Effect of Systematic Uncertainty Estimation on the Muon g - 2 Anomaly

The statistical significance that characterizes a discrepancy between a measurement and theoretical prediction is usually calculated assuming that the statistical and systematic uncertainties are known. Many types of systematic uncertainties are, however, estimated on the basis of approximate procedures and thus the values of the assigned errors are themselves uncertain. Here the impact of the uncertainty on the assigned uncertainty is investigated in the context of the muon g - 2 anomaly. The significance of the observed discrepancy between the Standard Model prediction of the muon’s anomalous magnetic moment and measured values are shown to decrease substantially if the relative uncertainty in the uncertainty assigned to the Standard Model prediction exceeds around 30%. The reduction in sensitivity increases for higher significance, so that establishing a 5σ effect will require not only small uncertainties but the uncertainties themselves must be estimated accurately to correspond to one standard deviation.

A Challenge of Macro-Meso-Micro Analysis Impacts on Multiracial Nationality Decision Making :Multiracial Thai-Japanese in Bangkok

Nationality decision making could impact immensely to country structural, society issues, and future population. Exploring the factors and model prediction could dramatical give benefits to Thailand-Japan or as a reference to other countries toward possibility of multiracial nationality decision making, policy approach to future population and international labor management. The case study of Thai-Japanese multiracial nationality decision making could crucial explain to 4 scenarioses under developing and developed country status contexts. The objectives in this exploratory basic factors research are (1) To study the possibility factors of multiracial decision making (2) To adjust the factors impact on multiracial decision making into a model prediction (3) To assess a model in separation of developing and developed country context toward multiracial decision making. This research conduted N=685 of multinaitonality Thai-Japanese in Bangkok (Thailand) under criteria control throught statistic processes requirement, questionnaire survey conducted in purposive sampling via online at Bangkok as the biggest majority province of Japanese migrant in Thailand (Thailand-Japan embassy,2019). PLS-SEM was considered to assess a formative measurement from lower to higher order and mediation model of macro, meso, and micro levels by using SMART-PLS 3.0. The results indicate that Thailand macro level should concern about “Political and governance”, and “Hospital and wellness” factors, Japan macro level should consider to “Economic”, and “Working environment” factors. Also, Thailand meso level have more issue on development factors than Japan, afterward meso factor as an individual background and experience reports that education and passport competency support to multiracial nationality decision making to both Thailand and Japan. As a result, Thailand macro, meso, and micro structure presents to the unrelevance between macro, meso, and micro which causes to unsupport of nationality decision making meanwhile Japan has a potential to escalate a policy toward macro and meso in better positive way with a significant support between macro, meso, and micro structure both direct and indirect to multiracial natonality decision making.

Robust Probabilistic Discriminative Model Prediction Tracker via Improved Model Update Strategy

In the visual object tracking, the tracking algorithm based on discriminative model prediction have shown favorable performance in recent years. Probabilistic discriminative model prediction (PrDiMP) is a typical tracker based on discriminative model prediction. The PrDiMP evaluates tracking results through output of the tracker to guide online update of the model. However, the tracker output is not always reliable, especially in the case of fast motion, occlusion or background clutter. Simply using the output of the tracker to guide the model update can easily lead to drift. In this paper, we present a robust model update strategy which can effectively integrate maximum response, multi-peaks and detector cues to guide model update of PrDiMP. Furthermore, we have analyzed the impact of different model update strategies on the performance of PrDiMP. Extensive experiments and comparisons with state-of-the-art trackers on the four benchmarks of VOT2018, VOT2019, NFS and OTB100 have proved the effectiveness and advancement of our algorithm.

Overview of Stochastic Model Updating in Aerospace Application Under Uncertainty Treatment

This chapter presents the technique route of model updating in the presence of imprecise probabilities. The emphasis is put on the inevitable uncertainties, in both numerical simulations and experimental measurements, leading the updating methodology to be significantly extended from deterministic sense to stochastic sense. This extension requires that the model parameters are not regarded as unknown-but-fixed values, but random variables with uncertain distributions, i.e. the imprecise probabilities. The final objective of stochastic model updating is no longer a single model prediction with maximal fidelity to a single experiment, but rather the calibrated distribution coefficients allowing the model predictions to fit with the experimental measurements in a probabilistic point of view. The involvement of uncertainty within a Bayesian updating framework is achieved by developing a novel uncertainty quantification metric, i.e. the Bhattacharyya distance, instead of the typical Euclidian distance. The overall approach is demonstrated by solving the model updating sub-problem of the NASA uncertainty quantification challenge. The demonstration provides a clear comparison between performances of the Euclidian distance and the Bhattacharyya distance, and thus promotes a better understanding of the principle of stochastic model updating, as no longer to determine the unknown-but-fixed parameters, but rather to reduce the uncertainty bounds of the model prediction and meanwhile to guarantee the existing experimental data to be still enveloped within the updated uncertainty space.