CrossVis: A visual analytics system for exploring heterogeneous multivariate data with applications to materials and climate sciences
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97 MATHEMATICS AND COMPUTING↗
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Results for “network science”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Science Use Case Design Patterns for Autonomous Experiments
Connecting scientific instruments and robot-controlled laboratories with computing and data resources at the edge, the Cloud or the high-performance computing (HPC) center enables autonomous experiments, self-driving laboratories, smart manufacturing, and artificial intelligence (AI)-driven design, discovery and evaluation. The Self-driven Experiments for Science / Interconnected Science Ecosystem (INTERSECT) Open Architecture enables science breakthroughs using intelligent networked systems, instruments and facilities with a federated hardware/software architecture for the laboratory of the future. It relies on a novel approach, consisting of (1) science use case design patterns, (2) a system of systems architecture, and (3) a microservice architecture. This paper introduces the science use case design patterns of the INTERSECT Architecture. It describes the overall background, the involved terminology and concepts, and the pattern format and classification. It further offers an overview of the 12 defined patterns and 4 examples of patterns of 2 different pattern classes. It also provides insight into building solutions from these patterns. The target audience are computer, computational, instrument and domain science experts working in the field of autonomous experiments.
Engelmann, Christian↗
INTERSECT Architecture Specification: Use Case Design Patterns (V.0.9)
Connecting scientific instruments and robot-controlled laboratories with computing and data resources at the edge, the Cloud or the high-performance computing (HPC) center enables autonomous experiments, self-driving laboratories, smart manufacturing, and artificial intelligence (AI)-driven design, discovery and evaluation. The Self-driven Experiments for Science / Interconnected Science Ecosystem (INTERSECT) Open Architecture enables science breakthroughs using intelligent networked systems, instruments and facilities with a federated hardware/software architecture for the laboratory of the future. It relies on a novel approach, consisting of (1) science use case design patterns, (2) a system of systems architecture, and (3) a microservice architecture. This document introduces the science use case design patterns of the INTERSECT Architecture. It describes the overall background, the involved terminology and concepts, and the pattern format and classification. It further details the 12 defined patterns and provides insight into building solutions from these patterns. The document also describes the application of these patterns in the context of several INTERSECT autonomous laboratories. The target audience are computer, computational, instrument and domain science experts working in the field of autonomous experiments.
97 MATHEMATICS AND COMPUTING↗
Scalable Bayesian optimization with randomized prior networks
Several fundamental problems in science and engineering consist of global optimization tasks involving unknown high-dimensional (black-box) functions that map a set of controllable variables to the outcomes of an expensive experiment. Bayesian Optimization (BO) techniques are known to be effective in tackling global optimization problems using a relatively small number objective function evaluations, but their performance suffers when dealing with high-dimensional outputs. To overcome the major challenge of dimensionality, here we propose a deep learning framework for BO and sequential decision making based on bootstrapped ensembles of neural architectures with randomized priors. Using appropriate architecture choices, we show that the proposed framework can approximate functional relationships between design variables and quantities of interest, even in cases where the latter take values in high-dimensional vector spaces or even infinite-dimensional function spaces. In the context of BO, we augmented the proposed probabilistic surrogates with re-parameterized Monte Carlo approximations of multiple-point (parallel) acquisition functions, as well as methodological extensions for accommodating black-box constraints and multi-fidelity information sources. We test the proposed framework against state-of-the-art methods for BO and demonstrate superior performance across several challenging tasks with high-dimensional outputs, including a constrained multi-fidelity optimization task involving shape optimization of rotor blades in turbo-machinery.
97 MATHEMATICS AND COMPUTING↗
Looking at Earth from Space: Teacher's Guide with Activities for Earth and Space Science
The Maryland Pilot Earth Science and Technology Education Network (MAPS-NET) project was sponsored by the National Aeronautics and Space Administration (NASA) to enrich teacher preparation and classroom learning in the area of Earth system science. This publication includes a teacher's guide that replicates material taught during a graduate-level course of the project and activities developed by the teachers. The publication was developed to provide teachers with a comprehensive approach to using satellite imagery to enhance science education. The teacher's guide is divided into topical chapters and enables teachers to expand their knowledge of the atmosphere, common weather patterns, and remote sensing. Topics include: weather systems and satellite imagery including mid-latitude weather systems; wave motion and the general circulation; cyclonic disturbances and baroclinic instability; clouds; additional common weather patterns; satellite images and the internet; environmental satellites; orbits; and ground station set-up. Activities are listed by suggested grade level and include the following topics: using weather symbols; forecasting the weather; cloud families and identification; classification of cloud types through infrared Automatic Picture Transmission (APT) imagery; comparison of visible and infrared imagery; cold fronts; to ski or not to ski (imagery as a decision making tool), infrared and visible satellite images; thunderstorms; looping satellite images; hurricanes; intertropical convergence zone; and using weather satellite images to enhance a study of the Chesapeake Bay. A list of resources is also included.
