Accurate Probability Estimates Research Articles

LFC-UNet: learned lossless medical image fast compression with U-Net.

In the field of medicine, the rapid advancement of medical technology has significantly increased the speed of medical image generation, compelling us to seek efficient methods for image compression. Neural networks, owing to their outstanding image estimation capabilities, have provided new avenues for lossless compression. In recent years, learning-based lossless image compression methods, combining neural network predictions with residuals, have achieved performance comparable to traditional non-learning algorithms. However, existing methods have not taken into account that residuals often concentrate excessively, hindering the neural network's ability to learn accurate residual probability estimation. To address this issue, this study employs a weighted cross-entropy method to handle the imbalance in residual categories. In terms of network architecture, we introduce skip connections from U-Net to better capture image features, thereby obtaining accurate probability estimates. Furthermore, our framework boasts excellent encoding speed, as the model is able to acquire all residuals and residual probabilities in a single inference pass. The experimental results demonstrate that the proposed method achieves state-of-the-art performance on medical datasets while also offering the fastest processing speed. As illustrated by an instance using head CT data, our approach achieves a compression efficiency of 2.30 bits per pixel, with a processing time of only 0.320 seconds per image.

Advanced Road Safety: Collective Perception for Probability of Collision Estimation of Connected Vehicles

In the dynamic landscape of vehicular communication systems, connected vehicles (CVs) present unprecedented capabilities in perception, cooperation, and, notably, probability of collision management. This paper’s main concern is the collision probability of collision estimation. Achieving effective collision estimation heavily relies on the sensor perception of obstacles and a critical collision probability prediction system. This paper is dedicated to refining the estimation of collision probability through the intentional integration of CV communications, with a specific focus on the collective perception of connected vehicles. The primary objective is to enhance the understanding of the potential probability of collisions in the surrounding environment by harnessing the collective insights gathered through inter-vehicular communication and collaboration. This improvement enables a superior anticipation capacity for both the driving system and the human driver, thereby enhancing road safety. Furthermore, the incorporation of extended perception strategies holds the potential for more accurate collision probability estimation, providing the driving system or human driver with increased time to react and make informed decisions, further fortifying road safety measures. The results underscore a significant enhancement in collision probability awareness, as connected vehicles collectively contribute to a more comprehensive collision probability landscape. Consequently, this heightened collective collision probability perception improves the anticipation capacity of both the driving system and the human driver, contributing to an elevated level of road safety. For future work, the exploration of our extended perception techniques to achieve real-time probability of collision estimation is proposed. Such endeavors aim to drive the development of robust and anticipatory autonomous driving systems that truly harness the benefits of connected vehicle technologies.

A Metamodel-Based Multi-Scale Reliability Analysis of FRP Truss Structures under Hybrid Uncertainties.

This study introduces a Radial Basis Function-Genetic Algorithm-Back Propagation-Importance Sampling (RBF-GA-BP-IS) algorithm for the multi-scale reliability analysis of Fiber-Reinforced Polymer (FRP) composite structures. The proposed method integrates the computationally powerful RBF neural network with GA, BP neural network and IS to efficiently calculate inner and outer optimization problems for reliability analysis with hybrid random and interval uncertainties. The investigation profoundly delves into incorporating both random and interval parameters in the reliability appraisal of FRP constructs, ensuring fluctuating parameters within designated boundaries are meticulously accounted for, thus augmenting analytic exactness. In application, the algorithm was subjected to diverse structural evaluations, including a seven-bar planar truss, an architectural space dome truss, and an intricate nonlinear truss bridge. Results demonstrate the algorithm's exceptional performance in terms of model invocation counts and accurate failure probability estimation. Specifically, within the seven-bar planar truss evaluation, the algorithm exhibited a deviation of 0.08% from the established failure probability benchmark.

