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At least 847 records · Page 47

Constellation: A science graph network for scalable data and knowledge discovery in extreme-scale scientific collaborations

Constellation's overarching goal is the federation of information from resources within an extreme-scale scientific collaboration to enable the scalable discovery of data and new knowledge pathways. The resource fabric is comprised of petascale supercomputers and storage systems, users, jobs, datasets and lifecycle artifacts. For an extreme-scale supercomputing center, normal operations can generate hundreds of millions of data products and metadata entries describing the resource fabric. Constellation federates the information extracted from the resources using a custom, transformative science graph network; constructs rich metadata indexes and higher-order derived metadata from the extracted information; and conducts scalable graph analytics to unravel hidden data pathways. Our implementation and deployment for a production, supercomputing facility shows that the graph can scale to more than 750 million vertices, its domain agnostic indexing can answer interesting science queries, and its analytics can aid in structural, topological and temporal analysis to identify usage hotspots.

Vazhkudai, Sudharshan S.↗

Damsel - A Data Model Storage Library for Exascale Science

The goal of this project is to enable exascale computational science applications to interact conveniently and efficiently with storage through abstractions that match their data models. We will accomplish this through three major activities: (1) identifying major data model motifs in computational science applications and developing representative benchmarks; (2) developing a data model storage library, called Damsel, that supports these motifs, provides efficient storage data layouts, incorporates optimizations to enable exascale operation, and is tolerant to failures; and (3) productizing Damsel and working with computational scientists to encourage adoption of this library by the scientific community.

97 MATHEMATICS AND COMPUTING↗

Damsel: A Data Model Storage Library for Exascale Science

The goal of this project is to enable exascale computational science applications to interact conveniently and efficiently with storage through abstractions that match their data models. We will accomplish this through three major activities: (1) identifying major data model motifs in computational science applications and developing representative benchmarks; (2) developing a data model storage library, called Damsel, that supports these motifs, provides efficient storage data layouts, incorporates optimizations to enable exascale operation, and is tolerant to failures; and (3) productizing Damsel and working with computational scientists to encourage adoption of this library by the scientific community.

97 MATHEMATICS AND COMPUTING↗

Carbon Storage Technical Viability Approach (CS TVA) Data Availability Results Database

The Carbon Storage Technical Viability Approach (CS TVA) Data Availability Results Database v1.0 is the output of a data availability assessment workflow that was developed by NETL to create a tool for spatial data availability assessment. This results database is the output of the first version of the workflow performed on the data with the CS TVA Database v2.0.

carbon storage↗

Outlier analyses of the Protein Data Bank archive using a probability-density-ranking approach

Outlier analyses are central to scientific data assessments. Conventional outlier identification methods do not work effectively for Protein Data Bank (PDB) data, which are characterized by heavy skewness and the presence of bounds and/or long tails. We have developed a data-driven nonparametric method to identify outliers in PDB data based on kernel probability density estimation. Unlike conventional outlier analyses based on location and scale, Probability Density Ranking can be used for robust assessments of distance from other observations. Analyzing PDB data from the vantage points of probability and frequency enables proper outlier identification, which is important for quality control during deposition-validation-biocuration of new three-dimensional structure data. Ranking of Probability Density also permits use of Most Probable Range as a robust measure of data dispersion that is more compact than Interquartile Range. The Probability-Density-Ranking approach can be employed to analyze outliers and data-spread on any large data set with continuous distribution.

59 BASIC BIOLOGICAL SCIENCES↗

Galaxies in X-ray selected clusters and groups in Dark Energy Survey data – II. Hierarchical Bayesian modelling of the red-sequence galaxy luminosity function

