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Thermal rate coefficients for some atomic collisions have been observed to be remarkably independent of the details of interatomic interactions at short range. This makes these rate coefficients universal functions of the long-range interaction parameters and masses, which was previously exploited to develop a self-defining atomic sensor for ambient pressure. Here, we employ rigorous quantum scattering calculations to examine the response of thermally averaged rate coefficients for atom-atom collisions to changes in the interaction potentials. We perform a comprehensive analysis of the universality, and the boundaries thereof, by treating the quantum scattering observables as probabilistic predictions determined by a distribution of interaction potentials. We show that there is a characteristic change of the resulting distributions of rate coefficients, separating light, few-electron atoms and heavy, polarizable atoms. We produce diagrams that illustrate the boundaries of the thermal collision universality at different temperatures and provide guidance for future experiments seeking to exploit the universality.

Atoms constitute promising quantum sensors for a variety of scenarios including vacuum metrology. Key to this application is knowledge of the collision rate coefficient of the sensor atom with the particles being detected. Prior work demonstrated that, for room-temperature collisions, the total collision rate coefficient and the trap depth dependence of the sensor atom loss rate from shallow traps are both universal, independent of the interaction potential at short range. It was also shown that measurements of the energy transferred to the sensor atom by the collision can be used to estimate the total collision rate coefficient. However, discrepancies found when comparing the results of this and other methods of deducing the rate coefficient call into question its accuracy. Here the universality hypothesis is re-examined and an important correction is presented. We find that measurements of the post-collision recoil energy of sensor atoms held in shallow magnetic traps only provide information about the interaction potential at the very largest inter-atomic distances (e.g.~the value of $C_6$ for a leading order term of $C_6/r^6$). As other non-negligible terms exist at medium and long ranges, the total collision rate coefficient, even if universal, can differ from that computed solely from the value of $C_6$. By incorporating these other long-range terms into a simple semi-classical (SC) calculation, we find the SC prediction matches that of full, multi-channel, quantum mechanical scattering calculations using the complete potential. This work resolves the discrepancies, demonstrates the simplicity of estimating the rate coefficients for universal collision partners, and provides guidance for using atoms as a self-calibrating primary quantum pressure standard., Comment: 20 pages, 9 figures

Atomic sensors have shown great promise for density and pressure metrology in the high, ultra-high, and extremely-high vacuum regimes. Specifically, the density of background gas particles in vacuum can be determined by measuring the collision rate between the particles and an ensemble of sensor atoms. This requires preparing the sensor atoms in a particular quantum state, observing the rate of changes of that state, and using the cross section coefficient for state-changing collisions to convert the rate into a corresponding density. The cross section can be known by various methods including by quantum scattering calculations using an ansatz for the interaction potential between the collision pair, by measurements of the post-collision sensor-atom momentum recoil distribution, or by empirical calibration of the sensor atom at a known density. Identifying systematic errors in the results of these methods can be aided by direct comparisons between them. Alternatively, measurements of different sensor atoms exposed to the same background gas offers another point of comparison free of the systematic errors inherent in creating a background gas at a known density. Here, we present such measurements for two sensor atoms, $^{87}$Rb and $^6$Li, and a variety of atomic and molecular background gases including H$_2$, N$_2$, Ar, Ne, Kr, and Xe. We find results consistent with, yet statistically different at the level of 3.5(5)%, from recent theoretical and experiment measurements. This work demonstrates a model-free method for transferring the primacy of one atomic standard to another sensor atom and highlights the utility of sensor-atom cross-calibration experiments to check the validity of direct measurements and theoretical predictions., Comment: 17 pages, 7 figures

We present a comprehensive mathematical model and experimental measurements for the evolution of a trapped particle ensemble driven by collisions with a room-temperature background vapor. The model accommodates any trap geometry, confining potential, initial trapped distribution, and other experimental details; it only depends on the the probability distribution function $P_t(E)$ for the collision-induced energy transfer to the trapped ensemble. We describe how to find $P_t(E)$ using quantum scattering calculations and how it can be approximated using quantum diffractive universality. We then compare our model to experimental measurements of a $^{87}$Rb ensemble energy evolution exposed to a room temperature background gas of Ar by means of a single parameter fit for the total collision rate $\Gamma$. We extracted a collision rate of $\Gamma = 0.646(1)\ \text{s}^{-1}$. This is compared to a value of $0.664(4)\ \text{s}^{-1}$ found by the commonly used method of zero-trap depth extrapolation, a $2.8\%$ correction that is a result of our model fully taking ensemble loss and heating into account. Finally, we report a five-fold increase in the precision of our collision rate extraction from the experimental data., Comment: 14 pages, 6 figures

