MICROSCOPE: systematic errors

The MICROSCOPE mission aims to test the Weak Equivalence Principle (WEP) in orbit with an unprecendented precision of 10-15 on the Eövös parameter thanks to electrostatic accelerometers on board a drag-free microsatellite. The precision of the test is determined by statistical errors, due to the environment and instrument noises, and by systematic errors to which this paper is devoted. Sytematic error sources can be divided into three categories: external perturbations, such as the residual atmospheric drag or the gravity gradient at the satellite altitude, perturbations linked to the satellite design, such as thermal or magnetic perturbations, and perturbations from the instrument internal sources. Each systematic error is evaluated or bounded in order to set a reliable upper bound on the WEP parameter estimation uncertainty.

Being Uncertain in Chromatographic Calibration—Some Unobvious Details in Experimental Design

This is an introductory tutorial and review about the uncertainty problem in chromatographic calibration. It emphasizes some unobvious, but important details influencing errors in the calibration curve estimation, uncertainty in prediction, as well as the connections and dependences between them, all from various perspectives of uncertainty measurement. Nonuniform D-optimal designs coming from Fedorov theorem are computed and presented. As an example, all possible designs of 24 calibration samples (3–8, 4–6, 6–4, 8–3 and 12–2, both uniform and D-optimal) are compared in context of many optimality criteria. It can be concluded that there are only two independent (orthogonal, but slightly complex) trends in optimality of these designs. The conclusions are important, as the uniform designs with many concentrations are not the best choices, contrary to some intuitive perception. Nonuniform designs are visibly better alternative in most calibration cases.

Stochastic satellite tracking with constrained budget via structured-chromosome genetic algorithms

This paper focuses on the scheduling under uncertainty of satellite tracking from a heterogeneous network of ground stations taking into account allocated resources. An optimisation-based approach is employed to efficiently select the optimal tracking schedule that minimises the final estimation uncertainty. Specifically, the scheduling is formulated as a variable-size problem, and a Structured-Chromosome Genetic Algorithm is developed to tackle the mixed-discrete global optimisation. The search algorithm employs genetic operators specifically revised to handle hierarchical search spaces. An orbit determination routine is run within each call to the fitness function to quantify the estimation uncertainty resulting from each candidate tracking schedule. The developed scheduler is tested on the tracking optimisation of a satellite in low Earth orbit, a highly perturbed dynamical regime. The obtained results show that the variable-size variants of Genetic Algorithms always outperform the fixed-size counterparts employed for comparison. In particular, Structured-Chromosome Genetic Algorithm is shown to find significantly better schedules under severely limited budgets.

A Wavelet Evaluation of Some Leading Business Cycle Indicators for the German Economy

Leading indicators are important variables in business cycle forecasting. We use wavelet analysis to investigate the lead-lag stability of German leading indicators in time-frequency space. This method permits a time-varying relation of the leading indicators to the reference cycle allowing simultaneously to focus on lead-lag stability at the specific business cycle frequencies. In this way we analyze an index of new orders, a survey-based index of business expectations, an index of stock market returns and the interest rate term spread. We confirm that most of these indicators are indeed leading the reference cycle most of the time, but the number of months leading varies considerably over time and is associated with a great deal of estimation uncertainty.

Novelty and uncertainty interact to regulate the balance between exploration and exploitation in the human brain.

Recent evidence suggests that both novelty and uncertainty act as potent features guiding exploration. However, these variables are often conflated with each other experimentally, and an understanding of how these attributes interact to regulate the balance between exploration and exploitation has proved elusive. Using a novel task designed to decouple stimulus novelty and estimation uncertainty, we identify separable behavioral and neural mechanisms by which exploration is colored. We show that uncertainty was avoided except when the information gained through exploration could be reliably exploited in the future. In contrast, and contrary to existing theory, novel options grew increasingly attractive relative to familiar counterparts irrespective of the opportunity to leverage their consequences and despite the uncertainty inherent to novel options. These findings led us to develop a formal computational framework in which uncertainty directed choice adapts to the prospective utility of exploration, while novel stimuli persistently draw favor as a result of inflated reward expectations biasing an exploitative strategy. Crucially, novelty is proposed to actively modulate uncertainty processing, effectively blunting the influence of uncertainty in shaping the subjective utility ascribed to novel stimuli. Both behavioral data and fMRI activity sampled from the ventromedial prefrontal cortex, frontopolar cortex and ventral striatum validate this model, thereby establishing a computational account that can not only explain behavior but also shed light on the functional contribution of these key brain regions to the exploration/exploitation trade-off. Our results point to multiple strategies and neural substrates charged with balancing the explore/exploit dilemma, with each targeting distinct aspects of the decision problem to foster a manageable decomposition of an otherwise intractable task.

Mendelian randomization informs shared genetic etiology underlying exposure and outcome by interrogating correlated horizontal pleiotropy

Mendelian randomization (MR) harnesses genetic variants as instrumental variables (IVs) to study the causal effect of exposure on trait/disease using summary statistics from genome-wide association studies. Classic MR assumptions are violated when IVs are associated with unmeasured confounders, i.e., when correlated horizontal pleiotropy (CHP) arises. Such confounders could be a shared gene or inter-connected pathways underlying exposure and outcome. We propose MR-CUE (MR with Correlated horizontal pleiotropy Unraveling shared Etiology and confounding), for estimating causal effect while identifying IVs with CHP and accounting for estimation uncertainty. For those IVs, we map their cis-associated genes and enriched pathways to inform shared genetic etiology underlying exposure and outcome. We apply MR-CUE to study the effects of interleukin 6 on multiple traits/diseases and identify several S100 genes involved in cross-traits etiology. We assess the effects of multiple exposures on type 2 diabetes across European and East Asian populations.

“Go Wild for a While!”: A New Test for Forecast Evaluation in Nested Models

In this paper, we present a new asymptotically normal test for out-of-sample evaluation in nested models. Our approach is a simple modification of a traditional encompassing test that is commonly known as Clark and West test (CW). The key point of our strategy is to introduce an independent random variable that prevents the traditional CW test from becoming degenerate under the null hypothesis of equal predictive ability. Using the approach developed by West (1996), we show that in our test, the impact of parameter estimation uncertainty vanishes asymptotically. Using a variety of Monte Carlo simulations in iterated multi-step-ahead forecasts, we evaluated our test and CW in terms of size and power. These simulations reveal that our approach is reasonably well-sized, even at long horizons when CW may present severe size distortions. In terms of power, results were mixed but CW has an edge over our approach. Finally, we illustrate the use of our test with an empirical application in the context of the commodity currencies literature.