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In most re-identification approaches, embedding vectors are compared to identify the best match for a given query. However, this comparison does not take into account whether the encoded information in the embedding vectors was extracted reliably from the input images. We propose the first attempt that illustrates how all three types of uncertainty, namely model uncertainty (also known as epistemic uncertainty), data uncertainty (also known as aleatoric uncertainty), and distributional uncertainty, can be estimated for embedding vectors. We provide evidence that we do indeed estimate these types of uncertainty, and that each type has its own value for improving re-identification performance. In particular, while the few state-of-the-art approaches that employ uncertainty for re-identification during inference utilize only data uncertainty to improve single-shot re-identification performance, we demonstrate that the estimated model uncertainty vector can be utilized to modify the feature vector. We explore the best method for utilizing the estimated model uncertainty based on the Market-1501 dataset and demonstrate that we are able to further enhance the performance above the already strong baseline UAL. Additionally, we show that the estimated distributional uncertainty resembles the degree to which the current sample is out-of-distribution. To illustrate this, we divide the distractor set of the Market-1501 dataset into four classes, each representing a different degree of out-of-distribution. By computing a score based on the estimated distributional uncertainty vector, we are able to correctly order the four distractor classes and to differentiate them from an in-distribution set to a significant extent. [ABSTRACT FROM AUTHOR]

Promptly predicting defects during an additive manufacturing process using only copious log data provides many advantages, albeit with computational limitations. We focus on predicting defects during electron beam melting with the black box nature of the manufacturing machine. For an accurate prediction of defects, which are rare ( < 2 % ), we extract temporal information to track abnormalities and formulate a feature selection algorithm that maximizes the expected value of a cost-sensitive accuracy. Correct identification of features responsible for the defects increases predictive power and informs manufacturers of potential corrective/preventive actions for process improvement. We solve the feature selection through resampling strategies integrated with ensemble procedures to handle data uncertainty and imbalance. Exploiting data uncertainty in our search leads to finding robust features with consistent predictive power. Our proposed methodology shows a 43% improvement in predicting defects (recall) without losing precision. Beyond additive manufacturing, this methodology has general application for rare-event prediction and imbalanced datasets. [ABSTRACT FROM AUTHOR]

We study a newsvendor problem in which demand is uncertain and newsvendor costs are highly time-varying. In this work, we propose a deep learning based newsvendor model that systematically considers uncertainty in inventory decision-making. Unlike the literature assuming a known demand distribution or considering fixed newsvendor costs, the proposed model is distributional assumption free and makes inventory decisions in response to time-varying costs. By exploiting two sources of uncertainty in decision-making, data and model uncertainty, we construct a forecast distribution and use this distribution for newsvendor inventory decisions. The numerical analysis evaluates the proposed model in the context of energy-commitment decision-making in a day-ahead market. The results demonstrate that the proposed model is promising when newsvendor costs change over time in terms of lowering inventory costs. Moreover, we verify that considering both data and model uncertainty enables us to reduce more inventory costs. [ABSTRACT FROM AUTHOR]

Some of the most notable human behavioral palimpsests result from warfare and its durable traces in the form of defensive architecture and strategic infrastructure. For premodern periods, this architecture is often understudied at the large scale, resulting in a lack of appreciation for the enormity of the costs and impacts of military spending over the course of human history. In this article, we compare the information gleaned from the study of the fortified cities of the Early Historic period of the Indian subcontinent (c. 3rd century BCE to 4th century CE) with the precolonial medieval era (9-17th centuries CE). Utilizing in-depth archaeological and historical studies along with local sightings and citizen-science blogs to create a comprehensive data set and map series in a "big-data" approach that makes use of heterogeneous data sets and presence-absence criteria, we discuss how the architecture of warfare shifted from an emphasis on urban defense in the Early Historic period to an emphasis on territorial offense and defense in the medieval period. Many medieval fortifications are known from only local reports and have minimal identifying information but can still be studied in the aggregate using a least-shared denominator approach to quantification and mapping. [ABSTRACT FROM AUTHOR]

As one of the most popular techniques for performance evaluation, Data Envelopment Analysis (DEA) has been widely applied in many areas. However, the self-evaluation used in DEA leaves it open to much criticism. Moreover, most researchers have ignored the fact that reality abounds with uncertainty and have assumed that the data used for evaluation is deterministic and accurate. Both assumptions make it difficult to evaluate the efficiency of real-world production processes correctly and reasonably. In this paper, we propose a series of robust cross-efficiency (RCE) models based on robust optimization theory and cross-efficiency to deal with these problems. First of all, the proposed RCE models allow the conservatism level to be adjusted easily to suit the attitude of the decision-maker towards uncertainty. In addition, the RCE models have better discrimination power than the existing robust CCR models. We present two applications to demonstrate the effectiveness and stability of our models. [ABSTRACT FROM AUTHOR]

