Low Uncertainty Research Articles

A reliability analysis and optimization method for a turbine shaft under combined high and low cycle fatigue loading

AbstractThe combined high and low cycle fatigue (CCF) loading condition and random uncertainty exert a considerable impact on the design of turbine shafts. To enhance the fatigue life and reliability, this research proposes a CCF reliability analysis and optimization method for turbine shafts. A CCF fatigue reliability analysis framework is established, which focuses on the quantification of CCF loading characteristics and random uncertainty. The consideration of CCF loading characteristics contain the loading frequency ratio of high cycle fatigue (HCF) to low cycle fatigue (LCF), the stress amplitude ratio of HCF to LCF, as well as their interaction. The consideration of random uncertainty contains material, geometry and load, and a surrogate model‐based method is introduced to improve the quantification efficiency. Through the validation by comparing with experimental data and traditional methods, the proposed method is with higher accuracy and efficiency. By integrating the proposed fatigue reliability analysis method with design optimization, optimal design values for the turbine shaft were identified. This method theoretically extends the shaft's CCF life and provides practical engineering guidance for its reliability analysis and design.

The impact of advice uncertainty and individual regulatory modes on advice taking

AbstractIn previous research on advice taking, researchers have mainly focused on certain types of advice. However, in practice, when people give advice to others, there is often a degree of uncertainty (e.g. I think that the distance between Beijing and Shanghai is between 800 and 1200 km). To date, only a few studies have examined the impact of uncertain advice on advice taking. Through two studies, the present research explores the influence of advice uncertainty and individuals’ regulatory mode predominance on advice taking and the mediating mechanism. In Study 1, the participants' chronic regulatory mode was measured by a questionnaire, and in Study 2, we induced the predominance of the participants’ situational regulatory mode using a recall task. We found that people are more likely to adopt advice with low uncertainty. The moderating effect of participants' regulatory mode on the impact of advice uncertainty on advice taking occurs only when the regulatory mode is induced by the situation. For the assessment‐predominant group, there was a significant difference between the no‐uncertainty group and the high‐uncertainty group, while for the locomotion‐predominant group, this difference was not significant. Additionally, our study revealed the mediating role of advice reliability, which existed only when the participants were able to compare low‐ and no‐uncertainty advice in a within‐participant design. That is, when decision makers adopt uncertainty advice within ranges, they not only consider reliability but also weigh multiple factors. Our findings contribute to understanding the mechanisms underlying individuals' preferences for uncertain advice and reasoning about individual differences.

Low Category Uncertainty and High Training Potential Instance Learning for Unsupervised Domain Adaptation

Recently, instance contrastive learning achieves good results in unsupervised domain adaptation. It reduces the distances between positive samples and the anchor, increases the distances between negative samples and the anchor, and learns discriminative feature representations for target samples. However, most recent methods for identifying positive and negative samples are based on whether the pseudo-labels of samples and the pseudo-label of the anchor correspond to the same class. Due to the lack of target labels, many uncertain data are mistakenly labeled during the training process, and many low training potential data are also utilized. To address these problems, we propose Low Category Uncertainty and High Training Potential Instance Learning for Unsupervised Domain Adaptation (LUHP). We first propose a weight to measure the category uncertainty of the target sample. We can effectively filter the samples near the decision boundary through category uncertainty thresholds which are calculated by weights. Then we propose a new loss to focus on samples with high training potential. Finally, for anchors with low category uncertainty, we propose a sample reuse strategy to make the model more robust. We demonstrate the effectiveness of LUHP by showing the results of four datasets widely used in unsupervised domain adaptation.

