Reducing Uncertainty in Aeroelastic Flutter Boundaries Using Experimental Data

Flutter prediction as currently practiced is usually deterministic, with a single structural model used to represent an aircraft. By using interval analysis to take into account structural variability, recent work has demonstrated that small changes in the structure can lead to very large changes in the altitude at which
utter occurs (Marques, Badcock, et al., J. Aircraft, 2010). In this follow-up work we examine the same phenomenon using probabilistic collocation (PC), an uncertainty quantification technique which can eficiently propagate multivariate stochastic input through a simulation code,
in this case an eigenvalue-based fluid-structure stability code. The resulting analysis predicts the consequences of an uncertain structure on incidence of
utter in probabilistic terms { information that could be useful in planning
flight-tests and assessing the risk of structural failure. The uncertainty in
utter altitude is confirmed to be substantial. Assuming that the structural uncertainty represents a epistemic uncertainty regarding the
structure, it may be reduced with the availability of additional information { for example aeroelastic response data from a flight-test. Such data is used to update the structural uncertainty using Bayes' theorem. The consequent
utter uncertainty is significantly reduced across the entire Mach number range.

Unconscious Vigilance: Worldview Defense without Adaptations for Terror, Coalition or Uncertainty Management

Individuals subtly reminded of death, coalitional challenges, or feelings of uncertainty display exaggerated preferences for affirmations and against criticisms of their cultural in-groups. Terror management, coalitional psychology, and uncertainty management theories postulate this “worldview defense” effectas the output of mechanisms evolved either to allay the fear of death, foster social support, or reduce anxiety by increasing adherence to cultural values. In 4 studies, we report evidence for an alternative perspective. We argue that worldview defense owes to unconscious vigilance, a state of accentuatedreactivity to affective targets (which need not relate to cultural worldviews) that follows detection of subtle alarm cues (which need not pertain to death, coalitional challenges, or uncertainty). In Studies 1 and 2, death-primed participants produced exaggerated ratings of worldview-neutral affective targets. In Studies 3 and 4, subliminal threat manipulations unrelated to death, coalitional challenges, or uncertaintyevoked worldview defense. These results are discussed as they inform evolutionary interpretations of worldview defense and future investigations of the influence of unconscious alarm on judgment.

TheShifting Sands of Uncertainty: Risk Construction and BSE/vCJD

It is more than a decade since scientists in the UK put forward evidence of a link between the emergence of a new variant of Creutzfeldt-Jakob Disease (vCJD) in humans, and a diminishing epidemic of Bovine Spongiform Encephalopathy, or BSE, in cattle. In the wake of this anniversary, the paper revisits two scientific narratives of risk, forged at different points along the developmental pathway of BSE science, including a series of advisory reports provided to the UK government between 1989 and 1994, and a symposium held in 2001 to assess the impact of the Phillips Inquiry. While the primary pathology of BSE became apparent relatively early on, uncertainties remain about the origins of BSE and its human variant, vCJD. The paper examines the handling of this sensitivity, and its communication, within these key documents, noting changes in patterns of uncertainty construction over time.

Chernobyl: Living with Risk and Uncertainty

The nuclear accident in Chernobyl in 1986 is a dramatic example of the type of incidents that are characteristic of a risk society. The consequences of the incident are indeterminate, the causes complex and future developments unpredictable. Nothing can compensate for its effects and it affects a broad population indiscriminately. This paper examines the lived experience of those who experienced biographical disruption as residents of the region on the basis of qualitative case studies carried out in 2003 in the Chernobyl regions of Russia, Ukraine and Belarus. Our analysis indicates that informants tend to view their future as highly uncertain and unpredictable; they experience uncertainty about whether they are already contaminated, and they have to take hazardous decisions about where to go and what to eat. Fear, rumours and experts compete in supplying information to residents about the actual and potential consequences of the disaster, but there is little trust in, and only limited awareness of, the information that is provided. Most informants continue with their lives and do what they must or even what they like, even where the risks are known. They often describe their behaviour as being due to economic circumstances; where there is extreme poverty, even hazardous food sources are better than none. Unlike previous studies, we identify a pronounced tendency among informants not to separate the problems associated with the disaster from the hardships that have resulted from the break-up of the USSR, with both events creating a deep-seated sense of resignation and fatalism. Although most informants hold their governments to blame for lack of information, support and preventive measures, there is little or no collective action to have these put in place. This contrasts with previous research which has suggested that populations affected by disasters attribute crucial significance to that incident and, as a consequence, become increasingly politicized with regard to related policy agendas.

CLOSE—A Data-Driven Approach to Speech Separation

This paper studies single-channel speech separation, assuming unknown, arbitrary temporal dynamics for the speech signals to be separated. A data-driven approach is described, which matches each mixed speech segment against a composite training segment to separate the underlying clean speech segments. To advance the separation accuracy, the new approach seeks and separates the longest mixed speech segments with matching composite training segments. Lengthening the mixed speech segments to match reduces the uncertainty of the constituent training segments, and hence the error of separation. For convenience, we call the new approach Composition of Longest Segments, or CLOSE. The CLOSE method includes a data-driven approach to model long-range temporal dynamics of speech signals, and a statistical approach to identify the longest mixed speech segments with matching composite training segments. Experiments are conducted on the Wall Street Journal database, for separating mixtures of two simultaneous large-vocabulary speech utterances spoken by two different speakers. The results are evaluated using various objective and subjective measures, including the challenge of large-vocabulary continuous speech recognition. It is shown that the new separation approach leads to significant improvement in all these measures.

Incorporating uncertainty in predictive species distribution modelling

Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and process-based models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.