General External Uncertainty Models of Three-Plane Intersection Point for 3D Absolute Accuracy Assessment of Lidar Point Cloud

The traditional practice to assess accuracy in lidar data involves calculating RMSEz (root mean square error of the vertical component). Accuracy assessment of lidar point clouds in full 3D (three dimension) is not routinely performed. The main challenge in assessing accuracy in full 3D is how to identify a conjugate point of a ground-surveyed checkpoint in the lidar point cloud with the smallest possible uncertainty value. Relatively coarse point-spacing in airborne lidar data makes it challenging to determine a conjugate point accurately. As a result, a substantial unwanted error is added to the inherent positional uncertainty of the lidar data. Unless we keep this additional error small enough, the 3D accuracy assessment result will not properly represent the inherent uncertainty. We call this added error “external uncertainty,” which is associated with conjugate point identification. This research developed a general external uncertainty model using three-plane intersections and accounts for several factors (sensor precision, feature dimension, and point density). This method can be used for lidar point cloud data from a wide range of sensor qualities, point densities, and sizes of the features of interest. The external uncertainty model was derived as a semi-analytical function that takes the number of points on a plane as an input. It is a normalized general function that can be scaled by smooth surface precision (SSP) of a lidar system. This general uncertainty model provides a quantitative guideline on the required conditions for the conjugate point based on the geometric features. Applications of the external uncertainty model were demonstrated using various lidar point cloud data from the U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) library to determine the valid conditions for a conjugate point from three-plane modeling.

Impact of Economic Policy Uncertainty on the Distribution of China’s Stock Returns: An External Perspective

This study examines the impact of external economic policy uncertainty on the distribution of China’s stock returns. The Chinese Economic Policy Uncertainty (CEPU) and global EPU (GEPU) indexes compiled by [1] are employed as a measurement of the external uncertainty. An empirical study is conducted using the GARCH-MIDAS framework. The first innovation of this study is extending the symmetric GARCH-MIDAS model to the case of GJR; the leverage effect is therefore considered. The second innovation is considering the impact of EPU on the overall distribution of returns, rather than on the mean or volatility. Full-sample fitting shows that CEPU can explain around 14% of the return volatility, and CEPU together with GEPU can explain about 17%. Out-of-sample recursive forecasting demonstrates that it is meaningful to extend the models to GJR; the EPU information improves the return distribution forecasting. However, the impact of EPUs is limited, which implies that external uncertainty is quite different from the “internal” economic policy uncertainty directly driving the China’s stock market.

Permissible Promise-Making Under Uncertainty

I outline four conditions on permissible promise-making: the promise must be for a morally permissible end, must not be deceptive, must be in good faith, and must involve a realistic assessment of oneself. I then address whether promises that you are uncertain you can keep can meet these four criteria, with a focus on campaign promises as an illustrative example. I argue that uncertain promises can meet the first two criteria, but that whether they can meet the second two depends on the source of the promisor's uncertainty. External uncertainty stemming from outside factors is unproblematic, but internal uncertainty stemming from the promisor's doubts about her own strength leads to promises that are in bad faith or unrealistic. I conclude that campaign promises are often subject to internal uncertainty and are therefore morally impermissible to make, all else being equal.

Imperfect Bayesian inference in visual perception

Optimal Bayesian models have been highly successful in describing human performance on perceptual decision-making tasks, such as cue combination and visual search. However, recent studies have argued that these models are often overly flexible and therefore lack explanatory power. Moreover, there are indications that neural computation is inherently imprecise, which makes it implausible that humans would perform optimally on any non-trivial task. Here, we reconsider human performance on a visual search task by using an approach that constrains model flexibility and tests for computational imperfections. Subjects performed a target detection task in which targets and distractors were tilted ellipses with orientations drawn from Gaussian distributions with different means. We varied the amount of overlap between these distributions to create multiple levels of external uncertainty. We also varied the level of sensory noise, by testing subjects under both short and unlimited display times. On average, empirical performance – measured as d’ – fell 18.1% short of optimal performance. We found no evidence that the magnitude of this suboptimality was affected by the level of internal or external uncertainty. The data were well accounted for by a Bayesian model with imperfections in its computations. This “imperfect Bayesian” model convincingly outperformed the “flawless Bayesian” model as well as all ten heuristic models that we tested. These results suggest that perception is founded on Bayesian principles, but with suboptimalities in the implementation of these principles. The view of perception as imperfect Bayesian inference can provide a middle ground between traditional Bayesian and anti-Bayesian views.Author summaryThe main task of perceptual systems is to make truthful inferences about the environment. The sensory input to these systems is often astonishingly imprecise, which makes human perception prone to error. Nevertheless, numerous studies have reported that humans often perform as accurately as is possible given these sensory imprecisions. This suggests that the brain makes optimal use of the sensory input and computes without error. The validity of this claim has recently been questioned for two reasons. First, it has been argued that a lot of the evidence for optimality comes from studies that used overly flexible models. Second, optimality in human perception is implausible due to limitations inherent to neural systems. In this study, we reconsider optimality in a standard visual perception task by devising a research method that addresses both concerns. In contrast to previous studies, we find clear indications of suboptimalities. Our data are best explained by a model that is based on the optimal decision strategy, but with imperfections in its execution.

Who Are Nonvoters?

Examining nonvoting at the individual level, this chapter identifies four types of nonvoters in both presidential and midterm elections. The chapter draws a theoretical distinction between external uncertainty found in the national campaign context and internal uncertainty among eligible citizens about whether a specific candidate will adequately address the external uncertainty. The four types of nonvoters respond to this internal uncertainty differently. The politically ignorant nonvoters do not follow the campaign or the candidates, so avoid internal uncertainty about them. The indifferent follow the campaign and candidates but see no differences between them as internal uncertainty remains. The dissatisfied know a good deal about the campaign context and candidates but see one or more candidates negatively; they do not vote because internal uncertainty about the candidates remains unresolved. The personal hardship nonvoters pay attention to the campaign and candidates but do not vote due to personal hardship associated with unemployment.