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At least 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Correlated Synthetic Time Series Generation using Fourier and ARMA

As the contribution of renewable energy grows in electricity markets, the complexity nuclear’s place within the energy mix increases, and likewise the need for robust simulation techniques. While decades of wind, solar, and demand pro- files can sometimes be obtained, this is too few samples to provide a statistically meaningful analysis of a system with baseload, peaker, and renewable generation. Synthetic time series generation presents itself as a suitable methodology to meet this need. One approach for synthetic series generation is training a model using Fourier series decomposition for seasonal patterns and Auto-Regressive Moving Average models (ARMA) to describe time-correlated statistical noise about the seasonal patterns. When combined, the Fourier plus ARMA (FARMA) model has been shown to provide an infinite set of independent, identically-distributed sample time series with the same statistical properties as the original data [1]. When considering an energy mix with renewable electricity production, several time series of energy, grid, and weather measurements are needed for each synthetic year modeled to statistically comprehend the efficiency of any given energy mix. These cannot be considered independent series in a given synthetic year. To capture and reproduce the correlations that might exist in the measured histories, the ARMA can further be extended as a Vector ARMA (VARMA). In the VARMA algorithm, covariance in statistical noise is captured both within a history as part of the autoregressive moving average, and with respect to the other variables in the time series. In this work the implementation of the Fourier VARMA in the RAVEN uncertainty quantification and risk analysis software framework [2] is presented, along with examples of correlated synthetic history generation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Ball-on-ring test validation for equibiaxial flexural strength testing of engineered ceramics

The validation of a ball-on-ring, equibiaxial flexural strength method to obtain the transverse rupture strength (TRS) of right cylindrical ceramic specimens was performed in this study. Validation of the test method was achieved using commercially available engineered high purity alumina disks and finite element (FE) model analysis. The validated fixture was then used to obtain the TRS and Weibull statistical analysis of MgO-partially stabilized zirconia (MSZ) and Y 2 O 3 -partially stabilized zirconia (YSZ) ceramic disks. TRS data for alumina, MSZ, and YSZ agreed with the TRS values reported in the literature. A statistically relevant number of samples (N > 30) for each material were tested to allow for a Weibull statistical analysis. Weibull parameters for these materials were within the expected values for engineered ceramics. The characteristic strength for alumina, MSZ, and YSZ were determined to be 289, 786, and 814 MPa, respectively. The Weibull modulus was determined between 10 and 25 for each material, which is typical of engineered ceramics. In addition, FE model results were in close agreement with experimental fracture values for the three ceramic materials tested in this study.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Identifying paleowater in California drinking water wells

Identification of paleowater in aquifers tapped for the public water supply is important both for sound water management, because paleowater is likely to be withdrawn unsustainably in high demand regions in the absence of large scale artificial recharge operations, and for contamination susceptibility assessment, because paleowater is typically isolated from contamination sources. In this study, paleowater, which herein includes pre-Holocene groundwater and also water that recharged well before the onset of significant human alteration of the hydrologic system in California, is identified using three key isotopic indicators of groundwater residence time: tritium ( 3 H), radiocarbon ( 14 C), and radiogenic helium-4 ( 4 He rad ). In this work, we compared results of these three tracers from a unique data set of more than 2000 wells that are predominantly long-screened drinking water production wells. Although considerable uncertainty is associated with calculated apparent ages for individual samples, non-parametric statistical tests indicate that the composite set of isotopic indicators support classification of samples into categories that allow identification of wells most likely to produce paleowater. Approximately 7% of the wells included in the study show strong evidence for producing paleowater, with screens extending greater than 146 m below ground surface, in which 3 H activity is less than 1 pCi/L, 14C activity is less than 40.9 pmC, and 4Herad concentration exceeds 7.4 × 10 –8 cm 3 STP/g water . An additional 22% of wells produce mixed-age water with a component of paleowater, with screens extending greater than 95 m below ground surface, in which 3 H is less than 5 pCi/L, and 14C activity is less than 95.91 pmC. Wells in desert basins of southeastern California and wells in the southwestern quadrant of the Central Valley are most likely to produce paleowater that is pre-Holocene in age. Very few wells in the northwestern portion of the state, the foothills and Sierra Nevada regions, and coastal basins with intensive artificial recharge activities are categorized as producing paleowater. Climate is the primary control on paleowater occurrence, with arid portions of the state that were wetter during the Pleistocene having the largest number of wells categorized as producing paleowater. Secondarily, paleowater is found at the end of very long flow paths in confined aquifers, e.g., in the center of the Northern Sacramento Valley. In contrast, paleowater may be masked in areas where unconfined or semi-confined conditions allow substantial mixing between modern recharge and paleowater. Modern, artificially recharged water has replaced very old groundwater on a large scale in urban coastal basins.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

