Principled Approach Research Articles

Secure Design: A Principled Approach for Systems Engineers

AbstractINCOSE's Systems Engineering Vision 2035 sets a goal that security will be as foundational a perspective in systems design as system performance and safety are today. Such a design needs to be inherently secure (i.e., eliminate hazards, susceptibilities, and vulnerabilities to the extent possible and control the remaining ones are, while still enabling the system to meet performance requirements) and provide a basis for necessary security functionality. This paper discusses a principled approach and concepts for creating such a design. Activities for secure design across multiple MITRE sponsors and work programs with desired outcomes to the Vision 2035 goal inform. These activities include community outreach through workshops and public review of publications.

How should we investigate variation in the relation between social media and well-being?

Most researchers studying the relation between social media use and well-being find small to no associations, yet policymakers and public stakeholders keep asking for more evidence. One way the field is reacting is by inspecting the variation around average relations—with the goal of describing individual social media users. Here, we argue that this approach produces findings that are not as informative as they could be. Our analysis begins by describing how the field got to this point. Then, we explain the problems with the current approach of studying variation and how it loses sight of one of the most important goals of a quantitative social science: generalizing from a sample to a population. We propose a principled approach to quantify, interpret, and explain variation in average relations by: (1) conducting model comparisons, (2) defining a region of practical equivalence and testing the theoretical distribution of relations against that region, (3) defining a smallest effect size of interest and comparing it against the theoretical distribution. We close with recommendations to either study moderators as systematic factors that explain variation or to commit to a person-specific approach and conduct N=1 studies and qualitative research.

Library of the Auditorium of Military Affairs in 1918—1921: History and Development of the Holding

The solution of urgent tasks to develop modern approaches to the organization of military libraries functioning is impossible without using the existing achievements in the field of military librarianship. Retrospective analysis of the work of the Information Historical and Scientific Center — Military Historical Library of the General Staff of the Armed Forces of the Russian Federation (IHSC — MHL GS AF RF) in the Soviet period can be used to improve the work of Russian military libraries at the present stage.Due to the lack of comprehensive studies on the history of the IHSC — MHL GS AF RF in the first years of Soviet power, this article attempts to fill this gap, in particular, to establish the sources of formation of the collection of the library, which changed its name. The aim of the study is to reconstruct the history of creation and formation of one of such sources — the library of the Auditorium of Military Affairs (later — the Department of Universal Military Training at the headquarters of the Petrograd District Commissariat for Military Affairs) in the context of the construction of the Workers’ and Peasants’ Red Army (WPRA) in 1918—1921, as well as to analyze the dynamics of the library holding.The relevance of the issue under study is conditioned, on the one hand, by its insufficient research, and on the other hand, by the demand for the historical experience of military libraries to develop principled approaches to their reform. The source base of the research is represented by the materials of Russian archives (the Russian State Military Archive, the Central State Archive of Literature and Art of St. Petersburg, the Current Archive of the IHSC — MHL GS AF RF), documents of the main and central bodies of military administration of the RSFSR, publications in the periodical press.The research has broadened the understanding of the fate of military book collections of the Russian Empire after its collapse and the initial stage of formation of military librarianship in Soviet Russia. The fate of some nationalized book collections before they were added to the holding of the library of the Auditorium of Military Affairs has been established. The documents revealing the main directions of the library’s activity have been introduced into scientific turnover; its organizational and staff structure has been analyzed; the interrelation of the library’s history with the history of the Red Army in the first years of Soviet power has been traced. The results obtained show that the impressive book collection accumulated by the library of the Auditorium of Military Affairs is now a part of the holding of the IHSC — MHL GS AF RF.

