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DOE OSTI2020

Track Seeding and Labelling with Embedded-space Graph Neural Networks

To address the unprecedented scale of HL-LHC data, the Exa.TrkX project is investigating a variety of machine learning approaches to particle track reconstruction. The most promising of these solutions, graph neural networks (GNN), process the event as a graph that connects track measurements (detector hits corresponding to nodes) with candidate line segments between the hits (corresponding to edges). Detector information can be associated with nodes and edges, enabling a GNN to propagate the embedded parameters around the graph and predict node-, edge- and graph-level observables. Previously, message-passing GNNs have shown success in predicting doublet likelihood, and we here report updates on the state-of-the-art architectures for this task. In addition, the Exa.TrkX project has investigated innovations in both graph construction, and embedded representations, in an effort to achieve fully learned end-to-end track finding. Hence, we present a suite of extensions to the original model, with encouraging results for hitgraph classification. In addition, we explore increased performance by constructing graphs from learned representations which contain non-linear metric structure, allowing for efficient clustering and neighborhood queries of data points. We demonstrate how this framework fits in with both traditional clustering pipelines, and GNN approaches. The embedded graphs feed into high-accuracy doublet and triplet classifiers, or can be used as an end-to-end track classifier by clustering in an embedded space. A set of post-processing methods improve performance with knowledge of the detector physics. Finally, we present numerical results on the TrackML particle tracking challenge dataset, where our framework shows favorable results in both seeding and track finding.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRSDate not supplied

Populating a Graph Database to Run a Usage-Based Discovery Tool

Most dataset discovery tools for Earth Observation data rely on descriptions and other metadata of the datasets, using keyword searches or attribute filtering to determine relevance. However, these descriptions often do not include the potential uses of the data. Thus, a user working on floods will rarely see few if any rainfall datasets show up in such a search. The Usage Based Discovery tool, on the other hand, offers usage instances to the user, either research articles or applications, along with the datasets that those usage instances used. This allows a user, particularly one new to the world of Earth Observation data, to investigate which datasets are used in similar cases. The information that powers Usage-Based Discovery is a graph database of relationships of usage to dataset and usage to topic, allowing the user to narrow their search for similar cases. In order to scale out to a graph database rich enough to provide a satisfactory user experience, we combine manual and automated processes to populate the graph. The initial content of the graph has been seeded primarily via human-aided data curation methods, using sites like Google Scholar. To scale up this effort, we’ve employed crowdsourcing. It is easy for anyone to contribute to our graph using their Open Researcher and Contributor Identifier for authorization. We’re now experimenting with Machine Learning and Natural Language Processing to help automate population of the graph, starting with the classification of research articles by topic. Finding adequate training data in the absence of a comprehensive and open research article API continues to be a significant challenge.

Vincent Inverso↗

DOE OSTI2022

The global spectrum of plant form and function: enhanced species-level trait dataset

Here we provide the ‘Global Spectrum of Plant Form and Function Dataset’, containing species mean values for six vascular plant traits. Together, these traits –plant height, stem specific density, leaf area, leaf mass per area, leaf nitrogen content per dry mass, and diaspore (seed or spore) mass – define the primary axes of variation in plant form and function. The dataset is based on ca. 1 million trait records received via the TRY database (representing ca. 2,500 original publications) and additional unpublished data. It provides 92,159 species mean values for the six traits, covering 46,047 species. The data are complemented by higher-level taxonomic classification and six categorical traits (woodiness, growth form, succulence, adaptation to terrestrial or aquatic habitats, nutrition type and leaf type). Data quality management is based on a probabilistic approach combined with comprehensive validation against expert knowledge and external information. Intense data acquisition and thorough quality control produced the largest and, to our knowledge, most accurate compilation of empirically observed vascular plant species mean traits to date.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Vacant niches help predict invasion risk by birds

