In this paper, we study the propagation of data uncertainty through the marching cubes algorithm for isosurface visualization for correlated uncertain data. Consideration of correlation has been shown paramount for avoiding errors in uncertainty quantification and visualization in multiple prior studies. Although the problem of isosurface uncertainty with spatial data correlation has been previously addressed, there are two major limitations to prior treatments. First, there are no analytical formulations for uncertainty quantification of isosurfaces when the data uncertainty is characterized by a Gaussian distribution with spatial correlation. Second, as a consequence of the lack of analytical formulations, existing techniques resort to a Monte Carlo sampling approach, which is expensive and difficult to integrate into visualization tools. To address these limitations, we present a closed-form framework to efficiently derive uncertainty in marching cubes level-sets for Gaussian uncertain data with spatial correlation (MAGIC). To derive closed-form solutions, we leverage the Hinkley’s derivation on the ratio of Gaussian distributions. With our analytical framework, we achieve a significant speed-up and enhanced accuracy of uncertainty quantification over classical Monte Carlo methods. We further accelerate our analytical solutions using many-core processors to achieve speed-ups up to 585× and integrability with production visualization tools for broader impact. We demonstrate the effectiveness of our correlation-aware uncertainty framework through experiments on meteorology, urban flow, and astrophysics simulation datasets.

Fluid-structure interaction (FSI) simulation of biological systems presents significant computational challenges, particularly for applications involving large structural deformations and contact mechanics, such as heart valve dynamics. Traditional ALE methods encounter fundamental difficulties with such problems due to mesh distortion, motivating immersed techniques. This work presents a novel open-source immersed FSI framework that strategically couples two mature finite element libraries: MFEM, a GPU-ready and scalable library with state-of-the-art parallel performance developed at Lawrence Livermore National Laboratory, and FEBio, a nonlinear finite element solver with sophisticated solid mechanics capabilities designed for biomechanics applications developed at the University of Utah. This coupling creates a unique synergy wherein the fluid solver leverages MFEM's distributed-memory parallelization and pathway to GPU acceleration, while the immersed solid exploits FEBio's comprehensive suite of hyperelastic and viscoelastic constitutive models and advanced solid mechanics modeling targeted for biomechanics applications. FSI coupling is achieved using a fictitious domain methodology with variational multiscale stabilization for enhanced accuracy on under-resolved grids expected with unfitted meshes used in immersed FSI. A fully implicit, monolithic scheme provides robust coupling for strongly coupled FSI characteristic of cardiovascular applications. The framework's modular architecture facilitates straightforward extension to additional physics and element technologies. Several test problems are considered to demonstrate the capabilities of the proposed framework, including a 3D semilunar heart valve simulation. This platform addresses a critical need for open-source immersed FSI software combining advanced biomechanics modeling with high-performance computing infrastructure. 

Scientific data is a critical input into scientific research. Yet the research data landscape is constantly changing as new datasets emerge, others are retired, or some disappear altogether. Data-usage descriptors can substantially advance research productivity by reducing the time that researchers spend finding new and relevant datasets in their research field. This paper describes how to generate data usage descriptors by finding how datasets are used in publications and then linking the dataset information to the publication metadata. It also shows how usage descriptors can be used to find other related datasets and their usage. It concludes by arguing that the approach represents a critical piece of foundational infrastructure that could be deployed in repositories as part of a referenceable, navigable, and contextual data framework. This article contains a reproducible workflow for constructing data-usage descriptors, based on analyzing the full text of publications in the Dimensions database. The illustrative use case is research on food security. The illustrative repository is the National Data Platform.

Modal analysis plays a crucial role in fluid dynamics, offering a powerful tool for reducing the complexity of high-dimensional fluid flow data while extracting meaningful insights into flow physics. This is particularly important in the study of cardiovascular flows, where modal techniques help characterize unsteady flow structures, improve reduced-order modeling, and inform disease diagnosis and rapid medical device design. The most commonly used method, proper orthogonal decomposition (POD), is highly interpretable but suffers from its linearity, which limits its ability to capture nonlinear interactions. In this work, we introduce decomposed sparse modal optimization (DESMO), a nonlinear, adaptive extension of POD that improves the accuracy of flow field reconstruction while requiring fewer modes. We use modern gradient descent-based optimization tools to optimize the spatial modes and temporal coefficients concurrently while using a sparsity-promoting loss term. We demonstrate these on a canonical fluid flow benchmark, flow over a cylinder, a real-world example, blood flow inside a brain aneurysm, and a turbulent channel flow. DESMO can identify spatial modes that resemble higher-order POD modes while uncovering entirely new spatial structures in some cases. Different versions of DESMO can leverage Fourier series for modeling temporal coefficients, an autoencoder for spatial mode optimization, and symbolic regression for discovering differential equations for temporal evolution. Our results demonstrate that DESMO not only provides a more accurate representation of fluid flows but also preserves the interpretability of classical POD by having an analytical modal decomposition equation, offering a promising approach for reduced-order modeling across engineering applications.