Digital eternity: The holographic resurrection of music icons

Holographic reincarnations blend the allure of the past with the possibilities of modern technology, navigating through ethical quandaries and the redefinition of performance art. Central to this exploration is the fusion of memory and digital perpetuity, pushing the limits of what constitutes a live performance. The holographic rebirth of legends such as Elvis Presley, Amy Winehouse and Whitney Houston sparks debate over the ethics of posthumous consent, the authenticity of such representations and the implications of monetizing the afterlife of artists. This shift towards digital eternity prompts a critical assessment of the moral dimensions involved in resurrecting artists through technology. Exploring the history and development of holography, from historical illusions to contemporary artificial intelligence-enhanced presentations, reveals both the technological achievements and the ethical challenges presented by these endeavours. The drive behind these resurrective projects shines a light on a lucrative industry focused on ‘Delebs’, turning the legacies of deceased celebrities into profitable ventures. Yet the quest to immortalize artists digitally invites scrutiny over potential misuse, privacy breaches and the erosion of the true essence of their legacies. Addressing these complex issues demands a concerted effort from ethicists, legal scholars, technology experts and cultural commentators. The journey into this uncharted domain highlights the necessity of balancing innovation with respect, advocating for a principled approach to honouring those who have passed. The concept of confining artists to a ‘digital purgatory’, where they exist solely for entertainment devoid of self-determination, emphasizes the urgency of establishing ethical guidelines. This digital afterlife, fraught with the risk of diminishing the soul of artistic works to mere commercial assets, calls for a vigilant approach to safeguard the dignity and volition of artists in the realm of digital legacy.

On Predictive Planning and Counterfactual Learning in Active Inference.

Given the rapid advancement of artificial intelligence, understanding the foundations of intelligent behaviour is increasingly important. Active inference, regarded as a general theory of behaviour, offers a principled approach to probing the basis of sophistication in planning and decision-making. This paper examines two decision-making schemes in active inference based on "planning" and "learning from experience". Furthermore, we also introduce a mixed model that navigates the data complexity trade-off between these strategies, leveraging the strengths of both to facilitate balanced decision-making. We evaluate our proposed model in a challenging grid-world scenario that requires adaptability from the agent. Additionally, our model provides the opportunity to analyse the evolution of various parameters, offering valuable insights and contributing to an explainable framework for intelligent decision-making.

Genome-wide nucleosome and transcription factor responses to genetic perturbations reveal chromatin-mediated mechanisms of transcriptional regulation.

Epigenetic mechanisms contribute to gene regulation by altering chromatin accessibility through changes in transcription factor (TF) and nucleosome occupancy throughout the genome. Despite numerous studies focusing on changes in gene expression, the intricate chromatin-mediated regulatory code remains largely unexplored on a comprehensive scale. We address this by employing a factor-agnostic, reverse-genetics approach that uses MNase-seq to capture genome-wide TF and nucleosome occupancies in response to the individual deletion of 201 transcriptional regulators in Saccharomyces cerevisiae, thereby assaying nearly one million mutant-gene interactions. We develop a principled approach to identify and quantify chromatin changes genome-wide, observing differences in TF and nucleosome occupancy that recapitulate well-established pathways identified by gene expression data. We also discover distinct chromatin signatures associated with the up- and downregulation of genes, and use these signatures to reveal regulatory mechanisms previously unexplored in expression-based studies. Finally, we demonstrate that chromatin features are predictive of transcriptional activity and leverage these features to reconstruct chromatin-based transcriptional regulatory networks. Overall, these results illustrate the power of an approach combining genetic perturbation with high-resolution epigenomic profiling; the latter enables a close examination of the interplay between TFs and nucleosomes genome-wide, providing a deeper, more mechanistic understanding of the complex relationship between chromatin organization and transcription.

Beyond analytics: Using computer‐aided methods in educational research to extend qualitative data analysis

AbstractThis study proposes and demonstrates how computer‐aided methods can be used to extend qualitative data analysis by quantifying qualitative data, and then through exploration, categorization, grouping, and validation. Computer‐aided approaches to inquiry have gained important ground in educational research, mostly through data analytics and large data set processing. We argue that qualitative data analysis methods can also be supported and extended by computer‐aided methods. In particular, we posit that computing capacities rationally applied can expand the innate human ability to recognize patterns and group qualitative information based on similarities. We propose a principled approach to using machine learning in qualitative education research based on the three interrelated elements of the assessment triangle: cognition, observation, and interpretation. Through the lens of the assessment triangle, the study presents three examples of qualitative studies in engineering education that have used computer‐aided methods for visualization and grouping. The first study focuses on characterizing students' written explanations of programming code, using tile plots and hierarchical clustering with binary distances to identify the different approaches that students used to self‐explain. The second study looks into students' modeling and simulation process and elicits the types of knowledge that they used in each step through a think‐aloud protocol. For this purpose, we used a bubble plot and a k‐means clustering algorithm. The third and final study explores engineering faculty's conceptions of teaching, using data from semi‐structured interviews. We grouped these conceptions based on coding similarities, using Jaccard's similarity coefficient, and visualized them using a treemap. We conclude this manuscript by discussing some implications for engineering education qualitative research.

