Data-scalable Hessian preconditioning for distributed parameter PDE-constrained inverse problems

Hessian preconditioners are the key to efficient numerical solution of large-scale distributed parameter PDE-constrained inverse problems with highly informative data. Such inverse problems arise in many applications, yet solving them remains computationally costly. With existing methods, the computational cost depends on spectral properties of the Hessian which worsen as more informative data are used to reconstruct the unknown parameter field. The best case scenario from a scientific standpoint (lots of high-quality data) is therefore the worst case scenario from a computational standpoint (large computational cost). In this dissertation, we argue that the best way to overcome this predicament is to build data-scalable Hessian/KKT preconditioners---preconditioners that perform well even if the data are highly informative about the parameter. We present a novel data-scalable KKT preconditioner for a diffusion inverse problem, a novel data-scalable Hessian preconditioner for an advection inverse problem, and a novel data-scalable domain decomposition preconditioner for an auxiliary operator that arises in connection with KKT preconditioning for a wave inverse problem. Our novel preconditioners outperform existing preconditioners in all three cases: they are robust to large numbers of observations in the diffusion inverse problem, large Peclet numbers in the advection inverse problem, and high wave frequencies in the wave inverse problem.Computational Science, Engineering, and Mathematic

Power poses – where do we stand?

<p>Dynamic results for Scenario 2.</p

Computational thermal, chemical, fluid, and solid mechanics for geosystems management.

This document summarizes research performed under the SNL LDRD entitled - Computational Mechanics for Geosystems Management to Support the Energy and Natural Resources Mission. The main accomplishment was development of a foundational SNL capability for computational thermal, chemical, fluid, and solid mechanics analysis of geosystems. The code was developed within the SNL Sierra software system. This report summarizes the capabilities of the simulation code and the supporting research and development conducted under this LDRD. The main goal of this project was the development of a foundational capability for coupled thermal, hydrological, mechanical, chemical (THMC) simulation of heterogeneous geosystems utilizing massively parallel processing. To solve these complex issues, this project integrated research in numerical mathematics and algorithms for chemically reactive multiphase systems with computer science research in adaptive coupled solution control and framework architecture. This report summarizes and demonstrates the capabilities that were developed together with the supporting research underlying the models. Key accomplishments are: (1) General capability for modeling nonisothermal, multiphase, multicomponent flow in heterogeneous porous geologic materials; (2) General capability to model multiphase reactive transport of species in heterogeneous porous media; (3) Constitutive models for describing real, general geomaterials under multiphase conditions utilizing laboratory data; (4) General capability to couple nonisothermal reactive flow with geomechanics (THMC); (5) Phase behavior thermodynamics for the CO2-H2O-NaCl system. General implementation enables modeling of other fluid mixtures. Adaptive look-up tables enable thermodynamic capability to other simulators; (6) Capability for statistical modeling of heterogeneity in geologic materials; and (7) Simulator utilizes unstructured grids on parallel processing computers

Grasping numbers: How numerical magnitude affects kinematics of reach to grasp actions

The SNARC (spatial-numerical association of response codes) effect is a well-studied phenomenon showing a spatial organisation of the magnitude of numbers. Smaller numbers (0-4) are associated with the left side of an individuals perceived surroundings, while larger numbers (6-9) are associated with the right side of an individuals perceived surroundings. The goal of our study was to determine if a larger numerical value on an object will increase the maximum distance between the thumb and index finger when reaching for the object also known as the maximum grip aperture. Further, it was of interest if the effect would be seen when using a cluster of small shapes as an indicator of numeracy as opposed to an Arabic numeral. Two sets of blocks were used: one with Arabic numerical values (1, 2, 8, 9) written on them and the other with heart-shaped stickers on them. For each set of blocks the values 1 and 9 were on the faces of the smaller block, while the values 2 and 8 were on faces of the larger block which served as a foil. A control block the same size as the smaller blocks with nothing on its face was also used. University students were recruited for the study, each participant was only tested using one set of blocks. Participants were tasked with grasping blocks after an audio cue. While reaching for the block, participants were instructed to say the number of hearts or Arabic numeral on the block. This was done for both the left and right hand of each participant. During each grasp of the block maximum grip aperture was recorded. Research is still undergoing with results to be finalized in the coming weeks. Based on prior experiments we hypothesize that maximum grip aperture should be altered depending on the size of the numerical value on the object. A tendency to have a larger maximum grip aperture when reaching for the larger number should occur, even though the objects are the same size. Using a cluster of shapes as an indicator of numeracy should also result in a similar difference to the one seen with numerical values. Since numerical processing occurs in the right hemisphere of the brain, the effect should be more pronounced in the left hand than the right. *Indicates presenter &nbsp

SACCHARIS: an automated pipeline to streamline discovery of carbohydrate active enzyme activities within polyspecific families and de novo sequence datasets

