标题： 通过定制动态混合建模的激发下量子点强度波动的综合学习 显示英文标题

标题： Integrative Learning of Quantum Dot Intensity Fluctuations under Excitation via Tailored Dynamic Mixture Modeling

摘要： 半导体纳米晶体，称为量子点（QDs），由于其独特的荧光特性而受到广泛关注。 在连续激发下，QDs发射的光子具有复杂的强度波动：在激发过程中，光子发射的强度会发生波动，这种波动模式在相同的实验条件下可能在不同的QDs之间有所不同。 增加复杂性的是，处理后的强度序列由于必要的阈值处理和归一化而呈现非高斯分布并被截断。 传统的基于正态性的单点分析方法无法应对这些复杂性。 在与化学家的合作中，我们开发了一种综合学习方法，以同时分析多个QDs的强度序列。 受数据结构的独特性和QDs假设行为的启发，我们的方法以著名的隐马尔可夫模型作为结构基础，以描述单个点的强度波动，同时假设在每个状态下，归一化的强度遵循0/1膨胀的Beta分布，状态/发射分布在QDs之间共享，状态转移动力学可以在几个QD簇之间变化。 该框架允许对强度波动模式进行精确的集体表征，并有潜力改变当前化学领域的实践。 将我们的方法应用于来自128个QDs的实验数据，我们揭示了三个共享的强度状态，并捕捉到了几种不同的强度转换模式，这突显了我们的方法在提供对QD行为及其设计和应用潜力更深入见解方面的有效性。 显示英文摘要

摘要： Semiconductor nano-crystals, known as quantum dots (QDs), have attracted significant attention for their unique fluorescence properties. Under continuous excitation, QDs emit photons with intricate intensity fluctuation: the intensity of photon emission fluctuates during the excitation, and such a fluctuation pattern can vary across different QDs even under the same experimental conditions. What adding to the complication is that the processed intensity series are non-Gaussian and truncated due to necessary thresholding and normalization. Conventional normality-based single-dot analysis fall short of addressing these complexities. In collaboration with chemists, we develop an integrative learning approach to simultaneously analyzing intensity series from multiple QDs. Motivated by the unique data structure and the hypothesized behaviors of the QDs, our approach leverages the celebrated hidden Markov model as its structural backbone to characterize individual dot intensity fluctuations, while assuming that, in each state the normalized intensity follows a 0/1 inflated Beta distribution, the state/emission distributions are shared across the QDs, and the state transition dynamics can vary among a few QD clusters. This framework allows for a precise, collective characterization of intensity fluctuation patterns and have the potential to transform current practice in chemistry. Applying our method to experimental data from 128 QDs, we reveal three shared intensity states and capture several distinct intensity transition patterns, underscoring the effectiveness of our approach in providing deeper insights into QD behaviors and their design and application potential.