When and How to Deviate From a Preregistration

As the practice of preregistration becomes more common, researchers need guidance in how to report deviations from their preregistered statistical analysis plan. A principled approach to the use of preregistration should not treat all deviations as problematic. Deviations from a preregistered analysis plan can both reduce and increase the severity of a test, as well as increase the validity of inferences. I provide examples of how researchers can present deviations from preregistrations and evaluate the consequences of the deviation when encountering 1) unforeseen events, 2) errors in the preregistration, 3) missing information, 4) violations of untested assumptions, and 5) falsification of auxiliary hypotheses. The current manuscript aims to provide a principled approach to deciding when to deviate from a preregistration and how to report deviations from an error-statistical philosophy grounded in methodological falsificationism. The goal is to help researchers reflect on the consequence of deviations from preregistrations by evaluating the test’s severity and the validity of the inference.

A deep learning method for comparing Bayesian hierarchical models.

Bayesian model comparison (BMC) offers a principled approach to assessing the relative merits of competing computational models and propagating uncertainty into model selection decisions. However, BMC is often intractable for the popular class of hierarchical models due to their high-dimensional nested parameter structure. To address this intractability, we propose a deep learning method for performing BMC on any set of hierarchical models which can be instantiated as probabilistic programs. Since our method enables amortized inference, it allows efficient re-estimation of posterior model probabilities and fast performance validation prior to any real-data application. In a series of extensive validation studies, we benchmark the performance of our method against the state-of-the-art bridge sampling method and demonstrate excellent amortized inference across all BMC settings. We then showcase our method by comparing four hierarchical evidence accumulation models that have previously been deemed intractable for BMC due to partly implicit likelihoods. Additionally, we demonstrate how transfer learning can be leveraged to enhance training efficiency. We provide reproducible code for all analyses and an open-source implementation of our method. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A Principled Approach to Characterize and Analyze Partially Observed Confounder Data from Electronic Health Records.

Partially observed confounder data pose challenges to the statistical analysis of electronic health records (EHR) and systematic assessments of potentially underlying missingness mechanisms are lacking. We aimed to provide a principled approach to empirically characterize missing data processes and investigate performance of analytic methods. Three empirical sub-cohorts of diabetic SGLT2 or DPP4-inhibitor initiators with complete information on HbA1c, BMI and smoking as confounders of interest (COI) formed the basis of data simulation under a plasmode framework. A true null treatment effect, including the COI in the outcome generation model, and four missingness mechanisms for the COI were simulated: completely at random (MCAR), at random (MAR), and two not at random (MNAR) mechanisms, where missingness was dependent on an unmeasured confounder and on the value of the COI itself. We evaluated the ability of three groups of diagnostics to differentiate between mechanisms: 1)-differences in characteristics between patients with or without the observed COI (using averaged standardized mean differences [ASMD]), 2)-predictive ability of the missingness indicator based on observed covariates, and 3)-association of the missingness indicator with the outcome. We then compared analytic methods including "complete case", inverse probability weighting, single and multiple imputation in their ability to recover true treatment effects. The diagnostics successfully identified characteristic patterns of simulated missingness mechanisms. For MAR, but not MCAR, the patient characteristics showed substantial differences (median ASMD 0.20 vs 0.05) and consequently, discrimination of the prediction models for missingness was also higher (0.59 vs 0.50). For MNAR, but not MAR or MCAR, missingness was significantly associated with the outcome even in models adjusting for other observed covariates. Comparing analytic methods, multiple imputation using a random forest algorithm resulted in the lowest root-mean-squared-error. Principled diagnostics provided reliable insights into missingness mechanisms. When assumptions allow, multiple imputation with nonparametric models could help reduce bias.

