supraspinatus Tendinosis
ClinicalTrials.gov study NCT04851951. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A versatile one-shot high-multiplex base editing strategy enables the generation of 20-plex knockout CAR T cells with enhanced anti-tumor efficacy
GEO Series GSE308760. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
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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.