Understanding the dynamics of population recovery in threatened species requires robust longitudinal monitoring datasets. However, evidence‐based decision‐making is often impeded by variable data collection approaches, necessitating critical evaluation of restricted available baselines. The Hainan gibbon, the world's rarest primate, had possibly declined to only seven or eight individuals in 1978 at Bawangling National Nature Reserve but has experienced subsequent population growth. Past population estimates lack detailed reporting of survey effort, and multiple conflicting estimates are available, hindering assessment of gibbon recovery. We investigated all reported estimates of Bawangling gibbon population size from 1978 to 2022, to evaluate the biological signal of population trends and the extent to which noise associated with varying survey effort, reporting and estimation may mask or misrepresent any underlying signal. This longitudinal dataset demonstrates that the Bawangling population experienced a series of bottlenecks and recoveries, with three successive periods of growth interspersed by population crashes (1978–1989, 1989–2000 and 2000–2022). The rate of gibbon population recovery was progressively slower over time in each successive period of growth, and this potential decline in recovery rate following serial bottlenecks suggests that additional management strategies may be required alongside "nature‐based solutions" for this species. However, population viability analysis suggests the 1978 founder population is unlikely to have been as low as seven individuals, raising concerns for interpreting reported historical population counts and understanding the dynamics of the species' recovery. We caution against overinterpreting potential signals within "messy" conservation datasets, and we emphasise the crucial importance of standardised replicable survey methods and transparent reporting of data and effort in all future surveys of Hainan gibbons and other highly threatened species. [ABSTRACT FROM AUTHOR]

This study introduces a novel mathematical tool for representing imprecise and ambiguous data: the multi-q cubic bipolar fuzzy soft set. Building upon established bipolar fuzzy sets and soft sets, this paper fist defines the concept of multi-q cubic bipolar fuzzy sets and their fundamental properties. Mathematical operations such as complement, union, and intersection are then developed for these sets. The core contribution lies in the introduction of multi-q cubic bipolar fuzzy soft sets. This new tool allows for a more nuanced representation of imprecise data compared to existing approaches. Key operations for manipulating these sets, including complement, restriction, and expansion, are defined. The applicability of multi-q cubic bipolar fuzzy soft sets extends to various domains, including multi-criteria decision making and problem solving. Illustrative examples demonstrate the practical utility of this innovative concept. [ABSTRACT FROM AUTHOR]

Aim: Climate change is altering habitat suitability for many organisms and modifying species ranges at a global scale. Here we explored the impact of climate change on 112 pine species (Pinus), fundamental elements of Northern terrestrial ecosystems. Location: Global. Methods: We applied a novel methodology for species distribution modelling that considers uncertainty in climatic projections and taxon sampling, and incorporates elements of species' recent evolutionary history. We based our niche calculations on climate and soil data and computed projections across multiple algorithms and IPCC scenarios, which were ensembled into one single suitability map. We then used phylogenetic methods to account for recent evolution in climatic requirements by estimating the evolution of climatic niche. Edaphoclimatic and evolutionary analyses were then combined to calibrate the projections in areas showing high uncertainty. We validated our models using naturalized occurrences of invasive pine species. Results: Our models predicted that by 2070, most pine species (58%) might face important reductions of habitat suitability, potentially leading to range losses and a decrease in species richness, particularly in some regions such as the Mediterranean Basin and South North America, albeit migration might mitigate these shifts in some cases. In contrast, our projections showed increased habitat suitability for approx. 20% of species, which may undergo range expansions under climate change. Moreover, the consideration of recent evolutionary trends modified projected scenarios, decreasing range loss and increasing range expansion for some species. The independent validation endorsed our models for many species and the influence of recent evolution in some cases. Conclusions: We predict that climate change will impose drastic changes in pine distribution and diversity across biogeographical regions, but the magnitude and direction of change will vary significantly across regions and taxa. Species‐level responses are likely to be influenced by regional conditions and the recent evolutionary history of each taxon. [ABSTRACT FROM AUTHOR]

Protein production in the biopharmaceutical industry necessitates the utilization of multiple analytical techniques and control methodologies to ensure both safety and consistency. To facilitate real‐time monitoring and control of cell culture processes, Raman spectroscopy has emerged as a versatile analytical technology. This technique, categorized as a Process Analytical Technology, employs chemometric models to establish correlations between Raman signals and key variables of interest. One notable approach for achieving real‐time monitoring is through the application of just‐in‐time learning (JITL), an industrial soft sensor modeling technique that utilizes Raman signals to estimate process variables promptly. The conventional Raman‐based JITL method relies on the K‐nearest neighbor (KNN) algorithm with Euclidean distance as the similarity measure. However, it falls short of addressing the impact of data uncertainties. To rectify this limitation, this study endeavors to integrate JITL with a variational autoencoder (VAE). This integration aims to extract dominant Raman features in a nonlinear fashion, which are expressed as multivariate Gaussian distributions. Three experimental runs using different cell lines were chosen to compare the performance of the proposed algorithm with commonly utilized methods in the literature. The findings indicate that the VAE–JITL approach consistently outperforms partial least squares, convolutional neural network, and JITL with KNN similarity measure in accurately predicting key process variables. [ABSTRACT FROM AUTHOR]