Watch Your Head: Assembling Projection Heads to Save the Reliability of Federated Models

Federated learning encounters substantial challenges with heterogeneous data, leading to performance degradation and convergence issues. While considerable progress has been achieved in mitigating such an impact, the reliability aspect of federated models has been largely disregarded. In this study, we conduct extensive experiments to investigate the reliability of both generic and personalized federated models. Our exploration uncovers a significant finding: federated models exhibit unreliability when faced with heterogeneous data, demonstrating poor calibration on in-distribution test data and low uncertainty levels on out-of-distribution data. This unreliability is primarily attributed to the presence of biased projection heads, which introduce miscalibration into the federated models. Inspired by this observation, we propose the "Assembled Projection Heads" (APH) method for enhancing the reliability of federated models. By treating the existing projection head parameters as priors, APH randomly samples multiple initialized parameters of projection heads from the prior and further performs targeted fine-tuning on locally available data under varying learning rates. Such a head ensemble introduces parameter diversity into the deterministic model, eliminating the bias and producing reliable predictions via head averaging. We evaluate the effectiveness of the proposed APH method across three prominent federated benchmarks. Experimental results validate the efficacy of APH in model calibration and uncertainty estimation. Notably, APH can be seamlessly integrated into various federated approaches but only requires less than 30% additional computation cost for 100x inferences within large models.

Uncertainty and credit conditions: Non-linear evidence from firm-level data

The financial frictions channel highlights the importance of credit conditions for the transmission of rising uncertainty. Using German firm-level survey data for the period 2003 to 2015, we document that a surge in a firm’s business uncertainty worsens its credit conditions. Particularly, we demonstrate that this effect depends on the level of uncertainty: low uncertainty nearly triples the effect compared to high uncertainty episodes. To provide an interpretation, we consider a process in which a firm’s credit conditions are driven by banks’ expectations about the future level of business uncertainty. Increases in uncertainty serve as a signal to update these expectations. Calibrating such a process using our dataset generates a stronger revision of expectations and a larger deterioration of credit conditions under low uncertainty.

Abstract 6375: Genetic testing station increases access to germline genetic testing

Abstract Despite evidence of clear benefit, genetic testing of pediatric oncology patients for germline variants remains rare due to a variety of factors, including insurance/financial barriers, limited knowledge about genetic testing, and competing interests for time and resources. Limited access to genetic testing, pre-test counseling and multidisciplinary care have been shown to disproportionately affect patients with limited resources. This represents a critical unmet need in pediatric oncology. Genetic testing station (GTS) is a pre-test counseling video tool that reviews what genetic testing is, why it might be indicated, and explains potential results. Similar video tools have been successfully used in other settings to increase rates of genetic testing, but there are no published reports on the effectiveness of a video education intervention to increase genetic testing in pediatric oncology patients. To test the effectiveness of GTS on genetic testing knowledge and feelings about genetic testing compared to in-person counseling, we use 5 previously validated tools pre- and post-intervention. The initial surveys measure genetic testing knowledge, decisional conflict, healthy literacy, evaluation of GTS and demographics. If participants complete genetic testing, we use the MICRA to assess the impact on their cancer risk assessment. Our GTS arm is recruited from UCLA Pediatric Sarcoma patients, and the in-person counseling arm is recruited from the UCLA Cancer Predisposition clinic. We have recruited 28 participants in the GTS arm and seven in the in-person counseling arm, of which 10 are patients over 18 years old, two are patients between 12-17 years old, and 23 are parents. In our GTS arm, there are 20 female and eight male participants. A majority identify as white or Latino. For the in-person counseling arm, there are four male and three female participants who identify as white, Middle Eastern, or Latino. A similar percentage score with high health literacy, 68% in the GTS arm and 71% for the in-person counseling arm. The majority of patients have no decisional conflict pre- or post-intervention for the GTS group, while the in-person counseling arm shows high initial decision conflict that is fully resolved with counseling. There is a statistically significant increase in genetic testing knowledge for five out of 10 questions comparing pre- and post-GTS. For GTS participants who have completed genetic testing and the MICRA, there is overall low distress and uncertainty, but higher scores for parents than adult or minor patients. In conclusion, there has been high interest in germline genetic testing in pediatric oncology patients with all families deciding to pursue genetic testing after viewing GTS. GTS is an effective tool in increasing genetic testing knowledge in participants. Therefore, GTS is a promising approach to increase access to genetic testing, while providing adequate pre-test counseling and no decisional conflict. Citation Format: Camille Hamilton, Mariana Niell Swiller, Franceska Bhansali, Alyssa Hoover, Stephanie Bottomley, Marisa Lewis, Bianca Russell, Julian A. Martinez, Beth Karlan, Noah Federman, Vivian Y. Chang. Genetic testing station increases access to germline genetic testing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6375.

