Structural Assumptions Research Articles

Scales and self-sustained mechanism of reattachment unsteadiness in separated shock wave/boundary layer interaction

The spatio-temporal scales, as well as a comprehensive self-sustained mechanism of the reattachment unsteadiness in shock wave/boundary layer interaction, are investigated in this study. Direct numerical simulations reveal that the reattachment unsteadiness of a Mach 7.7 laminar inflow causes over $26\,\%$ variation in wall friction and up to $20\,\%$ fluctuation in heat flux at the reattachment of the separation bubble. A statistical approach, based on the local reattachment upstream movement, is proposed to identify the spanwise and temporal scales of reattachment unsteadiness. It is found that two different types, i.e. self-induced and random processes, dominate different regions of reattachment. A self-sustained mechanism is proposed to comprehend the reattachment unsteadiness in the self-induced region. The intrinsic instability of the separation bubble transports vorticity downstream, resulting in an inhomogeneous reattachment line, which gives rise to baroclinic production of quasi-streamwise vortices. The pairing of these vortices initiates high-speed streaks and shifts the reattachment line upstream. Ultimately, viscosity dissipates the vortices, triggering instability and a new cycle of reattachment unsteadiness. The temporal scale and maximum vorticity are estimated with the self-sustained mechanism via order-of-magnitude analysis of the enstrophy. The advection speed of friction, derived from the assumption of coherent structures advecting with a Blasius-type boundary layer, aligns with the numerical findings.

A Systematic Literature Review of Modelling Approaches to Evaluate the Cost Effectiveness of PET/CT for Therapy Response Monitoring in Oncology.

This systematic literature review addresses model-based cost-effectiveness studies for therapy response monitoring with positron emission tomography (PET) generally combined with low-dose computed tomography (CT) for various cancer types. Given the known heterogeneity in therapy response events, studies should consider patient-level modelling rather than cohort-based modelling because of its flexibility in handling these events and the time to events. This review aims to identify the modelling methods used and includes a systematic assessment of the assumptions made in the current literature. This study was conducted and reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Information sources included electronic bibliographic databases, reference lists of review articles and contact with experts in the fields of nuclear medicine, health technology assessment and health economics. Eligibility criteria included peer-reviewed scientific publications and published grey literature. Literature searches, screening and critical appraisal were conducted by two reviewers independently. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) were used to assess the methodological quality. The Bias in Economic Evaluation (ECOBIAS) checklist was used to determine the risk of bias in the included publications. The search results included 2959 publications. The number of publications included for data extraction and synthesis was ten, representing eight unique studies. These studies addressed patients with lymphoma, advanced head and neck cancers, brain tumours, non-small cell lung cancer and cervical cancer. All studies addressed response to chemotherapy. No study evaluated response to immunotherapy. Most studies positioned PET/CT as an add-on modality and one study positioned PET/CT as a replacement for conventional imaging (X-ray and contrast-enhanced CT). Three studies reported decision-tree structures, four studies reported cohort-level state-transition models and one study reported a partitioned survival model. No patient-level models were reported. The simulation horizons adopted ranged from 1 year to lifetime. Most studies reported a probabilistic analysis, whereas two studies reported a deterministic analysis only. Two studies conducted a value of information analysis. Multiple studies did not adequately discuss model-specific aspects of bias. Most importantly and regularly observed were a high risk of structural assumptions bias, limited simulation horizon bias and wrong model bias. Model-based cost-effectiveness analysis for therapy response monitoring with PET/CT was based on cohorts of patients instead of individual patients in the current literature. Therefore, the heterogeneity in therapy response events was commonly not addressed appropriately. Further research should include more advanced and patient-level modelling approaches to accurately represent the complex context of clinical practice and, therefore, to be meaningful to support decision making. This review is registered in PROSPERO, the international prospective register of systematic reviews funded by the National Institute for Health Research, with CRD42023402581.

Event-horizon-scale Imaging of M87* under Different Assumptions via Deep Generative Image Priors

Reconstructing images from the Event Horizon Telescope (EHT) observations of M87*, the supermassive black hole at the center of the galaxy M87, depends on a prior to impose desired image statistics. However, given the impossibility of directly observing black holes, there is no clear choice for a prior. We present a framework for flexibly designing a range of priors, each bringing different biases to the image reconstruction. These priors can be weak (e.g., impose only basic natural-image statistics) or strong (e.g., impose assumptions of black hole structure). Our framework uses Bayesian inference with score-based priors, which are data-driven priors arising from a deep generative model that can learn complicated image distributions. Using our Bayesian imaging approach with sophisticated data-driven priors, we can assess how visual features and uncertainty of reconstructed images change depending on the prior. In addition to simulated data, we image the real EHT M87* data and discuss how recovered features are influenced by the choice of prior.

