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DOE OSTI2016

Integrating Symbolic and Statistical Methods for Testing Intelligent Systems Applications to Machine Learning and Computer Vision

Embedded intelligent systems ranging from tiny im- plantable biomedical devices to large swarms of autonomous un- manned aerial systems are becoming pervasive in our daily lives. While we depend on the flawless functioning of such intelligent systems, and often take their behavioral correctness and safety for granted, it is notoriously difficult to generate test cases that expose subtle errors in the implementations of machine learning algorithms. Hence, the validation of intelligent systems is usually achieved by studying their behavior on representative data sets, using methods such as cross-validation and bootstrapping.In this paper, we present a new testing methodology for studying the correctness of intelligent systems. Our approach uses symbolic decision procedures coupled with statistical hypothesis testing to. We also use our algorithm to analyze the robustness of a human detection algorithm built using the OpenCV open-source computer vision library. We show that the human detection implementation can fail to detect humans in perturbed video frames even when the perturbations are so small that the corresponding frames look identical to the naked eye.

Jha, Sumit Kumar↗

DOE OSTI2019

A Non-Exhaustive Search Algorithm to Identify Distribution Grid Operational Topology

Distribution grid, in general, is constructionally loopy but a radially operated network. Opening and closing of suitable tie-switches allow transition from one topology to another. Identifying the operational topology of a distribution system is essential for accurate control actions. This paper proposes a non-exhaustive search approach to detect the operational topology of the distribution feeder by estimating the switch statuses. In this work, a mixed-integer linear programming approach is proposed that finds the correct operational topology by identifying the system load demand and power flow variables that are closest to the corresponding measured values. The problem is constrained by distribution power flow and radial structure of the feeder. This formulation allows the inclusion of errors in power/line flow measurements making it more suitable for a practical distribution system. The proposed approach finds the correct operational topology without exhaustively searching for all possible topologies. We thoroughly validate the accuracy and effectiveness of the proposed approach in identifying correct operational topology using IEEE 123-node feeder with the help of numerous simulations studies. It is observed that the approach performs well even at very high percentages of load and flow measurement errors.

Missed detection ratio (MDR), Mixed-integer linear↗

DOE OSTI2014

A reduced-order modeling approach to represent subgrid-scale hydrological dynamics for land-surface simulations: application in a polygonal tundra landscape

Existing land surface models (LSMs) describe physical and biological processes that occur over a wide range of spatial and temporal scales. For example, biogeochemical and hydrological processes responsible for carbon (CO 2 , CH 4 ) exchanges with the atmosphere range from the molecular scale (pore-scale O 2 consumption) to tens of kilometers (vegetation distribution, river networks). Additionally, many processes within LSMs are nonlinearly coupled (e.g., methane production and soil moisture dynamics), and therefore simple linear upscaling techniques can result in large prediction error. In this paper we applied a reduced-order modeling (ROM) technique known as "proper orthogonal decomposition mapping method" that reconstructs temporally resolved fine-resolution solutions based on coarse-resolution solutions. We developed four different methods and applied them to four study sites in a polygonal tundra landscape near Barrow, Alaska. Coupled surface–subsurface isothermal simulations were performed for summer months (June–September) at fine (0.25 m) and coarse (8 m) horizontal resolutions. We used simulation results from three summer seasons (1998–2000) to build ROMs of the 4-D soil moisture field for the study sites individually (single-site) and aggregated (multi-site). The results indicate that the ROM produced a significant computational speedup (> 10 3 ) with very small relative approximation error (< 0.1%) for 2 validation years not used in training the ROM. We also demonstrate that our approach: (1) efficiently corrects for coarse-resolution model bias and (2) can be used for polygonal tundra sites not included in the training data set with relatively good accuracy (< 1.7% relative error), thereby allowing for the possibility of applying these ROMs across a much larger landscape. By coupling the ROMs constructed at different scales together hierarchically, this method has the potential to efficiently increase the resolution of land models for coupled climate simulations to spatial scales consistent with mechanistic physical process representation.

