We investigate the transfer of phosphorescent energy between co-assembled metallophosphors in crystalline nanostructures [Angew. Chem. Int. Ed. 57 7820(2018) and J. Am. Chem. Soc. 140 4269(2018)]. Neither Dexter's...We investigate the transfer of phosphorescent energy between co-assembled metallophosphors in crystalline nanostructures [Angew. Chem. Int. Ed. 57 7820(2018) and J. Am. Chem. Soc. 140 4269(2018)]. Neither Dexter's nor Forster's mechanism of resonance energy transfer(RET) could account fully for the observed rates, which exceed 85% with significant temperature dependence. But there exists an alternative pathway on RET mediated by intermediate states of resonantly confined exciton–polaritons. Such a mechanism was used to analyze artificial photosynthesis in organic fluorescents [Phys.Rev. Lett. 122 257402(2019)]. For metallophosphors, the confined modes act as extended states lying between the molecular S_(1) and T_(1) states, offering a bridge for the long-lived T_(1) excitons to migrate from donors to acceptors. Population dynamics with parameters taken entirely based on experiments fits the observed lifetimes of phosphorescence across a broad range of doping and temperature.展开更多
Recently deep learning has successfully achieved state-of-the-art performance on many difficulttasks. Deep neural networks allow for model flexibility and process features without the needof domain knowledge. Advantag...Recently deep learning has successfully achieved state-of-the-art performance on many difficulttasks. Deep neural networks allow for model flexibility and process features without the needof domain knowledge. Advantage learning (A-learning) is a popular method in dynamic treatment regime (DTR). It models the advantage function, which is of direct relevance to optimaltreatment decision. No assumptions on baseline function are made. However, there is a paucityof literature on deep A-learning. In this paper, we present a deep A-learning approach to estimate optimal DTR. We use an inverse probability weighting method to estimate the differencebetween potential outcomes. Parameter sharing of convolutional neural networks (CNN) greatlyreduces the amount of parameters in neural networks, which allows for high scalability. Convexified convolutional neural networks (CCNN) relax the constraints of CNN for optimisation purpose.Different architectures of CNN and CCNN are implemented for contrast function estimation.Both simulation results and application to the STAR*D (Sequenced Treatment Alternatives toRelieve Depression) trial indicate that the proposed methods outperform penalised least squareestimator.展开更多
基金Project supported by the National Natural Science Foundation of China (Grant No. 16Z103060007) (PA)。
文摘We investigate the transfer of phosphorescent energy between co-assembled metallophosphors in crystalline nanostructures [Angew. Chem. Int. Ed. 57 7820(2018) and J. Am. Chem. Soc. 140 4269(2018)]. Neither Dexter's nor Forster's mechanism of resonance energy transfer(RET) could account fully for the observed rates, which exceed 85% with significant temperature dependence. But there exists an alternative pathway on RET mediated by intermediate states of resonantly confined exciton–polaritons. Such a mechanism was used to analyze artificial photosynthesis in organic fluorescents [Phys.Rev. Lett. 122 257402(2019)]. For metallophosphors, the confined modes act as extended states lying between the molecular S_(1) and T_(1) states, offering a bridge for the long-lived T_(1) excitons to migrate from donors to acceptors. Population dynamics with parameters taken entirely based on experiments fits the observed lifetimes of phosphorescence across a broad range of doping and temperature.
基金This work was supported by National Institutes of Health[5P01CA142538].
文摘Recently deep learning has successfully achieved state-of-the-art performance on many difficulttasks. Deep neural networks allow for model flexibility and process features without the needof domain knowledge. Advantage learning (A-learning) is a popular method in dynamic treatment regime (DTR). It models the advantage function, which is of direct relevance to optimaltreatment decision. No assumptions on baseline function are made. However, there is a paucityof literature on deep A-learning. In this paper, we present a deep A-learning approach to estimate optimal DTR. We use an inverse probability weighting method to estimate the differencebetween potential outcomes. Parameter sharing of convolutional neural networks (CNN) greatlyreduces the amount of parameters in neural networks, which allows for high scalability. Convexified convolutional neural networks (CCNN) relax the constraints of CNN for optimisation purpose.Different architectures of CNN and CCNN are implemented for contrast function estimation.Both simulation results and application to the STAR*D (Sequenced Treatment Alternatives toRelieve Depression) trial indicate that the proposed methods outperform penalised least squareestimator.