Two sample test for covariance matrices in ultra-high dimension

In this paper, we propose a new test for testing the equality of two population covariance matrices in the ultra-high dimensional setting that the dimension is much larger than the sizes of both of the two samples. Our proposed methodology relies on a data splitting procedure and a comparison of a set of well selected eigenvalues of the sample covariance matrices on the split data sets. Compared to the existing methods, our methodology is adaptive in the sense that (i). it does not require specific assumption (e.g., comparable or balancing, etc.) on the sizes of two samples; (ii). it does not need quantitative or structural assumptions of the population covariance matrices; (iii). it does not need the parametric distributions or the detailed knowledge of the moments of the two populations. Theoretically, we establish the asymptotic distributions of the statistics used in our method and conduct the power analysis. We justify that our method is powerful under weak alternatives. We conduct extensive numerical simulations and show that our method significantly outperforms the existing ones both in terms of size and power. Analysis of two real data sets is also carried out to demonstrate the usefulness and superior performance of our proposed methodology. An R package UHDtst is developed for easy implementation of our proposed methodology.

On how finite β and three-dimensional magnetic perturbations affect the instability of toroidal ion temperature gradient mode

Abstract The effects of finite β (the ratio of plasma kinetic pressure to magnetic pressure) and three-dimensional (3D) magnetic perturbations (MPs) on the instability of toroidal ion temperature gradient (ITG) mode are studied in this work. The expression of ion magnetic drift frequency ω_di modified by the effects from both finite β and 3D MPs is firstly derived based on the local 3D equilibrium model. Then, under the assumptions of adiabatic electrons and localized mode structure around the outboard mid-plane (ϑ_p=0), the dispersion equation of the long wavelength toroidal ITG mode with considering the parallel ion dynamics is derived and solved. The results show that the distribution of ω_di around the outboard mid-plane, including both ω_(di,0)=├ ω_di ┤|_(ϑ_p=0) and ω_(di,0)^''≡├ (d^2 ω_di \/dϑ_p^2 )┤|_(ϑ_p=0) (twist parameter quantifying the degree of concave or convex of ω_di), is the key for affecting the toroidal ITG mode instability. The diamagnetic effects from finite β and the effects from 3D MPs can suppress the instability by reducing |ω_(di,0) |. Via reducing |ω_(di,0)^'' | under the prerequisite of unchanged sign of ω_(di,0)^'', there also exist stabilization effects on the instability from 3D MPs and the modification of local magnetic shear by finite β effects. In addition, the stabilization effects induced by reducing |ω_(di,0)^'' | are closely associated with the global magnetic shear. The mechanisms for the effects of finite β and 3D MPs on the instability of toroidal ITG mode revealed in this work are helpful to the comprehensive understanding of the relationship between internal kink mode induced non-axisymmetric flux surface distortion and internal transport barrier physics in tokamak plasmas.

Dynamic light scattering in aqueous solutions of γ-picoline

line-water solutions was established and their dynamics were studied depending on the temperature of the solution and the concentration of γ-picoline in the solution. Analysis of the research results confirms the assumption of the microheterogeneous structure of aqueous solutions in the vicinity of a singular point on the phase diagram of the state in temperatureconcentration coordinates. The microheterogeneity of the structure of an aqueous solution of γ-picoline determines the maximum intensity of light scattering and the negative dispersion of the speed of sound in the hypersonic frequency range.

Realized Random Graphs, with an Application to the Interbank Network

Abstract We introduce a new inferential methodology for dynamic network models driven by latent state variables. The main idea is to obtain a noisy representation of the state variables dynamics by computing a sequence of cross-sectional estimates of the network model at each point in time. The dynamic modeling of these cross-sectional estimates, that we name realized random graphs, transforms the original nonlinear state-space network model into a linear time-series model that can be easily estimated. Under the assumption of a mixed-membership blockmodel structure, the model parameters and the unobservable state variables can be consistently estimated when both the size of the network and the time-series length are large. By allowing for an extremely rich parameterization of the model in high dimensions, the proposed methodology describes the heterogeneous topology of real-world networks. We corroborate our findings by using this novel framework to estimate and forecast the dynamic common factors driving the evolution of the Italian electronic market of interbank deposits, and we show that the interbank lending rate is a key factor determining the network topology.

