Annals of a Publishing House

The uncertainty in a climate data records (CDRs) derived from Earth observations in part derives from the propagated uncertainty in the radiance record (the fundamental climate data record, FCDR) from which the geophysical estimates in the CDR are derived. A common barrier to providing uncertainty-quantified CDRs is the inaccessibility to CDR creators of appropriate radiance uncertainty information in the FCDR. Here, we propose radiance uncertainty information designed directly to facilitate estimation of propagated uncertainty in derived CDRs at full resolution and in gridded products. Errors in Earth observations are typically highly structured and complex, and the uncertainty information we propose is of intermediate complexity, sufficient to capture the main variability in propagated uncertainty in a CDR, while avoiding unfeasible complexity or data volume. The uncertainty and error correlation characteristics of uncertainty are quantified for three classes of error with different propagation properties: independent, structured and common radiance errors. The meaning, mathematical derivations, practical evaluation and example applications of this set of uncertainty information are presented.

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“…The optimisation described here has been implemented as a set of Python routines, built using the numpy [32] and numexpr libraries.…”

Section: Methods 3-practical Computationmentioning

confidence: 99%

Radiance Uncertainty Characterisation to Facilitate Climate Data Record Creation

et al. 2019

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“…Using the data analysis tools of the numpy [31], pandas [32] and scipy [33] Python libraries, statistical analysis of the available data was performed to determine the relationship between modal chromosome numbers and different sex chromosome karyotypes were analysed.…”

Section: Methodsmentioning

confidence: 99%

Meta-Analysis of Cancer Triploidy: Whole-Genome Rearrangements in Male Human Tumours Are Characterised by XXY Karyotypes

Nm¹,

Zayakin²,

Kleina³

et al. 2019

Preprint

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Triploidy in cancer is associated with poor prognosis but its origins remain unclear. Here, based on frequent X-chromosome doubling in male tumours we attempted to differentiate between a random chromosomal origin and whole-genome origin of cancer triploidy. In silico meta-analysis was performed on 15 male malignant and 5 benign tumour cohorts (2928 karyotypes) extracted from the Mitelman Database, comparing their modal chromosome numbers (ploidy) and combinations of sex chromosomes. Karyotype heterogeneity with a distinct near-triploid fraction was observed in all malignant tumour types, especially high in seminoma. For all tumour types, X-chromosome doubling, dominantly presented by XXY, strongly correlated with the near-triploid state (r≈0.9, p<0.001), negatively correlated with near-diploidy, and did not correlate with near-tetraploidy. The proportion of XX,-Y near-triploid karyotypes was variably increased in somatic tumours. A smaller near-triploid component with a doubled X-chromosome was also present in 3 of 5 benign tumour types, especially notable in colon adenoma. We conclude that doubling of the maternal genome followed by fusion with a paternal genome (similar to digyny) is likely responsible for the observed whole genome triploidy and may be causative for cancer initiation. The Y-chromosome may subsequently be lost due to secondary chromosome instability processes.

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“…The function for ReLU has been discussed along with its graph in Figure A1, which had been plotted using Numpy [37] and Matplotlib [38].…”

Section: Simple Capsnetmentioning

confidence: 99%

Detecting Pneumonia Using Convolutions and Dynamic Capsule Routing for Chest X-ray Images

Mittal

Kumar

Mittal³

et al. 2020

Sensors

119

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An entity’s existence in an image can be depicted by the activity instantiation vector from a group of neurons (called capsule). Recently, multi-layered capsules, called CapsNet, have proven to be state-of-the-art for image classification tasks. This research utilizes the prowess of this algorithm to detect pneumonia from chest X-ray (CXR) images. Here, an entity in the CXR image can help determine if the patient (whose CXR is used) is suffering from pneumonia or not. A simple model of capsules (also known as Simple CapsNet) has provided results comparable to best Deep Learning models that had been used earlier. Subsequently, a combination of convolutions and capsules is used to obtain two models that outperform all models previously proposed. These models—Integration of convolutions with capsules (ICC) and Ensemble of convolutions with capsules (ECC)—detect pneumonia with a test accuracy of 95.33% and 95.90%, respectively. The latter model is studied in detail to obtain a variant called EnCC, where n = 3, 4, 8, 16. Here, the E4CC model works optimally and gives test accuracy of 96.36%. All these models had been trained, validated, and tested on 5857 images from Mendeley.

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Annals of a Publishing House

Cited by 144 publications

References 0 publications

Radiance Uncertainty Characterisation to Facilitate Climate Data Record Creation

Radiance Uncertainty Characterisation to Facilitate Climate Data Record Creation

Meta-Analysis of Cancer Triploidy: Whole-Genome Rearrangements in Male Human Tumours Are Characterised by XXY Karyotypes

Detecting Pneumonia Using Convolutions and Dynamic Capsule Routing for Chest X-ray Images

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