Pau, G. S. H.↗

DOE OSTI2016

SU-F-E-02: A Feasibility Study for Application of Metal Artifact Reduction Techniques in MR-Guided Brachytherapy Gynecological Cancer with Titanium Applicators

Purpose: Utilization of Titanium Tandem and Ring (T&R) applicators in MR-guided brachytherapy has become widespread for gynecological cancer treatment. However, Titanium causes magnetic field disturbance and susceptibility artifact, which complicate image interpretation. In this study, metal artifact reduction techniques were employed to improve the image quality and reduce the metal related artifacts. Methods: Several techniques were employed to reduce the metal artifact caused by titanium T&R applicator. These techniques include Metal Artifact Reduction Sequence (MARS), View Angle Tilting (VAT) to correct in-plane distortion, and Slice Encoding for Metal Artifact Correction (SEMAC) for through-plane artifact correction. Moreover, MARS can be combined with VAT to further reduce the in-plane artifact by reapplying the selection gradients during the readout (MARS+VAT). SEMAC uses a slice selective excitation but acquires additional z-encodings in order to resolve off-resonant signal and to reduce through-plane distortions. Results: Comparison between the clinical sequences revealed that increasing the bandwidth reduces the error in measured diameter of T&R. However, the error is larger than 4mm for the best case with highest bandwidth and spatial resolution. MARS+VAT with isotropic resolution of 1mm reduced the error to 1.9mm which is the least among the examined 2D sequences. The measured diameter of tandem from SEMAC+VAT has the closest value to the actual diameter of tandem (3.2mm) and the error was reduced to less than 1mm. In addition, SEMAC+VAT significantly reduces the blooming artifact in the ring compared to clinical sequences. Conclusion: A higher bandwidth and spatial resolution sequence reduces the artifact and diameter of applicator with a slight compromise in SNR. Metal artifact reduction sequences decrease the distortion associated with titanium applicator. SEMAC+VAT sequence in combination with VAT revealed promising results for titanium imaging and can be utilized for MR-guided brachytherapy in gynecological cancer. The author is employee with Philips Healthcare.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

Sci—Sat AM: Stereo — 09: Accuracy of Liver Cancer Treatment on Cyberknife® with Synchrony™ Optical Tracking Throughout the Respiratory Cycle

The Cyberknife® robotic stereotactic body radiation therapy system is well-suited for treating liver lesions over the respiratory cycle as it includes room-mounted orthogonal x-ray tracking of internal fiducial markers and optical tracking of external markers. The Synchrony™ software generates a model of internal target positions during patient respiration and correlates it to the external optical tracking system for real-time optical-based position corrections of the linear accelerator during beam delivery. Although clinical studies have provided preliminary outcomes for liver lesions treated with the Cyberknife system, to date, there is little data demonstrating the ability of the Synchrony software to track targets in the liver, which deforms throughout the respiratory cycle. In this study, we investigated the respiratory motion model performance for predicting tumour motion. We conducted a retrospective analysis of fifteen liver cancer patients treated on the Cyberknife using the Synchrony optical tracking system. We analyzed Cyberknife tracking information stored in the log files to extract the left-right (LR), anterior-posterior (AP) and superior-inferior (SI) correlation errors between the model-predicted position and the internal fiducial centroid position determined by x-ray imaging. Only translational tracking and corrections were applied during treatment. Overall, the correlation errors were greatest in the SI direction. We calculated radial correlation errors, and determined that the 95{sup th}, 98{sup th} and 99{sup th} percentile errors were 3.4 mm, 4.4 mm and 5.1 mm, respectively. Based on translational correlation tracking errors we expect the clinical target volume will be within 3.4 mm of the planning target volume for 95 % of beam delivery time.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Comparing the DES-SN5YR and Pantheon+ SN cosmology analyses: investigation based on ‘evolving dark energy or supernovae systematics’?

Recent cosmological analyses measuring distances of type Ia supernovae (SNe Ia) and baryon acoustic oscillations (BAO) have all given similar hints at time-evolving dark energy. To examine whether underestimated SN Ia systematics might be driving these results, Efstathiou (2025) compared overlapping SN events between Pantheon+ and DES-SN5YR (20 per cent SNe are in common), and reported evidence for an $\sim$0.04 mag offset between the low- and high-redshift distance measurements of this subsample of events. If this offset is arbitrarily subtracted from the entire DES-SN5YR sample, the preference for evolving dark energy is reduced. In this paper, we show that this offset is mostly due to different corrections for Malmquist bias between the two samples; therefore, an object-to-object comparison can be misleading. Malmquist bias corrections differ between the two analyses for several reasons. First, DES-SN5YR used an improved model of SN Ia luminosity scatter compared to Pantheon+ but the associated scatter-model uncertainties are included in the error budget. Secondly, improvements in host mass estimates in DES-SN5YR also affected SN standardized magnitudes and their bias corrections. Thirdly, and most importantly, the selection functions of the two compilations are significantly different, hence the inferred Malmquist bias corrections. Even if the original scatter model and host properties from Pantheon+ are used instead, the evidence for evolving dark energy from CMB, DESI BAO Year 1 and DES-SN5YR is only reduced from 3.9$\sigma$ to 3.3$\sigma$, consistent with the error budget. Finally, in this investigation, we identify an underestimated systematic uncertainty related to host galaxy property uncertainties, which could increase the final DES-SN5YR error budget by 3 per cent. In conclusion, we confirm the validity of the published DES-SN5YR results.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Hierarchical mixture of experts and diagnostic modeling approach to reduce hydrologic model structural uncertainty

