The Parameter Houlihan: a solution to high-throughput identifiability indeterminacy for brutally ill-posed problems

One way to interject knowledge into clinically impactful forecasting is to use data assimilation, a nonlinear regression that projects data onto a mechanistic physiologic model, instead of a set of functions, such as neural networks. Such regressions have an advantage of being useful with particularly sparse, non-stationary clinical data. However, physiological models are often nonlinear and can have many parameters, leading to potential problems with parameter identifiability, or the ability to find a unique set of parameters that minimize forecasting error. The identifiability problems can be minimized or eliminated by reducing the number of parameters estimated, but reducing the number of estimated parameters also reduces the flexibility of the model and hence increases forecasting error. We propose a method, the parameter Houlihan, that combines traditional machine learning techniques with data assimilation, to select the right set of model parameters to minimize forecasting error while reducing identifiability problems. The method worked well: the data assimilation-based glucose forecasts and estimates for our cohort using the Houlihan-selected parameter sets generally also minimize forecasting errors compared to other parameter selection methods such as by-hand parameter selection. Nevertheless, the forecast with the lowest forecast error does not always accurately represent physiology, but further advancements of the algorithm provide a path for improving physiologic fidelity as well. Our hope is that this methodology represents a first step toward combining machine learning with data assimilation and provides a lower-threshold entry point for using data assimilation with clinical data by helping select the right parameters to estimate

Understanding the complex phase diagram of uranium: the role of electron-phonon coupling

We report an experimental determination of the dispersion of the soft phonon mode along [1,0,0] in uranium as a function of pressure. The energies of these phonons increase rapidly, with conventional behavior found by 20 GPa, as predicted by recent theory. New calculations demonstrate the strong pressure (and momentum) dependence of the electron-phonon coupling, whereas the Fermi-surface nesting is surprisingly independent of pressure. This allows a full understanding of the complex phase diagram of uranium, and the interplay between the charge-density wave and superconductivity

An Improved Distance and Mass Estimate for Sgr A* from a Multistar Orbit Analysis

We present new, more precise measurements of the mass and distance of our Galaxy's central supermassive black hole, Sgr A*. These results stem from a new analysis that more than doubles the time baseline for astrometry of faint stars orbiting Sgr A*, combining two decades of speckle imaging and adaptive optics data. Specifically, we improve our analysis of the speckle images by using information about a star's orbit from the deep adaptive optics data (2005 - 2013) to inform the search for the star in the speckle years (1995 - 2005). When this new analysis technique is combined with the first complete re-reduction of Keck Galactic Center speckle images using speckle holography, we are able to track the short-period star S0-38 (K-band magnitude = 17, orbital period = 19 years) through the speckle years. We use the kinematic measurements from speckle holography and adaptive optics to estimate the orbits of S0-38 and S0-2 and thereby improve our constraints of the mass ($M_{bh}$) and distance ($R_o$) of Sgr A*: $M_{bh} = 4.02\pm0.16\pm0.04\times10^6~M_{\odot}$ and $7.86\pm0.14\pm0.04$ kpc. The uncertainties in $M_{bh}$ and $R_o$ as determined by the combined orbital fit of S0-2 and S0-38 are improved by a factor of 2 and 2.5, respectively, compared to an orbital fit of S0-2 alone and a factor of $\sim$2.5 compared to previous results from stellar orbits. This analysis also limits the extended dark mass within 0.01 pc to less than $0.13\times10^{6}~M_{\odot}$ at 99.7% confidence, a factor of 3 lower compared to prior work.Comment: 56 pages, 14 figures, accepted to Ap