Development and validation of a clinical prediction model for the diagnostic management of acute pulmonary embolism: an individual participant data meta-analysis

Abstract Background/Introduction Risk stratification is used for decisions about imaging in patients with clinically suspected acute pulmonary embolism (PE). Purpose To develop a clinical prediction model that provides an individualized, accurate probability estimate for acute pulmonary embolism (PE) based on readily available clinical items and D-dimer concentrations. Methods An individual participant data meta-analysis was performed based on sixteen studies with data from 28,305 adult patients with clinically suspected PE from various clinical settings. A multilevel logistic regression model was built and validated including ten a priori defined objective candidate predictors to predict PE at baseline or during follow-up of 30-90 days. Multiple imputation was used for missing data. Backward elimination was performed with a P-value < 0.10. Discrimination (c-statistic with 95% CI and prediction intervals [PI]) and calibration (outcome:expected [O:E] ratio and calibration plot) were evaluated based on internal-external cross-validation. The accuracy of the model was compared to algorithms based on the Wells score and D-dimer testing. Results The final model included age (in years), sex, previous VTE, recent surgery/immobilization, hemoptysis, cancer, clinical signs of DVT, inpatient status, D-dimer (in μg/L), and an interaction term between age and D-dimer. The pooled c-statistic was 0.87 (95%-CI, 0.85-0.89; 95%-PI, 0.77-0.93) and overall calibration was very good (pooled O:E ratio, 0.99; 95% CI, 0.87-1.14; 95% PI, 0.55-1.79). In the lower range of probabilities, the model slightly overestimated VTE probability. Discrimination of the current model in the validation data sets was better than that of the Wells score combined with a D-dimer threshold based on age (c- statistic 0.73; 95%-CI, 0.70-0.75) or structured clinical pretest probability (c-statistic 0.79; 95%-CI, 0.76- 0.81). Conclusion(s) The present model provides an absolute, individualized probability of PE presence in a broad population of patients with suspected PE, with very good discrimination and calibration. Its clinical utility needs to be evaluated in a prospective management or impact study.Calibration plot of the new model

Direct Bayesian inference for fault severity assessment in Digital-Twin-Based fault diagnosis

For applications in condition-based maintenance of nuclear systems, the assessment of fault severity is crucial. In this work, we developed a framework that allows for direct inference of the probability distributions of possible faults in a system. We employed a model-based approach with model residuals generated from analytical redundancy relations provided by physics-based models of the system components. From real-time sensor readings, the values of the model residuals can be calculated, and the posterior probability distributions of the faults can be computed directly using the methods of Bayesian networks. From the posterior distribution of each fault, one can estimate the fault probability based on a chosen threshold and assess the severity of the fault. By eliminating the discretization and simplifications in middle steps, this approach allows us to leverage the available computational resources to provide more accurate fault probability estimates and severity assessments.

Diagnostic management of acute pulmonary embolism: a prediction model based on a patient data meta-analysis.

Risk stratification is used for decisions regarding need for imaging in patients with clinically suspected acute pulmonary embolism (PE). The aim was to develop a clinical prediction model that provides an individualized, accurate probability estimate for the presence of acute PE in patients with suspected disease based on readily available clinical items and D-dimer concentrations. An individual patient data meta-analysis was performed based on sixteen cross-sectional or prospective studies with data from 28 305 adult patients with clinically suspected PE from various clinical settings, including primary care, emergency care, hospitalized and nursing home patients. A multilevel logistic regression model was built and validated including ten a priori defined objective candidate predictors to predict objectively confirmed PE at baseline or venous thromboembolism (VTE) during follow-up of 30 to 90 days. Multiple imputation was used for missing data. Backward elimination was performed with a P-value <0.10. Discrimination (c-statistic with 95% confidence intervals [CI] and prediction intervals [PI]) and calibration (outcome:expected [O:E] ratio and calibration plot) were evaluated based on internal-external cross-validation. The accuracy of the model was subsequently compared with algorithms based on the Wells score and D-dimer testing. The final model included age (in years), sex, previous VTE, recent surgery or immobilization, haemoptysis, cancer, clinical signs of deep vein thrombosis, inpatient status, D-dimer (in µg/L), and an interaction term between age and D-dimer. The pooled c-statistic was 0.87 (95% CI, 0.85-0.89; 95% PI, 0.77-0.93) and overall calibration was very good (pooled O:E ratio, 0.99; 95% CI, 0.87-1.14; 95% PI, 0.55-1.79). The model slightly overestimated VTE probability in the lower range of estimated probabilities. Discrimination of the current model in the validation data sets was better than that of the Wells score combined with a D-dimer threshold based on age (c-statistic 0.73; 95% CI, 0.70-0.75) or structured clinical pretest probability (c-statistic 0.79; 95% CI, 0.76-0.81). The present model provides an absolute, individualized probability of PE presence in a broad population of patients with suspected PE, with very good discrimination and calibration. Its clinical utility needs to be evaluated in a prospective management or impact study. PROSPERO ID 89366.