Abstract Aim If communities have a ceiling determining the number of species that can coexist, then the ability of alien species to establish at any given location should be related to the quantity of vacant niches available. We developed a new approach to estimate the extent to which niches are vacant and then explored the relationship between vacant niches and alien species. Location Global with focal tests in Europe and North America. Taxon Birds. Methods Drawing on a global classification of trophic structures for birds, we calculated a ‘vacant niche ratio’ metric to quantify the expected level of saturation (i.e. number of vacant niches) for each 1° × 1° grid cell globally, based on the difference between expected and observed numbers of bird species for all trophic guilds. Next, we used random forests to examine if the presence of plant‐invertivore (whose food source represents plants, seeds, fruits and invertebrates) and granivore alien bird species was associated with the vacant niche ratio across well‐sampled regions. Results In Europe, we found a significant relationship between alien species and vacant niches, with greater numbers of alien species being found in communities that offered greater numbers of vacant niches overall, and across habitat types. In North America, we found no significant relationship between plant‐invertivores and vacant niches, while for granivores, we found that areas with fewer vacant niches had greater numbers of alien species, especially in forests. However, vacant niches alone correctly predict 69% of the presence of alien bird species when combining both regions and trophic guilds. Main Conclusions Most regions of the world have unsaturated bird communities, with the level of saturation in communities varying within regions and trophic guilds. We found that although often‐neglected, vacant niches are likely to be, at least partially, related with the successful establishment of alien bird species.

González‐del‐Pliego, Pamela↗

DOE OSTI2023

Improving an Acoustic Vehicle Detector Using an Iterative Self-Supervision Procedure

In many non-canonical data science scenarios, obtaining, detecting, attributing, and annotating enough high-quality training data is the primary barrier to developing highly effective models. Moreover, in many problems that are not sufficiently defined or constrained, manually developing a training dataset can often overlook interesting phenomena that should be included. To this end, we have developed and demonstrated an iterative self-supervised learning procedure, whereby models are successfully trained and applied to new data to extract new training examples that are added to the corpus of training data. Successive generations of classifiers are then trained on this augmented corpus. Using low-frequency acoustic data collected by a network of infrasound sensors deployed around the High Flux Isotope Reactor and Radiochemical Engineering Development Center at Oak Ridge National Laboratory, we test the viability of our proposed approach to develop a powerful classifier with the goal of identifying vehicles from continuously streamed data and differentiating these from other sources of noise such as tools, people, airplanes, and wind. Using a small collection of exhaustively manually labeled data, we test several implementation details of the procedure and demonstrate its success regardless of the fidelity of the initial model used to seed the iterative procedure. Finally, we demonstrate the method’s ability to update a model to accommodate changes in the data-generating distribution encountered during long-term persistent data collection.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS2012

Autonomous Information Unit for Fine-Grain Data Access Control and Information Protection in a Net-Centric System

As communication and networking technologies advance, networks will become highly complex and heterogeneous, interconnecting different network domains. There is a need to provide user authentication and data protection in order to further facilitate critical mission operations, especially in the tactical and mission-critical net-centric networking environment. The Autonomous Information Unit (AIU) technology was designed to provide the fine-grain data access and user control in a net-centric system-testing environment to meet these objectives. The AIU is a fundamental capability designed to enable fine-grain data access and user control in the cross-domain networking environments, where an AIU is composed of the mission data, metadata, and policy. An AIU provides a mechanism to establish trust among deployed AIUs based on recombining shared secrets, authentication and verify users with a username, X.509 certificate, enclave information, and classification level. AIU achieves data protection through (1) splitting data into multiple information pieces using the Shamir's secret sharing algorithm, (2) encrypting each individual information piece using military-grade AES-256 encryption, and (3) randomizing the position of the encrypted data based on the unbiased and memory efficient in-place Fisher-Yates shuffle method. Therefore, it becomes virtually impossible for attackers to compromise data since attackers need to obtain all distributed information as well as the encryption key and the random seeds to properly arrange the data. In addition, since policy can be associated with data in the AIU, different user access and data control strategies can be included. The AIU technology can greatly enhance information assurance and security management in the bandwidth-limited and ad hoc net-centric environments. In addition, AIU technology can be applicable to general complex network domains and applications where distributed user authentication and data protection are necessary. AIU achieves fine-grain data access and user control, reducing the security risk significantly, simplifying the complexity of various security operations, and providing the high information assurance across different network domains.