Abstract Background Deposition of new genetic sequences in online databases is expanding at an unprecedented rate. As a result, sequence identification continues to outpace functional characterization of carbohydrate active enzymes (CAZymes). In this paradigm, the discovery of enzymes with novel functions is often hindered by high volumes of uncharacterized sequences particularly when the enzyme sequence belongs to a family that exhibits diverse functional specificities (i.e., polyspecificity). Therefore, to direct sequence-based discovery and characterization of new enzyme activities we have developed an automated in silico pipeline entitled: Sequence Analysis and Clustering of CarboHydrate Active enzymes for Rapid Informed prediction of Specificity (SACCHARIS). This pipeline streamlines the selection of uncharacterized sequences for discovery of new CAZyme or CBM specificity from families currently maintained on the CAZy website or within user-defined datasets. Results SACCHARIS was used to generate a phylogenetic tree of a GH43, a CAZyme family with defined subfamily designations. This analysis confirmed that large datasets can be organized into sequence clusters of manageable sizes that possess related functions. Seeding this tree with a GH43 sequence from Bacteroides dorei DSM 17855 (BdGH43b, revealed it partitioned as a single sequence within the tree. This pattern was consistent with it possessing a unique enzyme activity for GH43 as BdGH43b is the first described α-glucanase described for this family. The capacity of SACCHARIS to extract and cluster characterized carbohydrate binding module sequences was demonstrated using family 6 CBMs (i.e., CBM6s). This CBM family displays a polyspecific ligand binding profile and contains many structurally determined members. Using SACCHARIS to identify a cluster of divergent sequences, a CBM6 sequence from a unique clade was demonstrated to bind yeast mannan, which represents the first description of an α-mannan binding CBM. Additionally, we have performed a CAZome analysis of an in-house sequenced bacterial genome and a comparative analysis of B. thetaiotaomicron VPI-5482 and B. thetaiotaomicron 7330, to demonstrate that SACCHARIS can generate “CAZome fingerprints”, which differentiate between the saccharolytic potential of two related strains in silico. Conclusions Establishing sequence-function and sequence-structure relationships in polyspecific CAZyme families are promising approaches for streamlining enzyme discovery. SACCHARIS facilitates this process by embedding CAZyme and CBM family trees generated from biochemically to structurally characterized sequences, with protein sequences that have unknown functions. In addition, these trees can be integrated with user-defined datasets (e.g., genomics, metagenomics, and transcriptomics) to inform experimental characterization of new CAZymes or CBMs not currently curated, and for researchers to compare differential sequence patterns between entire CAZomes. In this light, SACCHARIS provides an in silico tool that can be tailored for enzyme bioprospecting in datasets of increasing complexity and for diverse applications in glycobiotechnology

Radio Galaxy Zoo : unsupervised clustering of convolutionally auto-encoded radio-astronomical images

This paper demonstrates a novel and efficient unsupervised clustering method with the combination of a self-organizing map (SOM) and a convolutional autoencoder. The rapidly increasing volume of radio-astronomical data has increased demand for machine-learning methods as solutions to classification and outlier detection. Major astronomical discoveries are unplanned and found in the unexpected, making unsupervised machine learning highly desirable by operating without assumptions and labeled training data. Our approach shows SOM training time is drastically reduced and high-level features can be clustered by training on auto-encoded feature vectors instead of raw images. Our results demonstrate this method is capable of accurately separating outliers on a SOM with neighborhood similarity and K-means clustering of radio-astronomical features. We present this method as a powerful new approach to data exploration by providing a detailed understanding of the morphology and relationships of Radio Galaxy Zoo (RGZ) data set image features which can be applied to new radio survey data

Analysis and comparison of SARS-CoV-2 variant antibodies and neutralizing activity for 6 months after a booster mRNA vaccine in a healthcare worker population.

INTRODUCTION: In the context of recurrent surges of SARS-CoV-2 infections, a detailed characterization of antibody persistence over a 6-month period following vaccine booster dose is necessary to crafting effective public health policies on repeat vaccination. METHODS: To characterize the SARS-CoV-2 antibody profile of a healthcare worker population over a 6-month period following mRNA vaccination and booster dose. 323 healthcare workers at an academic medical center in Orange County, California who had completed primary vaccination and booster dose against SARS-CoV-2 were recruited for the study. A total of 690 blood specimens over a 6-month period were collected via finger-stick blood and analyzed for the presence of antibodies against 9 SARS-CoV-2 antigens using a coronavirus antigen microarray. RESULTS: The primary outcome of this study was the average SARS-CoV-2 antibody level as measured using a novel coronavirus antigen microarray. Additional outcomes measured include levels of antibodies specific to SARS-CoV-2 variants including Delta, Omicron BA.1, and BA.2. We also measured SARS-CoV-2 neutralization capacity for a subset of the population to confirm correlation with antibody levels. Although antibodies against SARS-CoV-2 wane throughout the 6-month period following a booster dose, antibody levels remain higher than pre-boost levels. However, a booster dose of vaccine based on the original Wuhan strain generates approximately 3-fold lower antibody reactivity against Omicron variants BA.1 and BA.2 as compared to the vaccine strain. Despite waning antibody levels, neutralization activity against the vaccine strain is maintained throughout the 6-month period. DISCUSSION: In the context of recurrent surges of SARS-CoV-2 infections, our data indicate that breakthrough infections are likely driven by novel variants with different antibody specificity and not by time since last dose of vaccination, indicating that development of vaccinations specific to these novel variants is necessary to prevent future surges of SARS-CoV-2 infections