Estimating Time-Varying Exposure Effects Through Continuous-Time Modelling in Mendelian Randomization.

Mendelian randomization is an instrumental variable method that utilizes genetic information to investigate the causal effect of a modifiable exposure on an outcome. In most cases, the exposure changes over time. Understanding the time-varying causal effect of the exposure can yield detailed insights into mechanistic effects and the potential impact of public health interventions. Recently, a growing number of Mendelian randomization studies have attempted to explore time-varying causal effects. However, the proposed approaches oversimplify temporal information and rely on overly restrictive structural assumptions, limiting their reliability in addressing time-varying causal problems. This article considers a novel approach to estimate time-varying effects through continuous-time modelling by combining functional principal component analysis and weak-instrument-robust techniques. Our method effectively utilizes available data without making strong structural assumptions and can be applied in general settings where the exposure measurements occur at different timepoints for different individuals. We demonstrate through simulations that our proposed method performs well in estimating time-varying effects and provides reliable inference when the time-varying effect form is correctly specified. The method could theoretically be used to estimate arbitrarily complex time-varying effects. However, there is a trade-off between model complexity and instrument strength. Estimating complex time-varying effects requires instruments that are unrealistically strong. We illustrate the application of this method in a case study examining the time-varying effects of systolic blood pressure on urea levels.

Educational assessment without numbers.

Psychometrics conceptualizes a person's proficiency (or ability, or competence), in a cognitive or educational domain, as a latent numerical quantity. Yet both conceptual and empirical studies have shown that the assumption of quantitative structure for such phenomena is unlikely to be tenable. A reason why most applications of psychometrics nevertheless continue to treat them as if they were numerical quantities may be that quantification is thought to be necessary to enable measurement. This is indeed true if one regards the task of measurement as the location of a measurand at a point on the real number line (the viewpoint adopted by, for example, the representational theory of measurement, the realist theory of measurement as the discovery of ratios, and Rasch measurement theory). But this is not the only philosophically respectable way of defining the notion of measurement. This paper suggests that van Fraassen's more expansive view of measurement as, in general, location in a logical space (which could be the real continuum, as in metrological applications in the physical sciences, but could be a different mathematical structure), provides a more appropriate conceptual framework for psychometrics. Taking educational measurement as a case study, it explores what that could look like in practice, drawing on fuzzy logic and mathematical order theory. It suggests that applying this approach to the assessment of intersubjectively constructed phenomena, such as a learner's proficiency in an inherently fuzzily-defined subject area, entails recognizing the theory-dependent nature of valid representations of such phenomena, which need not be conceived of structurally as values of quantities. Finally, some connections are made between this "qualitative mathematical" theorization of educational assessment, and the application of techniques from machine learning and artificial intelligence in this area.

An optimal control problem of traffic flow on a junction

We investigate how to control optimally a traffic flow through a junction on the line by acting only on speed reduction or traffic light at the junction. We show the existence of an optimal control and, under structure assumptions, provide optimality conditions. We use this analysis to investigate thoroughly the maximization of the flux on a space-time subset and show the existence of an optimal control which is bang-bang.

Fast convergence to non-isolated minima: four equivalent conditions for $${\textrm{C}^{2}}$$ functions

AbstractOptimization algorithms can see their local convergence rates deteriorate when the Hessian at the optimum is singular. These singularities are inescapable when the optima are non-isolated. Yet, under the right circumstances, several algorithms preserve their favorable rates even when optima form a continuum (e.g., due to over-parameterization). This has been explained under various structural assumptions, including the Polyak–Łojasiewicz condition, Quadratic Growth and the Error Bound. We show that, for cost functions which are twice continuously differentiable ($$\textrm{C}^2$$ C 2 ), those three (local) properties are equivalent. Moreover, we show they are equivalent to the Morse–Bott property, that is, local minima form differentiable submanifolds, and the Hessian of the cost function is positive definite along its normal directions. We leverage this insight to improve local convergence guarantees for safe-guarded Newton-type methods under any (hence all) of the above assumptions. First, for adaptive cubic regularization, we secure quadratic convergence even with inexact subproblem solvers. Second, for trust-region methods, we argue capture can fail with an exact subproblem solver, then proceed to show linear convergence with an inexact one (Cauchy steps).