The local black hole mass function derived from the M{sub BH}–P and the M{sub BH}–n relations

We present a determination of the supermassive black hole (SMBH) mass function for early- and late-type galaxies in the nearby universe (z < 0.0057), established from a volume-limited sample consisting of a statistically complete collection of the brightest spiral galaxies in the southern hemisphere. The sample is defined by limiting luminosity (redshift-independent) distance, D {sub L} = 25.4 Mpc, and a limiting absolute B-band magnitude, M{sub B}=−19.12. These limits define a sample of 140 spiral, 30 elliptical (E), and 38 lenticular (S0) galaxies. We established the Sérsic index distribution for early-type (E/S0) galaxies in our sample. Davis et al. established the pitch angle distribution for their sample, which is identical to our late-type (spiral) galaxy sample. We then used the pitch angle and the Sérsic index distributions in order to estimate the SMBH mass function for our volume-limited sample. The observational simplicity of our approach relies on the empirical relation between the mass of the central SMBH and the Sérsic index for an early-type galaxy or the logarithmic spiral-arm pitch angle for a spiral galaxy. Our SMBH mass function agrees well at the high-mass end with previous values in the literature. At the low-mass end, although inconsistencies exist in previous works that still need to be resolved, our work is more in line with expectations based on modeling of black hole evolution.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1986

Evolution of the luminosity function of extragalactic objects

A nonparametric procedure for determining the evolution of the luminosity function (LF) of extragalactic objects is described. The following steps are required: (1) selection of complete samples with known observational selection effects, (2) choice of a cosmological model, (3) statistical analysis of the sample and determination of the evolution of the luminosity function, and (4) the use of the luminosity function for the determination of the formation rate and the physical evolution of the objects. When the present technique is applied to a sample of optically selected quasars with redshifts less than two, it is shown that the LF evolves more strongly for higher luminosities, indicating a larger quasar activity at earlier epochs and a more rapid evolution of the objects during their higher luminosity phases.

Petrosian, V.↗
NASA NTRS2006

Convective Weather Forecast Quality Metrics for Air Traffic Management Decision-Making

