Didactic Example Research Articles

Class imbalance on medical image classification: towards better evaluation practices for discrimination and calibration performance.

This work aims to assess standard evaluation practices used by the research community for evaluating medical imaging classifiers, with a specific focus on the implications of class imbalance. The analysis is performed on chest X-rays as a case study and encompasses a comprehensive model performance definition, considering both discriminative capabilities and model calibration. We conduct a concise literature review to examine prevailing scientific practices used when evaluating X-ray classifiers. Then, we perform a systematic experiment on two major chest X-ray datasets to showcase a didactic example of the behavior of several performance metrics under different class ratios and highlight how widely adopted metrics can conceal performance in the minority class. Our literature study confirms that: (1) even when dealing with highly imbalanced datasets, the community tends to use metrics that are dominated by the majority class; and (2) it is still uncommon to include calibration studies for chest X-ray classifiers, albeit its importance in the context of healthcare. Moreover, our systematic experiments confirm that current evaluation practices may not reflect model performance in real clinical scenarios and suggest complementary metrics to better reflect the performance of the system in such scenarios. Our analysis underscores the need for enhanced evaluation practices, particularly in the context of class-imbalanced chest X-ray classifiers. We recommend the inclusion of complementary metrics such as the area under the precision-recall curve (AUC-PR), adjusted AUC-PR, and balanced Brier score, to offer a more accurate depiction of system performance in real clinical scenarios, considering metrics that reflect both, discrimination and calibration performance. This study underscores the critical need for refined evaluation metrics in medical imaging classifiers, emphasizing that prevalent metrics may mask poor performance in minority classes, potentially impacting clinical diagnoses and healthcare outcomes. Common scientific practices in papers dealing with X-ray computer-assisted diagnosis (CAD) systems may be misleading. We highlight limitations in reporting of evaluation metrics for X-ray CAD systems in highly imbalanced scenarios. We propose adopting alternative metrics based on experimental evaluation on large-scale datasets.

Re-weighting the randomized controlled trial for generalization: finite-sample error and variable selection

Abstract Randomized controlled trials (RCTs) may suffer from limited scope. In particular, samples may be unrepresentative: some RCTs over- or under-sample individuals with certain characteristics compared to the target population, for which one wants conclusions on treatment effectiveness. Re-weighting trial individuals to match the target population can improve the treatment effect estimation. In this work, we establish the expressions of the bias and variance of such re-weighting procedures—also called inverse propensity of sampling weighting (IPSW)—in presence of categorical covariates for any sample size. Such results allow us to compare the theoretical performance of different versions of IPSW estimates. Besides, our results show how the performance (bias, variance, and quadratic risk) of IPSW estimates depends on the two sample sizes (RCT and target population). A by-product of our work is the proof of consistency of IPSW estimates. In addition, we analyse how including covariates that are not necessary for identifiability of the causal effect may impact the asymptotic variance. Including covariates that are shifted between the two samples but not treatment-effect modifiers increases the variance while non-shifted but treatment-effect modifiers do not. We illustrate all the takeaways in a didactic example, and on a semi-synthetic simulation inspired from critical care medicine.

The Triple Product Rule and the Subtleties of the Mathematical Tools Used in Thermodynamics

We have clarified several formulas from mathematics and physics. Specifically, we have shown that several standard concepts from thermodynamics, which have been recently investigated and rejected in some papers, continue to be valid. Thus, we have studied the triple product rule, the first law of thermodynamics, Mayer's relation, work, function of state, the relationship between the coefficient of thermal expansion, coefficient of isothermal compressibility and thermal coefficient of pressure. We have used multivariable calculus and properties of Jacobians and other mathematical tools. Likewise, we have provided several didactic examples concerning the interpretation of thermodynamical and mathematical formulas. We have concluded that the basement of several thermodynamical formulas is correct but full of subtleties. Thus, those formulas continue to be valid despite some claims in the recent literature.

On the sigma-mu stochastic multicriteria analysis: Exact solutions for common particular cases

The sigma-mu approach is one of the recent innovations in the field of multicriteria decision aiding and composite indicators, extending the stochastic multicriteria acceptability analysis (SMAA) toolbox. The initial stage of this method involves computing the mean (mu) and standard deviation (sigma) for the composite value of the units under evaluation, considering a stochastic distribution on a set of admissible weights. This work develops closed-form formulas to obtain exact values for mu and sigma without needing approximations via Monte-Carlo simulations, which can be applied in some cases that are quite common. In terms of aggregation, these cases are characterized by an additive model, such as a weighted sum, a multiattribute value function, or PROMETHEE II. In terms of stochastic distributions, these cases include uniformly distributed unconstrained vectors of weights, rank-ordered vectors of weights, or lower-bounded weights. The developed formulas are applied to a didactic example and some open problems for future research are suggested.

