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At least 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Modeling interval trendlines: Symbolic singular spectrum analysis for interval time series

Abstract In this article we propose an extension of singular spectrum analysis for interval‐valued time series. The proposed methods can be used to decompose and forecast the dynamics governing a set‐valued stochastic process. The resulting components on which the interval time series is decomposed can be understood as interval trendlines, cycles, or noise. Forecasting can be conducted through a linear recurrent method, and we devised generalizations of the decomposition method for the multivariate setting. The performance of the proposed methods is showcased in a simulation study. We apply the proposed methods so to track the dynamics governing the Argentina Stock Market (MERVAL) in real time, in a case study over a period of turbulence that led to discussions of the government of Argentina with the International Monetary Fund.

de Carvalho, Miguel↗
DOE OSTI2023

Switchgrass sward establishment selection is consistent across multiple environments and fertilization levels

Strong selection can occur during switchgrass sward establishment. Differences in establishment selection due to environment or management could provide information on genotype-by-environment variation and could influence strategies for breeding perennial grasses. Leaf samples were collected before sward establishment and from 3-year-old swards for two breeding groups (lowland and hybrid) at three locations. Within two locations, samples were collected from paired fertilized (112 kg N ha –1 ) and unfertilized plots. Allele frequencies from pooled DNA samples were studied through multivariate analysis of variance, genomewide trait predictions (heading date and winter survivorship), and genomically estimated breeding values (GEBVs) for individual sward survival within an independent data set. This study found only minor variations in selection due to location or management. Predicted heading dates of the hybrid population had significant changes due to fertilization and location. There were strong correlations among sward establishment survival GEBVs between growing environments (hybrid r = 0.77; gulf r = 0.97). Interestingly, this study found a small number of genotypes that were over-represented in established swards across all growing environments. This study reinforces a prior report of selection during sward establishment and indicates that only a small degree of establishment selection is location-specific within these diverse growing conditions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Clinical characteristics of chronic lymphocytic leukemia occurring in chornobyl cleanup workers

Abstract The recently demonstrated radiation‐induction of chronic lymphocytic leukemia (CLL) raises the question as to whether the amount of radiation exposure influences any of the clinical characteristics of the disease. We evaluated the relationship between bone marrow radiation doses and clinical characteristics and survival of 79 CLL cases diagnosed during 1986–2006 in a cohort of 110 645 male workers who participated in the cleanup work of the Chornobyl nuclear accident in Ukraine in 1986. All diagnoses were confirmed by an independent International Hematology Panel. Patients were followed up to the date of death or end of follow‐up on 31 October 2010. The median age at diagnosis was 57 years. Median bone marrow dose was 22.6 milligray (mGy) and was not associated with time between exposure and clinical diagnosis of CLL (latent period), age, peripheral blood lymphocyte count or clinical stage of disease in univariate and multivariate analyses. Latent period was significantly shorter among those older at first exposure, smokers and those with higher frequency of visits to the doctor prior to diagnosis. A significant increase in the risk of death with increasing radiation dose was observed ( p = 0.03, hazard ratio = 2.38, 95% confidence interval: 1.11,5.08 comparing those with doses ≥22 mGy to doses <22 mGy). After adjustment for radiation dose, survival of CLL cases was significantly shorter among those with younger age at first exposure, higher peripheral blood lymphocyte count, more advanced clinical stage of disease and older age at diagnosis (all p < 0.05). This is the first study to examine association between bone marrow radiation doses from the Chornobyl accident and clinical manifestations of the CLL in Chornobyl cleanup workers. The current study provides new evidence on the association of radiation dose and younger age at first radiation exposure at Chornobyl with shorter survival after diagnosis. Future studies are necessary with more cases in order to improve the statistical power of these analyses and to determine their significance. Copyright © 2016 John Wiley & Sons, Ltd.

