Measurement of vertical atmospheric density profile from the X-ray Earth occultation of the Crab Nebula with Insight-HXMT

The X-ray Earth occultation sounding (XEOS) is an emerging method for measuring the neutral density in the lower thermosphere. In this paper, the X-ray Earth occultation (XEO) of the Crab Nebula is investigated by using the Insight-HXMT. The pointing observation data on the 30th September, 2018 recorded by the Low Energy X-ray telescope (LE) of Insight-HXMT are selected and analyzed. The extinction lightcurves and spectra during the X-ray Earth occultation process are extracted. A forward model for the XEO lightcurve is established and the theoretical observational signal for lightcurve is predicted. A Bayesian data analysis method is developed for the XEO lightcurve modeling and the atmospheric density retrieval. The posterior probability distribution of the model parameters is derived through the Markov Chain Monte Carlo (MCMC) algorithm with the NRLMSISE-00 model and the NRLMSIS 2.0 model as basis functions and the best-fit density profiles are retrieved respectively. It is found that in the altitude range of 105–200 km, the retrieved density profile is 88.8 % of the density of NRLMSISE-00 and 109.7 % of the density of NRLMSIS 2.0 by fitting the lightcurve in the energy range of 1.0–2.5 keV based on XEOS method. In the altitude range of 95–125 km, the retrieved density profile is 81.0 % of the density of NRLMSISE-00 and 92.3 % of the density of NRLMSIS 2.0 by fitting the lightcurve in the energy range of 2.5–6.0 keV based on XEOS method. In the altitude range of 85–110 km, the retrieved density profile is 87.7 % of the density of NRLMSISE-00 and 101.4 % of the density of NRLMSIS 2.0 by fitting the lightcurve in the energy range of 6.0–10.0 keV based on XEOS method. The measurements of density profiles are compared with the NRLMSISE-00/NRLMSIS 2.0 model simulations and the previous retrieval results with RXTE satellite. Finally, we find that the retrieved density profile from Insight-HXMT based on the NRLMSISE-00/NRLMSIS 2.0 models is qualitatively consistent with the previous retrieved results from RXTE. This study demonstrate that the XEOS from the X-ray astronomical satellite Insight-HXMT can provide an approach for the study of the upper atmosphere. The Insight-HXMT satellite can join the family of the XEOS. The Insight-HXMT satellite with other X-ray astronomical satellites in orbit can form a space observation network for XEOS in the future.

An EM Algorithm for Target Tracking with an Unknown Correlation Coefficient of Measurement Noise

In this paper, an expectation maximization based sequential modified unbiased converted measurement Kalman filter is proposed for target tracking with an unknown correlation coefficient of measurement noise between the range and the range rate. Firstly, a pseudo measurement is constructed by multiplying the range and the range rate to reduce the strong nonlinearity between the measurement and the target state. The mean and covariance of converted errors are subsequentlsubsequently derived by modified unbiased converted measurement to weaken the error caused by the linearization of the measurement equation, which is effectively to improve the dynamic accuracy of target tracking. Then, the converted errors of the position and the pseudo measurement are decorrelated by the Cholesky factorization and thus to obtain the posterior probability distribution of the state by using the sequential filtering in the Bayesian framework. Finally, the expectation maximization is introduced in the updating procedure of the pseudo measurement to jointly estimate the target state and the correlation coefficient. The target tracking scenario with an unknown correlation coefficient is built to demonstrate the validness and feasibility of the proposed algorithm. Simultaneously, the results of the normalized error squared validate the consistency of the modified unbiased converted measurement.

Robust LPV models identification approach based on shifted asymmetric Laplace distribution

This paper focuses on the robust parameters estimation algorithm of linear parameters varying (LPV) models. The classical robust identification techniques deal with the polluted training data, for example, outliers in white noise. The paper extends this robustness to both symmetric and asymmetric noise with outliers to achieve stronger robustness. Without the assumption of Gaussian white noise pollution, the paper employs asymmetric Laplace distribution to model broader noise, especially the asymmetrically distributed noise, since it is an asymmetric heavy-tailed distribution. Furthermore, the asymmetric Laplace (AL) distribution is represented as the product of Gaussian distribution and exponential distribution to decompose this complex AL distribution. Then, a shifted parameter is introduced as the regression term to connect the probabilistic models of the noise and the predict output that obeys shifted AL distribution. In this way, the posterior probability distribution of the unobserved variables could be deduced and the robust parameters estimation problem is solved in the general Expectation Maximization algorithm framework. To demonstrate the advantage of the proposed algorithm, a numerical simulation example is employed to identify the parameters of LPV models and to illustrate the convergence.