EvoImp: Multiple Imputation of Multi-label Classification data with a genetic algorithm.

Missing data is a prevalent problem that requires attention, as most data analysis techniques are unable to handle it. This is particularly critical in Multi-Label Classification (MLC), where only a few studies have investigated missing data in this application domain. MLC differs from Single-Label Classification (SLC) by allowing an instance to be associated with multiple classes. Movie classification is a didactic example since it can be "drama" and "bibliography" simultaneously. One of the most usual missing data treatment methods is data imputation, which seeks plausible values to fill in the missing ones. In this scenario, we propose a novel imputation method based on a multi-objective genetic algorithm for optimizing multiple data imputations called Multiple Imputation of Multi-label Classification data with a genetic algorithm, or simply EvoImp. We applied the proposed method in multi-label learning and evaluated its performance using six synthetic databases, considering various missing values distribution scenarios. The method was compared with other state-of-the-art imputation strategies, such as K-Means Imputation (KMI) and weighted K-Nearest Neighbors Imputation (WKNNI). The results proved that the proposed method outperformed the baseline in all the scenarios by achieving the best evaluation measures considering the Exact Match, Accuracy, and Hamming Loss. The superior results were constant in different dataset domains and sizes, demonstrating the EvoImp robustness. Thus, EvoImp represents a feasible solution to missing data treatment for multi-label learning.

New Approach to Solving Fuzzy Multiobjective Linear Fractional Optimization Problems

In this paper, an iterative approach based on the use of fuzzy parametric functions is proposed to find the best preferred optimal solution to a fuzzy multiobjective linear fractional optimization problem. From this approach, the decision-maker imposes tolerance values or termination conditions for each parametric objective function. Indeed, the fuzzy parametric values are computed iteratively, and each fuzzy fractional objective is transformed into a fuzzy non-fractional parametric function using these values of parameters. The core value of fuzzy numbers is used to transform the fuzzy multiobjective non-fractional problem into a deterministic multiobjective non-fractional problem, and the ε-constraint approach is employed to obtain a linear single objective optimization problem. Finally, by setting the value of parameter ε, the Dangtzig simplex method is used to obtain an optimal solution. Therefore, the number of solutions is equal to the number of used values, and the optimal solution is chosen according to the preference of the decision-maker. We have provided a didactic example to highlight the step of our approach and its numerical performances.

Physics-informed and black-box Identification of robotic actuator with a flexible joint

In robotics, precise models are critical for ensuring safety and functionality. However, acquiring a precise model characterizing a system’s dynamics can be challenging. One of the alternatives to address this issue is system Identification, which aims to obtain models through physical and experimental observations. In this manner, the developments in machine learning algorithms, such as neural networks, have significantly improved the modeling of complex and nonlinear phenomena. In this work, a mass-spring-damper (MSD) system and a low-cost original elastomer-based Series Elastic Actuators (eSEA) assembly are used to evaluate the performance of system Identification models. The black-box models selected are variations of the AutoRegressive Moving Average with eXogenous input (ARMAX) algorithm. The gray-box model aims to estimate the parameters of 4 friction models; the optimization is done utilizing physics-informed neural networks (PINNs). For both case studies, the PINNs outperformed the black-box models. In the didactic example, the parameters obtained are close to the ground truth, and the highest determinant coefficient obtained is 0.99. The friction model that best represents the robotic actuator is the LuGre model, with the parameters obtained using the PINNs, outperforming the best black-box model by lowering the mean absolute error (MAE) by 30.83%. With a determinant coefficient of 0.94, the model shows a high capacity for describing the multiple nonlinearities present in the system.

An Improved Copula‐Based Framework for Efficient Global Sensitivity Analysis

AbstractGlobal sensitivity analysis (GSA) enhances our understanding of computational models and simplifies model parameter estimation. VarIance‐based Sensitivity analysis using COpUlaS (VISCOUS) is a variance‐based GSA framework. The advantage of VISCOUS is that it can use existing model input‐output data (e.g., water model parameters‐responses) to estimate the first‐ and total‐order Sobol’ sensitivity indices. This study improves VISCOUS by refining its handling of marginal densities of the Gaussian mixture copula model (GMCM). We then evaluate VISCOUS using three types of generic functions relevant to water system models. We observe that its performance depends on function dimension, input‐output data size, and non‐identifiability. Function dimension refers to the number of uncertain input factors analyzed in GSA, and non‐identifiability refers to the inability to estimate GMCM parameters. VISCOUS proves powerful in estimating first‐order sensitivity with a small amount of input‐output data (e.g., 200 in this study), regardless of function dimension. It always ranks input factors correctly in both first‐ and total‐order terms. For estimating total‐order sensitivity, it is recommended to use VISCOUS when the function dimension is not very high (e.g., less than 20) due to the challenge of producing sufficient input‐output data for accurate GMCM inferences (e.g., more than 10,000 data). In cases where all input factors are equally important (a rarity in practice), VISCOUS faces non‐identifiability issues that impact its performance. We provide a didactic example and an open‐source Python code, pyVISCOUS, for broader user adoption.

