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Time-resolved sensing of fluorescence quanta provides exceptionally versatile information−including access to nanoscopic structure, chemical environment and nonclassical behavior of quantum emitters. Combined spectro-temporal information is typically obtained using spatial dispersion with photoelectron imaging such as streak-cameras or position-sensitive counting and, alternatively, sequential filtering with single-pixel detectors. However, such schemes require complex, expensive and low-sensitivity detectors or rely on scanning acquisition. Here, we demonstrate a single-pixel implementation of fluorescence emission spectroscopy entirely in the temporal domain compatible with (a) time-correlated single-photon counting (TCSPC) and (b) high-speed single-shot detection. Harnessing the near-field regime of the Time-Stretch Dispersive Fourier Transformation (TS-DFT), we encode spectral information via chromatic dispersion into temporal signals, and we demonstrate the retrieval of entwined information via a direct deconvolution using prior knowledge. Addressing high optical throughput for extended emitters, we introduce a high-bandwidth graded-index multimode fiber for TS-DFT. As proof-of-concept, we present rapid single-shot optical thermometry based on quantum-dot luminescence. Given its high speed, efficiency, and simplicity, we foresee broad applications for fast hyperspectral confocal fluorescence microscopy, low-light sensing, and highthroughput spectral screening.

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Health agencies rely upon survey-based physical measures to estimate the prevalence of key global health indicators such as hypertension. Such measures are usually collected by non-healthcare worker personnel and are potentially subject to measurement error due to variations in interviewer technique and setting, termed "interviewer effects". In the context of physical measurements, particularly in low- and middle-income countries, interviewer-induced biases have not yet been examined. Using blood pressure as a case study, we aimed to determine the relative contribution of interviewer effects on the total variance of blood pressure measurements in three large nationally-representative health surveys from the Global South. We utilized 169,681 observations between 2008 and 2019 from three health surveys (Indonesia Family Life Survey, National Income Dynamics Study of South Africa, and Longitudinal Aging Study in India). In a linear mixed model, we modeled systolic blood pressure as a continuous dependent variable and interviewer effects as random effects alongside individual factors as covariates. To quantify the interviewer effect-induced uncertainty in hypertension prevalence, we utilized a bootstrap approach comparing sub-samples of observed blood pressure measurements to their adjusted counterparts. Our analysis revealed that the proportion of variation contributed by in- terviewers to blood pressure measurements was statistically significant but small: approximately 0.24-2.2% depending on the cohort. Thus, hypertension prevalence estimates were not substantially impacted at national scales. However, individual extreme interviewers could account for measurement divergences as high as 12%. Thus, highly biased interviewers could have important impacts on hypertension estimates at the sub-district level.

Linear mixed models (LMMs) are suitable for clustered data and are common in e.g. biometrics, medicine, or small area estimation. It is of interest to obtain valid inference after selecting a subset of available variables. We construct confidence sets for the fixed effects in Gaussian LMMs that are estimated via a Lasso-type penalization which allows quantifying the joint uncertainty of both variable selection and estimation. To this end, we exploit the properties of restricted maximum likelihood (REML) estimators to separate the estimation of the regression coefficients and covariance parameters. We derive an appropriate normalizing sequence to prove the uniform Cramér consistency of the REML estimators. We then show that the resulting confidence sets for the fixed effects are uniformly valid over the parameter space of both the regression coefficients and the covariance parameters. Their superiority to naïve post-selection least-squares confidence sets is validated in simulations and illustrated with a study of the acid neutralization capacity of U.S. lakes.