Finch, Stuart C.↗
DOE OSTI2018

Factors controlling seasonal groundwater and solute flux from snow‐dominated basins

Abstract Critical zone influences on hydrologic partitioning, subsurface flow paths and reactions along these flow paths dictate the timing and magnitude of groundwater and solute flux to streams. To isolate first‐order controls on seasonal streamflow generation within highly heterogeneous, snow‐dominated basins of the Colorado River, we employ a multivariate statistical approach of end‐member mixing analysis using a suite of daily chemical and isotopic observations. Mixing models are developed across 11 nested basins (0.4 to 85 km 2 ) spanning a gradient of climatological, physical, and geological characteristics. Hydrograph separation using rain, snow, and groundwater as end‐members indicates that seasonal contributions of groundwater to streams is significant. Mean annual groundwater flux ranges from 12% to 33% whereas maximum groundwater contributions of 17% to 50% occur during baseflow. The direct relationship between snow water equivalent and groundwater flux to streams is scale dependent with a trend toward self‐similarity when basins exceed 5.5 km 2 . We find groundwater recharge increases in basins of high relief and within the upper subalpine where maximum snow accumulation is coincident with reduced conifer cover and lower canopy densities. The mixing model developed for the furthest downstream site did not transfer to upstream basins. The resulting error in predicted stream concentrations points toward weathering reactions as a function of source rock and seasonal shifts in flow path. Additionally, the potential for microbial sulfate reduction in floodplain sediments along a low‐gradient, meandering portion of the river is sufficient to modify hillslope contributions and alter mixing ratios in the analysis. Soil flushing in response to snowmelt is not included as an end‐member but is identified as an important mechanism for release of solutes from these mountainous watersheds. End‐member mixing analysis used in combination with high‐frequency observations reveals important aspects of catchment hydrodynamics across scale.

Carroll, Rosemary W. H.↗
DOE OSTI2018

Weight change since age 20 and incident risk of obesity‐related cancer in Japan: a pooled analysis of the Miyagi Cohort Study and the Ohsaki Cohort Study

It is unclear whether weight change during adulthood affects the risk of obesity‐related cancers such as those of the esophagus, colorectum, pancreas, breast, endometrium, and kidney among Japanese, where obesity is less frequent and less severe. We examined the association between weight change during adulthood and the risk of obesity‐related cancer among Japanese by conducting a pooled analysis of two prospective studies of residents in Miyagi Prefecture, Japan. A total of 78,743 persons (40,422 women and 38,321 men) aged 40–79 years participated in the Miyagi Cohort Study in 1990 and in the Ohsaki Cohort Study in 1994. Weight change since age 20 was divided into four categories (weight loss; stable weight; moderate weight gain; high weight gain). Cox proportional hazards regression analysis was used to estimate the multivariate hazard ratios (HRs) and 95% confidence intervals (CIs) for obesity‐related cancer incidence. During 1,057,899 person‐years of follow up, 4,467 cases of obesity‐related cancer (women; 1,916 cases, men; 2,551cases) were identified. In women, compared to the stable weight, weight gain was associated with an increased risk of obesity‐related cancer (moderate weight gain; HRs = 1.10, 95%CIs: 0.97–1.26, high weight gain; HRs = 1.29, 95%CIs: 1.14–1.47). The results indicate that weight gain since age 20 was associated with a significantly increased risk of obesity‐related cancer among Japanese women. By contrast, in men, our study found that weight change is not associated with the incidence of obesity‐related cancer.

Wakamatsu, Mano↗
DOE OSTI2014

Relationship of computed tomography perfusion and positron emission tomography to tumour progression in malignant glioma

Introduction: This study aimed to explore the potential for computed tomography (CT) perfusion and 18-Fluorodeoxyglucose positron emission tomography (FDG-PET) in predicting sites of future progressive tumour on a voxel-by-voxel basis after radiotherapy and chemotherapy. Methods: Ten patients underwent pre-radiotherapy magnetic resonance (MR), FDG-PET and CT perfusion near the end of radiotherapy and repeated post-radiotherapy follow-up MR scans. The relationships between these images and tumour progression were assessed using logistic regression. Cross-validation with receiver operating characteristic (ROC) analysis was used to assess the value of these images in predicting sites of tumour progression. Results: Pre-radiotherapy MR-defined gross tumour; near-end-of-radiotherapy CT-defined enhancing lesion; CT perfusion blood flow (BF), blood volume (BV) and permeability-surface area (PS) product; FDG-PET standard uptake value (SUV); and SUV:BF showed significant associations with tumour progression on follow-up MR imaging (P < 0.0001). The mean sensitivity (±standard deviation), specificity and area under the ROC curve (AUC) of PS were 0.64 ± 0.15, 0.74 ± 0.07 and 0.72 ± 0.12 respectively. This mean AUC was higher than that of the pre-radiotherapy MR-defined gross tumour and near-end-of-radiotherapy CT-defined enhancing lesion (both AUCs = 0.6 ± 0.1, P ≤ 0.03). The multivariate model using BF, BV, PS and SUV had a mean AUC of 0.8 ± 0.1, but this was not significantly higher than the PS only model. Conclusion: PS is the single best predictor of tumour progression when compared to other parameters, but voxel-based prediction based on logistic regression had modest sensitivity and specificity.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Deficient precipitation sensitivity to Sahel land surface forcings among CMIP5 models