Linear fractional optimization over the efficient set of multi-objective integer quadratic problem

In this paper, we introduce an exact algorithm for optimizing a linear fractional utility function over the efficient set of a multi objective integer quadratic problem. The algorithm is based on the “Branch and Cut” principle, which combines the branching process to ensure decision variables’ integrity and efficient cuts built off the non-increasing gradients’ directions of objective functions to eliminate inefficient integer solutions. The proposed approach accelerates the convergence to the efficient solution that optimizes the utility function. After presenting and describing the algorithm, a detailed didactic example is illustrated, followed by an experimental study to validate our approach and show computational costs.

Interpretable machine learning for psychological research: Opportunities and pitfalls.

In recent years, machine learning methods have become increasingly popular prediction methods in psychology. At the same time, psychological researchers are typically not only interested in making predictions about the dependent variable, but also in learning which predictor variables are relevant, how they influence the dependent variable, and which predictors interact with each other. However, most machine learning methods are not directly interpretable. Interpretation techniques that support researchers in describing how the machine learning technique came to its prediction may be a means to this end. We present a variety of interpretation techniques and illustrate the opportunities they provide for interpreting the results of two widely used black box machine learning methods that serve as our examples: random forests and neural networks. At the same time, we illustrate potential pitfalls and risks of misinterpretation that may occur in certain data settings. We show in which way correlated predictors impact interpretations with regard to the relevance or shape of predictor effects and in which situations interaction effects may or may not be detected. We use simulated didactic examples throughout the article, as well as an empirical data set for illustrating an approach to objectify the interpretation of visualizations. We conclude that, when critically reflected, interpretable machine learning techniques may provide useful tools when describing complex psychological relationships. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Missing Data in Sport Science: A Didactic Example Using Wearables in American Football

Data are often recorded from athletes to make decisions regarding the mitigation of injuries or the enhancement of performance. However, data collection in the real-world is difficult, and it is common for data to be missing from a particular training session due to equipment malfunction, athlete non-compliance, etc. The statistical community has long recognized that proper handling of missing data is vital to unbiased analyses and decision making, yet most dashboards in sport science and medicine do not recognize the issues introduced by missing data and practitioners are largely unaware that their displays are conveying biased information. The goal of this leading article is to show how real-world data can violate the 'missing completely at random' assumption in an American Football example and then demonstrate some potential imputation solutions which appear to maintain the underlying properties of the data in the presence of missingness. Whether data are aggregated on a dashboard as simple histograms and averages or with higher-level analytics, violating the 'missing completely at random' assumption results in a biased dashboard. Practitioners need to insist that dashboard developers perform missing data analyses and impute data as needed so valid data-driven decisions can be made.

Prespecification of Structure for the Optimization of Data Collection and Analysis

Data collection and research methodology represents a critical part of the research pipeline. On the one hand, it is important that we collect data in a way that maximises the validity of what we are measuring, which may involve the use of long scales with many items. On the other hand, collecting a large number of items across multiple scales results in participant fatigue, and expensive and time consuming data collection. It is therefore important that we use the available resources optimally. In this work, we consider how the representation of a theory as a causal/structural model can help us to streamline data collection and analysis procedures by not wasting time collecting data for variables which are not causally critical for answering the research question. This not only saves time and enables us to redirect resources to attend to other variables which are more important, but also increases research transparency and the reliability of theory testing. To achieve this, we leverage structural models and the Markov conditional independency structures implicit in these models, to identify the substructures which are critical for a particular research question. To demonstrate the benefits of this streamlining we review the relevant concepts and present a number of didactic examples, including a real-world example.

Analysis of diffuse scattering in electron diffraction data for the crystal structure determination of Pigment Orange 13, C32H24Cl2N8O2.

