Uncertainty Estimation for Strong-Noise Data

2018 ◽  
pp. 447-454
Author(s):  
Bin Shen ◽  
Binheng Song
Keyword(s):  
Uncertainty Estimation ◽  
Noise Data

Operational Noise Data for OH-58D Army Helicopters

1992 ◽  
Author(s):  
L. J. Benson ◽  
Micheal J. White ◽  
Kevin J. Murphy
Keyword(s):  
Noise Data

Development and Certification of Formononetin Reference Material for Quality Control of Functional Foods and Botanical Supplements

2019 ◽  
Vol 15 (5) ◽  
pp. 553-559
Author(s):  
Ningbo Gong ◽  
Baoxi Zhang ◽  
Kun Hu ◽  
Zhaolin Gao ◽  
Guanhua Du ◽  
...  
Keyword(s):  
Quality Control ◽  
Reference Material ◽  
Functional Foods ◽  
Certified Reference Materials ◽  
Coulometric Titration ◽  
Pharmaceutical Formulations ◽  
Uncertainty Estimation ◽  
Expanded Uncertainty ◽  
Scanning Calorimetry ◽  
Botanical Supplements

Background: Formononetin is a common soy isoflavonoid that can be found abundantly in many natural plants. Previous studies have shown that formononetin possesses a variety of activities which can be applied for various medicinal purposes. Certified Reference Materials (CRMs) play a fundamental role in the food, traditional medicine and dietary supplement fields, and can be used for method validation, uncertainty estimation, as well as quality control. Methods: The purity of formononetin was determined by Differential Scanning Calorimetry (DSC), Coulometric Titration (CT) and Mass Balance (MB) methods. Results: This paper reports the sample preparation methodology, homogeneity and stability studies, value assignment, and uncertainty estimation of a new certified reference material of formononetin. DSC, CT and MB methods proved to be sufficiently reliable and accurate for the certification purpose. The purity of the formononetin CRM was therefore found to be 99.40% ± 0.24 % (k = 2) based on the combined value assignments and the expanded uncertainty. Conclusion: This CRM will be a reliable standard for the validation of the analytical methods and for quality assurance/quality control of formononetin and formononetin-related traditional herbs, food products, dietary supplements and pharmaceutical formulations.

scholarly journals Calibration and uncertainty estimation of non-contact coordinate measurement systems based on Kriging models

2019 ◽  
Vol 57 ◽  
pp. 16-29 ◽  
Author(s):  
Lan Fei ◽  
Jean-Yves Dantan ◽  
Cyrille Baudouin ◽  
Shichang Du
Keyword(s):  
Uncertainty Estimation ◽  
Coordinate Measurement ◽  
Measurement Systems

scholarly journals Semi-Supervised Classification via Hypergraph Convolutional Extreme Learning Machine

2021 ◽  
Vol 11 (9) ◽  
pp. 3867
Author(s):  
Zhewei Liu ◽  
Zijia Zhang ◽  
Yaoming Cai ◽  
Yilin Miao ◽  
Zhikun Chen
Keyword(s):  
Extreme Learning Machine ◽  
Supervised Classification ◽  
Structured Data ◽  
Model Complex ◽  
Euclidean Domain ◽  
Noise Data ◽  
Data Points ◽  
Learning Machine ◽  
Random Hypergraph ◽  
Special Case

Extreme Learning Machine (ELM) is characterized by simplicity, generalization ability, and computational efficiency. However, previous ELMs fail to consider the inherent high-order relationship among data points, resulting in being powerless on structured data and poor robustness on noise data. This paper presents a novel semi-supervised ELM, termed Hypergraph Convolutional ELM (HGCELM), based on using hypergraph convolution to extend ELM into the non-Euclidean domain. The method inherits all the advantages from ELM, and consists of a random hypergraph convolutional layer followed by a hypergraph convolutional regression layer, enabling it to model complex intraclass variations. We show that the traditional ELM is a special case of the HGCELM model in the regular Euclidean domain. Extensive experimental results show that HGCELM remarkably outperforms eight competitive methods on 26 classification benchmarks.