Abstract The overall performance of the simulated seasonal precipitation response to local terrestrial forcings, namely vegetation abundance and soil moisture, in the Sahel among the Coupled Model Intercomparison Project Phase Five (CMIP5) Earth System Models (ESMs) is systematically investigated and compared with its observational counterpart using a multivariate statistical method. The observed seasonal precipitation response is evaluated against a large ensemble of observational, reanalysis, and satellite data sets to provide quantification of uncertainties. The behaviour of models with and without a Dynamic Global Vegetation Model (DGVM) component is also explored, along with the mechanisms responsible for terrestrial feedback on rainfall. In general, the CMIP5 models can reasonably capture the seasonal evolution of Sahel precipitation and soil moisture, albeit with wet biases during the pre‐monsoon period and dry biases during the peak monsoon period. The non‐DGVM ESMs simulate comparable leaf area indices (LAIs) with observations, while DGVM‐enabled ESMs simulate too much year‐round LAI. The variance of precipitation that is attributed to oceanic forcings in CMIP5 is comparable with observations; however, the variance of precipitation that is attributed to terrestrial forcings is smaller in CMIP5 models than observed, especially for non‐DGVM ESMs. CMIP5 models, especially those without DGVMs, undervalue precipitation's observed response strength to soil moisture anomalies. In both observations and CMIP5 models, none of the atmospheric variables show significant responses to direct vegetation forcing, except for the response in transpiration. Although vegetation has minimal direct effect on the atmospheric state, it can affect the atmosphere by modifying soil moisture and transpiration rate indirectly, which helps explain the more realistic simulation of rainfall in DGVM‐enabled ESMs than non‐DGVM ESMs. Coupling of an ESM to a DGVM is critical in generating reasonable land–atmosphere feedback and examining future ecological and climatic changes over the Sahel.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Validation of Stage N3 of the Eighth Edition AJCC Staging System for Nasopharyngeal Carcinoma

Objectives To validate stage nodal (N)3 of the 8th edition American Joint Committee on Cancer (AJCC) staging system for nasopharyngeal carcinoma (NPC). Methods This retrospective cohort study extracted NPC patients from the Surveillance, Epidemiology, and End Results database between 2004 and 2016. Pathologically confirmed patients with complete data of level IV, N3a, and N3b lymph node metastasis were investigated. The included patients were divided into level IV, N3a, and N3b groups. Five‐year overall survival (OS) and cancer‐specific survival (CSS) were compared among the three groups. Results A total of 693 patients were included: 285 (41.13%) patients in the level IV group, 124 (17.89%) patients in the N3a group, and 284 (40.98%) patients in the N3b group. The 5‐year OS (57.1%, 55.0%, and 55.2%) and CSS (64.4%, 63.5%, and 64.4%) were not different among the level IV, N3a, and N3b groups. Multivariate regression analysis revealed that N stage was not an independent prognostic factor for OS (hazard ratio [HR] = 1.03, 95% confidence interval [CI]: 0.91–1.17; P = .65) or CSS (HR = 1.03, 95% CI: 0.89–1.19; P = .70). Conclusion Stage N3 of the 8th edition AJCC staging system for NPC is reasonable. Level of Evidence III Laryngoscope , 131:535–540, 2021

Pan, Xin‐Bin↗
DOE OSTI2021

Survey on stochastic distribution systems: A full probability density function control theory with potential applications

