Analysis Of Rare Events Research Articles

Relative, Absolute, and Excess Risk of Second Primary Melanomas Among Multiple Racial and Ethnic Groups—Analysis of Rare Events

Relative, Absolute, and Excess Risk of Second Primary Melanomas Among Multiple Racial and Ethnic Groups—Analysis of Rare Events

Evaluating the Role of Data Enrichment Approaches towards Rare Event Analysis in Manufacturing.

Rare events are occurrences that take place with a significantly lower frequency than more common, regular events. These events can be categorized into distinct categories, from frequently rare to extremely rare, based on factors like the distribution of data and significant differences in rarity levels. In manufacturing domains, predicting such events is particularly important, as they lead to unplanned downtime, a shortening of equipment lifespans, and high energy consumption. Usually, the rarity of events is inversely correlated with the maturity of a manufacturing industry. Typically, the rarity of events affects the multivariate data generated within a manufacturing process to be highly imbalanced, which leads to bias in predictive models. This paper evaluates the role of data enrichment techniques combined with supervised machine learning techniques for rare event detection and prediction. We use time series data augmentation and sampling to address the data scarcity, maintaining its patterns, and imputation techniques to handle null values. Evaluating 15 learning models, we find that data enrichment improves the F1 measure by up to 48% in rare event detection and prediction. Our empirical and ablation experiments provide novel insights, and we also investigate model interpretability.

Active few-shot learning for rare bioacoustic feature annotation

The collection and annotation of bioacoustic data present several challenges to researchers. Bioacoustic monitoring of rare (sparse) or cryptic species generally encounter two main issues. The cost of collecting and processing field data and a lack of labelled datasets for the target species. The detection of invasive species incursions and probability of absence testing is especially challenging due to these species having population densities at or close to zero. We present a methodology specifically designed to aid in the analysis of rare acoustic events within long-term field recordings. This approach combines a wavelet-based segmentation method that automatically extracts transient features from within-field recordings. A few-shot active learning recommender system in a human-in-the-loop process prioritises the annotation of low-certainty samples. This process combines the accuracy of human classification and the speed of computational tools to greatly reduce the presence of non-target features in field recordings. We evaluate this approach using an invasive species identification case study. This methodology achieves a test accuracy of 98.4% as well as 81.2% test accuracy using 2-shot, 2-way prototypical learning without fine-tuning, demonstrating high performance at varying data availability contexts. Active learning using low-certainty samples achieves >90% test accuracy using only 20 training samples compared to 80 samples without active learning. This approach allows users to train custom audio classification models for any application with rare features. The model can be easily exported for use in the field making real-time bioacoustic monitoring of less-vocal species a possibility. All code and data are available at https://github.com/Listening-Lab/Annotator.

A single-loop reliability sensitivity analysis strategy for time-dependent rare events with both random variables and stochastic processes

To deal with the time-dependent reliability sensitivity (TDRS) analysis of rare events with both random variables and stochastic processes, a single-loop sampling procedure combined with the cross entropy-based importance sampling strategy (CEIS), namely SCEIS, is derived to lighten the computational burden without loss of precision. Firstly, the TDRS indices are derived to make them implementable by a single-loop importance sampling (IS) procedure. Then, the quasi-optimal IS density is obtained through the extension of the cross-entropy method, which is an adaptive procedure that overcomes the challenges of determining the position and number of design points in traditional IS. Furthermore, the SCEIS can be easily implemented to estimate the TDRS indices for problems with small failure probability. Three examples involving numerical and engineering problems are analyzed to demonstrate the performance of the proposed method. The results obtained from comparing with other existing methods reveal that the proposed method provides satisfactory TDRS analysis for rare events while significantly reducing computational costs.

Action formalism for geometric phases from self-closing quantum trajectories

When subject to measurements, quantum systems evolve along stochastic quantum trajectories that can be naturally equipped with a geometric phase observable via a post-selection in a final projective measurement. When post-selecting the trajectories to form a close loop, the geometric phase undergoes a topological transition driven by the measurement strength. Here, we study the geometric phase of a subset of self-closing trajectories induced by a continuous Gaussian measurement of a single qubit system. We utilize a stochastic path integral that enables the analysis of rare self-closing events using action methods and develop the formalism to incorporate the measurement-induced geometric phase therein. We show that the geometric phase of the most likely trajectories undergoes a topological transition for self-closing trajectories as a function of the measurement strength parameter. Moreover, the inclusion of Gaussian corrections in the vicinity of the most probable self-closing trajectory quantitatively changes the transition point in agreement with results from numerical simulations of the full set of quantum trajectories.