In recent times, MapReduce has become a popular tool for handling big data. At the same time, uncertainty is related to arbitrariness, fuzziness, ambiguity, irregularity and incomplete knowledge. In RS theory, the uncertainty behaviour of the data in the dataset of interest is managed by using upper and lower approximate sets and classification accuracy. The RS model is integrated with data clustering technique for optimal outcomes. With this motivation, this study designs an Optimal Clustering with RS‐Based Rule Generation Model (OC‐RSRGM) for data classification on MapReduce environment. The OC‐RSRGM technique aims to generate an optimal set of rules using RST for the data classification process and it involves a two‐stage process namely Optimal Fuzzy c‐Means Clustering (OFCM) and RSRGM‐based rule generation with classification. The OFCM technique is derived to eradicate the local optimal problem of the FCM (Fuzzy c‐Means) model using Barnacles Mating Optimizer (BMO). It provides the decision‐makers with all the information needed to design appropriate mechanisms to support their decision‐making activities. The Hadoop MapReduce tool is used to handle big data. The proposed method combines an FCM, BMO, RS theory to accomplish effective decision‐making. The OC‐RSRGM technique can be employed to continuous value dataset where data point does not offer any class details and it might be uncertain. To validate the performance of OC‐RSRGM technique, a detailed experimental analysis is carried out to highlights the betterment of OC‐RSRGM technique. The proposed OC‐RSRGM technique has obtained an effective outcome with the CT of 5.43 s. [ABSTRACT FROM AUTHOR]

Fairness of artificial intelligence and machine learning models, often caused by imbalanced datasets, has long been a concern. While many efforts aim to minimize model bias, this study suggests that traditional fairness evaluation methods may be biased, highlighting the need for a proper evaluation scheme with multiple evaluation metrics due to varying results under different criteria. Moreover, the limited data size of minority groups introduces significant data uncertainty, which can undermine the judgement of fairness. This paper introduces an innovative evaluation approach that estimates data uncertainty in minority groups through bootstrapping from majority groups for a more objective statistical assessment. Extensive experiments reveal that traditional evaluation methods might have drawn inaccurate conclusions about model fairness. The proposed method delivers an unbiased fairness assessment by adeptly addressing the inherent complications of model evaluation on imbalanced datasets. The results show that such comprehensive evaluation can provide more confidence when adopting those models.

COVID-19 surveillance across the United States is essential to tracking and mitigating the pandemic, but data representing cases and deaths may be impacted by attribute, spatial, and temporal uncertainties. COVID-19 case and death data are essential to understanding the pandemic and serve as key inputs for prediction models that inform policy-decisions; consistent information across datasets is critical to ensuring coherent findings. We implement an exploratory data analytic approach to characterize, synthesize, and visualize spatial-temporal dimensions of uncertainty across commonly used datasets for case and death metrics (Johns Hopkins University, the New York Times, USAFacts, and 1Point3Acres). We scrutinize data consistency to assess where and when disagreements occur, potentially indicating underlying uncertainty. We observe differences in cumulative case and death rates to highlight discrepancies and identify spatial patterns. Data are assessed using pairwise agreement (Cohen's kappa) and agreement across all datasets (Fleiss' kappa) to summarize changes over time. Findings suggest highest agreements between CDC, JHU, and NYT datasets. We find nine discrete type-components of information uncertainty for COVID-19 datasets reflecting various complex processes. Understanding processes and indicators of uncertainty in COVID-19 data reporting is especially relevant to public health professionals and policymakers to accurately understand and communicate information about the pandemic. [ABSTRACT FROM AUTHOR]

The Peace‐Athabasca Delta in Alberta, Canada has numerous perched basins that are primarily recharged after large ice jams cause floods (an ecological benefit). Previous studies have estimated that such large floods are likely to decrease in frequency under various climate projections. However, there is a sizable uncertainty range in these predicted flood probabilities, in part due to the short 60‐year systematic record that contained few large ice jam floods. An additional 50 years of historical data are available from various sources, with expert‐interpreted flood categories; however, these categorizations are uncertain in magnitude and occurrence. We developed a Bayesian framework that considers magnitude and occurrence uncertainties within a logistic regression model that predicts the annual probability of a large flood. The Bayesian regression estimates the joint distribution of parameters describing the effects of climatic factors and parameters that describe the probability that historical flood magnitudes were recorded as large (or not) when a truly large (or not) flood occurred. We compare four models for hindcasting and projecting large ice jam flood probabilities in future climates. The models consider: (a) historical data uncertainty, (b) no historical data uncertainty, (c) only the systematic record, and (d) the systematic record with a different model. Neglecting historical data uncertainty provides inaccurate estimates, while using only the systematic record provides wider prediction intervals than considering the full record with uncertain historical data. Thus, we demonstrate that including uncertain historical information can effectively extend the record length and make flood frequency analyses more accurate and precise. Key Points: We use a Bayesian logistic regression framework to estimate ice jam flood frequency while considering uncertainty in the historical recordWe compare annual flood probabilities from a model trained with a systematic record to a model trained with additional historical dataPrediction intervals for projected climates are narrower when uncertain historical data are used [ABSTRACT FROM AUTHOR]