Chow, Edward T.↗

DOE OSTI2026

Predicting U 3 O 8 powder processing conditions: An AI/ML approach analyzing deep learning embeddings of SEM micrographs

High-resolution SEM images of uranium-oxide powders encode micro- and nanoscale clues to their synthesis route and calcination temperature. We trained a ResNet-50 model on 11 commercial-scale U₃O₈ classes, ammonium diuranate (ADU) or uranyl peroxide (H₂O₂) precursors calcined at temperatures ranging from 400 to 750 °C and added a 256-D projection head before the classifier to analyze the learned representation. The best of eight seeds reached 92.4 % accuracy on reserved testing data, but our focus is the structure of the embedding space rather than the accuracy and labels. We quantify class relatedness in the original 256-D space using centroid similarity and distributional distances, and we use Uniform Manifold Approximation Projection (UMAP) for visualization. ‘Unknown’ images from different preparation methods, SEM operators, and from the literature localized near the expected classes under a nearest-centroid analysis without retraining, as well as clustered in similar UMAP space. In conclusion, this embedding-centered workflow complements black-box classification by providing quantitative, similarity-based comparisons of U₃O₈ morphologies and reduces storage space by up to 98 % for image data used in millisecond vector search comparisons.

36 MATERIALS SCIENCE↗

DOE OSTI2024

PAIReD jet: A multi-pronged resonance tagging strategy across all Lorentz boosts

We propose a new approach of jet-based event reconstruction that aims to optimally exploit correlations between the products of a hadronic multi-pronged decay across all Lorentz boost regimes. The new approach utilizes clustered small-radius jets as seeds to define unconventional jets, referred to as PAIReD jets. The constituents of these jets are subsequently used as inputs to machine learning-based algorithms to identify the flavor content of the jet. We demonstrate that this approach achieves higher efficiencies in the reconstruction of signal events containing heavy-flavor jets compared to other event reconstruction strategies at all Lorentz boost regimes. Classifiers trained on PAIReD jets also have significantly better background rejections compared to those based on traditional event reconstruction approaches using small-radius jets at low Lorentz boost regimes. The combined effect of a higher signal reconstruction efficiency and better classification performance results in a two to four times stronger rejection of light-flavor jets compared to conventional strategies at low Lorentz-boosts, and rejection rates similar to classifiers based on large-radius multi-pronged jets at high Lorentz-boost regimes.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2018

Laboratory Directed Research and Development Program - FY 2017 ORNL Annual Summary of Completed Projects

This report includes summaries of all ORNL LDRD research activities completed by September 30, 2017. It is sent to the ORNL Technical Officer for export control review and classification review prior to being delivered to the Office of Scientific and Technical Information. LDRD is a relatively small but vital program whose purpose is to meet the DOE mission. It allows ORNL and other DOE laboratories to improve their distinctive capabilities and to enhance their ability to conduct cutting-edge R&D for DOE and SPP sponsors. R&D projects funded through LDRD support the following general goals: maintain the scientific and technical vitality of the Laboratory; enhance the Laboratory’s ability to address future DOE missions; foster creativity and stimulate exploration of forefront areas of science and technology; serve as a proving ground for new concepts in R&D; and support high-risk, potentially high-value R&D. ORNL has established a program with three components to meet its LDRD objectives and to fulfill the particular needs of the Laboratory: the Director’s R&D Fund, the Seed Money Fund, and the Distinguished Fellowship Fund (Householder, Russell, Weinberg, and Wigner Fellowships). As outlined in Table 1, these three funds are complementary. The Director’s R&D Fund develops new capabilities in support of the Laboratory initiatives, the Seed Money Fund is open to all innovative ideas that have the potential for enhancing the Laboratory’s core scientific and technical competencies, and the Distinguished Fellowship Fund allows the Laboratory to hire exceptional early career scientists to refresh its scientific and technical capabilities. Provision for multiple routes of access to ORNL LDRD funds maximizes the likelihood that novel ideas with scientific and technological merit will be recognized and supported.

99 GENERAL AND MISCELLANEOUS↗