Complex systems seen either in general engineering practice or economics are subjected to ever increased uncertainties that are mostly represented as random variables or parameters, and the characteristics of random variables are represented by their probability density functions (PDFs). Controlling their PDFs means to shape their stochastic distributions and in general it would provide a full treatment for system analysis and operational control and optimization. This leads to the development of stochastic distribution control (SDC) systems theory in the past decades, where the original aim of the controller design is to realize a shape control of the distributions of certain random variables in their PDFs sense for some engineering processes. Indeed, once the PDFs of these random variables or parameters are used to describe their distribution characters, the control task is to obtain control signals so that the output PDFs of stochastic systems are made to follow their target PDFs. The subject of SDC was initially originated for non-Gaussian stochastic control systems design but has found a wide spectrum of applications in general systems in terms of data-driven modeling, analysis, signal processing (filtering), data mining via multivariable statistics, decision-making (optimization) for systems subjected to uncertainties and even in economics. In this context, SDC constitutes an effective primer tool for complex system analysis, control and operational optimizations. In this review paper, a detailed survey of the developments on the research of SDC systems will be made together with their wide spectrum applications and future perspectives.

42 ENGINEERING↗
DOE OSTI2018

Circumstances of suicide among individuals with a history of cancer

Abstract Objective Cancer can trigger psychological distress, which may be associated with risk of suicide. We explored precipitating circumstances of suicides among decedents with and without a history of cancer. Methods Coroner or medical examiner and law enforcement narratives of adult suicides were coded from 17 participating states in Centers for Disease Control and Prevention's National Violent Death Reporting System during 2004 to 2013. Bivariate and multivariate analyses examined associations between cancer history and factors that precipitated suicide. Results Of 90 581 suicides, 4182 decedents (4.6%) had a history of cancer. Significantly more decedents with a history of cancer (versus without) were male, non‐Hispanic white, married, veterans, and aged 55 or older ( P < .001). Decedents with a history of cancer were more likely to die of suicide by firearm and less likely to die of suicide by suffocation compared to poisoning. In matched case analyses controlling for demographic and recent circumstances, fewer decedents with a history of cancer had mental health problems, history of suicide attempts, alcohol use problems, intimate partner problems, financial problems, job problems, and recent crisis. Conclusions Findings highlight the potential to identify high‐risk populations for suicide prevention in clinical practice.

Massetti, Greta M.↗
DOE OSTI2023

Single-Filament Imaging Mass Spectrometry Lipidomics in Arthrospira platensis

Elucidating intra-organismal biochemical and lipid organization in photosynthetic biological cell factories of filamentous cyanobacteria, i.e., Arthrospira platensis (Spirulina), is important to track physiological response mechanisms during growth. Little is known about the filaments' biochemical organization and cellular structure and no label-free imaging techniques exist that provide molecular mapping. We applied ultra-high resolution mass spectrometry (7T FT-ICR-MS) matrix-assisted laser desorption ionization (MALDI) imaging to immobilized Spirulina filaments to investigate the localization of lipids across distinct physiological regions. We optimized matrix selection and deposition methods with the goal of facilitating high spatial, and intra-filament, resolution using untargeted multivariate statistical spectral deconvolution across MS pixels. Our results demonstrate an improved two step application with an optimized procedure for intra-organismal lipid profiling to improve analyte sensitivity and achieve higher spatial resolution, whereby we evaluate three conventional matrices 2,5-dihydroxybenzoic acid (DHB), a 9:1 ratio of DHB:superDHB (sDHB), 1,5- diaminonaphthalene (DAN) and a 50:50 mix of DHB:sDHB and compare delineation and pixel-based elucidation of intra-filament lipidomics. We identified a total of 1,626 features that could be putatively assigned a lipid-like formula based on database query and 46 unique features, with associated lipid assignments that were significantly distinct in their intra-filament location. MALDI- imaging MS with untargeted statistical spectral deconvolution was used to visualize intra-filament lipidomics organization in Spirulina filaments. Improvements in matrix deposition, including sequential sublimation and pneumatic spraying, increased signal abundance at high spatial resolution and allowed for identification of distinct lipid composition regions. Here in this work outlines a methodology that may be used for micro-ecological untargeted molecular phenotyping.