Since numerical weather prediction models are unable to accurately forecast the severity and the location of the storm cells several hours into the future when compared with observation data, there has been a growing interest in probabilistic description of convective weather. The classical approach for generating uncertainty bounds consists of integrating the state equations and covariance propagation equations forward in time. This step is readily recognized as the process update step of the Kalman Filter algorithm. The second well known method, known as the Monte Carlo method, consists of generating output samples by driving the forecast algorithm with input samples selected from distributions. The statistical properties of the distributions of the output samples are then used for defining the uncertainty bounds of the output variables. This method is computationally expensive for a complex model compared to the covariance propagation method. The main advantage of the Monte Carlo method is that a complex non-linear model can be easily handled. Recently, a few different methods for probabilistic forecasting have appeared in the literature. A method for computing probability of convection in a region using forecast data is described in Ref. 5. Probability at a grid location is computed as the fraction of grid points, within a box of specified dimensions around the grid location, with forecast convection precipitation exceeding a specified threshold. The main limitation of this method is that the results are dependent on the chosen dimensions of the box. The examples presented Ref. 5 show that this process is equivalent to low-pass filtering of the forecast data with a finite support spatial filter. References 6 and 7 describe the technique for computing percentage coverage within a 92 x 92 square-kilometer box and assigning the value to the center 4 x 4 square-kilometer box. This technique is same as that described in Ref. 5. Characterizing the forecast, following the process described in Refs. 5 through 7, in terms of percentage coverage or confidence level is notionally sound compared to characterizing in terms of probabilities because the probability of the forecast being correct can only be determined using actual observations. References 5 through 7 only use the forecast data and not the observations. The method for computing the probability of detection, false alarm ratio and several forecast quality metrics (Skill Scores) using both the forecast and observation data are given in Ref. 2. This paper extends the statistical verification method in Ref. 2 to determine co-occurrence probabilities. The method consists of computing the probability that a severe weather cell (grid location) is detected in the observation data in the neighborhood of the severe weather cell in the forecast data. Probabilities of occurrence at the grid location and in its neighborhood with higher severity, and with lower severity in the observation data compared to that in the forecast data are examined. The method proposed in Refs. 5 through 7 is used for computing the probability that a certain number of cells in the neighborhood of severe weather cells in the forecast data are seen as severe weather cells in the observation data. Finally, the probability of existence of gaps in the observation data in the neighborhood of severe weather cells in forecast data is computed. Gaps are defined as openings between severe weather cells through which an aircraft can safely fly to its intended destination. The rest of the paper is organized as follows. Section II summarizes the statistical verification method described in Ref. 2. The extension of this method for computing the co-occurrence probabilities in discussed in Section HI. Numerical examples using NCWF forecast data and NCWD observation data are presented in Section III to elucidate the characteristics of the co-occurrence probabilities. This section also discusses the procedure for computing throbabilities that the severity of convection in the observation data will be higher or lower in the neighborhood of grid locations compared to that indicated at the grid locations in the forecast data. The probability of coverage of neighborhood grid cells is also described via examples in this section. Section IV discusses the gap detection algorithm and presents a numerical example to illustrate the method. The locations of the detected gaps in the observation data are used along with the locations of convective weather cells in the forecast data to determine the probability of existence of gaps in the neighborhood of these cells. Finally, the paper is concluded in Section V.

Chatterji, Gano B.↗
DOE OSTI2016

SU-F-T-362: Quantification and Modelling of the Ionization Chamber Simulation Effective Points On Monaco Treatment Planning System

Purpose: Because of statistical noise in Monte Carlo dose calculations, effective point doses may not be accurate. Volume spheres are useful for evaluating dose in Monte Carlo plans, which have an inherent statistical uncertainty.We use a user-defined sphere volume instead of a point, take sphere sampling around effective point make the dose statistics to decrease the stochastic errors. Methods: Direct dose measurements were made using a 0.125cc Semiflex ion chamber (IC) 31010 isocentrically placed in the center of a homogeneous Cylindric sliced RW3 phantom (PTW, Germany).In the scanned CT phantom series the sensitive volume length of the IC (6.5mm) were delineated and defined the isocenter as the simulation effective points. All beams were simulated in Monaco in accordance to the measured model. In our simulation using 2mm voxels calculation grid spacing and choose calculate dose to medium and request the relative standard deviation ≤0.5%. Taking three different assigned IC over densities (air electron density(ED) as 0.01g/cm3 default CT scanned ED and Esophageal lumen ED 0.21g/cm3) were tested at different sampling sphere radius (2.5, 2, 1.5 and 1 mm) statistics dose were compared with the measured does. Results: The results show that in the Monaco TPS for the IC using Esophageal lumen ED 0.21g/cm3 and sampling sphere radius 1.5mm the statistical value is the best accordance with the measured value, the absolute average percentage deviation is 0.49%. And when the IC using air electron density(ED) as 0.01g/cm3 and default CT scanned EDthe recommented statistical sampling sphere radius is 2.5mm, the percentage deviation are 0.61% and 0.70%, respectivly. Conclusion: In Monaco treatment planning system for the ionization chamber 31010 recommend air cavity using ED 0.21g/cm3 and sampling 1.5mm sphere volume instead of a point dose to decrease the stochastic errors. Funding Support No.C201505006.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Suppression of Helium Induced Cracking in Laser Repair Welding of Highly Irradiated Stainless Steels

