LAUSR

A viscosity measurement technique for ultra-low sample volumes.

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0 Published in 2024 at "Soft matter"

DOI: 10.1039/d4sm00050a

Abstract: We describe a unique method to measure the viscosity of liquids based on the fluid mechanics of thin films. A drop of sample is spread over a substrate by contacting a blade with the drop… read more here.

Keywords: measurement technique; low sample; sample; viscosity measurement ... See more keywords

Segregation effects in DEEP2 galaxy groups

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1 Published in 2017 at "Monthly Notices of the Royal Astronomical Society"

DOI: 10.1093/mnras/stw2321

Abstract: We investigate segregation phenomena in galaxy groups in the range of $0.2 0.6$) redshift groups. Assuming that the color index ${(U-B)_0}$ can be used as a proxy for the galaxy type, we found that the… read more here.

Keywords: sample; segregation effects; high sample; low sample ... See more keywords

AggMapNet: enhanced and explainable low-sample omics deep learning with feature-aggregated multi-channel networks.

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1 Published in 2022 at "Nucleic acids research"

DOI: 10.1093/nar/gkac010

Abstract: Omics-based biomedical learning frequently relies on data of high-dimensions (up to thousands) and low-sample sizes (dozens to hundreds), which challenges efficient deep learning (DL) algorithms, particularly for low-sample omics investigations. Here, an unsupervised novel feature… read more here.

Keywords: low sample; deep learning; sample omics; feature ... See more keywords

Discriminating Tensor Spectral Clustering for High-Dimension-Low-Sample-Size Data

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0 Published in 2024 at "IEEE Transactions on Neural Networks and Learning Systems"

DOI: 10.1109/tnnls.2024.3422243

Abstract: Tensor spectral clustering (TSC) is a recently proposed approach to robustly group data into underlying clusters. Unlike the traditional spectral clustering (SC), which merely uses pairwise similarities of data in an affinity matrix, TSC aims… read more here.

Keywords: low sample; dimension; high dimension; dimension low ... See more keywords

Biobjective gradient descent for feature selection on high dimension, low sample size data

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0 Published in 2024 at "PLOS ONE"

DOI: 10.1371/journal.pone.0305654

Abstract: Even though deep learning shows impressive results in several applications, its use on problems with High Dimensions and Low Sample Size, such as diagnosing rare diseases, leads to overfitting. One solution often proposed is feature… read more here.

Keywords: sample size; low sample; biobjective gradient; feature ... See more keywords