Association between sitting time at work and the onset of major depressive episode: a 1-year prospective cohort study using the Bayesian regression

Background Although sedentary behavior is associated with the onset of major depressive disorder, it remains unclear whether sedentary behavior at work increases the risk of depression. The present study used the Bayesian approach to investigate the association between sitting time at work and the onset of major depressive episode (MDE). Methods A 1-year prospective cohort study was conducted among 233 Japanese workers without MDE (response rate: 4.3%). MDE onset was assessed using the self-reported WHO Composite International Diagnostic Interview version 3.0. A Bayesian Cox proportional hazard model was used to estimate the hazard ratio (HR) between long sitting time at work and MDE onset. Results A total of 231 workers were included in the analysis. During the follow-up, 1621 person-months were observed, and six participants experienced MDE onset. Incident rates per months were 0.34, 0.11, and 1.02% in short (< 7.2 h per day), medium (7.2–9.5 h), and long (9.5+ h) sitting time at work, respectively. The estimated median posterior probability distribution of the HR of long sitting time was 3.00 (95% highest density interval [HDI]: 0.73–12.03). The estimated median remained positive after adjustment for physical activity level and other covariates (HR = 2.11, 95% HDI: 0.42–10.22). The 10-base Bayesian factor for H1 (HR = 1.00) compared with the alternatives (H0, HR = 1.00) was 0.68 in the adjusted model. The analysis, which treated sitting time at work as a continuous variable, estimated that the median of the posterior probability distribution of the HR of sitting time was 0.79 (95% HDI: 0.58–1.07. The 10-base Bayesian factor was 2.73 in the linear association. Conclusions Long sitting time at work (9.5+ h per day) might be associated with MDE onset among workers. However, the linear association indicated conflicting results. Non-linear associations between sitting time and MDE onset might explain this inconsistency. The evidence for an adverse association between sitting time at work and MDE onset remains inconclusive.

COMPASS: Computations for Orientation and Motion Perception in Altered Sensorimotor States

Reliable perception of self-motion and orientation requires the central nervous system (CNS) to adapt to changing environments, stimuli, and sensory organ function. The proposed computations required of neural systems for this adaptation process remain conceptual, limiting our understanding and ability to quantitatively predict adaptation and mitigate any resulting impairment prior to completing adaptation. Here, we have implemented a computational model of the internal calculations involved in the orientation perception system’s adaptation to changes in the magnitude of gravity. In summary, we propose that the CNS considers parallel, alternative hypotheses of the parameter of interest (in this case, the CNS’s internal estimate of the magnitude of gravity) and uses the associated sensory conflict signals (i.e., difference between sensory measurements and the expectation of them) to sequentially update the posterior probability of each hypothesis using Bayes rule. Over time, an updated central estimate of the internal magnitude of gravity emerges from the posterior probability distribution, which is then used to process sensory information and produce perceptions of self-motion and orientation. We have implemented these hypotheses in a computational model and performed various simulations to demonstrate quantitative model predictions of adaptation of the orientation perception system to changes in the magnitude of gravity, similar to those experienced by astronauts during space exploration missions. These model predictions serve as quantitative hypotheses to inspire future experimental assessments.

Quantifying malaria acquired during travel and its role in malaria elimination on Bioko Island

Background Malaria elimination is the goal for Bioko Island, Equatorial Guinea. Intensive interventions implemented since 2004 have reduced prevalence, but progress has stalled in recent years. A challenge for elimination has been malaria infections in residents acquired during travel to mainland Equatorial Guinea. The present article quantifies how off-island contributes to remaining malaria prevalence on Bioko Island, and investigates the potential role of a pre-erythrocytic vaccine in making further progress towards elimination. Methods Malaria transmission on Bioko Island was simulated using a model calibrated based on data from the Malaria Indicator Surveys (MIS) from 2015 to 2018, including detailed travel histories and malaria positivity by rapid-diagnostic tests (RDTs), as well as geospatial estimates of malaria prevalence. Mosquito population density was adjusted to fit local transmission, conditional on importation rates under current levels of control and within-island mobility. The simulations were then used to evaluate the impact of two pre-erythrocytic vaccine distribution strategies: mass treat and vaccinate, and prophylactic vaccination for off-island travellers. Lastly, a sensitivity analysis was performed through an ensemble of simulations fit to the Bayesian joint posterior probability distribution of the geospatial prevalence estimates. Results The simulations suggest that in Malabo, an urban city containing 80% of the population, there are some pockets of residual transmission, but a large proportion of infections are acquired off-island by travellers to the mainland. Outside of Malabo, prevalence was mainly attributable to local transmission. The uncertainty in the local transmission vs. importation is lowest within Malabo and highest outside. Using a pre-erythrocytic vaccine to protect travellers would have larger benefits than using the vaccine to protect residents of Bioko Island from local transmission. In simulations, mass treatment and vaccination had short-lived benefits, as malaria prevalence returned to current levels as the vaccine’s efficacy waned. Prophylactic vaccination of travellers resulted in longer-lasting reductions in prevalence. These projections were robust to underlying uncertainty in prevalence estimates. Conclusions The modelled outcomes suggest that the volume of malaria cases imported from the mainland is a partial driver of continued endemic malaria on Bioko Island, and that continued elimination efforts on must account for human travel activity.