scholarly journals SCOUR: a stepwise machine learning framework for predicting metabolite-dependent regulatory interactions

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Justin Y. Lee ◽  
Britney Nguyen ◽  
Carlos Orosco ◽  
Mark P. Styczynski
Keyword(s):  
Machine Learning ◽  
Metabolic Networks ◽  
Sampling Frequency ◽  
Low Noise ◽  
Training Data ◽  
High Noise ◽  
Regulatory Interactions ◽  
Learning Framework ◽  
Metabolic Systems ◽  
Noise Data

Abstract Background The topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms—two characteristics that make it difficult to model metabolic networks accurately. While many computational methods have been built to unravel transcriptional regulation, there have been few approaches developed for systems-scale analysis and study of metabolic regulation. Here, we present a stepwise machine learning framework that applies established algorithms to identify regulatory interactions in metabolic systems based on metabolic data: stepwise classification of unknown regulation, or SCOUR. Results We evaluated our framework on both noiseless and noisy data, using several models of varying sizes and topologies to show that our approach is generalizable. We found that, when testing on data under the most realistic conditions (low sampling frequency and high noise), SCOUR could identify reaction fluxes controlled only by the concentration of a single metabolite (its primary substrate) with high accuracy. The positive predictive value (PPV) for identifying reactions controlled by the concentration of two metabolites ranged from 32 to 88% for noiseless data, 9.2 to 49% for either low sampling frequency/low noise or high sampling frequency/high noise data, and 6.6–27% for low sampling frequency/high noise data, with results typically sufficiently high for lab validation to be a practical endeavor. While the PPVs for reactions controlled by three metabolites were lower, they were still in most cases significantly better than random classification. Conclusions SCOUR uses a novel approach to synthetically generate the training data needed to identify regulators of reaction fluxes in a given metabolic system, enabling metabolomics and fluxomics data to be leveraged for regulatory structure inference. By identifying and triaging the most likely candidate regulatory interactions, SCOUR can drastically reduce the amount of time needed to identify and experimentally validate metabolic regulatory interactions. As high-throughput experimental methods for testing these interactions are further developed, SCOUR will provide critical impact in the development of predictive metabolic models in new organisms and pathways.

scholarly journals Quantifying the uncertainty of deep learning-based computer-aided diagnosis for patient safety

2019 ◽  
Vol 5 (1) ◽  
pp. 223-226
Author(s):  
Max-Heinrich Laves ◽  
Sontje Ihler ◽  
Tobias Ortmaier ◽  
Lüder A. Kahrs
Keyword(s):  
Monte Carlo ◽  
Patient Safety ◽  
Deep Learning ◽  
Epistemic Uncertainty ◽  
Computer Aided Diagnosis ◽  
Uncertainty Estimation ◽  
Test Time ◽  
Prediction Uncertainty ◽  
Computer Aided ◽  
Aided Diagnosis

AbstractIn this work, we discuss epistemic uncertainty estimation obtained by Bayesian inference in diagnostic classifiers and show that the prediction uncertainty highly correlates with goodness of prediction. We train the ResNet-18 image classifier on a dataset of 84,484 optical coherence tomography scans showing four different retinal conditions. Dropout is added before every building block of ResNet, creating an approximation to a Bayesian classifier. Monte Carlo sampling is applied with dropout at test time for uncertainty estimation. In Monte Carlo experiments, multiple forward passes are performed to get a distribution of the class labels. The variance and the entropy of the distribution is used as metrics for uncertainty. Our results show strong correlation with ρ = 0.99 between prediction uncertainty and prediction error. Mean uncertainty of incorrectly diagnosed cases was significantly higher than mean uncertainty of correctly diagnosed cases. Modeling of the prediction uncertainty in computer-aided diagnosis with deep learning yields more reliable results and is therefore expected to increase patient safety. This will help to transfer such systems into clinical routine and to increase the acceptance of machine learning in diagnosis from the standpoint of physicians and patients.

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