Abstract Understanding the dynamics of population recovery in threatened species requires robust longitudinal monitoring datasets. However, evidence‐based decision‐making is often impeded by variable data collection approaches, necessitating critical evaluation of restricted available baselines. The Hainan gibbon, the world's rarest primate, had possibly declined to only seven or eight individuals in 1978 at Bawangling National Nature Reserve but has experienced subsequent population growth. Past population estimates lack detailed reporting of survey effort, and multiple conflicting estimates are available, hindering assessment of gibbon recovery. We investigated all reported estimates of Bawangling gibbon population size from 1978 to 2022, to evaluate the biological signal of population trends and the extent to which noise associated with varying survey effort, reporting and estimation may mask or misrepresent any underlying signal. This longitudinal dataset demonstrates that the Bawangling population experienced a series of bottlenecks and recoveries, with three successive periods of growth interspersed by population crashes (1978–1989, 1989–2000 and 2000–2022). The rate of gibbon population recovery was progressively slower over time in each successive period of growth, and this potential decline in recovery rate following serial bottlenecks suggests that additional management strategies may be required alongside “nature‐based solutions” for this species. However, population viability analysis suggests the 1978 founder population is unlikely to have been as low as seven individuals, raising concerns for interpreting reported historical population counts and understanding the dynamics of the species' recovery. We caution against overinterpreting potential signals within “messy” conservation datasets, and we emphasise the crucial importance of standardised replicable survey methods and transparent reporting of data and effort in all future surveys of Hainan gibbons and other highly threatened species.

Facial Expression Recognition (FER) on unconstrained datasets poses a significant challenge, primarily due to data uncertainty stemming from human subjectivity and ambiguous facial expressions. Previous methods attempt to address this issue through relabeling strategies. However, this work reveals a relabel inconsistency problem. Specifically, the model weights are not updated simultaneously with the relabeling process. Consequently, the feature representations of the noisy samples remain associated with the previous label despite being relabeled. As a result, the relabeling mechanism reverts the new label back to the previous one, initiating a cycle between the two classes during the subsequent training. The failure to “shift” the feature representations closer to the new label centers hinders the model from learning discriminative features capable of handling data uncertainty, leading to degraded performance. In this work, a new framework based on embedding proximity is proposed to ensure consistent updating of feature representations with rectifications made during relabeling to overcome this limitation. This is achieved by pushing relabeled images closer to their newly assigned class centers and farther away from their previous class (wrong) centers in the feature embedding space. Through comprehensive experiments, this work utilizes existing models-SCN, RUL, and DMUE-to map the original feature space and then applies the proposed embedding proximity technique to update the feature representations. The updated models, denoted as SCN-C, RUL-C, and DMUE-C, demonstrate significant improvements in addressing inconsistency issues and enhancing overall performance. The proposed models outperform state-of-the-art methods, achieving accuracies of 65.73% on AffectNet, 89.51% on RAF-DB, and 71.83% on FER2013.

In general, salient object detection (SOD) datasets have ambiguity due to annotation accuracy and human subjectivity in determining saliency. Since this data uncertainty causes inaccurate prediction, many techniques tackling data uncertainty have been proposed so far. Previous works estimated data uncertainty in terms of predictive inaccuracy and adjusted the learning contribution so that a given model can focus more on specific data. However, inaccurate predictions can occur due to not only data uncertainty but also model uncertainty in which the model does not fully explain the data. As a result, a region that is inaccurately predicted due to model uncertainty is considered a region with high data uncertainty, resulting in insufficient learning. To solve this problem, we propose a novel uncertainty-aware learning scheme where model uncertainty is decomposed from prediction uncertainty and it is minimized. Also, we propose a refinement method to further improve performance by correcting the prediction result using data uncertainty in the inference step. The proposed uncertainty-aware method excludes data uncertainty from learning step and inference step more effectively, making the model more accurately detect salient object(s). The experimental results prove that the proposed method achieves state-of-the-art performance on several SOD datasets and qualitatively detects salient objects more accurately than the prior arts. The code will be uploaded on Github.

In most re-identification approaches, embedding vectors are compared to identify the best match for a given query. However, this comparison does not take into account whether the encoded information in the embedding vectors was extracted reliably from the input images. We propose the first attempt that illustrates how all three types of uncertainty, namely model uncertainty (also known as epistemic uncertainty), data uncertainty (also known as aleatoric uncertainty), and distributional uncertainty, can be estimated for embedding vectors. We provide evidence that we do indeed estimate these types of uncertainty, and that each type has its own value for improving re-identification performance. In particular, while the few state-of-the-art approaches that employ uncertainty for re-identification during inference utilize only data uncertainty to improve single-shot re-identification performance, we demonstrate that the estimated model uncertainty vector can be utilized to modify the feature vector. We explore the best method for utilizing the estimated model uncertainty based on the Market-1501 dataset and demonstrate that we are able to further enhance the performance above the already strong baseline UAL. Additionally, we show that the estimated distributional uncertainty resembles the degree to which the current sample is out-of-distribution. To illustrate this, we divide the distractor set of the Market-1501 dataset into four classes, each representing a different degree of out-of-distribution. By computing a score based on the estimated distributional uncertainty vector, we are able to correctly order the four distractor classes and to differentiate them from an in-distribution set to a significant extent.