Regularized sequence-context mutational trees capture variation in mutation rates across the human genome.

Germline mutation is the mechanism by which genetic variation in a population is created. Inferences derived from mutation rate models are fundamental to many population genetics methods. Previous models have demonstrated that nucleotides flanking polymorphic sites-the local sequence context-explain variation in the probability that a site is polymorphic. However, limitations to these models exist as the size of the local sequence context window expands. These include a lack of robustness to data sparsity at typical sample sizes, lack of regularization to generate parsimonious models and lack of quantified uncertainty in estimated rates to facilitate comparison between models. To address these limitations, we developed Baymer, a regularized Bayesian hierarchical tree model that captures the heterogeneous effect of sequence contexts on polymorphism probabilities. Baymer implements an adaptive Metropolis-within-Gibbs Markov Chain Monte Carlo sampling scheme to estimate the posterior distributions of sequence-context based probabilities that a site is polymorphic. We show that Baymer accurately infers polymorphism probabilities and well-calibrated posterior distributions, robustly handles data sparsity, appropriately regularizes to return parsimonious models, and scales computationally at least up to 9-mer context windows. We demonstrate application of Baymer in three ways-first, identifying differences in polymorphism probabilities between continental populations in the 1000 Genomes Phase 3 dataset, second, in a sparse data setting to examine the use of polymorphism models as a proxy for de novo mutation probabilities as a function of variant age, sequence context window size, and demographic history, and third, comparing model concordance between different great ape species. We find a shared context-dependent mutation rate architecture underlying our models, enabling a transfer-learning inspired strategy for modeling germline mutations. In summary, Baymer is an accurate polymorphism probability estimation algorithm that automatically adapts to data sparsity at different sequence context levels, thereby making efficient use of the available data.

Bi-fidelity Kriging model for reliability analysis of the ultimate strength of stiffened panels

A method based on a Bi-fidelity Kriging model is proposed for structural reliability analysis. It is based on adding low-fidelity data samples to the model to predict high-fidelity values, thus saving computational effort. Distance Correlation develops the correlation between the low and high-fidelity functions, initially proposed to assess the correlation between two variables. The bi-fidelity Kriging response surface model's efficiency as a surrogate model will be assessed for structural reliability problems that demand high computational costs, such as nonlinear finite element analysis structural models. The efficiency assessment is performed by comparing the accuracy of the failure probability predictions based on the Subset Simulation and First-order reliability method using the Bi-fidelity Kriging model as a surrogate for the performance function. The idea is illustrated by considering a representative component of marine structures analyzed by finite element analysis to create bi-fidelity scenarios to assess structural reliability with many variables. The results show that the proposed multi-fidelity method can provide an accurate failure probability estimation with less computational cost.

A systematic review of measurement uncertainty visualizations in the context of standardized assessments.

