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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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Constraining electroweak penguin graph contributions in measurements of the CKM phase alpha using B → π π and B → ρ ρ decays
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Supervised jet clustering with graph neural networks for Lorentz boosted bosons
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Role of the chiral anomaly in polarized deeply inelastic scattering: Finding the triangle graph inside the box diagram in Bjorken and Regge asymptotics
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Topological properties of the limited penetrable horizontal visibility graph family
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Predicting magnetic properties of van der Waals magnets using graph neural networks
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Fragile topology in line-graph lattices with two, three, or four gapped flat bands
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Graph model for the clustering of dark matter halos
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A Graph-Based QoS-Aware Resource Management Scheme for OFDMA Femtocell Networks
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TransKGQA: Enhanced Knowledge Graph Question Answering With Sentence Transformers
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Analyzing Cyber-Physical Modularity and Interdependence Using Bio-Inspired Graph Modeling
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Counting Triangles in Real-World Graph Streams: Dealing with Repeated Edges and Time Windows.
Abstract not provided.
Automated Histologic Grading from Free-Text Pathology Reports Using Graph-of-Words Features and Machine Learning
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Distributed memory parallel Markov random fields using graph partitioning
Markov random fields (MRF) based algorithms have attracted a large amount of interest in image analysis due to their ability to exploit contextual information about data. Image data generated by experimental facilities, though, continues to grow larger and more complex, making it more difficult to analyze in a reasonable amount of time. Applying image processing algorithms to large datasets requires alternative approaches to circumvent performance problems. Aiming to provide scientists with a new tool to recover valuable information from such datasets, we developed a general purpose distributed memory parallel MRF-based image analysis framework (MPI-PMRF). MPI-PMRF overcomes performance and memory limitations by distributing data and computations across processors. The proposed approach was successfully tested with synthetic and experimental datasets. Additionally, the performance of the MPI-PMRF framework is analyzed through a detailed scalability study. We show that a performance increase is obtained while maintaining an accuracy of the segmentation results higher than 98%. The contributions of this paper are: (a) development of a distributed memory MRF framework; (b) measurement of the performance increase of the proposed approach; (c) verification of segmentation accuracy in both synthetic and experimental, real-world datasets
Towards Practical and Robust Labeled Pattern Matching in Trillion-Edge Graphs
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Heuristic-based techniques for mapping irregular communication graphs to mesh topologies
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