scholarly journals Short communication: Inverse isochron regression for Re–Os, K–Ca and other chronometers

Geochronology ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 415-420
Author(s):  
Yang Li ◽  
Pieter Vermeesch
Keyword(s):  
Short Communication ◽  
Synthetic Data ◽  
Mass Spectrometric ◽  
Common Denominator ◽  
Data Simulation ◽  
Change Of Variables ◽  
Simple Change ◽  
Geological Complexity ◽  
Binary Mixing

Abstract. Conventional Re–Os isochrons are based on mass spectrometric estimates of 187Re/188Os and 187Os/188Os, which often exhibit strong error correlations that may obscure potentially important geological complexity. Using an approach that is widely accepted in 40Ar/39Ar and U–Pb geochronology, we here show that these error correlations are greatly reduced by applying a simple change of variables, using 187Os as a common denominator. Plotting 188Os/187Os vs. 187Re/187Os produces an “inverse isochron”, defining a binary mixing line between an inherited Os component whose 188Os/187Os ratio is given by the vertical intercept, and the radiogenic 187Re/187Os ratio, which corresponds to the horizontal intercept. Inverse isochrons facilitate the identification of outliers and other sources of data dispersion. They can also be applied to other geochronometers such as the K–Ca method and (with less dramatic results) the Rb–Sr, Sm–Nd and Lu–Hf methods. Conventional and inverse isochron ages are similar for precise datasets but may significantly diverge for imprecise ones. A semi-synthetic data simulation indicates that, in the latter case, the inverse isochron age is more accurate. The generalised inverse isochron method has been added to the IsoplotR toolbox for geochronology, which automatically converts conventional isochron ratios into inverse ratios, and vice versa.

scholarly journals Short communication: Inverse isochron regression for Re–Os, K–Ca and other chronometers

2021 ◽  
Author(s):  
Yang Li ◽  
Pieter Vermeesch
Keyword(s):  
Short Communication ◽  
Mass Spectrometric ◽  
Common Denominator ◽  
Change Of Variables ◽  
Simple Change ◽  
Geological Complexity ◽  
Binary Mixing

Abstract. Conventional Re–Os isochrons are based on mass spectrometric estimates of 187Re / 188Os and 187Os / 188Os. 188Os is usually far less abundant, and is therefore measured less precisely, than 187Os and 187Re. This causes strong error correlations between the two isochron ratios, which may obscure potentially important geological complexity. Using an approach that is widely accepted in 40Ar / 39Ar and U–Pb geochronology, we here show that these error correlations are greatly reduced by applying a simple change of variables, using 187Os as a common denominator. Plotting 188Os / 187Os vs. 187Re / 187Os produces an inverse isochron, defining a binary mixing line between an inherited Os-component whose 188Os / 187Os-ratio is given by the vertical intercept, and the radiogenic 187Re / 187Os-ratio, which corresponds to the horizontal intercept. Inverse isochrons facilitate the identification of outliers and other sources of data dispersion. They can also be applied to other geochronometers such as the K–Ca method and (with less dramatic results) the Rb–Sr, Sm–Nd and Lu–Hf methods. The generalised inverse isochron method has been added to the IsoplotR toolbox for geochronology, which automatically converts conventional isochron ratios into inverse ratios and vice versa.

scholarly journals Inferring viral occurrence patterns through a synthetic data simulation

2021 ◽  
Author(s):  
Ville N Pimenoff ◽  
Ramon Cleries
Keyword(s):  
Linear Models ◽  
Population Sample ◽  
Synthetic Data ◽  
Interaction Patterns ◽  
Viral Strain ◽  
Data Simulation ◽  
Synthetic Datasets ◽  
Pathogen Occurrence ◽  
Log Linear ◽  
Occurrence Patterns