Using ~100 X-ray selected clusters in the Dark Energy Survey Science Verification data, in this work we constrain the luminosity function (LF) of cluster red-sequence galaxies as a function of redshift. This is the first homogeneous optical/X-ray sample large enough to constrain the evolution of the LF simultaneously in redshift (0.1 < z < 1.05) and cluster mass ($13.5 \le \rm {log_{10}}(M_{200crit}) \sim \lt 15.0$). We pay particular attention to completeness issues and the detection limit of the galaxy sample. We then apply a hierarchical Bayesian model to fit the cluster galaxy LFs via a Schechter function, including its characteristic break (m*) to a faint end power-law slope (α). Our approach allows us to avoid known issues in similar analyses based on stacking or binning the clusters. We find weak and statistically insignificant (~1.9σ) evolution in the faint end slope α versus redshift. We also find no dependence in α or m* with the X-ray inferred cluster masses. Yet, the amplitude of the LF as a function of cluster mass is constrained to ${\sim } 20{{\ \rm per\ cent}}$ precision. As a by-product of our algorithm, we utilize the correlation between the LF and cluster mass to provide an improved estimate of the individual cluster masses as well as the scatter in true mass given the X-ray inferred masses. This technique can be applied to a larger sample of X-ray or optically selected clusters from the Dark Energy Survey, greatly improving the sensitivity of the analysis.

79 ASTRONOMY AND ASTROPHYSICS↗

Connect the Dots: In Situ 4-D Seismic Monitoring of CO 2 Storage With Spatio-Temporal CNNs

4-D seismic imaging has been widely used in CO 2 sequestration projects to monitor the fluid flow in the volumetric subsurface region that is not sampled by wells. Ideally, real-time monitoring and near-future forecasting would provide site operators with great insights to understand the dynamics of the subsurface reservoir and assess any potential risks. However, due to obstacles such as high deployment cost, availability of acquisition equipment, exclusion zones around surface structures, only very sparse seismic imaging data can be obtained during monitoring. That leads to an unavoidable and growing knowledge gap over time. The operator needs to understand the fluid flow throughout the project lifetime and the seismic data are only available at a limited number of times. This is insufficient for understanding reservoir behavior. To overcome those challenges, we have developed spatio-temporal neural-network-based models that can produce high-fidelity interpolated or extrapolated images effectively and efficiently. Specifically, our models are built on an autoencoder, and incorporate the long short-term memory (LSTM) structure with a new loss function regularized by optical flow. We validate the performance of our models using real 4-D post-stack seismic imaging data acquired at the Sleipner CO 2 sequestration field. We employ two different strategies in evaluating our models. Numerically, we compare our models with different baseline approaches using classic pixel-based metrics. We also conduct a blind survey and collect a total of 20 responses from domain experts to evaluate the quality of data generated by our models. Finally, via both numerical and expert evaluation, we conclude that our models can produce high-quality 2-D/3-D seismic imaging data at a reasonable cost, offering the possibility of real-time monitoring or even near-future forecasting of the CO 2 storage reservoir.

4-D seismic imaging↗

Information and Perception of Meaningful Patterns

The visual system needs to extract the most important elements of the external world from a large flux of information in a short time for survival purposes. It is widely believed that in performing this task, it operates a strong data reduction at an early stage, by creating a compact summary of relevant information that can be handled by further levels of processing. In this work we formulate a model of early vision based on a pattern-filtering architecture, partly inspired by high-speed digital data reduction in experimental high-energy physics (HEP). This allows a much stronger data reduction than models based just on redundancy reduction. We show that optimizing this model for best information preservation under tight constraints on computational resources yields surprisingly specific a-priori predictions for the shape of biologically plausible features, and for experimental observations on fast extraction of salient visual features by human observers. Interestingly, applying the same optimized model to HEP data acquisition systems based on pattern-filtering architectures leads to specific a-priori predictions for the relevant data patterns that these devices extract from their inputs. These results suggest that the limitedness of computing resources can play an important role in shaping the nature of perception, by determining what is perceived as ‘‘meaningful features’’ in the input data.