This systematic review summarized findings of 29 studies evaluating visual presentation formats appropriate for communicating measurement uncertainty associated with standardized clinical assessment instruments. Studies were identified through systematic searches of multiple databases (Medline, Embase, PsycInfo, ERIC, Scopus, and Web of Science). Strikingly, we found no studies which were conducted using samples of clinicians and included clinical decision-making scenarios. Included studies did however find that providing participants with information about measurement uncertainty may increase awareness of uncertainty and promote more optimal decision making. Formats which visualize the shape of the underlying probability distribution were found to promote more accurate probability estimation and appropriate interpretations of the underlying probability distribution shape. However, participants in the included studies did not seem to benefit from the additional information provided by such plots during decision-making tasks. Further explorations into how presentations of measurement uncertainty impact clinical decision making are needed to examine whether findings of the included studies generalize to clinician populations. This review provides an important overview of pitfalls associated with formats commonly used to communicate measurement uncertainty in clinical assessment instruments, and a potential starting point for further explorations into promising alternatives. Finally, our review offers specific recommendations on how remaining research questions might be addressed.

An efficient structural reliability analysis method with active learning Kriging-assisted robust adaptive importance sampling

Due to deterioration effects and a variety of manmade or natural hazards, structures and infrastructure systems can face serious challenges for the functionality and safety during the service life. Thus, it is crucial to accurately assess the structural reliability and perform reasonable management actions. Simulation methods can yield more accurate reliability analysis results compared with approximation methods. However, as structures and infrastructure systems become complex, evaluating the engineering models can be very time-consuming. To address the gap, this paper proposes an active learning Kriging-assisted method for efficient structural reliability analysis. The proposed method consists of two stages. In the first stage, Kriging-based random walk, i.e., MCMC, is performed to explore the failure domain. Then, in the second stage, a Gaussian mixture distribution is established as the quasi-optimal sampling distribution for importance sampling. With the active learning framework that enlarges the training database in the MCMC and importance sampling process, the proposed method can achieve robust exploration of the whole failure domain, and also accurate failure probability estimates. Two classical numerical examples first validate the performance and advantages of the proposed method for handling problems with multiple failure domains and rare events. Then, a real-world application demonstrates the feasibility of applying the proposed method for quantitatively assessing the failure risk for prestressed-concrete continuous rigid-frame bridges during the service life.

A precautionary approach to autonomous vehicles.

In this article, we defend an approach to autonomous vehicle ethics and policy based on the precautionary principle. We argue that a precautionary approach is warranted, given the significant scientific and moral uncertainties related to autonomous vehicles, especially higher-level ones. While higher-level autonomous vehicles may offer many important benefits to society, they also pose significant risks, which are not fully understood at this juncture. Risk management strategies traditionally used by government officials to make decisions about new technologies cannot be applied to higher-level autonomous vehicles because these strategies require accurate and reliable probability estimates concerning the outcomes of different policy options and extensive agreement about values, which are not currently available for autonomous vehicles. Although we describe our approach as precautionary, that does not mean that we are opposed to autonomous vehicle development and deployment, because autonomous vehicles offer benefits that should be pursued. The optimal approach to managing the risks of autonomous vehicles is to take reasonable precautions; that is, to adopt policies that attempt to deal with serious risks in a responsible way without depriving society of important benefits.

Hope from Despair*

Hope from Despair^*

Estimation of fault probability in medium voltage feeders through calibration techniques in classification models

Machine Learning is currently a well-suited approach widely adopted for solving data-driven problems in predictive maintenance. Data-driven approaches can be used as the main building block in risk-based assessment and analysis tools for Transmission and Distribution System Operators in modern Smart Grids. For this purpose, a suitable Decision Support System should be able of providing not only early warnings, such as the detection of faults in real time, but even an accurate probability estimate of outages and failures. In other words, the performance of classification systems, at least in these cases, needs to be assessed even in terms of reliable outputting posterior probabilities, a really important feature that, in general, classifiers very often do not offer. In this paper are compared several state-of-the-art calibration techniques along with a set of simple new proposed techniques, with the aim of calibrating fuzzy scoring values of a custom-made evolutionary-cluster-based hybrid classifier trained on a set of a real-world dataset of faults collected within the power grid that feeds the city of Rome, Italy. Comparison results show that in real-world cases calibration techniques need to be assessed carefully depending on the scores distribution and the proposed techniques are a valid alternative to the ones existing in the technical literature in terms of calibration performance, computational efficiency and flexibility.

