SEARCH · Search NASA
Results for “interpretability”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Constrained Voronoi models for interpreting surface microstructural measurements
Not provided.
Time dependent signatures: Moisture content interpretation in well logging applications with a D-T neutron generator
Not provided.
Interpreting effective hydrologic depth estimates derived from soil moisture remote sensing: A Bayesian non-linear modeling approach
Not provided.
Peakaboo: Advanced software for the interpretation of X-ray fluorescence spectra from synchrotrons and other intense X-ray sources
Abstract not provided
Clarifying the interpretation of carbon use efficiency in soil through methods comparison
Not Available
Opening the Black Box: Interpretable Machine Learning for Geneticists
Not provided.
Rapid Identification of X-ray Diffraction Patterns Based on Very Limited Data by Interpretable Convolutional Neural Networks
Large volumes of data from material characterizations call for rapid and automatic data analysis to accelerate materials discovery. Herein, we report a convolutional neural network (CNN) that was trained based on theoretical data and very limited experimental data for fast identification of experimental X-ray diffraction (XRD) patterns of metal–organic frameworks (MOFs). To augment the data for training the model, noise was extracted from experimental data and shuffled; then it was merged with the main peaks that were extracted from theoretical spectra to synthesize new spectra. For the first time, one-to-one material identification was achieved. Theoretical MOFs patterns (1012) were augmented to a whole data set of 72 864 samples. It was then randomly shuffled and split into training (58 292 samples) and validation (14 572 samples) data sets at a ratio of 4:1. For the task of discriminating, the optimized model showed the highest identification accuracy of 96.7% for the top 5 ranking on a test data set of 30 hold-out samples. Neighborhood component analysis (NCA) on the experimental XRD samples shows that the samples from the same material are clustered in groups in the NCA map. Analysis on the class activation maps of the last CNN layer further discloses the mechanism by which the CNN model successfully identifies individual MOFs from the XRD patterns. Furthermore, this CNN model trained by the data augmentation technique would not only open numerous potential applications for identifying XRD patterns for different materials, but also pave avenues to autonomously analyze data by other characterization tools such as FTIR, Raman, and NMR spectroscopies.
Theoretical Prediction and Interpretation of 237 Np Mössbauer Isomer Shifts
Different theoretical approaches for the calculation of 237 Np Mössbauer isomer shifts are investigated. The traditional contact density route is compared to a previously proposed alternative approach that uses energy derivatives with respect to the nuclear radius. Both approaches yield similar results as long as suitable basis sets augmented with large exponents and relativistic methods are used. Density functional theory (DFT) calculations do not show a strong dependency of the 237 Np isomer shift on the chosen functional. Wavefunction calculations show that dynamic electron correlation can be important when covalent bonding influences the isomer shift. Effects from spin–orbit coupling are small. The isomer shifts of ionic solids and Np(III) organometallic complexes are largely governed by the oxidation state of Np. Isomer shifts of organometallic Np(IV) complexes are strongly affected by donation bonding. Furthermore, detailed analysis of the wavefunction results with different active spaces demonstrates that correlation among the outer core Np and occupied ligand frontier orbitals contributes significantly to isomer shifts of Np(IV) compounds.
Extracting an Empirical Intermetallic Hydride Design Principle from Limited Data via Interpretable Machine Learning.
Abstract not provided.
Calculation and interpretation of classical turning surfaces in solids
Abstract Classical turning surfaces of Kohn–Sham potentials separate classically allowed regions (CARs) from classically forbidden regions (CFRs). They are useful for understanding many chemical properties of molecules but need not exist in solids, where the density never decays to zero. At equilibrium geometries, we find that CFRs are absent in perfect metals, rare in covalent semiconductors at equilibrium, but common in ionic and molecular crystals. In all materials, CFRs appear or grow as the internuclear distances are uniformly expanded. They can also appear at a monovacancy in a metal. Calculations with several approximate density functionals and codes confirm these behaviors. A classical picture of conduction suggests that CARs should be connected in metals, and disconnected in wide-gap insulators, and is confirmed in the limits of extreme compression and expansion. Surprisingly, many semiconductors have no CFR at equilibrium, a key finding for density functional construction. Nonetheless, a strong correlation with insulating behavior can still be inferred. Moreover, equilibrium bond lengths for all cases can be estimated from the bond type and the sum of the classical turning radii of the free atoms or ions.