In most water resources applications, a single model structure might be inadequate to capture the dynamic multi-scale interactions among different hydrological processes. Calibrating single models for dynamic catchments, where multiple dominant processes exist, can result in displacement of errors from structure to parameters, which in turn leads to over-correction and biased predictions. An alternative to a single model structure is to develop local expert structures that are effective in representing the dominant components of the hydrologic process and adaptively integrate them based on an indicator variable. In this study, the Hierarchical Mixture of Experts (HME) framework is applied to integrate expert model structures representing the different components of the hydrologic process. Various signature diagnostic analyses are used to assess the presence of multiple dominant processes and the adequacy of a single model, as well as to identify the structures of the expert models. The approaches are applied for two distinct catchments, the Guadalupe River (Texas) and the French Broad River (North Carolina) from the Model Parameter Estimation Experiment (MOPEX), using different structures of the HBV model. Furthermore, the results show that the HME approach has a better performance over the single model for the Guadalupe catchment, where multiple dominant processes are witnessed through diagnostic measures. Whereas, the diagnostics and aggregated performance measures prove that French Broad has a homogeneous catchment response, making the single model adequate to capture the response.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

When and why PINNs fail to train: A neural tangent kernel perspective

Physics-informed neural networks (PINNs) have lately received great attention thanks to their flexibility in tackling a wide range of forward and inverse problems involving partial differential equations. However, despite their noticeable empirical success, little is known about how such constrained neural networks behave during their training via gradient descent. More importantly, even less is known about why such models sometimes fail to train at all. Here in this work, we aim to investigate these questions through the lens of the Neural Tangent Kernel (NTK); a kernel that captures the behavior of fully-connected neural networks in the infinite width limit during training via gradient descent. Specifically, we derive the NTK of PINNs and prove that, under appropriate conditions, it converges to a deterministic kernel that stays constant during training in the infinite-width limit. This allows us to analyze the training dynamics of PINNs through the lens of their limiting NTK and find a remarkable discrepancy in the convergence rate of the different loss components contributing to the total training error. To address this fundamental pathology, we propose a novel gradient descent algorithm that utilizes the eigenvalues of the NTK to adaptively calibrate the convergence rate of the total training error. Finally, we perform a series of numerical experiments to verify the correctness of our theory and the practical effectiveness of the proposed algorithms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Anomalous behavior in high-pressure carbonaceous sulfur hydride

A new experimental study reported behavior in a high-pressure carbon-sulfur-hydrogen system that has been interpreted by the authors as superconductivity at room temperature. The sudden change of electrical resistance at a critical temperature and the change of the R vs. T behavior with an applied magnetic field point to superconductivity. This is a very exciting study in one of the most important areas of science, hence, it is crucial for the community to investigate these findings and hopefully reproduce these results. In this paper, we present calculations that expand upon the arguments put forth, and offer some speculations about physical mechanisms that might explain the observed data. Here, we show that there are errors in the analysis presented in the experimental paper, and with the correct analysis, the reported R vs. T data significantly deviate from the expected behavior. In particular, the extremely sharp change in resistance at the superconducting transition is not consistent with a strongly type II superconductor.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2017

Coupled Cluster and Density Functional Studies of Atomic Fluorine Chemisorption on Coronene as Model Systems for Graphene Fluorination

Potential energy curves (PECs) of atomic fluorine adsorption on coronene as a model for graphene or nanocarbon surfaces have been computed. The PECs were obtained by scanning the fluorine atom distance to one of the center carbon atoms of the coronene molecule as model system in a “top” position from 4.0 to1.0 Å in intervals of 0.1 Å using a variety of quantum chemical methods. Various density functional theory (DFT) functionals, such as B3LYP, PBE, PBE0, CAM-B3LYP, and LC-ωPBE; approximate DFT methods such as several levels of the density-functional tight-binding (DFTB) method, as well as ab initio wave function theory methods such as MP2, CCSD, CCSD(T), and G2MS extrapolations, were used to evaluate energies for B3LYP/cc-pVDZ PEC geometries. G2MS is an approximation to the highly accurate CCSD(T)/cc-pVTZ level of theory in our work. We found that fluorine is chemically adsorbed on coronene with a binding energy of 22.9 and 23.3 kcal/mol at the B3LYP/cc-pVDZ and G2MS levels of theory, respectively, and 18.3 and 19.3 kcal/mol after counterpoise correction. Here, we found that pure DFT functionals and their DFTB approximations fail to predict the correct dissociation limit due to the DFT-inherent self-interaction error and various limitations in the DFTB approximation itself.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Correction to Insights into the Performance Limits of the Li 7 P 3 S 11 Superionic Conductor: A Combined First-Principles and Experimental Study