Steele, Colleen↗
Composite Technologies for Science Missions
ASTROLAB, Lunar Geophysical Network, Mars Icebreaker and Mars PLACER Net missions propose use of various types of vehicles – such as rovers, landers and flying probes - to accomplish their scientific objectives. Although designs for such vehicles are myriad, many structural components can be made using composite manufacturing technologies. Composite materials are also myriad, by nature, but carbon fiber reinforced polymers (CFRP) in particular are sought after for their high specific strength and specific stiffness. CFRP components could be especially beneficial in vehicle designs for Moon or Mars missions due to their nearly negligible coefficient of thermal expansion (CTE). CTE is a critical design characteristics in environments where significant thermal swings are prevalent, such as those of atmosphere-less Moon and Mars.MSFC/EM42 houses several relevant composites technologies. Strong and rigid struts can be filament-wound. Shells and platforms can be laid-up using automated fiber placement (for repeatability) or by hand (for small features). Monolithic structures can even be designed to reduce part count and therefore reduce the risk of joint failures. Composites are also tailorable, allowing the incorporation of different material types or formats through the thickness of a part. This includes materials which shield from radiation or electrostatic discharge. Finally, the rigidity of composite parts minimizes overall vibration and compliance within a design.Additionally, missions such as Triton Ocean World Surveyor anticipate use of traditional, metallic fuel tanks. Composite overwrapped pressure vessels (COPVs) are suitable for use in this application as well. The Triton mission even cites a concern for heat retention, for which CFRP also offers improved characteristics over traditional metallic tanks.
Lightweight↗
Resilience of the slow component in timescale-separated synchronized oscillators
Physiological networks are usually made of a large number of biological oscillators evolving on a multitude of different timescales. Phase oscillators are particularly useful in the modelling of the synchronization dynamics of such systems. If the coupling is strong enough compared to the heterogeneity of the internal parameters, synchronized states might emerge where phase oscillators start to behave coherently. Here, we focus on the case where synchronized oscillators are divided into a fast and a slow component so that the two subsets evolve on separated timescales. We assess the resilience of the slow component by, first, reducing the dynamics of the fast one using Mori-Zwanzig formalism. Second, we evaluate the variance of the phase deviations when the oscillators in the two components are subject to noise with possibly distinct correlation times. From the general expression for the variance, we consider specific network structures and show how the noise transmission between the fast and slow components is affected. Interestingly, we find that oscillators that are among the most robust when there is only a single timescale, might become the most vulnerable when the system undergoes a timescale separation. We also find that layered networks seem to be insensitive to such timescale separations.
59 BASIC BIOLOGICAL SCIENCES↗
One thousand soils for molecular understanding of belowground carbon cycling
While significant progress has been made in understanding global carbon (C) cycling, the mechanisms regulating belowground C fluxes and storage are still uncertain. New molecular technologies have the power to elucidate these processes, yet we have no widespread standardized implementation of molecular techniques. To address this gap, we introduce the Molecular Observation Network (MONet), a decadal vision from the Environmental Molecular Sciences Laboratory (EMSL), to develop a national network for understanding the molecular composition, physical structure, and hydraulic and biological properties of soil and water. These data are essential for advancing the next generation of multiscale Earth systems models. In this paper, we discuss the 1000 Soils Pilot for MONet, including a description of standardized sampling materials and protocols and a use case to highlight the utility of molecular-level and microstructural measurements for assessing the impacts of wildfire on soil. While the 1000 Soils Pilot generated a plethora of data, we focus on assessments of soil organic matter (SOM) chemistry via Fourier-transform ion cyclotron resonance-mass spectrometry and microstructural properties via X-ray computed tomography to highlight the effects of recent fire history in forested ecosystems on belowground C cycling. We observed decreases in soil respiration, microbial biomass, and potential enzyme activity in soils with high frequency burns. Additionally, the nominal oxidation state of carbon in SOM increased with burn frequency in surface soils. This results in a quantifiable shift in the molecular signature of SOM and shows that wildfire may result in oxidation of SOM and structural changes to soil pore networks that persist into deeper soils.