Decoding task uncertainty: moderating effects on participative budgeting and budgetary slack dynamics

This research examines the effects of participative budgeting on budgetary slack under different levels of task uncertainty. It also investigates the mediating factors of goal orientation and job-relevant information in the cognitive and motivational aspects of participative budgeting in situations characterised by task uncertainty. Our comprehensive analytical methodology uses partial least squares structural equation modelling and conditional process modelling. Results reveal that participative budgeting has a significant positive effect on budgetary slack, with this relationship being mediated by goal orientation and job-relevant information. Task uncertainty levels mitigate the mediation effects. However, in situations of low task uncertainty, goal orientation and job-relevant information play an essential role in conveying the effects of participative budgeting on budgetary slack.

Sensitivity Analysis and Uncertainty Quantification of Nanoparticle Deposition from Tongue Morphological Variations.

The human tongue has highly variable morphology. Its role in regulating respiratory flows and deposition of inhaled aerosols remains unclear. The objective of this study was to quantify the uncertainty of nanoparticle deposition from the variability in tongue shapes and positions and to rank the importance of these morphological factors. Oropharyngeal models with different tongue postures were reconstructed by modifying an existent anatomically accurate upper airway geometry. An LRN k-ω model was applied to solve the multiregime flows, and the Lagrangian tracking approach with near-wall treatment was used to simulate the behavior and fate of inhaled aerosols. Once the database of deposition rates was completed, a surrogate model was trained using Gaussian process regression with polynomial kernels and was validated by comparing its predictions to new CFD simulations. Input sensitivity analysis and output updateability quantification were then performed using the surrogate model. Results show that particle size is the most significant parameter in determining nanoparticle deposition in the upper airway. Among the morphological factors, the shape variations in the central tongue had a higher impact on the total deposition than those in the back tongue and glottal aperture. When considering subregional deposition, mixed sensitivity levels were observed among morphological factors, with the back tongue being the major factor for throat deposition and the central tongue for oral deposition. Interaction effects between flow rate and morphological factors were much higher than the effects from individual parameters and were most significant in the throat (pharyngolaryngeal region). Given input normal variances, the nanoparticle deposition exhibits logarithmical normal distributions, with much lower uncertainty in 100-nm than 2-nm aerosols.

Characterization of defects in additively manufactured materials from mechanical properties

Minimization of defects in additively manufactured components is essential for material development and the determination of fabrication process windows. Here, a purely mechanical means of quantifying porosity in a material is developed based on the Mori–Tanaka mean field theory. The micromechanics based defect quantification approach developed here links the elastic properties of a material with the specific defect structures that must exist within it to obtain those properties. Laser powder bed fusion is used to fabricate aluminum 6061 with three different processing conditions to produce nominally identical samples with unique defect microstructures. Test samples are imaged with microcomputed X-ray tomography (μCT) and the images are classified with a machine learning method to identify the volume fraction of pores, solidification cracks, and the orientation distribution function of the solidification cracks. Tensile tests are then conducted in the z-axis and within the xy-plane to measure the respective elastic moduli and the Poisson’s ratio. These three commonly measured material properties are the only inputs to the new micromechanics based approach used to infer the same defect descriptors that were extracted from μCT. Excellent agreement is found between defect descriptors obtained with both methods, and quantities derived from the mechanical properties are found to carry substantially lower uncertainty. Full error propagation maps for the micromechanics based technique are calculated. Notably, defect descriptors from the micromechanics based approach are calculated from strain and load measurements, which are measured on much larger length scales than the defect size. This enables simple quantification of defects for materials containing small defects that are challenging to image.