The Grombalia aquifer constitutes a complex aquifer system formed by shallow, unconfined, semi-deep, and deep aquifers at different exploitation levels. In this study, we focused on the upper aquifer, the Wadi El Bey coastal aquifer. To assess natural aquifer recharge, we used a novel physiography-based method that uses soil texture-dependent potential infiltration coefficients and monthly rainfall data. The developed transient flow model was then applied to compute the temporal variation in the groundwater level in 34 observation wells from 1973 to 2020, taking into account the time series of spatially variable groundwater recharge, artificial groundwater recharge from 5 surface infiltration basins, pumping rates on 740 wells, and internal prescribed head cells to mimic water exchange between the wadis and aquifer. The quantified deviations in the computed hydraulic heads from measured water levels are acceptable because the database used to construct a scientifically sound and reliable groundwater model was limited. Further work is required to collect field data to quantitatively assess the local inflow and outflow rates between surface water and groundwater. The simulation of 12 climate scenarios highlighted a bi-structured north—south behaviour in the hydraulic heads: an increase in the north and a depletion in the south. A further increase in the pumping rate would, thus, be severe for the southern part of the Wadi El Bey aquifer. [ABSTRACT FROM AUTHOR]

Economic statistics are commonly published without estimates of their uncertainty. We conduct two waves of a randomized controlled online experiment to assess if and how the UK public understands data uncertainty. A control group observes only the point estimate of GDP. Treatment groups are presented with alternative qualitative and quantitative communications of GDP data uncertainty. We find that most of the public understands that GDP numbers are uncertain. Quantitative communications of data uncertainty help align the public's subjective probabilistic expectations of data uncertainty with objective estimates, but do not decrease trust in the statistical office. [ABSTRACT FROM AUTHOR]

Accurate traffic flow prediction is critically essential to transportation safety and Intelligent Transportation Systems (ITS). Existing approaches generally assume the traffic data are complete and reliable. However, in real scenarios, the traffic data are usually sparse and noisy due to the unreliability of the road sensors. Meanwhile, the global semantic traffic correlations among the road links over the road network are largely ignored by existing works. To address these issues, in this paper we study the novel problem of reliable traffic prediction with noisy and sparse traffic data and propose a Multi-View Bayesian Spatio-Temporal Graph Neural Network (MVB-STNet for short) to effectively address it. Specifically, we first construct the traffic flow graphs from two views, the structural traffic graph based on the topological closeness of the road sensors, and the semantic traffic graph which is constructed based on the traffic flow correlations among all the road sensors. Then the features of the two views are learned simultaneously to more broadly capture the spatial correlations. Inspired by the effectiveness of Bayesian neural networks in handling data uncertainty, we design the Bayesian Spatio-Temporal Long Short-Term Memory Net layer to more effectively learn the spatio-temporal features from the sparse and noisy traffic data. Extensive evaluations are conducted over two real traffic datasets. The results show that our proposal significantly improves current state-of-the-arts in terms of traffic flow prediction with sparse and noisy data. [ABSTRACT FROM AUTHOR]

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This study focuses on exploring and understanding measurement errors in analytical procedures involving miniaturized near-infrared instruments. Despite recent spreading in different application fields, there remains a lack of emphasis on the accuracy and reliability of these devices, which is a critical concern for accurate scientific outcomes. The study investigates multivariate measurement errors, revealing their complex nature and the influence that preprocessing techniques can have. The research introduces a possible workflow for practical error analysis in experiments involving diverse samples and instruments. Notably, it investigates how sample characteristics impact errors in the case of solid pills and tablets, typical pharmaceutical samples. ASCA was used for understanding critical instrumental factors and the potential and limitations of the method in the current application were discussed. The joint interpretation of multivariate error matrices and their resume through image histograms and K index are discussed in order to evaluate the impact of common preprocessing methods and to assess their influence on signals. [ABSTRACT FROM AUTHOR]

Securing visibility and dependable information from stakeholders poses a significant challenge for companies engaged in supply chain management. Timely and accurate information exchange is crucial for assessing factors such as carbon emissions and consumer demand. This paper explores the impact of digital and carbon reduction technologies on supply chain decisions, incorporating fuzzy logic and blockchain technology for enhanced decision-making. We specifically focus on a single buyer ordering from a vendor, where faulty items are promptly returned upon inspection. The lead time for the initial delivery includes both production and non-productive time, while successive deliveries only entail transportation time. The research employs intuitionistic triangular fuzzy numbers to represent all carbon factors and integrates blockchain technology to improve demand forecasting. The purpose is to determine the optimal strategies to minimize the carbon and joint total cost of the system. A solution methodology has been proposed for determining optimal strategies, illustrated through a numerical example. The optimal decisions are compared, both with and without blockchain, to validate the proposed model. The findings suggest that blockchain has a substantial impact on both optimal decision-making and the results of carbon reduction efforts. Sensitivity analysis reveals that a blockchain-based supply chain is both time- and cost-efficient, reduces costs and errors in data information, and efficiently shortens lead time in the network.

Abstract Earthquake occurrence modeling of large subduction events involves significant uncertainty, stemming from the scarcity of geological data and inaccuracy of dating techniques. The previous research on statistical modeling of full-margin ruptures of the Cascadia subduction zone attempted to address these issues. However, the adopted resampling method to account for the uncertain marine turbidite age data from the Cascadia subduction zone was not sufficient in the sample size. This study presents a statistical approach based on the Gaussian mixture model applied to significantly larger resampled Cascadia age data. The results suggest that the 3-component Gaussian mixture model outperforms the 2-component Gaussian mixture model and the 1-component renewal models by capturing the long gap and short-term clustering. The developed Gaussian mixture model is well suited to apply to probabilistic seismic and tsunami hazard analysis and the calculation of long-term probability of the future full-margin Cascadia events by considering the elapsed time since the last event.