Special issue PRO] A demonstration of estimands and sensitivity analyses for time-to-deterioration of patient reported outcomes

ABSTRACT In oncology trials, health-related quality of life (HRQoL), specifically patient-reported symptom burden and functional status, can support the interpretation of survival endpoints, such as progression-free survival. However, applying time-to-event endpoints to patient-reported outcomes (PRO) data is challenging. For example, in time-to-deterioration analyses clinical events such as disease progression are common in many settings and are often handled through censoring the patient at the time of occurrence; however, disease progression and HRQoL are often related leading to informative censoring. Special consideration to the definition of events and intercurrent events (ICEs) is necessary. In this work, we demonstrate time-to-deterioration of PRO estimands and sensitivity analyses to answer research questions using composite, hypothetical, and treatment policy strategies applied to a single endpoint of disease-related symptoms. Multiple imputation methods under both the missing-at-random and missing-not-at-random assumptions are used as sensitivity analyses of primary estimands. Hazard ratios ranged from 0.52 to 0.66 over all the estimands and sensitivity analyses modeling a robust treatment effect favoring the treatment in time to disease symptom deterioration or death. Differences in the estimands include how people who experience disease progression or discontinue the randomized treatment due to AEs are accounted for in the analysis. We use the estimand framework to define interpretable and principled approaches for different time-to-deterioration research questions and provide practical recommendations. Reporting the proportions of patient events and patient censoring by reason helps understand the mechanisms that drive the results, allowing for optimal interpretation.

Artificial intelligence ethics: from principles to the critical approach

The article discusses the problem of determining the methodological and conceptual foundations of the artificial intelligence ethics. It is shown that the principled approach is based on the theory of value embedding, which assumes that technical objects can either be the carriers of values themselves, or at least contribute to the realization of certain values. At the same time, it is highly dependent on stakeholders, and it rather declares ethics than ensures it. The person-centered approach is based on the idea of personal moral responsibility. The main problems of the personality-oriented approach are the gap in responsibility and the unpredictability of the actions of artificial intelligence. A critical approach is proposed, according to which the subject of artificial intelligence ethics is the impact of technology on people's ideas and values, their behavior and decision-making. The work introduces and discusses the concept of the scale paradox, resulting from the artificial intelligence use. This concept states that many ethically correct cases of using technology can lead to ethically unacceptable consequences. It is shown that one of the options for applying a critical approach can be the study of attitudes and stereotypes associated with artificial intelligence in the mass consciousness.

Interpretable linear dimensionality reduction based on bias-variance analysis

One of the central issues of several machine learning applications on real data is the choice of the input features. Ideally, the designer should select a small number of the relevant, nonredundant features to preserve the complete information contained in the original dataset, with little collinearity among features. This procedure helps mitigate problems like overfitting and the curse of dimensionality, which arise when dealing with high-dimensional problems. On the other hand, it is not desirable to simply discard some features, since they may still contain information that can be exploited to improve results. Instead, dimensionality reduction techniques are designed to limit the number of features in a dataset by projecting them into a lower dimensional space, possibly considering all the original features. However, the projected features resulting from the application of dimensionality reduction techniques are usually difficult to interpret. In this paper, we seek to design a principled dimensionality reduction approach that maintains the interpretability of the resulting features. Specifically, we propose a bias-variance analysis for linear models and we leverage these theoretical results to design an algorithm, Linear Correlated Features Aggregation (LinCFA), which aggregates groups of continuous features with their average if their correlation is “sufficiently large”. In this way, all features are considered, the dimensionality is reduced and the interpretability is preserved. Finally, we provide numerical validations of the proposed algorithm both on synthetic datasets to confirm the theoretical results and on real datasets to show some promising applications.

