Achieving Consistent Uncertainty Estimates with RANSAC-Based Algorithms

2016 ◽  
Author(s):  
Clark N. Taylor
Keyword(s):  
Uncertainty Estimates

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

scholarly journals The Infrared Absolute Radiance Interferometer (ARI) for CLARREO

2020 ◽  
Vol 12 (12) ◽  
pp. 1915
Author(s):  
Joe K. Taylor ◽  
Henry E. Revercomb ◽  
Fred A. Best ◽  
David C. Tobin ◽  
P. Jonathan Gero
Keyword(s):  
Brightness Temperature ◽  
New Technologies ◽  
Spectral Radiance ◽  
Overall Design ◽  
Societal Challenges ◽  
Ideal System ◽  
Uncertainty Estimates ◽  
Infrared Spectrometer ◽  
Global Coverage

The Absolute Radiance Interferometer (ARI) is an infrared spectrometer designed to serve as an on-orbit radiometric reference with the ultra-high accuracy (better than 0.1 K 3‑σ or k = 3 brightness temperature at scene brightness temperature) needed to optimize measurement of the long-term changes of Earth’s atmosphere and surface. If flown in an orbit that frequently crosses sun-synchronous orbits, ARI could be used to inter-calibrate the international fleet of infrared (IR) hyperspectral sounders to similar measurement accuracy, thereby establishing an observing system capable of achieving sampling biases on high-information-content spectral radiance products that are also < 0.1 K 3‑σ. It has been shown that such a climate observing system with <0.1 K 2‑σ overall accuracy would make it possible to realize times to detect subtle trends of temperature and water vapor distributions that closely match those of an ideal system, given the limit set by the natural variability of the atmosphere. This paper presents the ARI sensor's overall design, the new technologies developed to allow on-orbit verification and test of its accuracy, and the laboratory results that demonstrate its capability. In addition, we describe the techniques and uncertainty estimates for transferring ARI accuracy to operational sounders, providing economical global coverage. Societal challenges posed by climate change suggest that a Pathfinder ARI should be deployed as soon as possible.

scholarly journals Predictions of the geomagnetic secular variation based on the ensemble sequential assimilation of geomagnetic field models by dynamo simulations

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Sabrina Sanchez ◽  
Johannes Wicht ◽  
Julien Bärenzung
Keyword(s):  
Magnetic Field ◽  
Secular Variation ◽  
Geomagnetic Field ◽  
Wave Number ◽  
Main Magnetic Field ◽  
Candidate Model ◽  
Uncertainty Estimates ◽  
Geomagnetic Field Models ◽  
Dipole Configuration

Abstract The IGRF offers an important incentive for testing algorithms predicting the Earth’s magnetic field changes, known as secular variation (SV), in a 5-year range. Here, we present a SV candidate model for the 13th IGRF that stems from a sequential ensemble data assimilation approach (EnKF). The ensemble consists of a number of parallel-running 3D-dynamo simulations. The assimilated data are geomagnetic field snapshots covering the years 1840 to 2000 from the COV-OBS.x1 model and for 2001 to 2020 from the Kalmag model. A spectral covariance localization method, considering the couplings between spherical harmonics of the same equatorial symmetry and same azimuthal wave number, allows decreasing the ensemble size to about a 100 while maintaining the stability of the assimilation. The quality of 5-year predictions is tested for the past two decades. These tests show that the assimilation scheme is able to reconstruct the overall SV evolution. They also suggest that a better 5-year forecast is obtained keeping the SV constant compared to the dynamically evolving SV. However, the quality of the dynamical forecast steadily improves over the full assimilation window (180 years). We therefore propose the instantaneous SV estimate for 2020 from our assimilation as a candidate model for the IGRF-13. The ensemble approach provides uncertainty estimates, which closely match the residual differences with respect to the IGRF-13. Longer term predictions for the evolution of the main magnetic field features over a 50-year range are also presented. We observe the further decrease of the axial dipole at a mean rate of 8 nT/year as well as a deepening and broadening of the South Atlantic Anomaly. The magnetic dip poles are seen to approach an eccentric dipole configuration.

scholarly journals Updates to the Regional Seismic Travel Time (RSTT) Model: 2. Path-dependent Travel-time Uncertainty

2021 ◽  
Vol 178 (2) ◽  
pp. 313-339
Author(s):  
Michael L. Begnaud ◽  
Dale N. Anderson ◽  
Stephen C. Myers ◽  
Brian Young ◽  
James R. Hipp ◽  
...  
Keyword(s):  
Travel Time ◽  
Random Effects ◽  
Mean Squared Error ◽  
Random Effects Model ◽  
Location Error ◽  
Crust And Upper Mantle ◽  
Time Uncertainty ◽  
Uncertainty Estimates ◽  
Event Location ◽  
Travel Time Uncertainty

AbstractThe regional seismic travel time (RSTT) model and software were developed to improve travel-time prediction accuracy by accounting for three-dimensional crust and upper mantle structure. Travel-time uncertainty estimates are used in the process of associating seismic phases to events and to accurately calculate location uncertainty bounds (i.e. event location error ellipses). We improve on the current distance-dependent uncertainty parameterization for RSTT using a random effects model to estimate slowness (inverse velocity) uncertainty as a mean squared error for each model parameter. The random effects model separates the error between observed slowness and model predicted slowness into bias and random components. The path-specific travel-time uncertainty is calculated by integrating these mean squared errors along a seismic-phase ray path. We demonstrate that event location error ellipses computed for a 90% coverage ellipse metric (used by the Comprehensive Nuclear-Test-Ban Treaty Organization International Data Centre (IDC)), and using the path-specific travel-time uncertainty approach, are more representative (median 82.5% ellipse percentage) of true location error than error ellipses computed using distance-dependent travel-time uncertainties (median 70.1%). We also demonstrate measurable improvement in location uncertainties using the RSTT method compared to the current station correction approach used at the IDC (median 74.3% coverage ellipse).