Welding repair of irradiated nuclear reactor materials such as austenitic stainless steels used for the reactor internals has been challenging due to irradiation generated helium in the material matrix over an extended period of time. In this study, improved laser welding techniques, including the novel Auxiliary Beam Stress Improved (ABSI) technique that proactively manages the stresses during laser repair welding is investigated to substantially reduce or avoid the occurrence of intergranular helium-induced cracking (HeIC). Here, laser weld cladding, with and without ABSI technique, was applied to deposit weld metal on irradiated stainless steel (Type 304 L) blocks with various helium concentrations up to approximately 20 atomic parts per million (appm). Welded samples were cross-sectioned and analyzed using optical and electron microscopes. It was found that, with the improved laser welding techniques, no macroscopic (millimeter-sized) cracks were observed in any of the samples. Microscopic (less than 50 μm) cracks were present in the heat affected zone. Statistical analysis on the sample with the highest helium concentration (~20 appm) reveals that the ABSI technique could effectively reduce the size and amounts of microscopic cracks.

36 MATERIALS SCIENCE↗
NASA NTRS1982

Analysis of defect structure in silicon. Characterization of samples from UCP ingot 5848-13C

Statistically significant quantitative structural imperfection measurements were made on samples from ubiquitous crystalline process (UCP) Ingot 5848 - 13 C. Important trends were noticed between the measured data, cell efficiency, and diffusion length. Grain boundary substructure appears to have an important effect on the conversion efficiency of solar cells from Semix material. Quantitative microscopy measurements give statistically significant information compared to other microanalytical techniques. A surface preparation technique to obtain proper contrast of structural defects suitable for QTM analysis was perfected.

Natesh, R.↗
DOE OSTI2017

Graph Sampling for Visual Analytics

Effectively visualizing large graphs and capturing the statistical properties are two challenging tasks. To aid in these two tasks, many sampling approaches for graph simplification have been proposed, falling into three categories: node sampling, edge sampling, and traversal-based sampling. It is still unknown which approach is the best. We evaluate commonly used graph sampling methods through a combined visual and statistical comparison of graphs sampled at various rates. We conduct our evaluation on three graph models: random graphs, small-world graphs, and scale-free graphs. Initial results indicate that the effectiveness of a sampling method is dependent on the graph model, the size of the graph, and the desired statistical property. This benchmark study can be used as a guideline in choosing the appropriate method for a particular graph sampling task, and the results presented can be incorporated into graph visualization and analysis tools.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

COBRA:COMPUTED-TOMOGRAPHY BASED RANDOM-FIELD APPROXIMATION

SF-25-115 COBRA (COmputed-tomography Based Random-field Approximation) is a Python application for generating statistically equivalent random fields from CT-scan imagery. It leverages Karhunen–Loève expansions to model microstructural variability, enabling users to: Preprocess CT scans (filtering and Gaussian transformation); Fit covariance kernels fromempirical data; Solve eigenproblems to obtain KL modes; Sample random fields onsistent with fitted statistics; Postprocess samples back into the physical domain.

Hu, Tianchen↗
DOE OSTI2023

Understanding Biases in Sample Preparation Techniques for Coupled Scanning Electron Microscopy and MAMA PuO 2 Morphological Analysis