Online user studies of visualizations, visual encodings, and interaction techniques are ubiquitous in visualization research. Yet, designing, conducting, and analyzing studies effectively is still a major burden.Although various packages support such user studies, most solutions address only facets of the experiment life cycle, make reproducibility difficult, or do not cater to nuanced study designs or interactions. We introduce reVISit 2, a software framework that supports visualization researchers at all stages of designing and conducting browser-based user studies. ReVISit supports researchers in the design, debug & pilot, data collection, analysis, and dissemination experiment phases by providing both technical affordances (such as replay of participant interactions) and sociotechnical aids (such as a mindfully maintained community of support). It is a proven system that can be (and has been) used in publication-quality studies---which we demonstrate through a series of experimental replications. We reflect on the design of the system via interviews and an analysis of its technical dimensions. Through this work, we seek to elevate the ease with which studies are conducted, improve the reproducibility of studies within our community, and support the construction of advanced interactive studies.

Generative counterfactuals offer a promising avenue for explainable AI in medical imaging, yet ensuring these synthesized images are both anatomically faithful and clinically effective remains a significant challenge. This work presents a domain-specific diffusion framework for generating ”healthy” counterfactuals from chest X-rays with cardiomegaly, underpinned by a systematic metric-guided inference strategy. In contrast to methods relying on static sampling parameters, our approach iteratively explores the inference hyperparameter space to maximize our composite selection criterion, CF Score, that integrates our novel Faithfulness-Effectiveness Trade-off (F ET ) metric.

We extend the evaluation of counterfactual utility beyond simple classification shifts by conducting the simultaneous validation against radiologist annotations and eye-tracking data. Using the REFLACX dataset, we demonstrate that difference maps derived from our counterfactuals exhibit strong spatial alignment with expert visual attention and annotation. Quantified by Normalized Cross-Correlation, Hit Rate, pixel-wise ROC-AUC, and AUC-IoU, our results confirm that metric-guided counterfactuals provide dense and clinically relevant localizations of pathology that closely mirror human diagnostic reasoning.

Time series forecasting is a fundamental tool with wide ranging applications, yet recent debates question whether complex nonlinear architectures truly outperform simple linear models. Prior claims of dominance of the linear model often stem from benchmarks that lack diverse temporal dynamics and employ biased evaluation protocols. We revisit this debate through TimeSynth, a structured framework that emulates key properties of real world time series,including non-stationarity, periodicity, trends, and phase modulation by creating synthesized signals whose parameters are derived from real-world time series. Evaluating four model families Linear, Multi Layer Perceptrons (MLP), Convolutional Neural Networks (CNNs), and Transformers, we find a systematic bias in linear models: they collapse to simple oscillation regardless of signal complexity. Nonlinear models avoid this collapse and gain clear advantages as signal complexity increases. Notably, Transformers and CNN based models exhibit slightly greater adaptability to complex modulated signals compared to MLPs. Beyond clean forecasting, the framework highlights robustness differences under distribution and noise shifts and removes biases of prior benchmarks by using independent instances for train, test, and validation for each signal family. Collectively, TimeSynth provides a principled foundation for understanding when different forecasting approaches succeed or fail, moving beyond oversimplified claims of model equivalence. 

Hyper-differential sensitivity analysis with respect to model discrepancy was recently developed to enable uncertainty quantification for optimization problems. The approach consists of two primary steps: (i) Bayesian calibration of the discrepancy between high- and low-fidelity models, and (ii) propagating the model discrepancy uncertainty through the optimization problem. When high-fidelity model evaluations are limited, as is common in practice, the prior discrepancy distribution plays a crucial role in the uncertainty analysis. However, specification of this prior is challenging due to its mathematical complexity and many hyper-parameters. This article presents a novel approach to specify the prior distribution. Our approach consists of two parts: (1) an algorithmic initialization of the prior hyper-parameters that uses existing data to initialize a hyper-parameter estimate, and (2) a visualization framework to systematically explore properties of the prior and guide tuning of the hyper-parameters to ensure that the prior captures the appropriate range of uncertainty. We provide detailed mathematical analysis and a collection of numerical examples that elucidate properties of the prior that are crucial to ensure uncertainty quantification.