Viruses infecting humans are manifold and several of them provoke significant morbidity and mortality. Simulations creating large synthetic datasets from observed multiple viral strain infections in a limited population sample can be a powerful tool to infer significant pathogen occurrence and interaction patterns, particularly if limited number of observed data units is available. Here, to demonstrate diverse human papillomavirus (HPV) strain occurrence patterns, we used log-linear models combined with Bayesian framework for graphical independence network (GIN) analysis. That is, to simulate datasets based on modeling the probabilistic associations between observed viral data points, i.e different viral strain infections in a set of population samples. Our GIN analysis outperformed in precision all oversampling methods tested for simulating large synthetic viral strain-level prevalence dataset from observed set of HPVs data. Altogether, we demonstrate that network modeling is a potent tool for creating synthetic viral datasets for comprehensive pathogen occurrence and interaction pattern estimations.

PRUNING AND MODEL-SELECTING ALGORITHMS IN THE RBF FRAMEWORKS CONSTRUCTED BY SUPPORT VECTOR LEARNING

2006 ◽  
Vol 16 (04) ◽  
pp. 283-293 ◽  
Author(s):  
PEI-YI HAO ◽  
JUNG-HSIEN CHIANG
Keyword(s):  
Function Approximation ◽  
Synthetic Data ◽  
Support Vector ◽  
Detection Problem ◽  
Data Simulation ◽  
Penalty Term ◽  
Rbf Network ◽  
Sample Classification ◽  
Pruning Algorithms ◽  
And Function

This paper presents the pruning and model-selecting algorithms to the support vector learning for sample classification and function regression. When constructing RBF network by support vector learning we occasionally obtain redundant support vectors which do not significantly affect the final classification and function approximation results. The pruning algorithms primarily based on the sensitivity measure and the penalty term. The kernel function parameters and the position of each support vector are updated in order to have minimal increase in error, and this makes the structure of SVM network more flexible. We illustrate this approach with synthetic data simulation and face detection problem in order to demonstrate the pruning effectiveness.

scholarly journals Bayesian inverse estimation of urban CO2 emissions: Results from a synthetic data simulation over Salt Lake City, UT

Elem Sci Anth ◽  
2019 ◽  
Vol 7 ◽  
Author(s):  
Lewis Kunik ◽  
Derek V. Mallia ◽  
Kevin R. Gurney ◽  
Daniel L. Mendoza ◽  
Tomohiro Oda ◽  
...  
Keyword(s):  
Transport Model ◽  
Salt Lake ◽  
Salt Lake City ◽  
Synthetic Data ◽  
Emission Inventories ◽  
Model Data ◽  
Mismatch Error ◽  
Data Simulation ◽  
Salt Lake Valley ◽  
Lake City

Top-down, data-driven models possess ample power to improve the accuracy of bottom-up carbon dioxide (CO2) emission inventories, and more work is needed to explore the merger of top-down and bottom-up estimates to better inform the metrics used to monitor global CO2 fluxes. Here we present a Bayesian inverse modeling framework over Salt Lake City, Utah, which utilizes available CO2 emission inventories to establish a synthetic data simulation aimed at exploring model uncertainties. Prescribing a high-resolution, urban-scale data product (Hestia) as the “true” emissions in the model, we combine prior emissions with an atmospheric transport model to derive modeled afternoon CO2 enhancements at six monitoring sites within the Salt Lake Valley during the month of September 2015. A global high-resolution gridded emissions data product (ODIAC) is used as the prior, and objective uncertainty structures are defined for both the a priori estimates and the transport model-data relationship which consider non-negligible spatial and temporal covariances. Optimized (posterior) emissions over the Salt Lake Valley agree closely with the assumed “true” emissions during afternoon times, while results including unconstrained times (e.g. night-time) lack such agreement. Both spatial and temporal correlations of prior errors were found to be necessary for obtaining a robust posterior estimate. Model sensitivity analyses are performed, which examine correlation length and time scales, model-data mismatch error, and measurement site network variability. Through these analyses, one measurement site is identified as being particularly prone to introducing bias into posterior emissions due to influences from a nearby point source. Increasing model-data mismatch error at this site is shown to reduce bias in the posterior without significantly compromising agreement with monthly averaged true emissions.