Climate indices are used to reduce the complex climate system and its changes to simple measures. The data basis – whether observational data or climate model data – to which the climate indices are applied, is usually subject to uncertainties. For threshold-based climate indices, the data uncertainty influences the threshold value, and, hence, the uncertainty can influence the values for the climate index. What the actual impacts of these uncertainties are on threshold-based climate indices is examined in this paper. The focus is not only on the climate model uncertainty, but also on the observational data uncertainty. The general sensitivity of each of the chosen climate indices to arbitrary changes in the threshold is studied. This shows a higher sensitivity of indices assessing extremes (ice days, heavy precipitation days) to changes in the threshold than indices that integrate a quantity over a given time interval (coldsum, consecutive days). For assessing an ensemble of climate model data with respect to their ability to reproduce the index values for current climate, the reference data uncertainty is applied to the chosen threshold-based climate indices by changing their threshold value by its corresponding uncertainty. It is shown that the climate model uncertainty can be within the range of the reference data uncertainty. When using threshold-based climate indices to assess changes in future climate periods, uncertainties should always be taken into account and ideally corrected in an appropriate way. This is especially important for indices that assess extremes.

Abstract One challenge in Wide Area Measurement System is to design a reliable network which assures data availability. The availability of data in Phasor Measurement Unit (PMU) networks depends on two factors: 1‐Reliable data measurements which depend on performance of PMUs and transmission branches, 2‐Reliable data transmission which depends on the reliability of communication networks used to transfer data from PMUs to Phasor Data Concentrators (PDCs) and control centre. The reliability of data transmission can be improved by choosing proper locations for PDCs. In this paper, a novel approach for PDC placement is proposed based on two pre‐defined indices; observability reliability and Loss Of Data Expectation (LODE). In the proposed method, the probability of regional communication networks' failure and the uncertainty of failure data for all components are also included in the calculation of these two indices to achieve more realistic results. To handle uncertainty, fuzzy membership functions are obtained for repair time and failure rate of all components, and a fuzzy Monte Carlo method is presented to evaluate both indices for each possible PDCs' location. The minimum LODE is used to find the best locations for PDCs. The proposed method is investigated for three sample systems which prove the system efficacy.

Earthquake occurrence modeling of large subduction events involves significant uncertainty, stemming from the scarcity of geological data and inaccuracy of dating techniques. The previous research on statistical modeling of full-margin ruptures of the Cascadia subduction zone attempted to address these issues. However, the adopted resampling method to account for the uncertain marine turbidite age data from the Cascadia subduction zone was not sufficient in the sample size. This study presents a statistical approach based on the Gaussian mixture model applied to significantly larger resampled Cascadia age data. The results suggest that the 3-component Gaussian mixture model outperforms the 2-component Gaussian mixture model and the 1-component renewal models by capturing the long gap and short-term clustering. The developed Gaussian mixture model is well suited to apply to probabilistic seismic and tsunami hazard analysis and the calculation of long-term probability of the future full-margin Cascadia events by considering the elapsed time since the last event. [ABSTRACT FROM AUTHOR]

• Proposing physics-informed DCNN for HFI-SCB temperature field reconstruction. • Proposing Deep MC-QR method to quantify data uncertainty caused by noise. • Proposing HFI-SCB reliability analysis method based on interval multilevel BN. Temperature field reconstruction is essential for analyzing the reliability of a high-density functionally integrated satellite circuit board (HFI-SCB). As a representative deep learning model, the deep convolutional neural network (DCNN) is a powerful tool for reconstructing the HFI-SCB temperature field. However, DCNN needs a lot of labeled data to learn its parameters, which is contrary to the fact that actual satellite engineering can only acquire noisy unlabeled data. Thus, this paper proposes an unsupervised method, i.e., the physics-informed deep Monte Carlo quantile regression method, for reconstructing the HFI-SCB temperature field and quantifying the data uncertainty caused by sensor noise. The proposed method combines a DCNN with known physics knowledge to reconstruct an accurate HFI-SCB temperature field using only monitoring point temperatures. Besides, the proposed method can quantify the data uncertainty by the Monte Carlo quantile regression. Based on the reconstructed temperature field and the quantified data uncertainty, this paper builds an interval multilevel Bayesian Network to analyze the HFI-SCB reliability. Two case studies are used to validate the proposed method. [ABSTRACT FROM AUTHOR]

The paper examines the sensitivity for the solution of linear programming problems using Bayesian techniques, when samples for the coefficients of the objective function are uncertain. When data is available, we estimate the solution of the linear program and provide statistical measures of uncertainty through the posterior distributions of the solution in the light of the data. When data is not available, these techniques examine the sensitivity of the solution to random variation in the coefficients of the linear problem. The new techniques are based on two posteriors emerging from the inequalities of Karush–Kuhn–Tucker conditions. The first posterior is asymptotic and does not require data. The second posterior is finite-sample-based and is used whenever data is available or if random samples can be drawn from the joint distribution of coefficients. A by-product of our framework is a robust solution. We illustrate the new techniques in two empirical applications to the case of uncertain Data Envelopment Analysis efficiency, involving two large samples, of US commercial banks and a sample of European commercial banks regulated by the Single Supervisory Mechanism. We analyse whether some pre-determined criteria, associated with size and new supervisory framework, can adequately affect the solution of linear program. The results provide evidence of substantial improvements in statistical structure with respect to sensitivities and robustification. Our methodology can serve as a consistency check of the statistical inference for the solution of linear programming problems in efficiency under uncertainty in data. [ABSTRACT FROM AUTHOR]