The crystallographic study of two polymorphs of the industrial pyrazolone Pigment Orange 13 (P.O.13) is reported. The crystal structure of the β phase was determined using single-crystal X-ray analysis of a tiny needle. The α phase was investigated using three-dimensional electron diffraction. The electron diffraction data contain sharp Bragg reflections and strong diffuse streaks, associated with severe stacking disorder. The structure was solved by careful analysis of the diffuse scattering, and similarities of the unit-cell parameters with the β phase. The structure solution is described in detail and this provides a didactic example of solving molecular crystal structures in the presence of diffuse scattering. Several structural models were constructed and optimized by lattice-energy minimization with dispersion-corrected DFT. A four-layer model was found, which matches the electron diffraction data, including the diffuse scattering, and agrees with X-ray powder data. Additionally, five further phases of P.O.13 are described.

Granular approximations: A novel statistical learning approach for handling data inconsistency with respect to a fuzzy relation

Inconsistency in classification and regression problems occurs when instances that relate in a certain way on the condition attributes, do not follow the same relation on the decision attribute. It typically appears as a result of perturbation in data caused by incomplete knowledge (missing attributes) or by random effects that occur during data generation (instability in the assessment of decision attribute values). Inconsistencies with respect to a crisp preorder relation (expressing either dominance or indiscernibility between instances) can be handled with set-theoretic approaches like rough sets and by using statistical/machine learning approaches that involve optimization methods. In particular, the Kotłowski-Słowiński (KS) approach relabels the objects from a dataset such that inconsistencies are removed, and such that the new class labels are as close as possible to the original ones in terms of a given loss function. In this paper, we generalize the KS approach to handle inconsistency determined by a fuzzy preorder relation rather than a crisp one. The method produces a consistent fuzzy relabeling of the instances and may be used as a preprocessing tool with algorithms for binary classification and regression. As the obtained fuzzy sets can be represented as unions of meaningful simple fuzzy sets or granules, we call them granular approximations. We provide statistical foundations for our method, develop appropriate optimization procedures, provide didactic examples, and prove several important properties.

Investigaciones sobre el uso de analogías en el aula de ciencias: una revisión sistemática

Experimental science teachers are faced with the task of explaining scientific topics that are difficult for students to understand, so they usually resort to analogues that make them easier to understand by comparison. It is important to know the scientific research published on didactic sequences with analogies and their implementation in the classroom. Thus, this work shows a systematic review of the scientific literature in the Web of Science and Scopus databases on the subject between 2000 and 2020.  Results show 10 articles that meet the established inclusion criteria and, after their analysis, it is concluded that, although the sample is small, they are relevant studies, as they provide useful didactic examples for teaching, common analogies to address certain contents, aspects to consider in teacher training and students' perceptions

NATIONAL AND CULTURAL FEATURES OF THE SPEECH ACT CONGRATULATIONS IN THE MODERN GERMAN LANGUAGE (compared to Russian)

The proposed paper analyzes the features of the speech act congratulation in modern German in comparison with Russian. The author proceeds from the understanding of etiquette speech acts as representatives of the communicative category politeness, characterizes these phenomena as socially conditioned, changeable and, as a result, requiring repeated study. The purpose of the article is the comparative consideration of congratulation in informal communication in German and Russian. The tasks of the work include a review of available research papers on the issue, a comparative description of congratulations in two nationalcultural communities taking into account traditions and customs of the people and the specifics of language units. The dictionary platforms of the German, authentic didactic resources and examples from the author’s personal correspondence are used as practical material. An analysis of the semantic and syntactic properties of the performatives constituting the speech act congratulation in both languages allows to draw conclusions regarding the use of etiquette formulas in close connection with a holidays certain category: in contrast of the Russian verb поздравлять, the German verb gratulieren is used in congratulations on personal events of the addressee, at that time how congratulations on state or church holidays are good wishes, expressed by nominal phrases. The novelty of the research lies also in addressing the issues of stereotyping and modification of the semantic meanings of particular holidays in German culture, emerging of new significant occasions under the influence of democratization processes in society, due to the spread of political correctness ideas, which determines the relevance of the corresponding congratulatory phrases.

Using agent-based models to address non-communicable diseases: a review of models and their application to policy