In this project, the scanning electron microscopy (SEM) sampling method used during the statistical design study (SDS) was investigated to determine if any sampling biases were present in the analyzed data. Using standard particle size distribution powders from the National Institute of Standards and Technology (NIST 1984 standard reference material) with the origin wet dispersion method, it was determined that a bias to smaller particles was present. This was supported by theoretical calculations using Stokes’ law to determine the settling rate of spherical particles of roughly the same size and mass as those found in the SDS. Based on the theoretical calculations, it was determined that the settling rate for each of the 76 powder sets in the SDS could be unique based on specific particle shape and mass distributions, making a universal correction factor/formula not applicable. Therefore, priority shifted to developing an improved wet dispersion method that significantly reduced the particle settling rate for all particle size and shapes. This was achieved by replacing the original solvent (isopropyl alcohol) with a heavy liquid (lithium heteropolytungstates), which dramatically slowed the settling rate and allowed for the capture of a suitable homogeneous aliquot. SEM imaging and Morphological Analysis for Material Attribution (MAMA) software analysis were conducted on the NIST standard, and the SEM/MAMA data were compared to data captured by a dynamic image analysis particle size analyzer. The resulting data confirmed that the new wet dispersion method does indeed deliver an improved representative aliquot to the SEM stub. For instance, in the NIST certificate, the average particle size is ~17.1 µm ± 2.2 µm with a normal distribution. The initial wet dispersion method resulted in a drastically reduced average particle size of 6.1 µm in addition to a non-representative heavy bi-modal distribution whereas the improved LST wet dispersion method resulting in an average particle size that was much closer to the NIST certificate (12.7 µm) with a similar normal distribution. Although the improved method was still short of the NIST certificate average, atomic force microscopy analysis determined that the resulting ~20-25% reduction in size was due to particles sinking into the carbon sticky tape used for SEM imaging. It is believed that that this bias can be calibrated in a much more predicable manner than the original settling rate bias. In addition, the matching normal distribution curves between the NIST certificate and the heavy liquid method indicate a much-improved representative aliquot has been sampled and imaged. A surrogate CeO 2 powder was used to reflect PuO 2 more accurately and to aid in implementing radiological controls and shielding. The resulting data sets from the SEM/MAMA method and the particle size analyzer give almost identical average particle sizes and particle distribution statistics. Future work will re-analyze several select runs from the SDS to determine if morphological signatures can be found with the improved sampling method.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Carbon Dioxide Sealing Capacity: Textural or Compositional Controls? (Final Scientific/Technical Report)