scholarly journals Uncertainty-Aware Deep Learning-Based Cardiac Arrhythmias Classification Model of Electrocardiogram Signals

Computers ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 82
Author(s):  
Ahmad O. Aseeri
Keyword(s):  
Deep Learning ◽  
Cardiac Arrhythmias ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Probabilistic Approach ◽  
Classification Model ◽  
Gating Mechanism ◽  
Uncertainty Estimates ◽  
Wide Range

Deep Learning-based methods have emerged to be one of the most effective and practical solutions in a wide range of medical problems, including the diagnosis of cardiac arrhythmias. A critical step to a precocious diagnosis in many heart dysfunctions diseases starts with the accurate detection and classification of cardiac arrhythmias, which can be achieved via electrocardiograms (ECGs). Motivated by the desire to enhance conventional clinical methods in diagnosing cardiac arrhythmias, we introduce an uncertainty-aware deep learning-based predictive model design for accurate large-scale classification of cardiac arrhythmias successfully trained and evaluated using three benchmark medical datasets. In addition, considering that the quantification of uncertainty estimates is vital for clinical decision-making, our method incorporates a probabilistic approach to capture the model’s uncertainty using a Bayesian-based approximation method without introducing additional parameters or significant changes to the network’s architecture. Although many arrhythmias classification solutions with various ECG feature engineering techniques have been reported in the literature, the introduced AI-based probabilistic-enabled method in this paper outperforms the results of existing methods in outstanding multiclass classification results that manifest F1 scores of 98.62% and 96.73% with (MIT-BIH) dataset of 20 annotations, and 99.23% and 96.94% with (INCART) dataset of eight annotations, and 97.25% and 96.73% with (BIDMC) dataset of six annotations, for the deep ensemble and probabilistic mode, respectively. We demonstrate our method’s high-performing and statistical reliability results in numerical experiments on the language modeling using the gating mechanism of Recurrent Neural Networks.

A New Generation of Tools for the Design of Tall Buildings Subjected to Wind Loads

2008 ◽  
Author(s):  
Emil Simiu ◽  
Rene D. Gabbai
Keyword(s):  
Structural Engineering ◽  
Tall Buildings ◽  
Building Envelope ◽  
Force Balance ◽  
Wind Loads ◽  
Individual Member ◽  
Engineering Practice ◽  
Wind Effects ◽  
Uncertainty Estimates ◽  
Internal Forces

Current approaches to the estimation of wind-induced wind effects on tall buildings are based largely on 1970s and 1980s technology, and were shown to result in some cases in errors of up to 40%. Improvements are needed in: (i) the description of direction-dependent aerodynamics; (ii) the description of the direction-dependent extreme wind climate; (iii) the estimation of inertial wind effects induced by fluctuating aerodynamic forces acting on the entire building envelope; (iv) the estimation of uncertainties inherent in the wind effects; and (v) the use of applied wind forces, calculated inertial forces, and uncertainty estimates, to obtain via influence coefficients accurate and risk-consistent estimates of wind-induced internal forces or demand-to-capacity ratios for any individual structural member. Methods used in current wind engineering practice are especially deficient when the distribution of the wind loads over the building surface and their effects at levels other than the building base are not known, as is the case when measurements are obtained by the High-Frequency Force Balance method, particularly in the presence of aerodynamic interference effects due to neighboring buildings. The paper describes a procedure that makes it possible to estimate wind-induced internal forces and demand-to-capacity ratios in any individual member by: developing aerodynamic and wind climatological data sets, as well as aerodynamic/climatological directional interaction models; significantly improving the quality of the design via rigorous structural engineering methods made possible by modern computational resources; and properly accounting for knowledge uncertainties. The paper covers estimates of wind effects required for allowable stress design, wherein knowledge uncertainties pertaining to the parameters that determine the wind loading are not considered, as well as estimates required for strength design, in which these uncertainties need to be accounted for explicitly.

scholarly journals Methods for estimating uncertainty in factor analytic solutions

2014 ◽  
Vol 7 (3) ◽  
pp. 781-797 ◽  
Author(s):  
P. Paatero ◽  
S. Eberly ◽  
S. G. Brown ◽  
G. A. Norris
Keyword(s):  
Environmental Protection Agency ◽  
Synthetic Data ◽  
Analytic Solutions ◽  
Data Sets ◽  
Random Errors ◽  
Data Set ◽  
Factor Analytic ◽  
Uncertainty Estimates ◽  
Multilinear Engine ◽  
Analytic Models

Abstract. The EPA PMF (Environmental Protection Agency positive matrix factorization) version 5.0 and the underlying multilinear engine-executable ME-2 contain three methods for estimating uncertainty in factor analytic models: classical bootstrap (BS), displacement of factor elements (DISP), and bootstrap enhanced by displacement of factor elements (BS-DISP). The goal of these methods is to capture the uncertainty of PMF analyses due to random errors and rotational ambiguity. It is shown that the three methods complement each other: depending on characteristics of the data set, one method may provide better results than the other two. Results are presented using synthetic data sets, including interpretation of diagnostics, and recommendations are given for parameters to report when documenting uncertainty estimates from EPA PMF or ME-2 applications.

Sign in / Sign up

Export Citation Format

Share Document