59 BASIC BIOLOGICAL SCIENCES↗

Marine ARM GPCI Investigation of Clouds Infrared Sea Surface Temperature Autonomous Radiometer (ISAR) Field Campaign Report

Sea surface temperature (SST) is one of the most appropriate and important climate parameters: a widespread increase is an indicator of global warming and modifications of the geographical distribution of SST are an extremely sensitive indicator of climate change. There is high demand for accurate, reliable, high-spatial-and-temporal-resolution SST measurements for the parameterization of ocean-atmosphere heat, momentum, and gas (SST is therefore critical to understanding the processes controlling the global carbon dioxide budget) fluxes, for detailed diagnostic and process-orientated studies to better understand the behavior of the climate system, as model boundary conditions, for assimilation into climate models, and for the rigorous validation of climate model output. In order to achieve an overall net flux uncertainty < 10 W/m 2 (Bradley and Fairall, 2006), the sea surface (skin) temperature (SSST) must be measured to an error < 0.1 C and a precision of 0.05 C. Anyone experienced in shipboard meteorological measurements will recognize this is a tough specification. These demands require complete confidence in the content, interpretation, accuracy, reliability, and continuity of observational SST data—criteria that can only be fulfilled by the successful implementation of an ongoing data product validation strategy.

54 ENVIRONMENTAL SCIENCES↗

Data Validation Package April 2016 Groundwater and Surface Water Sampling at the Monticello, Utah, Disposal and Processing Sites August 2016

This semiannual event includes sampling groundwater and surface water at the Monticello Disposal and Processing Sites. Sampling and analyses were conducted as specified in the Sampling and Analysis Plan for U.S. Department of Energy Office of Legacy Management Sites (LMS/PRO/S04351, continually updated) and Program Directive MNT-2016-01. Complete sample sets were collected from 42 of 48 planned locations (9 of 9 former mill site wells, 13 of 13 downgradient wells, 7 of 9 downgradient permeable reactive barrier wells, 4 of 7 seeps and wetlands, and 9 of 10 surface water locations). Planned monitoring locations are shown in Attachment 1, Sampling and Analysis Work Order. Locations R6-M3, SW00-01, Seep 1, Seep 2, and Seep 5 were not sampled due to insufficient water availability. A partial sample was collected at location R4-M3 due to insufficient water. All samples from the permeable reactive barrier wells were filtered as specified in the program directive. Duplicate samples were collected from surface water location Sorenson and from monitoring wells 92-07 and RlO-Ml. Water levels were measured at all sampled wells and an additional set of wells. See Attachment2, Trip Report for additional details. The contaminants of concern (COCs) for the Monticello sites are arsenic, manganese, molybdenum, nitrate+ nitrite as nitrogen (nitrate+ nitrite as N), selenium, uranium, and vanadium. Locations with COCs that exceeded remediation goals are listed in Table 1 and Table 2. Time-concentration graphs of the COCs for all groundwater and surface water locations are included in Attachment 3, Data Presentation. An assessment of anomalous data is included in Attachment 4.

54 ENVIRONMENTAL SCIENCES↗

KBase Educators Handbook

The KBase Educators Handbook is a community resource for educators teaching biology, computational biology, and bioinformatics using KBase. The Handbook includes supporting documentation on how to join and access community-developed resources for teaching with KBase, best practices, and guidelines on how to contribute to the KBase Educators Community.

59 BASIC BIOLOGICAL SCIENCES↗

Hierarchical Modeling and Statistical Calibration for Photometric Redshifts

The cosmological exploitation of modern photometric galaxy surveys requires both accurate (unbiased) and precise (narrow) redshift probability distributions derived from broadband photometry. Existing methodologies do not meet these requirements. Standard template fitting delivers interpretable models and errors, but lacks the flexibility to learn inaccuracies in the observed photometry or spectral templates. Machine learning addresses those issues, but requires representative training data, and the resulting models and uncertainties cannot be interpreted in the context of a physical model or outside of the training data. We present a hierarchical modeling approach simultaneously addressing the issues of flexibility, interpretability, and generalization. It combines template fitting with flexible (machine-learning-like) models to correct the spectral templates, model their redshift distributions, and recalibrate the photometric observations. By optimizing the full posterior distribution of the model and solving for its (thousands of) parameters, one can perform a global statistical calibration of the data and the spectral energy distribution (SED) model. We apply this approach to the public Dark Energy Survey Science Verification data and show that it provides more accurate and compact redshift posterior distributions than existing methods, as well as insights into residual photometric and SED systematics. Here, the model is causal and makes predictions for future data (e.g., additional photometric bandpasses), and its internal parameters and components are interpretable. This approach does not formally require the training data to be complete or representative; in principle, it can even work in regimes in which few or no spectroscopic redshifts are available.