This research project is aiming to assess the carbon dioxide sealing capacity of most common seal-rocks, such as shales and non-fractured limestones, by analyzing the role of textural and compositional parameters of those rocks. We hypothesize that sealing capacity is controlled by textural and/or compositional parameters of caprocks. In this research, we seek to evaluate the importance of textural and compositional parameters affecting the sealing capacity of caprocks. The conceptual framework involves two testable end-member hypotheses concerning the sealing capacity of carbon dioxide reservoir caprocks. Better understanding of the elements controlling sealing quality will advance our knowledge regarding the sealing capacity of shales and carbonates. Due to relatively low permeability, shale and non-fractured carbonate units are considered relatively impermeable formations which can retard reservoir fluid flow by forming high capillary pressure. Similarly, these unites can constitute reliable seals for carbon dioxide capture and sequestration purposes. This project is a part of the comprehensive project with the final aim of studying the caprock sealing properties and the relationship between microscopic and macroscopic characteristics of seal rocks in depleted gas fields of Oklahoma Panhandle. Through this study we examined various seal rock characteristics to infer about their respective effects on sealing capacity in special case of replacing reservoir fluid with super critical carbon dioxide (scCO 2 ). To assess the effect of textural and compositional properties on scCO 2 maximum retention column height we collected 30 representative core samples in caprock formations in three counties (Cimarron, Texas, Beaver) in Oklahoma Panhandle. Core samples were collected from various seal formations (e.g., Cherokee, Keys, Morrowan) at different depths. We studied the compositional and textural properties of the core samples using several techniques. Mercury Injection Porosimetry (MIP), Scanning Electron Microscopy SEM, and Sedigraph measurements are used to assess the pore-throat-size distribution, sorting, texture, and grain size of the samples. Also, displacement pressure at 10% mercury saturation (Pd) and graphically derived threshold pressure (P c ) were determined by MIP technique. SEM images were used for qualitative study of the minerals and pores texture of the core samples. Moreover, EDS (Energy Dispersive X-Ray Spectrometer), BET specific surface area, and Total Organic Carbon (TOC) measurements were performed to study various parameters and their possible effects on sealing capacity of the samples. We found that shales have the relatively higher average sealing threshold pressure (P c ) than carbonate and sandstone samples. Based on these observations, shale formations could be considered as a promising caprock in terms of retarding scCO 2 flow and leakage into above formations. We hypothesized that certain characteristics of shales (e.g., 3 fine pore size, pore size distribution, high specific surface area, and strong physical chemical interaction between wetting phase and mineral surface) make them an efficient caprock for sealing super critical CO 2 . We found that the displacement pressure at 10% mercury saturation could not be the ultimate representative of the sealing capacity of the rock sample. On the other hand, we believe that graphical method, introduced by Cranganu (2004) is a better indicator of the true sealing capacity. Based on statistical analysis of our samples from Oklahoma Panhandle we assessed the effects of each group of properties (textural and compositional) on maximum supercritical CO 2 height that can be hold by the caprock. We conclude that there is a relatively strong positive relationship (+.40 to +.69) between supercritical CO 2 column height based on P c and hard/ soft mineral content index (ratio of minerals with Mohs hardness more than 5 over minerals with Mohs hardness less than 5) in both shales and limestone samples. Average median pore radius and porosity display a strong negative correlation with supercritical CO 2 retention column height. Also, increasing bulk density is positively correlated with the supercritical CO 2 retention column height. One of the most important factors affecting sealing capacity and consequently the height of supercritical CO 2 column is sorting of the pore throats. We observed a strong positive correlation between pore throat sorting and height of CO 2 retention column, especially in shales. This correlation could not be observed in limestone samples. It suggests that the pore throat sorting is more controlling the sealing capacity in shales and shales with well sorted pore throats are the most reliable lithology as seal. We observed that Brunauer–Emmett–Teller (BET) surface area shows a very strong correlation with CO 2 retention column height in limestone samples while BET surface area did not display significant correlation in shales. Pore structure based on SEM micrographs exhibits strong correlation with CO 2 retention column height in limestones. Both intercrystalline and vuggy structures have negative correlations while intergranular texture has positive correlation in limestone with respect to CO 2 retention column height. Textural effects observed on SEM micrographs did not show statistically significant correlation with supercritical CO 2 retention column height in shale samples. Finally, we showed that increasing hard/soft mineral index is strongly correlated with the displacement pressure in limestone samples. Vuggy texture displays a relatively strong and negative correlation with displacement pressure values at 10% mercury saturation in shale samples.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Air, surface, and wastewater surveillance of SARS-CoV-2; a multimodal evaluation of COVID-19 detection in a built environment

Environmental surveillance of infectious organisms holds tremendous promise to reduce human-to-human transmission in indoor spaces through early detection. In this study we determined the applicability and limitations of wastewater, indoor high-touch surfaces, in-room air, and rooftop exhaust air sampling methods for detecting SARS-CoV-2 in a real world building occupied by residents recently diagnosed with COVID-19. We concurrently examined the results of three 24-hour environmental surveillance techniques, indoor surface sampling, exhaust air sampling and wastewater surveillance, to the known daily census fluctuations in a COVID-19 isolation dormitory. Additionally, we assessed the ability of aerosol samplers placed in the large volume lobby to detect SARS-CoV-2 multiple times per day. Our research reveals an increase in the number of individuals confirmed positive with COVID-19 as well as their estimated human viral load to be associated with statistically significant increases in viral loads detected in rooftop exhaust aerosol samples (p = 0.0413), wastewater samples (p = 0.0323,), and indoor high-touch surfaces (p < 0.001)). We also report that the viral load detected in lobby aerosol samples was statistically higher in samples collected during presence of occupants whose COVID-19 diagnostic tests were confirmed positive via qPCR compared to periods when the lobby was occupied by either contact-traced (suspected positive) individuals or during unoccupied periods (p = 0.0314 and <2e–16). We conclude that each daily (24h) surveillance method, rooftop exhaust air, indoor high-touch surfaces, and wastewater, provide useful detection signals for building owner/operator(s). Furthermore, we demonstrate that exhaust air sampling can provide spatially resolved signals based upon ventilation exhaust zones. Additionally, we find that indoor lobby air sampling can provide temporally resolved signals useful during short duration sampling periods (e.g., 2-4 hours) even with intermittent occupancy by occupants diagnosed with COVID-19.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Preserving correlations between trajectories for efficient path sampling