The total room-temperature, velocity-averaged cross section for atom-atom and atom-molecule collisions is well approximated by a universal function depending only on the magnitude of the leading order dispersion coefficient, $C_6$. This feature of the total cross section together with the universal function for the energy distribution transferred by glancing angle collisions ($P_{\rm{QDU}6}$) can be used to empirically determine the total collision cross section and realize a self-calibrating, vacuum pressure standard. This was previously validated for Rb+N$_2$ and Rb+Rb collisions. However, the post-collision energy distribution is expected to deviate from $P_{\rm{QDU}6}$ in the limit of small $C_6$ and small reduced mass. Here we observe this deviation experimentally by performing a direct cross-species loss rate comparison between Rb+H$_2$ and Li+H$_2$ and using the \textit{ab initio} value of $\langle \sigma_{\rm{tot}} \, v \rangle_{\rm{Li+H}_2}$. We find a velocity averaged total collision cross section ratio, $R = \langle \sigma_{\rm{tot}} \, v \rangle_{\rm{Li+H}_2} : \langle \sigma_{\rm{tot}} \, v \rangle_{\rm{Rb+H}_2} = 0.83(5)$. Based on an \textit{ab initio} computation of $\langle \sigma_{\rm{tot}} \, v \rangle_{\rm{Li+H}_2} = 3.13(6)\times 10^{-15}$ m$^3$/s, we deduce $\langle \sigma_{\rm{tot}} \, v \rangle_{\rm{Rb+H}_2} = 3.8(2) \times 10^{-15}$ m$^3$/s, in agreement with a Rb+H$_2$ \textit{ab initio} value of $\langle \sigma_{\mathrm{tot}} v \rangle_{\mathrm{Rb+H_2}} = 3.57 \times 10^{-15} \mathrm{m}^3/\mathrm{s}$.By contrast, fitting the Rb+H$_2$ loss rate as a function of trap depth to the universal function we find $\langle \sigma_{\rm{tot}} \, v \rangle_{\rm{Rb+H}_2} = 5.52(9) \times 10^{-15}$ m$^3$/s. Finally, this work demonstrates how to perform a cross-calibration of sensor atoms to extend and enhance the cold atom based pressure sensor., Comment: 14 pages, 9 figures

Collisions between trapped atoms or trapped molecules with room temperature particles in the surrounding vacuum induce loss of the trapped population at a rate proportional to the density of the background gas particles. The total velocity-averaged loss rate coefficient $\langle \sigma_\mathrm{tot} v \rangle$ for such collisions and the variation of the loss rate with trap depth has been shown to depend only on the long range interaction potential between the collision partners. This collision universality was previously used to realize a self-calibrating, atom-based, primary pressure standard and was validated by indirect comparison with an orifice flow standard. Here, we use collision universality to measure $\langle \sigma_\mathrm{tot} v \rangle = 6.44(11)(5) \times 10^{-15}~\rm{m^3/s}$ for Rb-Rb collisions and deduce the corresponding $C_6 = 4688(198)(95)~E_ha_0^6$, in excellent agreement with predictions based upon $\textit{ab initio}$ calculated and previously measured $C_6$ values., Comment: 9 pages, 4 figures

The known connection between shrinkage estimation, empirical Bayes, and mixed effects models is explored and applied to balanced and unbalanced designs in which the responses are correlated. As an illustration, a mixed model is proposed for predicting the outcome of English Premier League games that takes into account both home and away team effects. Results based on empirical best linear unbiased predictors obtained from fitting mixed linear models are compared with fully Bayesian predictors that utilize prior information from the previous season., Comment: 15 pages, 6 figures

It is known that the estimating equations for quantile regression (QR) can be solved using an EM algorithm in which the M-step is computed via weighted least squares, with weights computed at the E-step as the expectation of independent generalized inverse-Gaussian variables. This fact is exploited here to extend QR to allow for random effects in the linear predictor. Convergence of the algorithm in this setting is established by showing that it is a generalized alternating minimization (GAM) procedure. Another modification of the EM algorithm also allows us to adapt a recently proposed method for variable selection in mean regression models to the QR setting. Simulations show the resulting method significantly outperforms variable selection in QR models using the lasso penalty. Applications to real data include a frailty QR analysis of hospital stays, and variable selection for age at onset of lung cancer and for riboflavin production rate using high-dimensional gene expression arrays for prediction., Comment: arXiv admin note: text overlap with arXiv:1910.11479