Using Bayesian Inference to Estimate Plausible Muscle Forces in Musculoskeletal Models

Background: Musculoskeletal modeling is currently a preferred method for estimating the muscle forces that underlie observed movements. However, these estimates are sensitive to a variety of assumptions and uncertainties, which creates difficulty when trying to interpret the muscle forces from musculoskeletal simulations. Here, we describe an approach that uses Bayesian inference to identify plausible ranges of muscle forces for a simple motion while representing uncertainty in the measurement of the motion and the objective function used to solve the muscle redundancy problem. Methods: We generated a reference elbow flexion-extension motion by simulating a set of muscle excitation signals derived from the computed muscle control tool built into OpenSim. We then used a Markov Chain Monte Carlo (MCMC) algorithm to sample from a posterior probability distribution of muscle excitations that would result in the reference elbow motion trajectory. We constructed a prior over the excitation parameters which down-weighted regions of the parameter space with greater muscle excitations. We used muscle excitations to find the corresponding kinematics using OpenSim, where the error in position and velocity trajectories (likelihood function) was combined with the sum of the cubed muscle excitations integrated over time (prior function) to compute the posterior probability density. Results: We evaluated the muscle forces that resulted from the set of excitations that were visited in the MCMC chain (five parallel chains, 450,000 iterations per chain, runtime = 71 hours). The estimated muscle forces compared favorably with the reference motion from computed muscle control, while the elbow angle and velocity from MCMC matched closely with the reference with an average RMSE for angle and velocity equal to 0.008° and 0.18°/s, respectively. However, our rank plot analysis and potential scale reduction statistics, which we used to evaluate convergence of the algorithm, indicated that the parallel chains did not fully mix. Conclusions: While the results from this process are a promising step towards characterizing uncertainty in muscle force estimation, the computational time required to search the solution space with, and the lack of MCMC convergence indicates that further developments in MCMC algorithms are necessary for this process to become feasible for larger-scale models.

Entropy, Information, and the Updating of Probabilities

This paper is a review of a particular approach to the method of maximum entropy as a general framework for inference. The discussion emphasizes pragmatic elements in the derivation. An epistemic notion of information is defined in terms of its relation to the Bayesian beliefs of ideally rational agents. The method of updating from a prior to posterior probability distribution is designed through an eliminative induction process. The logarithmic relative entropy is singled out as a unique tool for updating (a) that is of universal applicability, (b) that recognizes the value of prior information, and (c) that recognizes the privileged role played by the notion of independence in science. The resulting framework—the ME method—can handle arbitrary priors and arbitrary constraints. It includes the MaxEnt and Bayes’ rules as special cases and, therefore, unifies entropic and Bayesian methods into a single general inference scheme. The ME method goes beyond the mere selection of a single posterior, and also addresses the question of how much less probable other distributions might be, which provides a direct bridge to the theories of fluctuations and large deviations.

Global Vision Localization of Indoor Service Robot Based on Improved Iterative Extended Kalman Particle Filter Algorithm

Localization is the primary problem of mobile robot navigation. Monte Carlo localization based on particle filter has better accuracy and is easier to implement, but there is also the problem of particle degradation. In this paper, the iterative extended Kalman filter is optimized by the Levenberg-Marquardt optimization method. An improved particle filter algorithm based on the upon optimized iterative Kalman filter is proposed, and the importance probability density function of the particle filter is generated by the maximum posterior probability estimation of the improved iterative Kalman filter. Simulation results of the improved particle filter algorithm show that the algorithm can approximate the state posterior probability distribution more closely with fewer sampled particles under the premise of ensuring sufficient state estimation accuracy. Meanwhile, the computation is reduced and the real-time performance is enhanced. Finally, the algorithm is validated on the indoor mobile service robot. The experimental results show that the localization algorithm’s accuracy meets requirement for real-time localizing of the restaurant service robot.

Bayesian estimation of Earth's climate sensitivity and transient climate response from observational warming and heat content datasets

Future climate change projections, impacts, and mitigation targets are directly affected by how sensitive Earth's global mean surface temperature is to anthropogenic forcing, expressed via the climate sensitivity (S) and transient climate response (TCR). However, the S and TCR are poorly constrained, in part because historic observations and future climate projections consider the climate system under different response timescales with potentially different climate feedback strengths. Here, we evaluate S and TCR by using historic observations of surface warming, available since the mid-19th century, and ocean heat uptake, available since the mid-20th century, to constrain a model with independent climate feedback components acting over multiple response timescales. Adopting a Bayesian approach, our prior uses a constrained distribution for the instantaneous Planck feedback combined with wide-ranging uniform distributions of the strengths of the fast feedbacks (acting over several days) and multi-decadal feedbacks. We extract posterior distributions by applying likelihood functions derived from different combinations of observational datasets. The resulting TCR distributions when using two preferred combinations of historic datasets both find a TCR of 1.5 (1.3 to 1.8 at 5–95 % range) ∘C. We find the posterior probability distribution for S for our preferred dataset combination evolves from S of 2.0 (1.6 to 2.5) ∘C on a 20-year response timescale to S of 2.3 (1.4 to 6.4) ∘C on a 140-year response timescale, due to the impact of multi-decadal feedbacks. Our results demonstrate how multi-decadal feedbacks allow a significantly higher upper bound on S than historic observations are otherwise consistent with.