Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher.
Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?
Some links on this page may take you to non-federal websites. Their policies may differ from this site.
-
BARG, ALEXANDER; Sason, Igal; Loeliger, Hans-Andrea; Richardson, Tom; Vardy, Alexander; Wornell, Gregory (Ed.)A continuity structure of correlations among arms in multi-armed bandit can bring a significant acceleration of exploration and reduction of regret, in particular, when there are many arms. However, it is often latent in practice. To cope with the latent continuity, we consider a transfer learning setting where an agent learns the structural information, parameterized by a Lipschitz constant and an embedding of arms, from a sequence of past tasks and transfers it to a new one. We propose a simple but provably-efficient algorithm to accurately estimate and fully exploit the Lipschitz continuity at the same asymptotic order of lower bound of sample complexity in the previous tasks. The proposed algorithm is applicable to estimate not only a latent Lipschitz constant given an embedding, but also a latent embedding, while the latter requires slightly more sample complexity. To be specific, we analyze the efficiency of the proposed framework in two folds: (i) our regret bound on the new task is close to that of the oracle algorithm with the full knowledge of the Lipschitz continuity under mild assumptions; and (ii) the sample complexity of our estimator matches with the information-theoretic fundamental limit. Our analysis reveals a set of useful insights on transfer learning for latent Lipschitz continuity. From a numerical evaluation based on real-world dataset of rate adaptation in time-varying wireless channel, we demonstrate the theoretical findings and show the superiority of the proposed framework compared to baselines.more » « less
-
Azide moieties, unique linear species containing three nitrogen atoms, represent an attractive class of molecular tag for hyperpolarized magnetic resonance imaging (HP-MRI). Here we demonstrate ( 15 N) 3 -azide-containing molecules exhibit long-lasting hyperpolarization lifetimes up to 9.8 min at 1 T with remarkably high polarization levels up to 11.6% in water, thus establishing ( 15 N) 3 -azide as a powerful spin storage for hyperpolarization. A single ( 15 N)-labeled azide has also been examined as an effective alternative tag with long-lived hyperpolarization. A variety of biologically important molecules are studied in this work, including choline, glucose, amino acid, and drug derivatives, demonstrating great potential of 15 N-labeled azides as universal hyperpolarized tags for nuclear magnetic resonance imaging applications.more » « less
An official website of the United States government