79 ASTRONOMY AND ASTROPHYSICS↗

Tachyon: Intelligent Multi-Scale Modeling of Distributed Resilient Infrastructure and Workflows for Data Intensive HEP Analyses

The DOE High Energy Physics (HEP) program in Neutrino and Collider science drives data-intensive science and simulation on extreme-scale platforms. Modeling and optimizing the complex distributed components from experimental to leadership computing facilities are essential for HEP workflows to achieve required response times and resilience under various conditions. Tachyon proposes a framework for scalable modeling, simulation, and validation of key performance characteristics for the distributed infrastructure between FNAL and ALCF, along with associated HEP workflows.

Carothers, Chris [Rensselaer Poly.]↗

Tachyon: Intelligent Multi-Scale Modeling of Distributed Resilient Infrastructure and Workflows for Data Intensive HEP Analyses

The DOE High Energy Physics (HEP) program in Neutrino and Collider science drives data-intensive science and simulation on extreme-scale platforms. Modeling and optimizing the complex distributed components from experimental to leadership computing facilities are essential for HEP workflows to achieve required response times and resilience under various conditions. Tachyon proposes a framework for scalable modeling, simulation, and validation of key performance characteristics for the distributed infrastructure between FNAL and ALCF, along with associated HEP workflows.

Carothers, Chris [Rensselaer Poly.]↗

Primary transcripts: From the discovery of RNA processing to current concepts of gene expression ‐ Review

The main purpose of this review is to recall for investigators - and in particular students -, some of the early data and concepts in molecular genetics and biology that are rarely cited in the current literature and are thus invariably overlooked. There is a growing tendency among editors and reviewers to consider that only data produced in the last 10–20 years or so are pertinent. However this is not the case. In exact science, sound data and lucid interpretation never become obsolete, and even if forgotten, will resurface sooner or later. In the field of gene expression, covered in the present review, recent post-genomic data have indeed confirmed many of the earlier results and concepts developed in the mid-seventies, well before the start of the recombinant DNA revolution. Human brains and even the most powerful computers, have difficulty in handling and making sense of the overwhelming flow of data generated by recent high-throughput technologies. This was easier when low throughput, more integrative methods based on biochemistry and microscopy dominated biological research. Nowadays, the need for organising concepts is ever more important, otherwise the mass of available data can generate only {sup b}uilding ruins{sup }- the bricks without an architect. Concepts such as pervasive transcription of genomes, large genomic domains, full domain transcripts (FDTs) up to 100 kb long, the prevalence of post-transcriptional events in regulating eukaryotic gene expression, and the 3D-genome architecture, were all developed and discussed before 1990, and are only now coming back into vogue. Thus, to review the impact of earlier concepts on later developments in the field, I will confront former and current data and ideas, including a discussion of old and new methods. Whenever useful, I shall first briefly report post-genomic developments before addressing former results and interpretations. Equally important, some of the terms often used sloppily in scientific discussions will be clearly defined. As a basis for the ensuing discussion, some of the issues and facts related to eukaryotic gene expression will first be introduced. In chapter 2 the evolution in perception of biology over the last 60 years and the impact of the recombinant DNA revolution will be considered. Then, in chapter 3 data and theory concerning the genome, gene expression and genetics will be reviewed. The experimental and theoretical definition of the gene will be discussed before considering the 3 different types of genetic information - the {sup T}riad{sup }- and the importance of post-transcriptional regulation of gene expression in the light of the recent finding that 90% of genomic DNA seems to be transcribed. Some previous attempts to provide a conceptual framework for these observations will be recalled, in particular the {sup C}ascade Regulation Hypothesis{sup }(CRH) developed in 1967–85, and the {sup G}ene and Genon{sup }concept proposed in 2007. A knowledge of the size of primary transcripts is of prime importance, both for experimental and theoretical reasons, since these molecules represent the primary units of the {sup R}NA genome{sup }on which most of the post-transcriptional regulation of gene expression occurs. In chapter 4, I will first discuss some current post-genomic topics before summarising the discovery of the high Mr-RNA transcripts, and the investigation of their processing spanning the last 50 years. Since even today, a consensus concerning the real form of primary transcripts in eukaryotic cells has not yet been reached, I will refer to the viral and specialized cellular models which helped early on to understand the mechanisms of RNA processing and differential splicing which operate in cells and tissues. As a well-studied example of expression and regulation of a specific cellular gene in relation to differentiation and pathology, I will discuss the early and recent work on expression of the globin genes in nucleated avian erythroblasts. An important concept is that the primary transcript not only embodies protein-coding information and regulation of its expression, but also the 3D-structure of the genomic DNA from which it was derived. The wealth of recent post-genomic data published in this field emphasises the importance of a fundamental principle of genome organisation and expression that has been overlooked for years even though it was already discussed in the 1970–80ties. These issues are addressed in chapter 5 which focuses on the involvement of the nuclear matrix and nuclear architecture in DNA and RNA biology. This section will make reference to the Unified Matrix Hypothesis (UMH), which was the first molecular model of the 3D organisation of DNA and RNA. The chapter on the {sup R}NA-genome and peripheral memories{sup }discusses experimental data on the ribonucleoprotein complexes containing pre-mRNA (pre-mRNPs) and mRNA (mRNPs) which are organised in nuclear and cytoplasmic spaces respectively. Finally, {sup O}utlook {sup }will enumerate currently unresolved questions in the field, and will propose some ideas that may encourage further investigation, and comprehension of available experimental data still in need of interpretation. In chapter 8, some propositions and paradigms basic to the authors own analysis are discussed. {sup I}n conclusion{sup }the raison d'être of this review is recalled and positioned within the overall framework of scientific endeavour.

