Multi-band interference and imaging via joint-detection of two-photon beats from thermal radiation.

We present a theoretical discussion of multi-band two-photon interference via joint detection by "slow" detectors and extend it to a technique for multi-band ghost imaging. This technique exploits the advantage of two-photon optical beats over classical optical beats with multi-band thermal light, where the beat frequency can be resolved from intensity fluctuation correlation measurement with two relatively slow photodetectors. The underlying two-photon beats represent a two-photon interference phenomenon: a pair of randomly created and randomly paired photons interfering with the pair itself. A notable implication of the two-photon beats is that they can be turbulence-resistant, which makes our result not only of fundamental interest but also practically useful.

Using family planning service statistics to inform model-based estimates of modern contraceptive prevalence.

The annual assessment of Family Planning (FP) indicators, such as the modern contraceptive prevalence rate (mCPR), is a key component of monitoring and evaluating goals of global FP programs and initiatives. To that end, the Family Planning Estimation Model (FPEM) was developed with the aim of producing survey-informed estimates and projections of mCPR and other key FP indictors over time. With large-scale surveys being carried out on average every 3-5 years, data gaps since the most recent survey often exceed one year. As a result, survey-based estimates for the current year from FPEM are often based on projections that carry a larger uncertainty than data informed estimates. In order to bridge recent data gaps we consider the use of a measure, termed Estimated Modern Use (EMU), which has been derived from routinely collected family planning service statistics. However, EMU data come with known limitations, namely measurement errors which result in biases and additional variation with respect to survey-based estimates of mCPR. Here we present a data model for the incorporation of EMU data into FPEM, which accounts for these limitations. Based on known biases, we assume that only changes in EMU can inform FPEM estimates, while also taking inherent variation into account. The addition of this EMU data model to FPEM allows us to provide a secondary data source for informing and reducing uncertainty in current estimates of mCPR. We present model validations using a survey-only model as a baseline comparison and we illustrate the impact of including the EMU data model in FPEM. Results show that the inclusion of EMU data can change point-estimates of mCPR by up to 6.7 percentage points compared to using surveys only. Observed reductions in uncertainty were modest, with the width of uncertainty intervals being reduced by up to 2.7 percentage points.