DiffSED: Sound Event Detection with Denoising Diffusion

Sound Event Detection (SED) aims to predict the temporal boundaries of all the events of interest and their class labels, given an unconstrained audio sample. Taking either the split-and-classify (i.e., frame-level) strategy or the more principled event-level modeling approach, all existing methods consider the SED problem from the discriminative learning perspective. In this work, we reformulate the SED problem by taking a generative learning perspective. Specifically, we aim to generate sound temporal boundaries from noisy proposals in a denoising diffusion process, conditioned on a target audio sample. During training, our model learns to reverse the noising process by converting noisy latent queries to the ground-truth versions in the elegant Transformer decoder framework. Doing so enables the model generate accurate event boundaries from even noisy queries during inference. Extensive experiments on the Urban-SED and EPIC-Sounds datasets demonstrate that our model significantly outperforms existing alternatives, with 40+% faster convergence in training. Code: https://github.com/Surrey-UPLab/DiffSED

AdvST: Revisiting Data Augmentations for Single Domain Generalization

Single domain generalization (SDG) aims to train a robust model against unknown target domain shifts using data from a single source domain. Data augmentation has been proven an effective approach to SDG. However, the utility of standard augmentations, such as translate, or invert, has not been fully exploited in SDG; practically, these augmentations are used as a part of a data preprocessing procedure. Although it is intuitive to use many such augmentations to boost the robustness of a model to out-of-distribution domain shifts, we lack a principled approach to harvest the benefit brought from multiple these augmentations. Here, we conceptualize standard data augmentations with learnable parameters as semantics transformations that can manipulate certain semantics of a sample, such as the geometry or color of an image. Then, we propose Adversarial learning with Semantics Transformations (AdvST) that augments the source domain data with semantics transformations and learns a robust model with the augmented data. We theoretically show that AdvST essentially optimizes a distributionally robust optimization objective defined on a set of semantics distributions induced by the parameters of semantics transformations. We demonstrate that AdvST can produce samples that expand the coverage on target domain data. Compared with the state-of-the-art methods, AdvST, despite being a simple method, is surprisingly competitive and achieves the best average SDG performance on the Digits, PACS, and DomainNet datasets. Our code is available at https://github.com/gtzheng/AdvST.

Robust chromatin state annotation.

With the goal of mapping genomic activity, international projects have recently measured epigenetic activity in hundreds of cell and tissue types. Chromatin state annotations produced by segmentation and genome annotation (SAGA) methods have emerged as the predominant way to summarize these epigenomic data sets in order to annotate the genome. These chromatin state annotations are essential for many genomic tasks, including identifying active regulatory elements and interpreting disease-associated genetic variation. However, despite the widespread applications of SAGA methods, no principled approach exists to evaluate the statistical significance of chromatin state assignments. Here, we propose the first method for assigning calibrated confidence scores to chromatin state annotations. Toward this goal, we performed a comprehensive evaluation of the reproducibility of the two most widely used existing SAGA methods, ChromHMM and Segway. We found that their predictions are frequently irreproducible. For example, when applying the same SAGA method on two sets of experimental replicates, 27%-69% of predicted enhancers fail to replicate. This suggests that a substantial fraction of predicted elements in existing chromatin state annotations cannot be relied upon. To remedy this problem, we introduce SAGAconf, a method for assigning a measure of confidence (r-value) to chromatin state annotations. SAGAconf works with any SAGA method and assigns an r-value to each genomic bin of a chromatin state annotation that represents the probability that the label of this bin will be reproduced in a replicated experiment. Thus, SAGAconf allows a researcher to select only the reliable predictions from a chromatin annotation for use in downstream analyses.

Bridging the gap between movement data and connectivity analysis using the Time-Explicit Habitat Selection (TEHS) model

BackgroundUnderstanding how to connect habitat remnants to facilitate the movement of species is a critical task in an increasingly fragmented world impacted by human activities. The identification of dispersal routes and corridors through connectivity analysis requires measures of landscape resistance but there has been no consensus on how to calculate resistance from habitat characteristics, potentially leading to very different connectivity outcomes.MethodsWe propose a new model, called the Time-Explicit Habitat Selection (TEHS) model, that can be directly used for connectivity analysis. The TEHS model decomposes the movement process in a principled approach into a time and a selection component, providing complementary information regarding space use by separately assessing the drivers of time to traverse the landscape and the drivers of habitat selection. These models are illustrated using GPS-tracking data from giant anteaters (Myrmecophaga tridactyla) in the Pantanal wetlands of Brazil.ResultsThe time model revealed that the fastest movements tended to occur between 8 p.m. and 5 a.m., suggesting a crepuscular/nocturnal behavior. Giant anteaters moved faster over wetlands while moving much slower over forests and savannas, in comparison to grasslands. We also found that wetlands were consistently avoided whereas forest and savannas tended to be selected. Importantly, this model revealed that selection for forest increased with temperature, suggesting that forests may act as important thermal shelters when temperatures are high. Finally, using the spatial absorbing Markov chain framework, we show that the TEHS model results can be used to simulate movement and connectivity within a fragmented landscape, revealing that giant anteaters will often not use the shortest-distance path to the destination patch due to avoidance of certain habitats.ConclusionsThe proposed approach can be used to characterize how landscape features are perceived by individuals through the decomposition of movement patterns into a time and a habitat selection component. Additionally, this framework can help bridge the gap between movement-based models and connectivity analysis, enabling the generation of time-explicit connectivity results.