54 ENVIRONMENTAL SCIENCES↗
LaserNetUS. Final report
In this proposal we have formed a consortium in response to a request from the DoE Office of Fusion Energy Sciences to establish a national network of Petawatt-class laser systems to act as domestic user facilities that can enable a broad range of frontier scientific research. This new entity is called LaserNet US. The network formed here is directly responsive to recommendations made in a recently released National Academy of Sciences Report with regard to US strategy for high intensity laser research, “Opportunities in Intense Ultrafast Lasers: Reaching for the Brightest Light”. As detailed in this report, the research in this area has the potential to transform science in a number of research fields and to open up new areas of fundamental research. High field science was initiated in the US in the 1990’s, and subsequently Europe and Asia have embraced this research field, investing more than a billion dollars in this area over the past several years. Our network includes five academic and two national lab-based high intensity laser facilities. These facilities are distributed geographically throughout the US and the network provides access to complementary facilities which have unique world-class laser and experimental capabilities. LaserNet US will make these existing Petawatt-class laser facilities available to users from around the country who until now have not had regular access to such machines. Consequently, the network, which will develop over time, will also become a key element in driving forward national research in high field and high energy density plasma science. In particular, the University of Michigan provides access to the Hercules laser facility and the T-cubed laser facility for collaborative experiments as part of the first year operation. In the second year - to begin in summer 2019 - the experiments will be chosen via proposal submission and review by an outside panel formed by DOE.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Detecting hidden layers from spreading dynamics on complex networks
When dealing with spreading processes on networks it can be of the utmost importance to test the reliability of data and identify potential unobserved spreading paths. In this paper we address these problems and propose methods for hidden layer identification and reconstruction. We also explore the interplay between difficulty of the task and the structure of the multilayer network describing the whole system where the spreading process occurs. Our methods stem from an exact expression for the likelihood of a cascade in the susceptible-infected model on an arbitrary graph. We then show that by imploring statistical properties of unimodal distributions and simple heuristics describing joint likelihood of a series of cascades one can obtain an estimate of both existence of a hidden layer and its content with success rates far exceeding those of a null model. Furthermore, we conduct our analyses on both synthetic and real-world networks providing evidence for the viability of the approach presented.
97 MATHEMATICS AND COMPUTING↗
Stabilized reference frequency distribution for radio science with the Cassini spacecraft and the Deep Space Network
This paper provides an overview and update of the end to end high performance frequency and timing subsystm.
frequency distribution fiber optic frequency distr↗
Stabilized reference frequency distribution for radio science with the Cassini spacecraft and the Deep Space Network
Explore the source record for details and available documents.
SFODA CSO reference frequency stability phase nois↗
Relay Support for the Mars Science Laboratory and the Coming Decade of Mars Relay Network Evolution
Mars Relay Network is prepared to support MSL: a) ODY/MRO/MEX will all provide critical event comm support during EDL. b) New Electra/Electra-Lite capabilities on the MSL-MRO link will support >250 Mb/sol MSL data return. 2013 MAVEN orbiter will replenish on-orbit relay infrastructure as prior orbiters approach end-of-life. While NASA has withdrawn from the 2016 EMTGO and 2018 Joint Rover missions, analysis of the potential link shows a path to Gbit/sol relay capability 2012.
critical event telemetry↗
Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability
Abstract Neural networks have become increasingly prevalent within the geosciences, although a common limitation of their usage has been a lack of methods to interpret what the networks learn and how they make decisions. As such, neural networks have often been used within the geosciences to most accurately identify a desired output given a set of inputs, with the interpretation of what the network learns used as a secondary metric to ensure the network is making the right decision for the right reason. Neural network interpretation techniques have become more advanced in recent years, however, and we therefore propose that the ultimate objective of using a neural network can also be the interpretation of what the network has learned rather than the output itself. We show that the interpretation of neural networks can enable the discovery of scientifically meaningful connections within geoscientific data. In particular, we use two methods for neural network interpretation called backward optimization and layerwise relevance propagation, both of which project the decision pathways of a network back onto the original input dimensions. To the best of our knowledge, LRP has not yet been applied to geoscientific research, and we believe it has great potential in this area. We show how these interpretation techniques can be used to reliably infer scientifically meaningful information from neural networks by applying them to common climate patterns. These results suggest that combining interpretable neural networks with novel scientific hypotheses will open the door to many new avenues in neural network‐related geoscience research.