A method of combined metamodel and subset simulation for reliability analysis of rare events

Reliability analysis of large, complex structures with high reliability has always been a challenging task. The Subset Simulation (SUS) has proven highly effective for high-dimensional and small failure probability problems. However, the computational demands of time-consuming numerical simulations in the field of mechanical engineering remain substantial. Metamodeling techniques provide an effective means to reduce simulation computational costs. This paper introduces a novel approach, termed SUSAK, which combines the Subset Simulation with Kriging metamodels to address the challenge of small failure probability calculations. By using the original distribution of input variables representing the structural system, the SUSAK method establishes an adaptive metamodel and iteratively optimizes it with intermediate failure domain samples corresponding to the intermediate events. By replacing the performance function in subsequent calculations of the probability associated with intermediate events with metamodels, the enhanced method significantly reduces the computational burden compared to using the SUS method. This approach does not rely on solving for the design point, is not constrained by the shape of the failure domain, and retains the advantages of the SUS method in high-dimensional small failure probability calculations. As a result, it is well-suited for calculating small failure probabilities in nonlinear, discontinuous failure domains, multiple failure domains, and high-dimensional problems.

Molecular Dynamics Simulation of Complex Reactivity with the Rapid Approach for Proton Transport and Other Reactions (RAPTOR) Software Package.

Simulating chemically reactive phenomena such as proton transport on nanosecond to microsecond and beyond time scales is a challenging task. Ab initio methods are unable to currently access these time scales routinely, and traditional molecular dynamics methods feature fixed bonding arrangements that cannot account for changes in the system's bonding topology. The Multiscale Reactive Molecular Dynamics (MS-RMD) method, as implemented in the Rapid Approach for Proton Transport and Other Reactions (RAPTOR) software package for the LAMMPS molecular dynamics code, offers a method to routinely sample longer time scale reactive simulation data with statistical precision. RAPTOR may also be interfaced with enhanced sampling methods to drive simulations toward the analysis of reactive rare events, and a number of collective variables (CVs) have been developed to facilitate this. Key advances to this methodology, including GPU acceleration efforts and novel CVs to model water wire formation are reviewed, along with recent applications of the method which demonstrate its versatility and robustness.

A double-loop adaptive relevant vector machine combined with Harris Hawks optimization-based importance sampling

PurposeEnsuring the safety of structures is important. However, when a structure possesses both an implicit performance function and an extremely small failure probability, traditional methods struggle to conduct a reliability analysis. Therefore, this paper proposes a reliability analysis method aimed at enhancing the efficiency of rare event analysis, using the widely recognized Relevant Vector Machine (RVM).Design/methodology/approachDrawing from the principles of importance sampling (IS), this paper employs Harris Hawks Optimization (HHO) to ascertain the optimal design point. This approach not only guarantees precision but also facilitates the RVM in approximating the limit state surface. When the U learning function, designed for Kriging, is applied to RVM, it results in sample clustering in the design of experiment (DoE). Therefore, this paper proposes a FU learning function, which is more suitable for RVM.FindingsThree numerical examples and two engineering problem demonstrate the effectiveness of the proposed method.Originality/valueBy employing the HHO algorithm, this paper innovatively applies RVM in IS reliability analysis, proposing a novel method termed RVM-HIS. The RVM-HIS demonstrates exceptional computational efficiency, making it eminently suitable for rare events reliability analysis with implicit performance function. Moreover, the computational efficiency of RVM-HIS has been significantly enhanced through the improvement of the U learning function.

Strength in numbers: Unleashing the potential of trans-scale scope AMATERAS for massive cell quantification.

Singularity biology is a scientific field that targets drastic state changes in multicellular systems, aiming to discover the key cells that induce the state change and investigate the mechanisms behind them. To achieve this goal, we developed a trans-scale optical imaging system (trans-scale scope), that is capable of capturing both macroscale changes across the entire system and the micro-scale behavior of individual cells, surpassing the cell observation capabilities of traditional microscopes. We developed two units of the trans-scale scope, named AMATERAS-1 and -2, which demonstrated the ability to observe multicellular systems consisting of over one million cells in a single field of view with sub-cellular resolution. This flagship instrument has been used to observe the dynamics of various cell species, with the advantage of being able to observe a large number of cells, allowing the detection and analysis of rare events and cells such as leader cells in multicellular pattern formation and cells that spontaneously initiate calcium waves. In this paper, we present the design concept of AMATERAS, the optical configuration, and several examples of observations, and demonstrate how the strength-in-numbers works in life sciences.

