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  1. ; ; ; ; ( , Large-Scale Scientific Computing. LSSC 2021, Springer Lecture Notes in Computer Science)
    Diffusion State Distance (DSD) is a data-dependent metric that compares data points using a data-driven diffusion process and provides a powerful tool for learning the underlying structure of high-dimensional data. While finding the exact nearest neighbors in the DSD metric is computationally expensive, in this paper, we propose a new random-walk based algorithm that empirically finds approximate k-nearest neighbors accurately in an efficient manner. Numerical results for real-world protein-protein interaction networks are presented to illustrate the efficiency and robustness of the proposed algorithm. The set of approximate k-nearest neighbors performs well when used to predict proteins’ functional labels.
    Free, publicly-accessible full text available March 18, 2023
  2. ; ; ; ; ; ; ; ; ; ; et al ( , Geophysical research letters)
    Baffin Bay exports Arctic Water to the North Atlantic while receiving northward flowing Atlantic Water. Warm Atlantic Water has impacted the retreat of tidewater glaciers draining the Greenland Ice Sheet. Periods of enhanced Atlantic Water transport into Baffin Bay have been observed, but the oceanic processes are still not fully explained. At the end of 2010 the net transport at Davis Strait, the southern gateway to Baffin Bay, reversed from southward to northward for a month, leading to significant northward oceanic heat transport into Baffin Bay. This was associated with an extreme high in the Greenland Blocking Index and amore »stormtrack path that shifted away from Baffin Bay. Thus fewer cyclones in the Irminger Sea resulted in less frequent northerly winds along the western coast of Greenland, allowing anomalous northward penetration of warm waters, reversing the volume and heat transport at Davis Strait.« less
    Free, publicly-accessible full text available August 18, 2022
  3. ; ; ; ; ( , Metadata and Semantic Research. MTSR 2020. Communications in Computer and Information Science)
    Garoufallou, E ; Ovalle-Perandones, M.A. (Ed.)
    This paper introduces Helping Interdisciplinary Vocabulary Engineering for Materials Science (HIVE-4-MAT), an automatic linked data ontology application. The paper provides contextual background for materials science, shared ontology infrastructures, and knowledge extraction applications. HIVE-4-MAT's three key features are reviewed: 1) Vocabulary browsing, 2) Term search and selection, and 3) Knowledge Extraction/Indexing, as well as the basics of named entity recognition (NER). The discussion elaborates on the importance of ontology infrastructures and steps taken to enhance knowledge extraction. The conclusion highlights next steps surveying the ontology landscape, including NER work as a step toward relation extraction (RE), and support for better ontologies.
    Accepted Manuscript Full Text Available
  4. ; ; ; ; ( , ICLR)
    Accepted Manuscript Full Text Available
  5. ; ; ; ; ; ; ; ; ; ; et al ( , GLSVLSI '21: Proceedings of the 2021 on Great Lakes Symposium on VLSI)
    We present a hybrid optical-electrical analog deep learning (DL) accelerator, the first work to use incoherent optical signals for DL workloads. Incoherent optical designs are more attractive than coherent ones as the former can be more easily realized in practice. However, a significant challenge in analog DL accelerators, where multiply-accumulate operations are dominant, is that there is no known solution to perform accumulation using incoherent optical signals. We overcome this challenge by devising a hybrid approach: accumulation is done in the electrical domain, while multiplication is performed in the optical domain. The key technology enabler of our design is themore »transistor laser, which performs electrical-to-optical and optical-to-electrical conversions efficiently to tightly integrate electrical and optical devices into compact circuits. As such, our design fully realizes the ultra high-speed and high-energy-efficiency advantages of analog and optical computing. Our evaluation results using the MNIST benchmark show that our design achieves 2214× and 65× improvements in latency and energy, respectively, compared to a state-of-the-art memristor-based analog design.« less
    Full Text Available 
  6. ; ; ; ; ( , International Conference on Learning Representations (ICLRR))
    url: https://openreview.net/pdf?id=LGgdb4TS4Z
    Accepted Manuscript Full Text Available
  7. ; ; ; ; ; ; ; ; ( , ArXivorg)
    Accepted Manuscript Full Text Available
  8. ; ; ; ; ; ; ( , IEEE Transactions on Computers)
    Full Text Available 
  9. ; ; ; ; ( , Cloud Computing – CLOUD 2020. CLOUD 2020. Lecture Notes in Computer Science)
    Zhang, Q. ; Wang, Y. ; Zhang, LJ. (Ed.)
    Full Text Available 
  10. ; ; ; ; ; ( , SIAM Journal on Scientific Computing)
    Full Text Available