Time-Resolved Directional Brain–Heart Interplay Measurement Through Synthetic Data Generation Models
Sign Up to like & getrecommendations! Published in 2019 at "Annals of Biomedical Engineering"
DOI: 10.1007/s10439-019-02251-y
Abstract: Although a plethora of synthetic data generation models have been proposed to validate biomarkers of brain and cardiovascular dynamics separately, a limited number of computational methods estimating directed brain–heart information flow are currently available in… read more here.
Keywords: data generation; heart; synthetic data; generation models ... See more keywords
Pixel-Wise Crowd Understanding via Synthetic Data
Sign Up to like & getrecommendations! Published in 2021 at "International Journal of Computer Vision"
DOI: 10.1007/s11263-020-01365-4
Abstract: Crowd analysis via computer vision techniques is an important topic in the field of video surveillance, which has wide-spread applications including crowd monitoring, public safety, space design and so on. Pixel-wise crowd understanding is the… read more here.
Keywords: crowd; crowd understanding; understanding via; synthetic data ... See more keywords
Generation of synthetic training data for SEEG electrodes segmentation
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Computer Assisted Radiology and Surgery"
DOI: 10.1007/s11548-022-02585-4
Abstract: Stereoelectroencephalography (SEEG) is a minimally invasive surgical procedure, used to locate epileptogenic zones. An accurate identification of the metallic contacts recording the SEEG signal is crucial to ensure effectiveness of the upcoming treatment. However, due… read more here.
Keywords: synthetic data; training data; segmentation; seeg ... See more keywords
Next-generation deep learning based on simulators and synthetic data
Sign Up to like & getrecommendations! Published in 2022 at "Trends in Cognitive Sciences"
DOI: 10.1016/j.tics.2021.11.008
Abstract: Deep learning (DL) is being successfully applied across multiple domains, yet these models learn in a most artificial way: they require large quantities of labeled data to grasp even simple concepts. Thus, the main bottleneck… read more here.
Keywords: synthetic data; deep learning; next generation; based simulators ... See more keywords
Machine Learning with Enormous “Synthetic” Data Sets: Predicting Glass Transition Temperature of Polyimides Using Graph Convolutional Neural Networks
Sign Up to like & getrecommendations! Published in 2022 at "ACS Omega"
DOI: 10.1021/acsomega.2c04649
Abstract: In the present work, we address the problem of utilizing machine learning (ML) methods to predict the thermal properties of polymers by establishing “structure–property” relationships. Having focused on a particular class of heterocyclic polymers, namely… read more here.
Keywords: graph convolutional; synthetic data; methodology; machine learning ... See more keywords
Machine learning analysis of self-assembled colloidal cones.
Sign Up to like & getrecommendations! Published in 2022 at "Soft matter"
DOI: 10.1039/d1sm01466h
Abstract: Optical and confocal microscopy is used to image the self-assembly of microscale colloidal particles. The density and size of self-assembled structures is typically quantified by hand, but this is extremely tedious. Here, we investigate whether… read more here.
Keywords: self assembled; synthetic data; machine learning; colloidal cones ... See more keywords
Synthetic data sets for the identification of key ingredients for RNA-seq differential analysis
Sign Up to like & getrecommendations! Published in 2018 at "Briefings in Bioinformatics"
DOI: 10.1093/bib/bbw092
Abstract: Numerous statistical pipelines are now available for the differential analysis of gene expression measured with RNA-sequencing technology. Most of them are based on similar statistical frameworks after normalization, differing primarily in the choice of data… read more here.
Keywords: data sets; differential analysis; sets identification; key ingredients ... See more keywords
On the importance of benchmarking algorithms under realistic noise conditions
Sign Up to like & getrecommendations! Published in 2020 at "Geophysical Journal International"
DOI: 10.1093/gji/ggaa025
Abstract: Testing with synthetic data sets is a vital stage in an algorithm’s development for benchmarking the algorithm’s performance. A common addition to synthetic data sets is White, Gaussian Noise (WGN) which is used to mimic… read more here.
Keywords: algorithms realistic; data sets; realistic noise; synthetic data ... See more keywords
Demonstrating an approach for evaluating synthetic geospatial and temporal epidemiologic data utility: results from analyzing >1.8 million SARS-CoV-2 tests in the United States National COVID Cohort Collaborative (N3C)
Sign Up to like & getrecommendations! Published in 2022 at "Journal of the American Medical Informatics Association : JAMIA"
DOI: 10.1093/jamia/ocac045
Abstract: Abstract Objective This study sought to evaluate whether synthetic data derived from a national coronavirus disease 2019 (COVID-19) dataset could be used for geospatial and temporal epidemic analyses. Materials and Methods Using an original dataset… read more here.
Keywords: data utility; synthetic data; zip; covid ... See more keywords
Generating synthetic mixed discrete-continuous health records with mixed sum-product networks
Sign Up to like & getrecommendations! Published in 2022 at "Journal of the American Medical Informatics Association : JAMIA"
DOI: 10.1093/jamia/ocac184
Abstract: OBJECTIVE Privacy is a concern whenever individual patient health data is exchanged for scientific research. We propose using mixed sum-product networks (MSPNs) as private representations of data and take samples from the network to generate… read more here.
Keywords: health; synthetic data; mixed discrete; sum product ... See more keywords
Fine Grain Synthetic Educational Data: Challenges and Limitations of Collaborative Learning Analytics
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3156073
Abstract: While data privacy is a key aspect of Learning Analytics, it often creates difficulty when promoting research into underexplored contexts as it limits data sharing. To overcome this problem, the generation of synthetic data has… read more here.
Keywords: educational data; synthetic data; challenges limitations; data real ... See more keywords