Sequential Sampling-Based Asymptotic Probability Estimation of High-Dimensional Rare Events

Abstract Accurate analysis of rare failure events with an affordable computational cost is often challenging in many engineering applications, particularly for problems with high-dimensional system inputs. The extremely low probabilities of occurrence often lead to large probability estimation errors and low computational efficiency. Thus, it is vital to develop advanced probability analysis methods that are capable of providing robust estimations of rare event probabilities with narrow confidence bounds. The general method of determining confidence intervals of an estimator using the central limit theorem faces the critical obstacle of low computational efficiency. This is a side effect of the widely used Monte Carlo method, which often requires a large number of simulation samples to derive a reasonably narrow confidence interval. In this paper, a new probability analysis approach is developed which can be used to derive the estimates of rare event probabilities efficiently with narrow estimation bounds simultaneously for high-dimensional problems and complex engineering systems. The asymptotic behavior of the developed estimator is proven theoretically without imposing strong assumptions. An asymptotic confidence interval is established for the developed estimator. The presented study offers important insights into the robust estimations of the probability of occurrences for rare events. The accuracy and computational efficiency of the developed technique are assessed with numerical and engineering case studies. Case study results have demonstrated that narrow bounds can be obtained efficiently using the developed approach with the true values consistently located within the estimation bounds.

Computing non-equilibrium trajectories by a deep learning approach

Predicting the occurrence of rare and extreme events in complex systems is a fundamental problem in non-equilibrium physics. These events can have huge impacts on human societies. New approaches have emerged in recent years, which better estimate tail distributions. They often use large deviation concepts without the need to perform heavy direct ensemble simulations. In particular, a well-known approach is to derive a minimum action principle and find its minimizers.The analysis of rare reactive events in non-equilibrium systems without detailed balance is notoriously difficult both theoretically and computationally. They can often be described in the limit of small noise by the Freidlin-Wentzell action. Here, we propose a method which minimizes the geometric action instead using neural networks: it is called deep gMAM. It relies on a natural and simple machine-learning formulation of the classical gMAM approach when the Lagrangian is known. We provide a detailed description of the method as well as many examples: from bimodal switches in nonlinear stochastic (partial) differential equations, including a codimension-1 nucleation in an Allen-Cahn-Ginzburg-Landau 5-D stochastic partial differential equation, to quasi-potential estimates and extreme events in Burgers turbulence.

MSR63 Analysis of Rare Events in Outcomes Research Using Department of Defense Data: Intravenous Immune Globulin Therapy for Bulluous Pemphiogoid

In outcomes research, we deal with rare cases frequently. If medications is just approved building a sample size on the claims datasets, which has 4-6 months lag may take some time and you had to work with small numbers for the initial studies. For certain cases, the diagnosis is real so even have a researcher have enough coverage, can not get enough sample size due to characteristics of the disease. Using a Department of Defense dataset, we analyzed a rare events and proposed a model that mitigates the bias. In particular, we analyzed the probability of using IVIg Therapy for a rare skin condition called Bullous pemphigoid.

A Lyapunov approach to stability of positive semigroups: an overview with illustrations

The stability analysis of possibly time varying positive semigroups on non-necessarily compact state spaces, including Neumann and Dirichlet boundary conditions is a notoriously difficult subject. These crucial questions arise in a variety of areas of applied mathematics, including nonlinear filtering, rare event analysis, branching processes, physics and molecular chemistry. This article presents an overview of some recent Lyapunov-based approaches, focusing principally on practical and powerful tools for designing Lyapunov functions. These techniques include semigroup comparisons as well as conjugacy principles on non-necessarily bounded manifolds with locally Lipschitz boundaries. All the Lyapunov methodologies discussed in the article are illustrated in a variety of situations, ranging from conventional Markov semigroups on general state spaces to more sophisticated conditional stochastic processes possibly restricted to some non-necessarily bounded domains, including locally Lipschitz and smooth hypersurface boundaries, Langevin diffusions as well as coupled harmonic oscillators.

Police unions and police fatalities in the United States, 1990−2018

As of 2018, thirty-one states granted collective bargaining rights to police officers that require local government management to recognize and collectively bargain in good faith with police unions. Although scholars have identified factors related to police fatalities, the influence of police unions has gone understudied. Research in other occupational areas shows that union strength is associated with fewer workplace injuries and fatalities. This research analyzes the influence of union strength and collective bargaining rights on police fatalities. Using state-level data from 1990 to 2018, a rare-event analysis of police fatalities indicates that states with strong public employee unions experience fewer accidental line-of-duty deaths of police officers. Further, this analysis shows that states which grant collective bargaining rights to police officers have fewer felonious line-of-duty deaths of police officers compared with those that do not grant police collective bargaining rights. This article concludes with a discussion of the policy implications and the role of collective bargaining in police safety.

