502 research outputs found
Organic solar cells based on non-fullerene acceptors.
Organic solar cells (OSCs) have been dominated by donor:acceptor blends based on fullerene acceptors for over two decades. This situation has changed recently, with non-fullerene (NF) OSCs developing very quickly. The power conversion efficiencies of NF OSCs have now reached a value of over 13%, which is higher than the best fullerene-based OSCs. NF acceptors show great tunability in absorption spectra and electron energy levels, providing a wide range of new opportunities. The coexistence of low voltage losses and high current generation indicates that new regimes of device physics and photophysics are reached in these systems. This Review highlights these opportunities made possible by NF acceptors, and also discuss the challenges facing the development of NF OSCs for practical applications
ssROC: Semi-Supervised ROC Analysis for Reliable and Streamlined Evaluation of Phenotyping Algorithms
High-throughput phenotyping will accelerate the use of
electronic health records (EHRs) for translational research. A critical
roadblock is the extensive medical supervision required for phenotyping
algorithm (PA) estimation and evaluation. To address this challenge, numerous
weakly-supervised learning methods have been proposed to estimate PAs. However,
there is a paucity of methods for reliably evaluating the predictive
performance of PAs when a very small proportion of the data is labeled. To fill
this gap, we introduce a semi-supervised approach (ssROC) for estimation of the
receiver operating characteristic (ROC) parameters of PAs (e.g., sensitivity,
specificity).
ssROC uses a small labeled dataset to
nonparametrically impute missing labels. The imputations are then used for ROC
parameter estimation to yield more precise estimates of PA performance relative
to classical supervised ROC analysis (supROC) using only labeled data. We
evaluated ssROC through in-depth simulation studies and an extensive evaluation
of eight PAs from Mass General Brigham.
In both simulated and real data, ssROC produced ROC
parameter estimates with significantly lower variance than supROC for a given
amount of labeled data. For the eight PAs, our results illustrate that ssROC
achieves similar precision to supROC, but with approximately 60% of the amount
of labeled data on average.
ssROC enables precise evaluation of PA performance to
increase trust in observational health research without demanding large volumes
of labeled data. ssROC is also easily implementable in open-source
software.
When used in conjunction with weakly-supervised PAs,
ssROC facilitates the reliable and streamlined phenotyping necessary for
EHR-based research
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