In this paper, we consider a bilevel polynomial optimization problem, where the constraint functions of both the upper-level and lower-level problems involve uncertain parameters. We employ the deterministic robust optimization approach to examine the bilevel polynomial optimization problem under data uncertainties by providing lower bound approximations and convergences of sum-of-squares (SOS) relaxations for the robust bilevel polynomial optimization problem. More precisely, we show that under the convexity of the lower-level problem and either the boundedness of the feasible set or the coercivity of the objective function, the global optimal values of SOS relaxation problems are lower bounds of the global optimal value of the robust bilevel polynomial problem and they converge to this global optimal value when the degrees of SOS polynomials in the relaxation problems tend to infinity. Moreover, an application to an electric vehicle charging scheduling problem with renewable energy sources demonstrates that using the proposed SOS relaxation schemes, we obtain more stable optimal values than applying a direct solution approach as the SOS relaxations are capable of solving these models involving data uncertainties in dynamic charging price and weather conditions. [ABSTRACT FROM AUTHOR]

The paper is focused on solving the modern scientific and applied problem related to development and practical use in Decision Support Systems (DSS) of information technologies directed towards forecasting of non-linear non- stationary processes (NNP) that take place in economy and finances as well as in many other areas of activities. Thus, object of study are non-linear non-stationary processes taking place in economy and financial sphere. The basic problem of the study is development of new mathematical models and methods of analysis and forecasting non-linear non-stationary processes in economy and finances, improvement of information decision support technologies that would help to enhance quality of forecast estimates and respective decisions in conditions of uncertainties and risk. The methods given in the paper are used for automating the process of intellectual data analysis that describe the processes under study and automatizing model constructing procedures. As a result of the study performed the information technology was developed to be used in DSS based upon system analysis principles, taking into consideration possible data uncertainties, regression and intellectual data analysis. The technology provides for constructing adequate models of the process under study and computing high quality forecast estimates. The particular feature of the approach proposed is that it provides for high quality of experimental results due to taking into consideration special features of non-linear non-stationary processes that take place in various spheres of activities and their evolution is influenced by many specific factors. The use of the technology proposed in decision support systems of enterprises, state governmental organs, and local self-government will create basis for effective solving the tasks of governing development of non-linear non- stationary processes that take place in many spheres of activities. The approaches proposed in the paper can be used in practice as separately as well as parts of existing information systems at enterprises and organizations. [ABSTRACT FROM AUTHOR]

Climate and hydrologic hazards pose a threat to the distribution of watersheds’ water resources in time and space, necessitating planning for sustainable resilience and adaptation. Hydrologic modelling has emerged as a potential solution for understanding watershed responses to projected climate change, and a prediction model that can deliver actionable information is necessary, although it requires basin-scale observations to calibrate the model to reliably predict basin-scale water resources hazards. Such luxury is not always tenable in watersheds with inadequate ground-based observation. However, satellite-based evapotranspiration (ET) data coupled with a machine learning feature selection as a data refinement process has made integrated water balance modelling widely regarded as a viable alternative for improving the capability of watershed modelling processes in data-sparse regions. This study developed a convincing hydrologic model framework to sufficiently calibrate and provide accurate behavioural solutions for all model responses. The framework was applied to four sub-basins that form the larger Lake Chad basin. The model results were applied to assess the dynamic changes in projected blue and green water resource sustainability in response to climate change in one of the sub-basins. Study findings indicate that hydrologic fluxes can be simulated accurately with varying degrees of acceptability, with R2 and NSE values in the range of 0.69–0.88 and 0.45–0.77 for calibration and 0.69–0.79 and 0.34–0.63 for validation, respectively, and captured within a satisfactory uncertainty range of P-factor and R-factor values of 0.68–0.93 and 0.73–1.31, respectively, in 83%, 67%, 85.7%, and 81.3% of the sub-watersheds based on multi-site simulation despite distinct watershed morphology, although there are significant trade-offs in parameter sensitivity. Whilst green water is the dominant freshwater component across the basin relative to blue water, climate change may be a significant factor influencing changes in the projected green water sustainability status, and the combination of socioeconomic drivers and climate change may significantly impact the projected blue water sustainability status across the basin. Projected changes in the green and blue water sustainability status have shown that more than 50% of the watershed will become ecologically fragile. In addition, the identified freshwater geographic sustainability hotspots may be beyond restoration without adequate long-term river basin water resource plans.

In this paper, we propose the introduction of a neutrosophic chi-square-test for consistency, incorporating neutrosophic statistics. Our aim is to modify the existing chi-square -test for consistency in order to analyze imprecise data. We present a novel test statistic for the neutrosophic chi-square -test for consistency, which accounts for the uncertainties inherent in the data. To evaluate the performance of the proposed test, we compare it with the traditional chi-square -test for consistency based on classical statistics. By conducting a comparative analysis, we assess the efficiency and effectiveness of our proposed neutrosophic chi-square -test for consistency. Furthermore, we illustrate the application of the proposed test through a numerical example, demonstrating how it can be utilized in practical scenarios. Through this implementation, we aim to provide empirical evidence of the improved performance of our proposed test when compared to the traditional chi-square-test for consistency based on classical statistics. We anticipate that the proposed neutrosophic chi-square -test for consistency will outperform its classical counterpart, offering enhanced accuracy and reliability when dealing with imprecise data. This advancement has the potential to contribute significantly to the field of statistical analysis, particularly in situations where data uncertainty and imprecision are prevalent.

