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  1. ; ; ; ( , Computer Graphics Forum)
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  2. ( , Proceedings of the AAAI Conference on Artificial Intelligence)
    Accepted Manuscript Full Text Available
  3. ; ; ; ; ; ; ; ( , Biochemistry)
    Booker, S (Ed.)
    Applying enzymatic reactions to produce useful molecules is a central focus of chemical biology. Iron and 2-oxoglutarate (Fe/2OG) enzymes are found in all kingdoms of life and catalyze a broad array of oxidative transformations. Herein, we demonstrate that the activity of an Fe/2OG enzyme can be redirected when changing the targeted carbon hybridization from sp3 to sp2. During leucine 5-hydroxylase catalysis, installation of an olefin group onto the substrate redirects the Fe(IV)−oxo species reactivity from hydroxylation to asymmetric epoxidation. The resulting epoxide subsequently undergoes intramolecular cyclization to form the substituted piperidine, 2S,5S-hydroxypipecolic acid.
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  4. ; ; ; ( , Springer LNCS Wireless Algorithms, Systems, and Applications)
    Mobile edge computing (MEC) is an emerging paradigm that integrates computing resources in wireless access networks to process computational tasks in close proximity to mobile users with low latency. In this paper, we propose an online double deep Q networks (DDQN) based learning scheme for task assignment in dynamic MEC networks, which enables multiple distributed edge nodes and a cloud data center to jointly process user tasks to achieve optimal long-term quality of service (QoS). The proposed scheme captures a wide range of dynamic network parameters including non-stationary node computing capabilities, network delay statistics, and task arrivals. It learns themore »optimal task assignment policy with no assumption on the knowledge of the underlying dynamics. In addition, the proposed algorithm accounts for both performance and complexity, and addresses the state and action space explosion problem in conventional Q learning. The evaluation results show that the proposed DDQN-based task assignment scheme significantly improves the QoS performance, compared to the existing schemes that do not consider the effects of network dynamics on the expected long-term rewards, while scaling reasonably well as the network size increases.« less
    Accepted Manuscript Full Text Available
  5. ; ; ; ; ; ; ( , AMIA Jt Summits Transl Sci Proc.)
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  6. ; ; ; ; ; ; ; ( , European Conference on Computer Vision)
    Accepted Manuscript Full Text Available
  7. ( , Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition)
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  8. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review B)
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  9. ; ; ; ; ; ; ; ( , Proceedings of the 2019 SIAM International Conference on Data Mining)
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  10. ; ; ; ; ; ; ( , IEEE Conference on Computer Vision and Pattern Recognition)
    Accepted Manuscript Full Text Available