Inferring primary particle size distribution and thermal accommodation coefficient of soot aggregate across diverse ambient temperatures using laser-induced incandescence

Laser-induced incandescence (LII) technique has proven to be a powerful tool for non-invasive, in-situ particle sizing of soot aggregates in flames, and the key lies in interpreting LII signals with a heat transfer model. However, the reliability of model-derived primary particle size distribution (PPSD) is sensitive to the uncertainty of thermal accommodation coefficient (TAC), which is typically regarded as a pre-determined parameter in the LII heat transfer model. To overcome this problem, we have proposed a trivariate inversion strategy, i.e., inferring two PPSD parameters and TAC simultaneously from LII signals, which has been theoretically validated at room temperature, but its feasibility in the flame case remains to be investigated. In this study, the trivariate inversion strategy is extensively evaluated over a wide distribution of both ambient temperature and laser fluence. For different cases ranging from room temperature to flame temperature, numerical tests show that the inversion results of all three variables exhibit significantly high accuracy and low uncertainty with proper selection of laser fluence. Furthermore, our study shows that the trivariate inversion strategy can maintain robust performance in ambient temperature fluctuation caused by spatial inhomogeneity of the flame. All these results indicate that this trivariate inversion strategy has a promising application in combustion scenarios.

Enhancing deforestation monitoring in the Brazilian Amazon: A semi-automatic approach leveraging uncertainty estimation

Official governmental monitoring of deforestation in the Brazilian Amazon relies on human experts conducting visual analyzes of remote sensing images, an approach that is very expensive and time-consuming due to the enormous geographic extent to be inspected. The main obstacle to the adoption of fully automatic methods is the requirement for the highest possible deforestation detection accuracy, which arises from the official nature of the deforestation reports, used as a basis for deforestation prevention policies and international treaties. This work addresses such a high accuracy constraint by introducing a semi-automatic method that aims at limiting visual analysis to areas where automatic classification is characterized by high uncertainty. We hypothesize that classification uncertainty is correlated with classification error. Therefore, by trusting automatic detections with low uncertainty and subjecting areas of high uncertainty to visual audit, the amount of human intervention in the whole process could be limited to a small fraction of the forest area, while very high accuracies would be achieved. In this work we investigate various uncertainty estimation strategies, including multiple and single inference approaches, and evaluate different uncertainty metrics. Additionally, we conducted a comprehensive analysis to identify the natural factors and deforestation practices that contribute to high uncertainty in automatic classification. We draw interesting conclusions from the analysis of selected regions of two distinct target sites, which exemplify common causes of uncertainty and indicate how uncertainty may be related with ongoing forest degradation processes. Through experimental evaluation at those sites, we demonstrate that the proposed semi-automatic methodology can achieve impressive accuracies in terms of F1-score values, exceeding 97%, while reducing the total human audit workload to just 3% of the target area.

Enhanced group decision-making through an intelligent algorithmic approach for multiple-attribute credit evaluation with 2-tuple linguistic neutrosophic sets

With the development of the internet economy, e-commerce has rapidly risen, and a large number of small and micro e-commerce enterprises have emerged. However, these enterprises have low financial information transparency, small scale, and high development uncertainty. Therefore, combining the characteristics of the internet economy, it is of great significance to dynamically evaluate credit risk. This not only helps to enhance the quality and rationality of credit risk evaluation results, but also helps to improve financing efficiency and reduce financing risks. The credit evaluation for small and micro enterprises is a multiple-attribute group decision-making (MAGDM). Recently, the TODIM (an acronym in Portuguese of interactive and multicriteria decision making) and TOPSIS method has been inaugurated to cope with MAGDM issues. The 2-tuple linguistic neutrosophic sets (2TLNSs) are inaugurated as an effective tool for characterizing uncertain information during the credit evaluation for small and micro enterprises. In this paper, the 2-tuple linguistic neutrosophic TODIM-TOPSIS (2TLNN-TODIM-TOPSIS) method is inaugurated to solve the MAGDM under 2TLNSs. Finally, a numerical case study for credit evaluation for small and micro enterprises is inaugurated to confirm the proposed method. The prime contribution of this paper are outlined: (1) The information entropy based on score function and accuracy function are built on the 2TLNSs to obtain weight information; (2) an integrated the 2-tuple linguistic neutrosophic TODIM-TOPSIS (2TLNN-TODIM-TOPSIS) method is established to cope with MAGDM; (3) An illustrative example for credit evaluation for small and micro enterprises has accomplished to illustrate the 2TLNN-TODIM-TOPSIS; (4) some comparative analysis are employed to verify the 2TLNN-TODIM-TOPSIS method.