Existing deep image classification techniques strive to suppress data uncertainty for various reasons such as blur, occlusion, noise, and label error and advance to higher accuracy. However, they ignore data uncertainty-induced imprecision and thus do not work as intended. In this paper, we propose a deep open-source framework to mine and reason the imprecision of such training and test sets, which can present better performance and reduce the risk of misclassification. First, we design a label reassignment mechanism. It allows the network to reassign training labels and allow imperfect training samples with multiple labels. As a result, they are removed from the original classes and considered new imprecise samples to represent partial ignorance. Second, we propose a new imbalanced data enhancement architecture to learn a generalized representation of each (precise and imprecise) class. It helps the network fuse the auxiliary information from both precise and imprecise classes, which is beneficial to extract more distinctive class features from single-labeled samples and characterize uncertainty-induced imprecision in the test set by imprecise test samples. Afterward, methodological analyses and empirical evaluations are conducted. The proposed framework is demonstrated to present better performance on different typical networks (Resnet50, MobileNetV2, DenseNet121, EfficientNetB0, ShuffleNetV2, SENet, SqueezeNet, Xception) based on five publicly available datasets (Imagewoof-5, Flowers, Monkey, Butterfly, and Cifar-10). In addition, several targeted deep techniques for uncertain images or imprecise results are also employed as comparisons to prove the superiority of the proposed framework. • We develop a deep open-source framework to mine and reason imprecise information. • We provide a multi-label reassignment mechanism to select imprecise samples. • We design an imbalanced data enhancement architecture to fuse different information. • The method can expand training samples and reduce the gap of different classes. [ABSTRACT FROM AUTHOR]

Usage of public restrooms, provided by the Indian government under "Clean India Mission", is limited due to negligence in manual supervision. This paper proposes an Internet of Things-based smart application for sanitation to keep check on geographically distributed restrooms autonomously through soft-hard sensors. Due to a substantial increase in the number of sensing devices in recent years, the application aims to re-utilize data from hard sensors co-located near the restroom locations. A private Sensing as a Service (SaS) paradigm is proposed on the fog node that provides sensor data as a service at the network edge to reduce new sensor deployments. The data from re-utilized sensors is vendor-specific, and, therefore, have heterogeneous protocols and file formats, unknown to the application vendor. A soft-hard fusion framework is proposed to handle data heterogeneity of re-utilized data and perform time-series fusion of hard-sensor data (vendor-specific or application-specific) with uncertain soft sensor data at the fog node for having complete and accurate information about the toilet. The proposed framework takes approximately 0.145 s to resolve heterogeneity, handle soft uncertainty and perform fusion with low resource consumption. Moreover, it has a good system as well as network performance with increased classification accuracy in predicting the cleaning requirement of every toilet. [ABSTRACT FROM AUTHOR]

Absolute value equations (AVEs) have attracted much attention in recent studies. However, the problem data may be contaminated by noises that yield a meaningless solution, even if these coefficients are uncertain within a certain range. To address this issue, we import the idea of robust optimization and present their robust counterpart models with data uncertainty in the l1 and l∞ norm balls. In particular, we prove that these models are equivalent to the linear programming problems. Numerical experiments demonstrate that the true solution of these AVEs can be recovered by solving the equivalent linear programming models with open-resource packages JuMP and HiGHS in Julia language. [ABSTRACT FROM AUTHOR]

Facial action units (AUs) refer to a comprehensive set of atomic facial muscle movements. Recent works have focused on exploring complementary information by learning the relationships among AUs. Most existing approaches process AU co-occurrence and enhance AU recognition by learning the dependencies among AUs from labels, however, the complementary information among features of different AUs are ignored. Moreover, ground truth annotations suffer from a large intra-class variance and their associated intensity levels may vary depending on the annotators’ experience. In this paper, we propose the Region Attentive AU intensity estimation method with Uncertainty Weighted Multi-task Learning (RA-UWML). A RoI-Net is first used to extract features from the pre-defined facial patches where the AUs locate. Then, we use the co-occurrence of AUs using both within patch and between patches representation learning. Within a given patch, we propose sharing representation learning in a multi-task manner. To achieve complementarity and avoid redundancy between different image patches, we propose to use a multi-head self-attention mechanism to adaptively and attentively encode each patch specific representation. Moreover, the AU intensity is represented as a Gaussian distribution, instead of a single value, where the mean value indicates the most likely AU intensity and the variance indicates the uncertainty of the estimated AU intensity. The estimated variances are leveraged to automatically weight the loss of each AU in the multitask learning model. In extensive experiments on the Disfa, Fera2015 and Feafa benchmarks, it is shown that the proposed AU intensity estimation model achieves better results compared to the state-of-the-art models. [ABSTRACT FROM AUTHOR]