09 BIOMASS FUELS↗
DOE OSTI2014

Characterizing Sub-Daily Flow Regimes: Implications of Hydrologic Resolution on Ecohydrology Studies

Natural variability in flow is a primary factor controlling geomorphic and ecological processes in riverine ecosystems. Within the hydropower industry, there is growing pressure from environmental groups and natural resource managers to change reservoir releases from daily peaking to run-of-river operations on the basis of the assumption that downstream biological communities will improve under a more natural flow regime. In this paper, we discuss the importance of assessing sub-daily flows for understanding the physical and ecological dynamics within river systems. We present a variety of metrics for characterizing sub-daily flow variation and use these metrics to evaluate general trends among streams affected by peaking hydroelectric projects, run-of-river projects and streams that are largely unaffected by flow altering activities. Univariate and multivariate techniques were used to assess similarity among different stream types on the basis of these sub-daily metrics. For comparison, similar analyses were performed using analogous metrics calculated with mean daily flow values. Our results confirm that sub-daily flow metrics reveal variation among and within streams that are not captured by daily flow statistics. Using sub-daily flow statistics, we were able to quantify the degree of difference between unaltered and peaking streams and the amount of similarity between unaltered and run-of-river streams. The sub-daily statistics were largely uncorrelated with daily statistics of similar scope. Furthermore, on short temporal scales, sub-daily statistics reveal the relatively constant nature of unaltered streamreaches and the highly variable nature of hydropower-affected streams, whereas daily statistics show just the opposite over longer temporal scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A hidden Markov model for population-level cervical cancer screening data

The Cancer Registry of Norway has been administrating a national cervical cancer screening program since 1992 by coordinating triennial cytology exam screenings for the female population between 25 and 69 years of age. Up to 80% of cancers are prevented through mass screening, but this comes at the expense of considerable screening activity and leads to overtreatment of clinically asymptomatic precancers. Here, we present a continuous-time, time-inhomogeneous hidden Markov model which was developed to understand the screening process and cervical cancer carcinogenesis in detail. By leveraging 1.7 million individual's multivariate time-series of medical exams performed over a 25-year period, we simultaneously estimate all model parameters. We show that an age-dependent model reflects the Norwegian screening program by comparing empirical survival curves from observed registry data and data simulated from the proposed model. The model can be generalized to include more detailed individual-level covariates as well as new types of screening exams. By utilizing individual screening histories and covariate data, the proposed model shows potential for improving strategies for cancer screening programs by personalizing recommended screening intervals.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Decoding the Mechanisms of Phase Transitions from In Situ Microscopy Observations

Abstract Analysis of the temperature‐ and stimulus‐dependent imaging data toward elucidation of the physical transformations is an ubiquitous problem in multiple fields. Here, temperature‐induced phase transition in BaTiO 3 is explored using the machine learning analysis of domain morphologies visualized via variable‐temperature scanning transmission electron microscopy (STEM) imaging data. This approach is based on the multivariate statistical analysis of the time or temperature dependence of the statistical descriptors of the system, derived in turn from the categorical classification of observed domain structures or projection on the continuous parameter space of the feature extraction‐dimensionality reduction transform. The proposed workflow offers a powerful tool for the exploration of the dynamic data based on the statistics of image representation as a function of the external control variable to visualize the transformation pathways during phase transitions and chemical reactions. This can include the mesoscopic STEM data as demonstrated here, but also optical, chemical imaging, etc., data. It can further be extended to the higher dimensional spaces, for example, analysis of the combinatorial libraries of materials compositions.

Valleti, Sai Mani Prudhvi↗
DOE OSTI2024

A Novel Non‐Destructive Rapid Tool for Estimating Amino Acid Composition and Secondary Structures of Proteins in Solution

Abstract Amino‐acid protein composition plays an important role in biology, medicine, and nutrition. Here, a groundbreaking protein analysis technique that quickly estimates amino acid composition and secondary structure across various protein sizes, while maintaining their natural states is introduced and validated. This method combines multivariate statistics and the thermostable Raman interaction profiling (TRIP) technique, eliminating the need for complex preparations. In order to validate the approach, the Raman spectra are constructed of seven proteins of varying sizes by utilizing their amino acid frequencies and the Raman spectra of individual amino acids. These constructed spectra exhibit a close resemblance to the actual measured Raman spectra. Specific vibrational modes tied to free amino and carboxyl termini of the amino acids disappear as signals linked to secondary structures emerged under TRIP conditions. Furthermore, the technique is used inversely to successfully estimate amino acid compositions and secondary structures of unknown proteins across a range of sizes, achieving impressive accuracy ranging between 1.47% and 5.77% of root mean square errors (RMSE). These results extend the uses for TRIP beyond interaction profiling, to probe amino acid composition and structure.