The beneficial role of curiosity on route memory in children

IntroductionThere has been a growing interest in the role of innate curiosity on facets of human cognition, such as in spatial learning and memory. Yet, it is unclear how state level curiosity evoked by the current environment could interact differentially with trait curiosity, to impact spatial memory performance.MethodsWe assessed the influence of trait and state curiosity on route memory. Forty-two 10-year-old children with low and high-trait curiosity (20 Females; 22 Males) actively explored virtual environments that elicited varying levels of uncertainty (i.e., state-curiosity).ResultsAs trait curiosity increased, so did memory performance in low and high uncertainty conditions, suggesting that high-curiosity children can better recruit cognitive resources within non-optimal environments. Children with high compared to low trait curiosity also reported greater feelings of presence during exploration. Importantly, in environments with medium uncertainty, children with low trait curiosity were able to perform as well as those with high curiosity.DiscussionResults show that individual differences in trait curiosity influence route learning and these interact dynamically with state-curiosity invoked within different environments.

A global estimate of monthly vegetation and soil fractions from spatiotemporally adaptive spectral mixture analysis during 2001–2022

Abstract. Multifaceted regime shifts of Earth's surface are ongoing dramatically and – in turn – considerably alter the global carbon budget, energy balance and biogeochemical cycles. Sustainably managing terrestrial ecosystems necessitates a deeper comprehension of the diverse and dynamic nature of multicomponent information within these environments. However, comprehensive records of global-scale fractional vegetation and soil information that encompass these structural and functional complexities remain limited. Here, we provide a globally comprehensive record of monthly vegetation and soil fractions during the period 2001–2022 using a spatiotemporally adaptive spectral mixture analysis framework. This product is designed to continuously represent Earth's terrestrial surface as a percentage of five physically meaningful vegetation and soil endmembers, including photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), bare soil (BS), ice or snow (IS) and dark surface (DA), with high accuracy and low uncertainty compared to the previous vegetation index and vegetation continuous-field product as well as traditional fully constrained linear spectral mixture models. We also adopt nonparametric seasonal Mann–Kendall tested fractional dynamics to identify shifts based on interactive changes in these fractions. Our results – superior to previous portrayals of the greening planet – not only report a +9.35 × 105 km2 change in photosynthetic vegetation, but also explore decreases in nonphotosynthetic vegetation (−2.19 × 105 km2), bare soil (−5.14 × 105 km2) and dark surfaces (−2.27 × 105 km2). In addition, interactive changes in these fractions yield multifaceted regime shifts with important implications, such as a simultaneous increase in PV and NPV in central and southwestern China during afforestation activities, an increase in PV in cropland of China and India due to intensive agricultural development, a decrease in PV and an increase in BS in tropical zones resulting from deforestation. These advantages emphasize that our dataset provides locally relevant information on multifaceted regime shifts at the required scale, enabling scalable modeling and effective governance of future terrestrial ecosystems. The data about five fractional surface vegetation and soil components are available in the Science Data Bank (https://doi.org/10.57760/sciencedb.13287, Sun and Sun, 2023).