Importance sampling of trajectories has proved a uniquely successful strategy for exploring rare dynamical behaviors of complex systems in an unbiased way. Carrying out this sampling, however, requires an ability to propose changes to dynamical pathways that are substantial, yet sufficiently modest to obtain reasonable acceptance rates. Satisfying this requirement becomes very challenging in the case of long trajectories, due to the characteristic divergences of chaotic dynamics. Here, we examine schemes for addressing this problem, which engineer correlation between a trial trajectory and its reference path, for instance using artificial forces. Our analysis is facilitated by a modern perspective on Markov chain Monte Carlo sampling, inspired by non-equilibrium statistical mechanics, which clarifies the types of sampling strategies that can scale to long trajectories. Viewed in this light, the most promising such strategy guides a trial trajectory by manipulating the sequence of random numbers that advance its stochastic time evolution, as done in a handful of existing methods. In cases where this “noise guidance” synchronizes trajectories effectively, as the Glauber dynamics of a two-dimensional Ising model, we show that efficient path sampling can be achieved for even very long trajectories.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Large-scale application of free energy perturbation calculations for antibody design

Abstract Alchemical free energy perturbation (FEP) is a rigorous and powerful technique to calculate the free energy difference between distinct chemical systems. Here we report our implementation of automated large-scale FEP calculations, using the Amber software package, to facilitate antibody design and evaluation. In combination with Hamiltonian replica exchange, our FEP simulations aim to predict the effect of mutations on both the binding affinity and the structural stability. Importantly, we incorporate multiple strategies to faithfully estimate the statistical uncertainties in the FEP results. As a case study, we apply our protocols to systematically evaluate variants of the m396 antibody for their conformational stability and their binding affinity to the spike proteins of SARS-CoV-1 and SARS-CoV-2. By properly adjusting relevant parameters, the particle collapse problems in the FEP simulations are avoided. Furthermore, large statistical errors in a small fraction of the FEP calculations are effectively reduced by extending the sampling, such that acceptable statistical uncertainties are achieved for the vast majority of the cases with a modest total computational cost. Finally, our predicted conformational stability for the m396 variants is qualitatively consistent with the experimentally measured melting temperatures. Our work thus demonstrates the applicability of FEP in computational antibody design.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Point Adversarial Self-Mining: A Simple Method for Facial Expression Recognition

In this article, we propose a simple yet effective approach, called point adversarial self mining (PASM), to improve the recognition accuracy in facial expression recognition (FER). Unlike previous works focusing on designing specific architectures or loss functions to solve this problem, PASM boosts the network capability by simulating human learning processes: providing updated learning materials and guidance from more capable teachers. Specifically, to generate new learning materials, PASM leverages a point adversarial attack method and a trained teacher network to locate the most informative position related to the target task, generating harder learning samples to refine the network. The searched position is highly adaptive since it considers both the statistical information of each sample and the teacher network capability. Other than being provided new learning materials, the student network also receives guidance from the teacher network. After the student network finishes training, the student network changes its role and acts as a teacher, generating new learning materials and providing stronger guidance to train a better student network. Here, the adaptive learning materials generation and teacher/student update can be conducted more than one time, improving the network capability iteratively. Extensive experimental results validate the efficacy of our method over the existing state of the arts for FER.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1981

Statistical classification techniques for engineering and climatic data samples

Fisher's sample linear discriminant function is modified through an appropriate alteration of the common sample variance-covariance matrix. The alteration consists of adding nonnegative values to the eigenvalues of the sample variance covariance matrix. The desired results of this modification is to increase the number of correct classifications by the new linear discriminant function over Fisher's function. This study is limited to the two-group discriminant problem.

Temple, E. C.↗