HAWC observations strongly favor pulsar interpretations of the cosmic-ray positron excess
Not Available
Interpretation of proton radiography experiments of hohlraums with three-dimensional simulations [Interpretation of proton radiography experiments of hohlraums with 3D simulations]
Proton radiography experiments of laser-irradiated hohlraums performed at the OMEGA laser facility are analyzed using three-dimensional (3D) hydrodynamic simulations coupled to a proton trajectography package. Experiments with three different laser irradiation patterns were performed, and each produced a distinct proton image. By comparing these results with synthetic proton images obtained by sending protons through plasma profiles in the hohlraum obtained from 3D radiation hydrodynamic simulations, it is found that the simulated images agree favorably with the experimental images when electric fields, due to the electron pressure gradients that arise from 3D structures occurring during plasma expansion, are included. Furthermore, these comparisons provide quantitative estimates of the electric field present inside the hohlraums.
Detection of recurrent pancreatic cancer: value of second-opinion interpretations of cross-sectional images by subspecialized radiologists
Purpose: To investigate the value of second-opinion interpretation of cross-sectional images by subspecialized radiologists to diagnose recurrent pancreatic cancer after surgery. Methods: The IRB approved and issued a waiver of informed consent for this retrospective study. Initial and second-opinion interpretations of 69 consecutive submitted MRI or CT follow-up after pancreatic cancer resection between January 1, 2009 and December 31, 2013 were evaluated by one oncologic imaging radiologist, who was blinded to patient’s clinical details and histopathologic data. The reviewer was asked to classify each interpretation in reference of the diagnosis of PDAC recurrence. It was also recorded if the radiologic interpretation recommended additional imaging studies to confirm recurrence. The diagnosis of recurrence was determined by pathology when available, otherwise by imaging follow-up, clinical, or laboratory assessments. Cohen’s kappa statistic was used to assess agreement between initial and second-opinion interpretations. The differences between the initial and second-opinion interpretations were examined using McNemar test or Bowker’s test of symmetry. Results: Disagreement on recurrence between the initial report and the second-opinion interpretation was observed in 32% of cases (22/69; k = 0.44). Second-opinion interpretations had a higher sensitivity and a higher specificity on recurrence compared to the initial interpretations (0.93 vs. 0.75 and 0.90 vs. 0.68, respectively), and the difference in specificity was significant (p = 0.016). Additional imaging studies were recommended more frequently in the initial interpretation (22% vs. 6%, p = 0.006). Conclusions: Our study shows the second-opinion interpretation by subspecialized radiologists improves the detection of pancreatic cancer recurrence after surgical resection.
Definitions, methods, and applications in interpretable machine learning
Machine-learning models have demonstrated great success in learning complex patterns that enable them to make predictions about unobserved data. In addition to using models for prediction, the ability to interpret what a model has learned is receiving an increasing amount of attention. However, this increased focus has led to considerable confusion about the notion of interpretability. In particular, it is unclear how the wide array of proposed interpretation methods are related and what common concepts can be used to evaluate them. We aim to address these concerns by defining interpretability in the context of machine learning and introducing the predictive, descriptive, relevant (PDR) framework for discussing interpretations. The PDR framework provides 3 overarching desiderata for evaluation: predictive accuracy, descriptive accuracy, and relevancy, with relevancy judged relative to a human audience. Moreover, to help manage the deluge of interpretation methods, we introduce a categorization of existing techniques into model-based and post hoc categories, with subgroups including sparsity, modularity, and simulatability. To demonstrate how practitioners can use the PDR framework to evaluate and understand interpretations, we provide numerous real-world examples. These examples highlight the often underappreciated role played by human audiences in discussions of interpretability. Finally, based on our framework, we discuss limitations of existing methods and directions for future work. We hope that this work will provide a common vocabulary that will make it easier for both practitioners and researchers to discuss and choose from the full range of interpretation methods.