Entropy-based adaptive design for contour finding and estimating reliability

In reliability analysis, methods used to estimate failure probability are often limited by the costs associated with model evaluations. Many of these methods, such as multifidelity importance sampling (MFIS), rely upon a computationally efficient surrogate model like a Gaussian process (GP) to quickly generate predictions. The quality of the GP fit, particularly in the vicinity of the failure region(s), is instrumental in supplying accurately predicted failures for such strategies. We introduce an entropy-based GP adaptive design that, when paired with MFIS, provides more accurate failure probability estimates and with higher confidence. We show that our greedy data acquisition strategy better identifies multiple failure regions compared to existing contour-finding schemes. We then extend the method to batch selection, without sacrificing accuracy. Illustrative examples are provided on benchmark data as well as an application to an impact damage simulator for National Aeronautics and Space Administration (NASA) spacesuits.

A New Method To Find All The High-Probability Word-Oriented Truncated Differentials: Application To Midori, SKINNY And CRAFT

Abstract This paper proposes a new method to find high-probability truncated differentials using matrix muliplication. For Markov cipher with similar round function, suppose that the transition probability matrix of round function is $\mathcal{D}$, then $\mathcal{D}^{r}$ contains all the differential probabilities of an $r$-round block cipher. To reduce the matrix dimension, we consider the word-oriented truncated differential and the truncated transition probability matrix $\mathcal{T}$. Regardless of the effect of the $S$-box, we focus on whether there is a non-zero difference on one cell instead of the value of the difference. In this case, the matrix dimension reduces significantly and we can calculate $\mathcal{T}^{r}$ using a workstation. Then all the $r$-round truncated differential probabilities can be found from $\mathcal{T}^{r}$. And the probability in $\mathcal{T}^{r}$ is the probability of the whole truncated differential hull but not a single or several truncated differential characteristics. Besides, we make a more accurate probability estimation of the truncated differential of lightweight block cipher. Combined with the truncated differential hull, we found some longer truncated differential distinguishers. And as $\mathcal{T}^{r}$ stores all the truncated differential probabilities, we can also find all the impossible truncated differentials.

Adaptive network reliability analysis: Methodology and applications to power grid

Flow network models can capture the underlying physics and operational constraints of many networked systems including the power grid and transportation and water networks. However, analyzing systems’ reliability using computationally expensive flow-based models faces substantial challenges, especially for rare events. Existing actively trained meta-models, which present a new promising direction in reliability analysis, are not applicable to networks due to the inability of these methods to handle high-dimensional problems as well as discrete or mixed variable inputs. This study presents the first Adaptive surrogate-based Network Reliability (ANR) analysis through integration of Bayesian Additive Regression Trees (BART) and Monte Carlo simulation (MCS). An active learning method is developed that identifies the most valuable training samples based on the credible intervals derived by BART over the space of predictor variables as well as the proximity of the points to the estimated limit state. Benchmark power grids including IEEE 30, 57, 118, and 300-bus systems and their power flow models for cascading failure analysis are considered to investigate ANR-BART, MCS, subset simulation, and passively-trained optimal BART and deep neural networks. Results indicate that ANR-BART is robust and yields accurate estimates of network failure probability, while significantly reducing the computational cost of reliability analysis.