We show that the estimating equations for quantile regression can be solved using a simple EM algorithm in which the M-step is computed via weighted least squares, with weights computed at the E-step as the expectation of independent generalized inverse-Gaussian variables. We compute the variance-covariance matrix for the quantile regression coefficients using a kernel density estimator that results in more stable standard errors than those produced by existing software. A natural modification of the EM algorithm that involves fitting a linear mixed model at the M-step extends the methodology to mixed effects quantile regression models. In this case, the fitting method can be justified as a generalized alternating minimization algorithm. Obtaining quantile regression estimates via the weighted least squares method enables model diagnostic techniques similar to the ones used in the linear regression setting. The computational approach is compared with existing software using simulated data, and the methodology is illustrated with several case studies., Comment: a newer version of the paper which focuses on large-P variable selection is available in arXiv:2104.08595

This work demonstrates that quantum diffractive collisions, those that result in very small momentum and energy transfer, are universal. Specifically, the cumulative energy distribution transferred to an initially stationary sensor particle by a quantum diffractive collision follows a universal function that depends only on the sensor particle mass and the thermally-averaged, total collision cross section. The characteristic energy scale corresponds to the localization length associated with the collision-induced quantum measurement, and the shape of the universal function is determined {\it only} by the analytic form of the interaction potential at long range. Using cold $^{87}$Rb sensor atoms confined in a magnetic trap, we observe experimentally the universal function specific to van der Waals collisions, and realize a \emph{self-defining} particle pressure sensor that can be used for any ambient gas. This provides the first primary and quantum definition of the Pascal, applicable to any species and therefore represents a key advance for vacuum and pressure metrology. The quantum pressure standard realized here was compared with a state-of-the-art orifice flow standard transferred by an ionization gauge calibrated for N$_2$. The pressure measurements agreed at the 0.5\% level., Comment: 31 pages, 5 figures

High-dimensional mixed data as a combination of both continuous and ordinal variables are widely seen in many research areas such as genomic studies and survey data analysis. Estimating the underlying correlation among mixed data is hence crucial for further inferring dependence structure. We propose a semiparametric latent Gaussian copula model for this problem. We start with estimating the association among ternary-continuous mixed data via a rank-based approach and generalize the methodology to p-level-ordinal and continuous mixed data. Concentration rate of the estimator is also provided and proved. At last, we demonstrate the performance of the proposed estimator by extensive simulations and two case studies of real data examples of algorithmic risk score evaluation and cancer patients survival data., Comment: arXiv admin note: text overlap with arXiv:1703.04957 by other authors

A new empirical Bayes approach to variable selection in the context of generalized linear models is developed. The proposed algorithm scales to situations in which the number of putative explanatory variables is very large, possibly much larger than the number of responses. The coefficients in the linear predictor are modeled as a three-component mixture allowing the explanatory variables to have a random positive effect on the response, a random negative effect, or no effect. A key assumption is that only a small (but unknown) fraction of the candidate variables have a non-zero effect. This assumption, in addition to treating the coefficients as random effects facilitates an approach that is computationally efficient. In particular, the number of parameters that have to be estimated is small, and remains constant regardless of the number of explanatory variables. The model parameters are estimated using a Generalized Alternating Maximization algorithm which is scalable, and leads to significantly faster convergence compared with simulation-based fully Bayesian methods., Comment: arXiv admin note: text overlap with arXiv:1510.03781

3D Morphable Models (3DMMs) are powerful statistical models of 3D facial shape and texture, and among the state-of-the-art methods for reconstructing facial shape from single images. With the advent of new 3D sensors, many 3D facial datasets have been collected containing both neutral as well as expressive faces. However, all datasets are captured under controlled conditions. Thus, even though powerful 3D facial shape models can be learnt from such data, it is difficult to build statistical texture models that are sufficient to reconstruct faces captured in unconstrained conditions ("in-the-wild"). In this paper, we propose the first, to the best of our knowledge, "in-the-wild" 3DMM by combining a powerful statistical model of facial shape, which describes both identity and expression, with an "in-the-wild" texture model. We show that the employment of such an "in-the-wild" texture model greatly simplifies the fitting procedure, because there is no need to optimize with regards to the illumination parameters. Furthermore, we propose a new fast algorithm for fitting the 3DMM in arbitrary images. Finally, we have captured the first 3D facial database with relatively unconstrained conditions and report quantitative evaluations with state-of-the-art performance. Complementary qualitative reconstruction results are demonstrated on standard "in-the-wild" facial databases. An open source implementation of our technique is released as part of the Menpo Project.