Molecular Investigation of the Self-Assembly Mechanism Underlying Polydopamine Coatings: The Synergistic Effect of Typical Building Blocks Acting on Interfacial Adhesion.

Polydopamine (PDA) is well known as a mussel-inspired adhesive material composed of oligomeric heteropolymers. However, the conventional eumelanin-like structural assumption of PDA seems deficient in explaining its interfacial adhesion. To determine the decisive mechanism of PDA coating formation, experiments and simulations were performed in this study. 5,6-Dihydroxyindole (DHI), the signature building block of eumelanin, was introduced as the control group. Various typical building blocks in PDA were quantified by physicochemical characterizations, and the polar-group-dominated interfacial interaction was evaluated by classic molecular dynamics and metadynamics methods. Aminoethyl has been proven to be the key functional group inducing the adsorption of PDA on the hydroxylated silica substrates, while DHI shows limited adhesion to the substrate due to the absence of aminoethyl as the catechol-indole structure of DHI exhibits poor affinity to the silica surface. Pyrrole carboxylic acid, as an oxidative product detected from PDA/DHI, is unfavorable for its adhesion to silica substrates. Overall, the coating formation and self-aggregating precipitation of PDA are two competitive aminoethyl-consuming paths; thus, the in situ oxidative coupling of dopamine is indispensable for the PDA coating preparation. The collected PDA precipitates can no longer present satisfactory coating forming behavior, resulting from a shortage of aminoethyl moieties.

Modeling First-Line Daratumumab Use for Newly Diagnosed, Transplant-Ineligible, Multiple Myeloma: A Cost-Effectiveness and Risk Analysis for Healthcare Payers.

This study aimed to assess the cost-effectiveness of two regimens regarded as the standard of care for the treatment of newly diagnosed, transplant-ineligible multiple myeloma in Singapore: (1) daratumumab, lenalidomide, and dexamethasone and (2) bortezomib, lenalidomide, and dexamethasone. Additionally, it aimed to explore potential strategies to manage decision uncertainty and mitigate financial risk. A cost-effectiveness analysis from the healthcare system perspective was conducted using a partitioned survival model to estimate lifetime costs and quality-adjusted life years (QALYs) associated with daratumumab-based treatment and the bortezomib-based regimen. The analysis used data from the MAIA and SWOG S0777 trials and incorporated local real-world data where available. Sensitivity analyses were performed to evaluate the robustness of the findings, and a risk analysis was conducted to analyze various payer strategies in terms of their payer strategy and uncertainty burden (P-SUB), which account for the decision uncertainty and the additional cost of choosing a suboptimal intervention. The incremental cost-effectiveness ratio (ICER) for daratumumab, lenalidomide, and dexamethasone (DRd) compared with bortezomib, lenalidomide, and dexamethasone (VRd) was US $90,364 per QALY gained. The results were sensitive to variations in survival for DRd, postprogression treatment costs, cost of hospice care, and hazard ratio for progression-free survival. The scenarios explored indicated that structural assumptions, such as the time horizon of the analysis, significantly influenced the results due to uncertainties arising from immature trial data and treatment efficacy over time. Among the various payer strategies compared, an upfront price discount for daratumumab emerged as the best approach with the lowest P-SUB at US $14,708. In conclusion, this study finds that daratumumab as a first-line treatment for myeloma exceeds the cost-effectiveness threshold considered in this evaluation. An upfront price reduction is the recommended strategy to manage uncertainties and mitigate financial risks. These findings highlight the importance of targeted payer strategies to address specific types and sources of uncertainty.