58 GEOSCIENCES↗
Indicator Patterns of Forced Change Learned by an Artificial Neural Network
Abstract Many problems in climate science require the identification of signals obscured by both the “noise” of internal climate variability and differences across models. Following previous work, we train an artificial neural network (ANN) to predict the year of a given map of annual‐mean temperature (or precipitation) from forced climate model simulations. This prediction task requires the ANN to learn forced patterns of change amidst a background of climate noise and model differences. We then apply a neural network visualization technique (layerwise relevance propagation) to visualize the spatial patterns that lead the ANN to successfully predict the year. These spatial patterns thus serve as “reliable indicators” of the forced change. The architecture of the ANN is chosen such that these indicators vary in time, thus capturing the evolving nature of regional signals of change. Results are compared to those of more standard approaches like signal‐to‐noise ratios and multilinear regression in order to gain intuition about the reliable indicators identified by the ANN. We then apply an additional visualization tool (backward optimization) to highlight where disagreements in simulated and observed patterns of change are most important for the prediction of the year. This work demonstrates that ANNs and their visualization tools make a powerful pair for extracting climate patterns of forced change.
54 ENVIRONMENTAL SCIENCES↗
The Nexus for Exoplanet System Science
NExSS is a research coordination network dedicated to the study of planetary habitability. A NASA cross-division initiative bringing astrophysicists, planetary scientists, Earth scientists, and heliophysicists together to bring a systems science approach to this problem. NExSS's goals being to investigate the diversity of planets, understanding how planet history, geology, and climate interact to create the conditions for life. Also, to put planets into an architectural context as stellar systems built over time by dynamical processes and sculpted by stars. Use experience from solar system (including Earth) history to identify where habitable niches are most likely to occur and which planets are most likely to be habitable. Leverage NASA investments in research and missions to accelerate discovery and characterization of potential life-bearing worlds.
coordination↗
Combination of deep neural network with attention mechanism enhances the explainability of protein contact prediction
Abstract Deep learning has emerged as a revolutionary technology for protein residue‐residue contact prediction since the 2012 CASP10 competition. Considerable advancements in the predictive power of the deep learning‐based contact predictions have been achieved since then. However, little effort has been put into interpreting the black‐box deep learning methods. Algorithms that can interpret the relationship between predicted contact maps and the internal mechanism of the deep learning architectures are needed to explore the essential components of contact inference and improve their explainability. In this study, we present an attention‐based convolutional neural network for protein contact prediction, which consists of two attention mechanism‐based modules: sequence attention and regional attention. Our benchmark results on the CASP13 free‐modeling targets demonstrate that the two attention modules added on top of existing typical deep learning models exhibit a complementary effect that contributes to prediction improvements. More importantly, the inclusion of the attention mechanism provides interpretable patterns that contain useful insights into the key fold‐determining residues in proteins. We expect the attention‐based model can provide a reliable and practically interpretable technique that helps break the current bottlenecks in explaining deep neural networks for contact prediction. The source code of our method is available at https://github.com/jianlin-cheng/InterpretContactMap .
59 BASIC BIOLOGICAL SCIENCES↗
The INTERSECT Open Federated Architecture for the Laboratory of the Future
A federated instrument-to-edge-to-center architecture is needed to autonomously collect, transfer, store, process, curate, and archive scientific data and reduce human-in-the-loop needs with (a) common interfaces to leverage community and custom software, (b) pluggability to permit adaptable solutions, reuse, and digital twins, and (c) an open standard to enable adoption by science facilities world-wide. The Selfdriven Experiments for Science/Interconnected Science Ecosystem (INTERSECT) Open Architecture enables science breakthroughs using intelligent networked systems, instruments and facilities with autonomous experiments, “self-driving” laboratories, smart manufacturing and artificial intelligence (AI) driven design, discovery and evaluation. It creates an open federated architecture for the laboratory of the future using a novel approach, consisting of (1) science use case design patterns, (2) a system of systems architecture, and (3) a microservice architecture.
Engelmann, Christian↗