We present highly efficient electroluminescent devices using size-separated silicon nanocrystals (ncSi) as light emitting material. The emission color can be tuned from the deep red down to the yellow-orange spectral region by using very monodisperse size-separated nanoparticles. High external quantum efficiencies up to 1.1% as well as low turn-on voltages are obtained for red emitters. In addition, we demonstrate that size-separation of ncSi leads to drastically improved lifetimes of the devices and much less sensitivity of the emission wavelength to the applied drive voltage.
Developing interactive software, such as websites or games, is a particularly engaging way to learn computer science. However, teaching and giving feedback on such software is time-consuming -standard approaches require instructors to manually grade student-implemented interactive programs. As a result, online platforms that serve millions, like Code.org, are unable to provide any feedback on assignments for implementing interactive programs, which critically hinders students' ability to learn. One approach toward automatic grading is to learn an agent that interacts with a student's program and explores states indicative of errors via reinforcement learning. However, existing work on this approach only provides binary feedback of whether a program is correct or not, while students require finer-grained feedback on the specific errors in their programs to understand their mistakes. In this work, we show that exploring to discover errors can be cast as a meta-exploration problem. This enables us to construct a principled objective for discovering errors and an algorithm for optimizing this objective, which provides fine-grained feedback. We evaluate our approach on a set of over 700K real anonymized student programs from a Code.org interactive assignment. Our approach provides feedback with 94.3% accuracy, improving over existing approaches by 17.7% and coming within 1.5% of human-level accuracy. Project web page: https://ezliu.github.io/dreamgrader.
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