Page 7847. The authors express regret that one mistake was made in their paper. In Figure 4b, the “Endo-Exo” direction was supposed to be reversed. When the orientation is corrected, T g is assigned an endothermic process and T c is assigned an exothermic process. A corrected Figure 4b (along with its original caption) is provided. Although the error was present, it does not affect the interpretation and conclusions of the manuscript as a whole. The authors apologize for any inconvenience this may have caused the readers.

36 MATERIALS SCIENCE↗

DOE OSTI2019

A Measurement‐Model Fusion Approach for Improved Wet Deposition Maps and Trends

Abstract Air quality models provide spatial fields of wet deposition (WD) and dry deposition that explicitly account for the transport and transformation of emissions from thousands of sources. However, many sources of uncertainty in the air quality model including errors in emissions and meteorological inputs (particularly precipitation) and incomplete descriptions of the chemical and physical processes governing deposition can lead to bias and error in the simulation of WD. We present an approach to bias correct Community Multiscale Air Quality model output over the contiguous United States using observation‐based gridded precipitation data generated by the Parameter‐elevation Regressions on Independent Slopes Model and WD observations at the National Atmospheric Deposition Program National Trends Network sites. A cross‐validation analysis shows that the adjusted annual accumulated WD for NO 3 − , NH 4 + , and SO 4 2− from 2002 to 2012 has less bias and higher correlation with observed values than the base model output without adjustment. Temporal trends in observed WD are captured well by the adjusted model simulations across the entire contiguous United States. Consistent with previous trend analyses, WD NO 3 − and SO 4 2− are shown to decrease during this period in the eastern half of the United States, particularly in the Northeast, while remaining nearly constant in the West. Trends in WD of NH 4 + are more spatially and temporally heterogeneous, with some positive trends in the Great Plains and Central Valley of CA and slightly negative trends in the south.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Erratum: Planck 2018 results: VI. Cosmological parameters

In the original version, the bounds given in Eqs. (87a) and (87b) on the contribution to the early-time optical depth, (15,30), contained a numerical error in deriving the 95th percentile from the Monte Carlo samples. The corrected 95% upper bounds are: τ(15,30) < 0:018 (lowE, flat τ(15, 30), FlexKnot), (1) τ(15, 30) < 0:023 (lowE, flat knot, FlexKnot): (2) These bounds are a factor of 3 larger than the originally reported results. Consequently, the new bounds do not significantly improve upon previous results from Planck data presented in Millea & Bouchet (2018) as was stated, but are instead comparable. Equations (1) and (2) give results that are now similar to those of Heinrich & Hu (2021), who used the same Planck 2018 data to derive a 95% upper bound of 0.020 using the principal component analysis (PCA) model and uniform priors on the PCA mode amplitudes.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

The nonconforming Virtual Element Method for eigenvalue problems

In this work, we analyse the nonconforming Virtual Element Method (VEM) for the approximation of elliptic eigenvalue problems. The nonconforming VEM allows to treat in the same formulation the two- and three-dimensional case. We present two possible formulations of the discrete problem, derived respectively by the nonstabilized and stabilized approximation of the $L^2$-inner product, and we study the convergence properties of the corresponding discrete eigenvalue problem. The proposed schemes provide a correct approximation of the spectrum, in particular we prove optimal-order error estimates for the eigenfunctions and the usual double order of convergence of the eigenvalues. Finally we show a large set of numerical tests supporting the theoretical results, including a comparison with the conforming Virtual Element choice.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Practical GW scheme for electronic structure of 3 d -transition-metal monoxide anions: ScO - , TiO - , CuO - , and ZnO -