Finding Dangerous Waves—Review of Methods to Obtain Wave Impact Design Loads for Marine Structures

AbstractGreen water and slamming wave impacts can lead to severe damage or operability issues for marine structures. It is therefore essential to consider their probability and loads in design. This is difficult, as impacts are both hydrodynamically complex and relatively rare. The complexity requires high-fidelity modeling (experiments or CFD), whereas a statistically sound analysis of rare events requires long durations. High-fidelity tools are too demanding to run a Monte–Carlo simulation; low-fidelity tools do not include sufficient physical details. The use of extreme value theory and/or multi-fidelity modeling is therefore required. The present paper reviews the state-of-the-art methods to find wave impact design loads, which include response-conditioning methods, screening methods, and adaptive sampling methods. Their benefits and shortcomings are discussed, as well as challenges for the wave impact problem. One challenge is the role of wave non-linearity. Another is the validation of the different methods; it is hard to obtain long-duration high-fidelity wave impact data.

Determinants of Bus Dwell Time

Bus dwell time data collection typically involves labor-intensive ride checks. This paper reports an analysis of bus dwell times that use archived automatic vehicle location (AVL)/automatic passenger counter (APC) data reported at the level of individual bus stops. The archived data provide a large number of observations that serve to better understand the determinants of dwells, including analysis of rare events, such as lift operations. The analysis of bus dwell times at bus stops is applicable to TriMet, the transit provider for the Portland metropolitan area, and transit agencies in general. The determinants of dwell time include passenger activity, lift operations, and other effects, such as low floor bus, time of day, and route type.

Moving-zone renewal strategy combining adaptive Kriging and truncated importance sampling for rare event analysis

Moving-zone renewal strategy combining adaptive Kriging and truncated importance sampling for rare event analysis

Impact of red blood cell lysing on rare event analysis.

The challenges associated with analyzing rare cells are dependent on a series of factors, which usually require large numbers of cells per sample for successful resolution. Among these is determining the minimum number of total events needed to be acquired as defined by the expected frequency of the target cell population. The choice of markers that identify the target population, as well as the event rate and the number of aborted events/second, will also determine the statistically significant detection of rare cell events. Sample preparation is another important but often overlooked factor in rare cell analysis, and in this study we examine Poisson theory and methods to determine the effect of sample manipulation on rare cell detection. After verifying the applicability of this theory, we have evaluated the potential impact of red cell lysis on rare cell analysis, and how cell rarity can be underestimated or overestimated based on erythrolytic sensitivity or resistance of healthy leukocytes and pathological rare cells.

The potential of Meudon spectroheliograph for investigating long term solar activity and variability

Observations of the chromosphere with Deslandres’s spectroheliograph started at Paris Observatory in 1893 and were followed by systematic observations at Meudon since 1908. The solar collection of Hα and CaII K images is probably the longest available worldwide, with associated products such as synoptic maps and tables. Since 2018, Meudon spectroheliograph is the only instrument producing data-cubes of full line profiles of CaII H, CaII K and Hα, for each pixel of the solar disk. Slices of the cubes provide monochromatic images. We summarize in this paper the capabilities of the successive generations of the instrument, and explore the potential of the collection and products for analysis of rare events, investigations of past solar activity and studies of long term variability.

An Algorithm for Restoring a Function from Different Functionals for Predicting Rare Events in the Economy

This paper aims to restore some parameters of functionals using cubic splines to forecast rare events in finance and economics. The article considers the mathematical method for recovering an unknown function from many different functionals, such as the value of a function, the value of its first derivative, second derivative, as well as a definite integral over a certain interval. Moreover, all observations can occur with an error. Therefore, the author uses a method of recovering a function from different functionals observed with an error. The function is restored in the form of a cubic spline, which has a value-second derivative representation. The optimization problem consists in minimizing several sums of squares of the deviation at once, for ordinary values, for the first derivatives, for the second derivatives, for integrals, and for roughness penalty. For greater flexibility, weights have been introduced both for each group of observations and for each individual observation separately. The article shows in detail how the elements of each corresponding matrix are filled in. The appendix provides an implementation of the method as a FunctionalSmoothingSpline function in R language. Examples of using the method for the analysis and forecasting of rare (discrete) events in the economy are given. Formulas for calculating the cross-validation score CV (α) for the automatic procedure for determining the smoothing parameter α from the data in our problem of recovering a function by many functionals are shown. The paper concludes that the presented method makes it possible to analyze and predict rare events, which will allow you to prepare for such future events, get some benefit from this, or reduce possible risks or losses.