Estimating a mode choice model with incomplete data

1989 ◽  
Vol 16 (6) ◽  
pp. 917-923
Author(s):  
Gerald Brown ◽  
Siovache Kahkeshan
Keyword(s):  
Survey Data ◽  
Incomplete Data ◽  
Choice Model ◽  
Mode Choice ◽  
Synthetic Data ◽  
Level Of Service ◽  
Data Simulation ◽  
Data Set ◽  
Policy Model ◽  
Mode Choice Model

This study develops and uses a synthetic data base to calibrate a logit mode choice model of work trips in metropolitan Vancouver. Missing survey data entries for perceived measures of travel time and waiting time by bus, as well as operating and parking cost by car, are calculated using statistical methods to increase the survey sample of 275 complete cases to 621 usable cases. The synthesized data set is used to specify random utility functions for two planning assumptions. The short-term policy specification using only level of service variables does not produce a usable model, but the specification based on a long-term planning assumption using a combination of level of service and socioeconomic variables produces plausible results. The inconclusive results from the policy model could be due to survey data problems, data simulation, and (or) the lack of conceptual validity of perceived measures of transportation attributes. The planning model provides insight into mode split prediction and transportation management for cities that are undergoing dynamic demographic and social changes. Key words: mode choice, incomplete data, socioeconomic factors, logit model.

scholarly journals A new approach to analyticity of Dirichlet-Neumann operators

2001 ◽  
Vol 131 (6) ◽  
pp. 1411-1433 ◽  
Author(s):  
David P. Nicholls ◽  
Fernando Reitich
Keyword(s):  
Theoretical Background ◽  
Perturbative Calculation ◽  
New Approach ◽  
Change Of Variables ◽  
Simple Change

This paper outlines the theoretical background of a new approach towards an accurate and well-conditioned perturbative calculation of Dirichlet-Neumann operators (DNOs) on domains that are perturbations of simple geometries. Previous work on the analyticity of DNOs has produced formulae that, as we have found, are very ill-conditioned. We show how a simple change of variables can lead to recursions that satisfy analyticity estimates without relying on subtle cancellation properties at the heart of previous formulae.

scholarly journals Bone phenotypes in rheumatology – there is more to bone than just bone

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Christian S. Thudium ◽  
Signe Holm Nielsen ◽  
Samra Sardar ◽  
Ali Mobasheri ◽  
Willem Evert van Spil ◽  
...  
Keyword(s):  
Bone Formation ◽  
Skeletal Development ◽  
Bone Matrix ◽  
Matrix Composition ◽  
Endochondral Bone Formation ◽  
Common Denominator ◽  
Endochondral Bone ◽  
Pathological Conditions ◽  
Simple Change ◽  
Rheumatological Diseases

AbstractOsteoarthritis, rheumatoid arthritis, psoriatic arthritis, and ankylosing spondylitis, all have one clear common denominator; an altered turnover of bone. However, this may be more complex than a simple change in bone matrix and mineral turnover. While these diseases share a common tissue axis, their manifestations in the area of pathology are highly diverse, ranging from sclerosis to erosion of bone in different regions. The management of these diseases will benefit from a deeper understanding of the local versus systemic effects, the relation to the equilibrium of the bone balance (i.e., bone formation versus bone resorption), and the physiological and pathophysiological phenotypes of the cells involved (e.g., osteoblasts, osteoclasts, osteocytes and chondrocytes). For example, the process of endochondral bone formation in chondrocytes occurs exists during skeletal development and healthy conditions, but also in pathological conditions. This review focuses on the complex molecular and cellular taxonomy of bone in the context of rheumatological diseases that alter bone matrix composition and maintenance, giving rise to different bone turnover phenotypes, and how biomarkers (biochemical markers) can be applied to potentially describe specific bone phenotypic tissue profiles.

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