The GW approximation to many-body perturbation theory is a reliable tool for describing charged electronic excitations, and it has been successfully applied to a wide range of extended systems for several decades using a plane-wave basis. However, the GW approximation has been used to test limited spectral properties of a limited set of finite systems (e.g., frontier orbital energies of closed-shell sp molecules) only for about a decade using a local-orbital basis. Here, we calculate the quasiparticle spectra of closed- and open-shell molecular anions with partially and completely filled 3 d shells (shallow and deep 3 d states, respectively), ScO - , TiO - , CuO - , and ZnO - , using various levels of GW theory, and compare them to experiments to evaluate the performance of the GW approximation on the electronic structure of small molecules containing 3 d transition metals. We find that the G -only eigenvalue self-consistent GW scheme with W fixed to the PBE level ($G_nW_0$@PBE), which gives the best compromise between accuracy and efficiency for solids, also gives good results for both localized ( d ) and delocalized ( sp ) states of 3 d -transition-metal oxide molecules. The success of $G_nW_0$@PBE in predicting electronic excitations in these systems reasonably well is likely due to the fortuitous cancellation effect between the overscreening of the Coulomb interaction by PBE and the underscreening by the neglect of vertex corrections. Together with the absence of the self-consistent field convergence error (e.g., spin contamination in open-shell systems) and the GW multisolution issue, the $G_nW_0$@PBE scheme gives the possibility to predict the electronic structure of complex real systems (e.g., molecule-solid and sp-d hybrid systems) accurately and efficiently.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Impact and mitigation of spectroscopic systematics on DESI DR1 clustering measurements

The large scale structure catalogs within DESI Data Release 1 (DR1) use nearly 6 million galaxies and quasars as tracers of the large-scale structure of the universe to measure the expansion history with baryon acoustic oscillations and the growth of structure with redshift-space distortions. In order to take advantage of DESI's unprecedented statistical power, we must ensure that the galaxy clustering measurements are unaffected by non-cosmological density fluctuations. One source of spurious fluctuations comes from variation in galaxy density with spectroscopic observing conditions, lowering the redshift efficiency (and thus galaxy density) in certain areas of the sky. We measure the uniformity of the redshift success rate for DESI luminous red galaxies (LRG), bright galaxies (BGS) and quasars (QSO), complementing the detailed discussion of emission line galaxy (ELG) systematics in a companion paper [1]. We find small but significant fluctuations of up to 3% in redshift success rate with the effective spectroscopic signal-to-noise, and create and describe weights that remove these fluctuations. We also describe the process to identify and remove data from certain poorly performing fibers from DESI DR1, and measure the stability of the redshift success rate with time. Finally, we find small but significant correlations of redshift success rate with position on the focal plane, survey speed, and number of exposures required, and show the impact of weights correcting these trends on the power spectrum multipoles and on cosmological parameters from BAO and RSD fits. These corrections change the best-fit parameters by <15% of their statistical errors, and thus contribute negligibly to the overall DESI error budget.

cosmological parameters from LSS↗

DOE OSTI2022

Measurement of integrated luminosities at BESIII for data samples at center-of-mass energies between 4.0 and 4.6 GeV*

Abstract The integrated luminosities of data samples collected in the BESIII experiment in 2016–2017 at center-of-mass energies between 4.19 and 4.28 GeV are measured with a precision better than 1% by analyzing large-angle Bhabha scattering events. The integrated luminosities of old datasets collected in 2010–2014 are updated by considering corrections related to detector performance, offsetting the effect of newly discovered readout errors in the electromagnetic calorimeter, which can haphazardly occur.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Impacts of floating-point non-associativity on reproducibility for HPC and deep learning applications

Run to run variability in parallel programs caused by floating-point non-associativity has been known to significantly affect reproducibility in iterative algorithms, due to accumulating errors. Non-reproducibility can critically affect the efficiency and effectiveness of correctness testing for stochastic programs. Recently, the sensitivity of deep learning training and inference pipelines to floating-point non-associativity has been found to sometimes be extreme. It can prevent certification for commercial applications, accurate assessment of robustness and sensitivity, and bug detection. New approaches in scientific computing applications have coupled deep learning models with high-performance computing, leading to an aggravation of debugging and testing challenges. Here we perform an investigation of the statistical properties of floating-point non-associativity within modern parallel programming models, and analyze performance and productivity impacts of replacing atomic operations with deterministic alternatives on GPUs. We examine the recently-added deterministic options in PyTorch within the context of GPU deployment for deep learning, uncovering and quantifying the impacts of input parameters triggering run to run variability and reporting on the reliability and completeness of the documentation. Finally, we evaluate the strategy of exploiting automatic determinism that could be provided by deterministic hardware, using the Groq LPUTM accelerator for inference portions of the deep learning pipeline. We demonstrate the benefits that a hardware-based strategy can provide within reproducibility and correctness efforts.

Shanmugavelu, Sanjif↗