Different model assumptions about plant hydraulics and photosynthetic temperature acclimation yield diverging implications for tropical forest gross primary production under warming

AbstractTropical forest photosynthesis can decline at high temperatures due to (1) biochemical responses to increasing temperature and (2) stomatal responses to increasing vapor pressure deficit (VPD), which is associated with increasing temperature. It is challenging to disentangle the influence of these two mechanisms on photosynthesis in observations, because temperature and VPD are tightly correlated in tropical forests. Nonetheless, quantifying the relative strength of these two mechanisms is essential for understanding how tropical gross primary production (GPP) will respond to climate change, because increasing atmospheric CO2 concentration may partially offset VPD‐driven stomatal responses, but is not expected to mitigate the effects of temperature‐driven biochemical responses. We used two terrestrial biosphere models to quantify how physiological process assumptions (photosynthetic temperature acclimation and plant hydraulic stress) and functional traits (e.g., maximum xylem conductivity) influence the relative strength of modeled temperature versus VPD effects on light‐saturated GPP at an Amazonian forest site, a seasonally dry tropical forest site, and an experimental tropical forest mesocosm. By simulating idealized climate change scenarios, we quantified the divergence in GPP predictions under model configurations with stronger VPD effects compared with stronger direct temperature effects. Assumptions consistent with stronger direct temperature effects resulted in larger GPP declines under warming, while assumptions consistent with stronger VPD effects resulted in more resilient GPP under warming. Our findings underscore the importance of quantifying the role of direct temperature and indirect VPD effects for projecting the resilience of tropical forests in the future, and demonstrate that the relative strength of temperature versus VPD effects in models is highly sensitive to plant functional parameters and structural assumptions about photosynthetic temperature acclimation and plant hydraulics.

Large/small eddy simulations: A high-fidelity method for studying high-Reynolds number turbulent flows

Direct numerical simulations (DNS) are one of the main ab initio tools to study turbulent flows. However, due to their considerable computational cost, DNS are primarily restricted to canonical flows at moderate Reynolds numbers, in which turbulence is isolated from the realistic, large-scale flow dynamics. In contrast, lower fidelity techniques, such as large eddy simulations (LES), are employed for modeling real-life systems. Such approaches rely on closure models that make multiple assumptions, including turbulent equilibrium, small-scale universality, etc., which require prior knowledge of the flow and can be violated. We propose a method, which couples a lower-fidelity, unresolved, time-dependent calculation of an entire system (LES) with an embedded small eddy simulation (SES) that provides a high-fidelity, fully resolved solution in a sub-region of interest of the LES. Such coupling is achieved by continuous replacement of the large SES scales with a low-pass filtered LES velocity field. The method is formulated in physical space, with no assumptions of equilibrium, small-scale structure, and boundary conditions. A priori tests of both steady and unsteady homogeneous, isotropic turbulences are used to demonstrate the method's accuracy in recovering turbulence properties, including spectra, probability density functions of the intermittent quantities, and sub-grid dissipation. Finally, SES is compared with two alternative approaches: one embedding a high-resolution region through static mesh refinement and a generalization of the traditional volumetric spectral forcing. Unlike these methods, SES is shown to achieve DNS-level accuracy at a fraction of the cost of the full DNS, thus opening the possibility to study high-Re flows.

Testing covariance separability for continuous functional data

Analyzing the covariance structure of data is a fundamental task of statistics. While this task is simple for low‐dimensional observations, it becomes challenging for more intricate objects, such as multi‐variate functions. Here, the covariance can be so complex that just saving a non‐parametric estimate is impractical and structural assumptions are necessary to tame the model. One popular assumption for space‐time data is separability of the covariance into purely spatial and temporal factors. In this article, we present a new test for separability in the context of dependent functional time series. While most of the related work studies functional data in a Hilbert space of square integrable functions, we model the observations as objects in the space of continuous functions equipped with the supremum norm. We argue that this (mathematically challenging) setup enhances interpretability for users and is more in line with practical preprocessing. Our test statistic measures the maximal deviation between the estimated covariance kernel and a separable approximation. Critical values are obtained by a non‐standard multiplier bootstrap for dependent data. We prove the statistical validity of our approach and demonstrate its practicability in a simulation study and a data example.

Exploration of diverse solutions for the calibration of imperfect climate models

Abstract. The calibration of Earth system model parameters is subject to data, time, and computational constraints. The high dimensionality of this calibration problem, combined with errors arising from model structural assumptions, makes it impossible to find model versions fully consistent with historical observations. Therefore, the potential for multiple plausible configurations presenting different trade-offs between skills in various variables and spatial regions remains usually untested. In this study, we lay out a formalism for making different assumptions about how ensemble variability in a perturbed physics ensemble relates to model error, proposing an empirical but practical solution for finding diverse near-optimal solutions. A meta-model is used to predict the outputs of a climate model reduced through principal component analysis. Then, a subset of input parameter values yielding results similar to a reference simulation is identified. We argue that the effective degrees of freedom in the model performance response to parameter input (the “parametric component”) are, in fact, relatively small, illustrating why manual calibration is often able to find near-optimal solutions. The results explore the potential for comparably performing parameter configurations that have different trade-offs in model errors. These model candidates can inform model development and could potentially lead to significantly different future climate evolution.