Efficient detection of Parkinson's disease using deep learning techniques over medical data

AbstractParkinson's disease is a degenerative disease that leads to brain disorder and nonfunctioning of different body parts. Deep learning tools like artificial neural network (ANN), convolution neural network (CNN), regression Analysis (RA), and so on, has been considered to a great extent in recent days. Several data sets based on the motor and nonmotor symptoms are applied to different classifier for correct identification of Parkinson's patient from healthy people. In this paper, hybridization of two deep learning tools such as, RA and ANN are done for effective diagnosis of the disease by probability estimation. The communal merits of individual approaches of the existing approaches are realized in this context for accurate probability estimation. Data preprocessing and probability estimation of preprocessed data is done in RA. The second existing approach is used to identify the PD patient by comparing with a predefined threshold value of a neuron. The estimation is performed on the data set of speech recognition, iron content, and pulse rate among a group of people. The proposed approach is compared with the existing approaches like, SVM and k‐NN classifier. The computed result reveals the superiority of the proposed algorithm with 93.46% accuracy.

A machine-learning based probabilistic perspective on dynamic security assessment

Probabilistic security assessment and real-time dynamic security assessments (DSA) are promising to better handle the risks of system operations. The current methodologies of security assessments may require many time-domain simulations for some stability phenomena that are unpractical in real-time. Supervised machine learning is promising to predict DSA as their predictions are immediately available. Classifiers are offline trained on operating conditions and then used in real-time to identify operating conditions that are insecure. However, the predictions of classifiers can be sometimes wrong and hazardous if an alarm is missed for instance.A probabilistic output of the classifier is explored in more detail and proposed for probabilistic security assessment. An ensemble classifier is trained and calibrated offline by using Platt’ scaling to provide accurate probability estimates of the output. Imbalances in the training database and a cost-skewness addressing strategy are proposed for considering that missed alarms are significantly worse than false alarms. Subsequently, risk-minimised predictions can be made in real-time operation by applying cost-sensitive learning. Through case studies on a real data-set of the French transmission grid and on the IEEE 6 bus system using static security metrics, it is showcased how the proposed approach reduces inaccurate predictions and risks. The sensitivity on the likelihood of contingency is studied as well as on expected outage costs. Finally, the scalability to several contingencies and operating conditions are showcased.

Fast and accurate structure probability estimation for simultaneous alignment and folding of RNAs with Markov chains

MotivationSimultaneous alignment and folding (SA&F) of RNAs is the indispensable gold standard for inferring the structure of non-coding RNAs and their general analysis. The original algorithm, proposed by Sankoff, solves the theoretical problem exactly with a complexity of O(n^6) in the full energy model. Over the last two decades, several variants and improvements of the Sankoff algorithm have been proposed to reduce its extreme complexity by proposing simplified energy models or imposing restrictions on the predicted alignments.ResultsHere, we introduce a novel variant of Sankoff’s algorithm that reconciles the simplifications of PMcomp, namely moving from the full energy model to a simpler base pair-based model, with the accuracy of the loop-based full energy model. Instead of estimating pseudo-energies from unconditional base pair probabilities, our model calculates energies from conditional base pair probabilities that allow to accurately capture structure probabilities, which obey a conditional dependency. This model gives rise to the fast and highly accurate novel algorithm Pankov (Probabilistic Sankoff-like simultaneous alignment and folding of RNAs inspired by Markov chains).ConclusionsPankov benefits from the speed-up of excluding unreliable base-pairing without compromising the loop-based free energy model of the Sankoff’s algorithm. We show that Pankov outperforms its predecessors LocARNA and SPARSE in folding quality and is faster than LocARNA.

Using Bayesian Updating to Improve Decisions under Uncertainty

Decision making requires managers to constantly estimate the probability of uncertain outcomes and update those estimates in light of new information. This article provides guidance to managers on how they can improve that process by more explicitly adopting a Bayesian approach. Clear understanding and application of the Bayesian approach leads to more accurate probability estimates, resulting in better informed decisions. More importantly, adopting a Bayesian approach, even informally, promises to improve the quality of managerial thinking, analysis, and decisions in a variety of additional ways.