BackgroundAgent based models are a computational methodology in which systems of simulated heterogeneous agents interact with one another and their environment; they are a research tool with the potential to provide greater understanding of the complex, interdependent, and systemic determinants of population health challenges, particularly when co-produced with the decision makers, practitioners, and public who understand and experience these challenges from a variety of perspectives. Although agent-based modelling is becoming more widely used in health research, this methodology is currently underutilised in non-communicable disease prevention. We aim to highlight the potential role of agent-based modelling in supporting policy and practice decision-making in non-communicable disease prevention, using an obesity example to show how an agent-based model can capture the social influences in diet. MethodsWe identified agent-based models addressing non-communicable disease prevention from PubMed, ScienceDirect, Google Scholar, and Web of Science, using the search terms “agent-based model” and “public health” between Jan 1, 2000, and Dec 31, 2022. Case studies were selected that best illustrated the use of agent-based models for UK public health challenges. Selection criteria also included open access code documentation and models that reported core methodologies. FindingsWe identified three key case studies that best exemplified the use of agent-based models for addressing non-communicable disease prevention. A further three core sociological agent-based model case studies that were seen as core to methodology were also identified. Although we did not find any agent-based models that had been used for policy implementation to date, we did identify a model of obesity spread in networked agents. This model simulated how individuals influence each other with respect to food consumption and physical activity. We deconstructed this model to find that it is a good didactic example of agent-based methodology and shows the potential for agent-based models to be used to inform policy and practice decision-making in the future. InterpretationWell-designed agent-based models that are developed in collaboration with public health stakeholders have the potential to provide new insights into non-communicable disease prevention and provide an opportunity to test policy scenarios in silico before they are applied in the real world. FundingPopulation health Agent-based Simulation nEtwork (PHASE).

Partially Confirmatory Approach to Factor Analysis with Bayesian Learning: A LAWBL Tutorial

Different from traditional practice that considers factor analysis as either exploratory or confirmatory, different amounts of substantive information can be available in between the confirmatory and exploratory extremes under the partially confirmatory approach. Based on Bayesian Lasso methods, three models were recently proposed for various types of data under the new approach: the partially confirmatory factor analysis (PCFA), generalized PCFA, and partially confirmatory item response model. All models with related variants can be implemented in the R package LAWBL, which is available free of charge. This article introduces the theoretical and statistical foundation of the three models in a unified framework, including model formulation, identification, variants, and Bayesian inference and estimation with regularizations. Didactic examples covering different scenarios are employed to illustrate the implementation of the models and their variants in LAWBL step by step. Guidelines and suggestions are given to researchers and practitioners in a discussion.

Is smart urban governance possible in developing countries? Constraints, resources, innovative tools and strategies

Until recently, we were talking about urban management, local management and territorial management. We are now talking about smart city or intelligent city, terms that have become fashionable in specialized literature and even in everyday language to such an extent that those who ignore them could consider themselves overtaken by the passage of time. But what does the term "smart city" actually mean? Smart city is a city equipped with the tools enabling it to observe, learn and react from the data collected. To observe itself, the smart city needs a large set of various sensors and materials. To this definition, some prefer to oppose another, which would define the smart city as a city that is more liveable, less polluting and in which the user is given a place. This definition obviously does not exclude neither the fact of the connection and the acquisition of essential information necessarily multiple and numerous, nor the complexity of the internal system of analysis and automated decision-making oriented towards the comfort of the user. Some see the smart city as a process and an approach and not as an end. So what about in developing countries where society is undergoing profound and rapid change? Can we adapt the concept in the implementation of a new governance of the city? Answering these questions is not easy. To get around the difficulty, we will instead offer the concept of a sustainable city where the management of essential information can be made less complex. In this article we will successively deal with the theoretical foundations of the "transition to the smart city" and sustainability, the need for the establishment of a new vision of urban governance and smart governance tools. To illustrate our point, we will present a few simple and didactic examples for the Algerian city of medium importance. Keywords: smart city, sustainability, local governance

Supporting Decisions by Unleashing Multiple Mindsets Using Pairwise Comparisons Method

Inconsistency in pairwise comparison judgements is often perceived as an unwanted phenomenon and researchers have proposed a number of techniques to either reduce it or to correct it. We take a viewpoint that this inconsistency unleashes different mindsets of the decision maker(s) that should be taken into account when generating recommendations as decision support. With this aim we consider the spanning trees analysis which is a recently emerging idea for use with the pairwise comparison approach that represents the plurality of mindsets (in terms of a plurality of vectors corresponding to different spanning trees). Until now, the multiplicity of the vectors supplied by the spanning trees approach have been amalgamated into a single preference vector, losing the information about the plurality of mindsets. To preserve this information, we propose a novel methodology taking an approach similar to Stochastic Multi-criteria Acceptability Analysis. Considering all the rankings of alternatives corresponding to the different mindsets, our methodology gives the probability that an alternative attains a given ranking position as well as the probability that an alternative is preferred to another one. Since the exponential number of spanning trees makes their enumeration prohibitive, we propose computing approximate probabilities using statistical sampling of the spanning trees. Our approach is also appealing because it can be applied also to incomplete sets of pairwise comparisons. We demonstrate its usefulness with a didactic example as well as with an application to a real-life case of selecting a Telecom backbone infrastructure for rural areas.