Effect of Model Fidelity on High-Speed Aeroelastic Behavior of a Cantilever Plate

Turbulence, flow separation, and shock dynamics challenge the modeling and analysis of high-speed aeroelastic behavior. Motivated by this, the importance of modeling the fidelity of the flow is explored in the aeroelastic response of a cantilever plate in an Ma=2.0 separating turbulent flow using unsteady Reynolds-averaged Navier–Stokes (URANS) and URANS-enriched local piston theory (LPT). Structural modeling assumptions are also evaluated using both linear and nonlinear representations. Close agreement in the predicted aeroelastic steady state is observed. However, large discrepancies in the dynamic aeroelastic response predictions are found and ultimately linked to the neglect of deformation-induced cavity pressure fluctuations and dynamic flow separation in the LPT model. Interestingly, the dynamic flow separation induces a fluid-driven limit cycle oscillation in the postflutter regime. Furthermore, structural nonlinearity is not found to have a strong impact on the conditions and configurations considered.

Leveraged Matrix Completion With Noise.

Completing low-rank matrices from subsampled measurements has received much attention in the past decade. Existing works indicate that O(nrlog2(n)) datums are required to theoretically secure the completion of an n ×n noisy matrix of rank r with high probability, under some quite restrictive assumptions: 1) the underlying matrix must be incoherent and 2) observations follow the uniform distribution. The restrictiveness is partially due to ignoring the roles of the leverage score and the oracle information of each element. In this article, we employ the leverage scores to characterize the importance of each element and significantly relax assumptions to: 1) not any other structure assumptions are imposed on the underlying low-rank matrix and 2) elements being observed are appropriately dependent on their importance via the leverage score. Under these assumptions, instead of uniform sampling, we devise an ununiform/biased sampling procedure that can reveal the "importance" of each observed element. Our proofs are supported by a novel approach that phrases sufficient optimality conditions based on the Golfing scheme, which would be of independent interest to the wider areas. Theoretical findings show that we can provably recover an unknown n×n matrix of rank r from just about O(nrlog2 (n)) entries, even when the observed entries are corrupted with a small amount of noisy information. The empirical results align precisely with our theories.

Multimodal Approach Reveals the Symmetry-Breaking Pathway to the Broken Helix in EuIn2As2

Understanding and manipulating emergent phases, which are themes at the forefront of quantum-materials research, rely on identifying their underlying symmetries. This general principle has been particularly prominent in materials with coupled electronic and magnetic degrees of freedom, in which magnetic order influences the electronic band structure and can lead to exotic topological effects. However, identifying symmetry of a magnetically ordered phase can pose a challenge, particularly in the presence of small domains. Here we introduce a multimodal approach for determining magnetic structures, which combines symmetry-sensitive optical probes, scattering, and group-theoretical analysis. We apply it to EuIn2As2, a material that has received attention as a candidate axion insulator. While first-principles calculations predict this state on the assumption of a simple collinear antiferromagnetic structure, subsequent neutron-scattering measurements reveal a much more intricate magnetic ground state characterized by two coexisting magnetic wave vectors reached by successive thermal phase transitions. The proposed high- and low-temperature phases are a spin helix and a state with interpenetrating helical and Néel antiferromagnetic order termed a “broken helix,” respectively. Employing a multimodal approach, we identify the magnetic structure associated with these two phases of EuIn2As2. We find that the higher-temperature phase is characterized by a variation of the magnetic moment amplitude from layer to layer, with the moment vanishing entirely in every third Eu layer. The lower-temperature structure is similar to the broken helix, with one important difference: Because of local strain, the relative orientation of the magnetic structure and the lattice is not fixed. Consequently, the symmetry required to protect the axion phase is not generically protected in EuIn2As2, but we show that it can be restored if the magnetic structure is tuned with uniaxial strain. Finally, we present a spin Hamiltonian that identifies the spin interactions that account for the complex magnetic order in EuIn2As2. Our work highlights the importance of a multimodal approach in determining the symmetry of complex order parameters. Published by the American Physical Society 2024