Time-first approach for land cover mapping using big Earth observation data time-series in a data cube – a case study from the Lake Geneva region (Switzerland)

ABSTRACT Accurate, consistent, and high-resolution Land Use & Cover (LUC) information is fundamental for effectively monitoring landscape dynamics and better apprehending drivers, pressures, state, and impacts on land systems. Nevertheless, the availability of such national products with high thematic accuracy is still limited and consequently researchers and policymakers are constrained to work with data that do not necessarily reflect on-the-ground realities impending to correctly capture details of landscape features as well as limiting the identification and quantification of drivers and rate of change. Hereafter, we took advantage of the Switzerland’s official LUC statistical sampling survey and dense time-series of Sentinel-2 data, combining them with Machine and Deep Learning methods to produce an accurate and high spatial resolution land cover map over the Lake Geneva region. Findings suggest that time-first approach is a valuable alternative to space-first approaches, accounting for the intra-annual variability of classes, hence improving classification performances. Results demonstrate that Deep Learning methods outperform more traditional Machine Learning ones such as Random Forest, providing more accurate predictions with lower uncertainty. The produced land cover map has a high accuracy, an improved spatial resolution, while at the same time preserving the statistical significance (i.e. class proportion) of the official national dataset. This work paves the way towards the objective to produce a yearly high resolution land cover map of Switzerland and potentially implement a continuous land change monitoring capability. However further work is required to properly address challenges such as the need for increased temporal resolution for LUC information or the quality of training samples.

An improved algorithm for the estimation of the root mean square value as an optimal solution for commercial measurement equipment

This paper demonstrates that direct changes in the algorithm for the estimation of the root mean square value of a voltage signal of an arbitrary waveform can lead to improved performances and lower measurement uncertainty of commercially available instruments without requiring any upgrade of their existing hardware. The research conducted and presented here is an original contribution to the development of estimation techniques and mathematical models for measurement oriented purposes regardless of the number of samples in the given period relying on mathematical calculation of the equal complexity as in the methods already in use. The theoretical approach examines the problem of numerical integration focusing on modified Simpson's 1/3 rule and modified Simpson's 3/8 rule used for the purpose of the estimation of the root mean square value when a small number of samples per period is available. It highlights the limitations of Simpson's 1/3 rule and Simpson's 3/8 rule, and shows that the newly proposed algorithm is optimal with respect to measurement accuracy and precision even in cases when the ratio of the sampling frequency and the signal's fundamental frequency is low. All theoretical results have been validated experimentally.

Graph Neural Network-Accelerated Multitasking Genetic Algorithm for Optimizing PdxTi1-xHy Surfaces under Various CO2 Reduction Reaction Conditions.

Palladium (Pd) hydride-based catalysts have been reported to have excellent performance in the CO2 reduction reaction (CO2RR) and hydrogen evolution reaction (HER). Our previous work on doped PdH and Pd alloy hydrides showed that Ti-doped and Ti-alloyed Pd hydrides could improve the performance of the CO2 reduction reaction compared with pure Pd hydride. Compositions and chemical orderings of the surfaces with only one adsorbate under certain reaction conditions are linked to their stability, activity, and selectivity toward the CO2RR and HER, as shown in our previous work. In fact, various coverages, types, and mixtures of the adsorbates, as well as state variables such as temperature, pressure, applied potential, and chemical potential, could impact their stability, activity, and selectivity. However, these factors are usually fixed at common values to reduce the complexity of the structures and the complexity of the reaction conditions in most theoretical work. To address the complexities above and the huge search space, we apply a deep learning-assisted multitasking genetic algorithm to screen for PdxTi1-xHy surfaces containing multiple adsorbates for CO2RR under different reaction conditions. The ensemble deep learning model can greatly speed up the structure relaxations and retain a high accuracy and low uncertainty of the energy and forces. The multitasking genetic algorithm simultaneously finds globally stable surface structures under each reaction condition. Finally, 23 stable structures are screened out under different reaction conditions. Among these, Pd0.56Ti0.44H1.06 + 25%CO, Pd0.31Ti0.69H1.25 + 50%CO, Pd0.31Ti0.69H1.25 + 25%CO, and Pd0.88Ti0.12H1.06 + 25%CO are found to be very active for CO2RR and suitable to generate syngas consisting of CO and H2.