Chemistry↗
DOE OSTI2023

Field evaluation of semi‐automated moisture estimation from geophysics using machine learning

Geophysical methods can provide three-dimensional (3D), spatially continuous estimates of soil moisture. However, point-to-point comparisons of geophysical properties to measure soil moisture data are frequently unsatisfactory, resulting in geophysics being used for qualitative purposes only. This is because (1) geophysics requires models that relate geophysical signals to soil moisture, (2) geophysical methods have potential uncertainties resulting from smoothing and artifacts introduced from processing and inversion, and (3) results from multiple geophysical methods are not easily combined within a single soil moisture estimation framework. To investigate these potential limitations, an irrigation experiment was performed wherein soil moisture was monitored through time, and several surface geophysical datasets indirectly sensitive to soil moisture were collected before and after irrigation: ground penetrating radar, electrical resistivity tomography (ERT), and frequency domain electromagnetics (FDEM). Data were exported in both raw and processed form, and then snapped to a common 3D grid to facilitate moisture prediction by standard calibration techniques, multivariate regression, and machine learning. A combination of inverted ERT data, raw FDEM, and inverted FDEM data was most informative for predicting soil moisture using a random regression forest model (one-thousand 60/40 training/test cross-validation folds produced root mean squared errors ranging from 0.025–0.046 cm 3 /cm 3 ). This cross-validated model was further supported by a separate evaluation using a test set from a physically separate portion of the study area. Machine learning was conducive to a semi-automated model-selection process that could be used for other sites and datasets to locally improve accuracy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Lowering post‐construction yield assessment uncertainty through better wind plant power curves

Abstract Many operational analyses of wind power plants require a statistical relationship, which can be called the wind plant power curve, to be developed between wind plant energy production and concurrent atmospheric variables. Currently, a univariate linear regression at monthly resolution is the industry standard for post‐construction yield assessments. Here, we evaluate the benefits in augmenting this conventional approach by testing alternative regressions performed with multiple inputs, at a finer time resolution, and using nonlinear machine‐learning algorithms. We utilize the National Renewable Energy Laboratory's open‐source software package OpenOA to assess wind plant power curves for 10 wind plants. When a univariate generalized additive model at daily or hourly resolution is used, regression uncertainty is reduced, in absolute terms, by up to 1.0 % and 1.2 % (corresponding to a −59 % and −80 % relative change), respectively, compared to a univariate linear regression at monthly resolution; also, a more accurate assessment of the mean long‐term wind plant production is achieved. Additional input variables also reduce the regression uncertainty: when temperature is added as an input to the conventional monthly linear regression, the operational analysis uncertainty connected to regression is reduced, in absolute terms, by up to 0.5 % (−43 % relative change) for wind power plants with strong seasonal variability. Adding input variables to the machine‐learning model at daily resolution can further reduce regression uncertainty, with up to a −10 % relative change. Based on these results, we conclude that a multivariate nonlinear regression at daily or hourly resolution should be recommended for assessing wind plant power curves.

17 WIND ENERGY↗
DOE OSTI2021

Scalable statistical inference of photometric redshift via data subsampling

Handling big data has largely been a major bottleneck in traditional statistical models. Consequently, when accurate point prediction is the primary target, machine learning models are often preferred over their statistical counterparts for bigger problems. But full probabilistic statistical models often outperform other models in quantifying uncertainties associated with model predictions. We develop a data-driven statistical modeling framework that combines the uncertainties from an ensemble of statistical models learned on smaller subsets of data carefully chosen to account for imbalances in the input space. We demonstrate this method on a photometric redshift estimation problem in cosmology, which seeks to infer a distribution of the redshift—the stretching effect in observing the light of far-away galaxies—given multivariate color information observed for an object in the sky. Our proposed method performs balanced partitioning, graph-based data subsampling across the partitions, and training of an ensemble of Gaussian process models.

data subsampling↗