The roots of polarization in the individual reward system.

Polarization raises concerns for democracy and society, which have expanded in the internet era where (mis)information has become ubiquitous, its transmission faster than ever, and the freedom and means of opinion expressions are expanding. The origin of polarization however remains unclear, with multiple social and emotional factors and individual reasoning biases likely to explain its current forms. In the present work, we adopt a principled approach and show that polarization tendencies can take root in biased reward processing of new information in favour of choice confirmatory evidence. Through agent-based simulations, we show that confirmation bias in individual learning is an independent mechanism and could be sufficient for creating polarization at group level independently of any additional assumptions about the opinions themselves, a priori beliefs about them, information transmission mechanisms or the structure of social relationship between individuals. This generative process can interact with polarization mechanisms described elsewhere, but constitutes an entrenched biological tendency that helps explain the extraordinary resilience of polarization against mitigating efforts such as dramatic informational change in the environment.

Treemendous: an R package for integrating taxonomic information across backbones.

Standardizing and translating species names from different databases is key to the successful integration of data sources in biodiversity research. There are numerous taxonomic name-resolution applications that implement increasingly powerful name-cleaning and matching approaches, allowing the user to resolve species relative to multiple backbones simultaneously. Yet there remains no principled approach for combining information across these underlying taxonomic backbones, complicating efforts to combine and merge species lists with inconsistent and conflicting taxonomic information. Here, we present Treemendous, an open-source software package for the R programming environment that integrates taxonomic relationships across four publicly available backbones to improve the name resolution of tree species. By mapping relationships across the backbones, this package can be used to resolve datasets with conflicting and inconsistent taxonomic origins, while ensuring the resulting species are accepted and consistent with a single reference backbone. The user can chain together different functionalities ranging from simple matching to a single backbone, to graph-based iterative matching using synonym-accepted relations across all backbones in the database. In addition, the package allows users to 'translate' one tree species list into another, streamlining the assimilation of new data into preexisting datasets or models. The package provides a flexible workflow depending on the use case, and can either be used as a stand-alone name-resolution package or in conjunction with existing packages as a final step in the name-resolution pipeline. The Treemendous package is fast and easy to use, allowing users to quickly merge different data sources by standardizing their species names according to the regularly updated database. By combining taxonomic information across multiple backbones, the package increases matching rates and minimizes data loss, allowing for more efficient translation of tree species datasets to aid research into forest biodiversity and tree ecology.

A principled approach to non-discrimination in cost-effectiveness

The US Inflation Reduction Act (IRA) prohibits the Centers for Medicare and Medicaid Services (CMS) from using standard quality-adjusted life-years or other value assessment methods that discriminate against the aged, terminally ill, or disabled when setting maximum fair prices for prescription drugs. This policy has reignited interest in methods for assessing value without discrimination. Equal value of life-years gained (EVL), healthy years in total (HYT), and Generalized Risk-Adjusted Cost-Effectiveness (GRACE) have emerged as proposals. Neither EVL nor HYT rests on well-articulated microeconomic foundations. We show that they produce decisions that are inconsistent over time in a variety of ways, including: (1) failure to support additivity and indirect comparison in cases where the standard-of-care therapy changes over time; (2) strictly negative value of survival gains that accrue from a new, better standard-of-care, particularly for the disabled themselves; (3) unbounded average value of survival gains; and (4) non-convex survival preferences. We propose an alternative method that relies on GRACE and its microeconomic foundations.