High-resolution digital mapping of soil erodibility in China

Soil erodibility (K) is the intrinsic susceptibility of a soil to water erosion. Currently, its detailed and accurate spatial distribution information especially over large areas is urgently required for national and regional soil erosion assessment and soil conservation decision making. This study combined pedotransfer function with digital soil mapping techniques to develop a high-resolution map of soil erodibility across China. The First, based on a recent national soil survey, we adopted the erosion-productivity impact calculator (EPIC) to calculate soil erodibility values at 4710 soil sampling points. Then, with the caclulated values of points, we used five techniques including polygon linking (PL), ordinary kriging (OK), Cubist, extreme gradient boosting (XGBoost), and random forests (RF) to generate spatial distribution of soil erodibility. The three latter machine learning techniques modeled the quantitative relationships between soil erodibility and a set of environmental covariates. The results showed that machine learning methods exhibited much more spatial details than the PL and OK did. Among the five techniques the RF achieved the highest accuracy with R2 of 0.49 and RMSE of 0.0077 t ha h ha−1 MJ−1 mm−1 based on 10-fold cross-validation. Spatial uncertainty analysis of the RF predictions showed that high uncerntainty occurred in northwestern China and low uncertainty in the center and southeast. We found that topographical and climatic variables are major environmental factors indirectly controlling spatial variation of soil erodibility while the soil particle composition and SOC contents directly influence the variation.

The pupil dilation response as an indicator of visual cue uncertainty and auditory outcome surprise.

In everyday perception, we combine incoming sensory information with prior expectations. Expectations can be induced by cues that indicate the probability of following sensory events. The information provided by cues may differ and hence lead to different levels of uncertainty about which event will follow. In this experiment, we employed pupillometry to investigate whether the pupil dilation response to visual cues varies depending on the level of cue-associated uncertainty about a following auditory outcome. Also, we tested whether the pupil dilation response reflects the amount of surprise about the subsequently presented auditory stimulus. In each trial, participants were presented with a visual cue (face image) which was followed by an auditory outcome (spoken vowel). After the face cue, participants had to indicate by keypress which of three auditory vowels they expected to hear next. We manipulated the cue-associated uncertainty by varying the probabilistic cue-outcome contingencies: One face was most likely followed by one specific vowel (low cue uncertainty), another face was equally likely followed by either of two vowels (intermediate cue uncertainty) and the third face was followed by all three vowels (high cue uncertainty). Our results suggest that pupil dilation in response to task-relevant cues depends on the associated uncertainty, but only for large differences in the cue-associated uncertainty. Additionally, in response to the auditory outcomes, the pupil dilation scaled negatively with the cue-dependent probabilities, likely signalling the amount of surprise.

Multi-objective calibration and uncertainty analysis for the event-based modelling of flash floods

ABSTRACT This study investigates the best approach to calibrate an event-based conceptual Hydrologiska Byråns Vattenbalansavdelning (HBV) model, comparing different trials of single-objective, single-event multi-objective (SEMO), and multi-event-multi-objective (MEMO) model calibrations using root mean squared error (RMSE), Nash-Sutcliffe model efficiency coefficient (NSE), and Bias as objective functions. Model performance was validated for several peak events via 90% confidence interval (CI)-based output uncertainty quantification of relative error of discharges. Multi-objective optimization yielded more accurate and robust solutions compared to single-objective calibrations. Ensembles of Pareto solutions from the multi-objective calibrations better characterized the flood peaks within the uncertainty intervals. MEMO calibration exhibited lower uncertainties and better prediction of peak events versus SEMO calibration. Moreover, the MEMO_6D (six-dimensional) approach outperformed the SEMO_3D and MEMO_3D in capturing the larger peak events. This study suggests that the MEMO_6D is the best approach for predicting large flood events with lower model output uncertainties when the calibration is performed with a better combination of peak events.