60 APPLIED LIFE SCIENCES↗

Templates for developing and versioning data standards and reporting formats using GitHub

This data package contains three templates that can be used for creating README files and Issue Templates, written in the markdown language, that support community-led data reporting formats. We created these templates based on the results of a systematic review (see related references) that explored how groups developing data standard documentation use the Version Control platform GitHub, to collaborate on supporting documents. Based on our review of 32 GitHub repositories, we make recommendations for the content of README Files (e.g., provide a user license, indicate how users can contribute) and so 'README_template.md' includes headings for each section. The two issue templates we include ('issue_template_for_all_other_changes.md' and 'issue_template_for_documentation_change.md') can be used in a GitHub repository to help structure user-submitted issues, or can be modified to suit the needs of data standard developers. We used these templates when establishing ESS-DIVE's community space on GitHub (https://github.com/ess-dive-community) that includes documentation for community-led data reporting formats. We also include file-level metadata 'flmd.csv' that describes the contents of each file within this data package. Lastly, the temporal range that we indicate in our metadata is the time range during which we searched for data standards documented on GitHub.

54 ENVIRONMENTAL SCIENCES↗

Data-centric machine learning in quantum information science

Abstract We propose a series of data-centric heuristics for improving the performance of machine learning systems when applied to problems in quantum information science. In particular, we consider how systematic engineering of training sets can significantly enhance the accuracy of pre-trained neural networks used for quantum state reconstruction without altering the underlying architecture. We find that it is not always optimal to engineer training sets to exactly match the expected distribution of a target scenario, and instead, performance can be further improved by biasing the training set to be slightly more mixed than the target. This is due to the heterogeneity in the number of free variables required to describe states of different purity, and as a result, overall accuracy of the network improves when training sets of a fixed size focus on states with the least constrained free variables. For further clarity, we also include a ‘toy model’ demonstration of how spurious correlations can inadvertently enter synthetic data sets used for training, how the performance of systems trained with these correlations can degrade dramatically, and how the inclusion of even relatively few counterexamples can effectively remedy such problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