Mean flow (MeanFlow) enables efficient, high-fidelity image generation, yet its single-function evaluation (1-NFE) generation often cannot yield compelling results. We address this issue by introducing RMFlow, an efficient multimodal generative model that integrates a coarse 1-NFE MeanFlow transport with a subsequent tailored noise-injection refinement step. RMFlow approximates the average velocity of the flow path using a neural network trained with a new loss function that balances minimizing the Wasserstein distance between probability paths and maximizing sample likelihood. RMFlow achieves near state-of-the-art results on text-to-image, context-to-molecule, and time-series generation using only 1-NFE, at a computational cost comparable to the baseline MeanFlows. 

The conformational activation of α_IIbβ₃integrin is crucial for platelet aggregation, a central event in hemostasis and thrombosis. Although activation can be triggered by extracellular arginine-glycine-aspartic acid (RGD)-containing ligands as well as mechanical forces, how these biochemical and mechanical cues exactly govern the structural dynamics of α_IIbβ₃remains unclear. Here, using all-atom molecular dynamics simulations, we show that mechanical force and RGD binding promote activation α_IIbβ₃through distinct mechanisms. Mechanical force applied to the RGD-binding site induces long-range, correlated motions of distant parts of the receptor, facilitating head–leg separation. In contrast, RGD binding increases localized, non-correlated fluctuations that weaken leg coordination but do not generate long-range motions. Despite these differences, both cues stabilize the open, extended conformation of α_IIbβ₃. Together, these findings suggest that mechanical and biochemical stimuli play complementary yet distinct roles in integrin conformational activation. A balance between global coordination and local fluctuations likely governs integrin activation in complex environments where the dominance of mechanical or biochemical cues could lead to distinct activation pathways and functional outcomes.

This study validates a finite deformation, nonlinear viscoelastic constitutive model for the collagen matrix of immature bovine articular cartilage, using reactive viscoelasticity. Tissue samples underwent proteoglycan (PG) digestion, losing more than 98% of their initial PG content to increase their hydraulic permeability. To verify that PG-digestion eliminated flow-dependent viscoelasticity, samples were subjected to a gravitational permeation experiment, demonstrating that their hydraulic permeability, k=268±152 mm⁴/N⋅s (n=8), was five orders of magnitude greater than reported for untreated cartilage. Digested cartilage plugs were subjected to unconfined compression stress relaxation (four consecutive 10% strain ramp-hold profiles) to fit the load response and extract material properties (RMSE_fit=1.86±0.61 kPa, n=8). Successful curve-fitting served as a necessary condition for validating the model. Then, a separate unconfined compression stress-relaxation test was performed on the same samples, to 40% compressive strain at the same ramp rate. The model was able to faithfully predict this experimental response using fitted material properties (RMSE_pred=3.95±1.33 kPa, with 0=stresses=155±37 kPa), providing a sufficient condition for validation in unconfined compression stress-relaxation. A computational model showed that flow-independent viscoelasticity of cartilage collagen can enhance the stress response by ~15% at fast strain rates, over flow-dependent effects. However, we estimate from prior studies that flow-independent viscoelasticity may enhance the stress response of cartilage by up to 200%, implying that PGs probably contribute significantly to the tissue?s flow-independent viscoelasticity.

Finite element (FE) simulations emulating transcatheter pulmonary valve (TPV) system deployment in patient-specific right ventricular outflow tracts (RVOT) assume material properties for the RVOT and adjacent tissues. Sensitivity of the deployment to variation in RVOT material properties is unknown. Moreover, the effect of a transannular patch stiffness and location on simulated TPV deployment has not been explored. A sensitivity analysis on the material properties of a patient-specific RVOT during TPV deployment, modeled as an uncoupled HGO material, was conducted using FEBioUncertainSCI. Further, the effects of a transannular patch during TPV deployment were analyzed by considering two patch locations and four patch stiffnesses. Visualization of results and quantification were performed using custom metrics implemented in SlicerHeart and FEBio. Sensitivity analysis revealed that the shear modulus of the ground matrix (c), fiber modulus (k1), and fiber mean orientation angle (gamma) had the greatest effect on 95th %ile stress, whereas only c had the greatest effect on 95th %ile Lagrangian strain. First-order sensitivity indices contributed the greatest to the total-order sensitivity indices. Simulations using a transannular patch revealed that peak stress and strain were dependent on patch location. As stiffness of the patch increased, greater stress was observed at the interface connecting the patch to the RVOT, and stress in the patch itself increased while strain decreased. The total enclosed volume by the TPV device remained unchanged across all simulated patch cases. This study highlights that while uncertainties in tissue material properties and patch locations may influence functional outcomes, FE simulations provide a reliable framework for evaluating these outcomes in TPVR. 

READMEs shape first impressions of software projects, yet what constitutes a good README varies across audiences and contexts. Research software needs reproducibility details, while open-source libraries might prioritize quick-start guides. Through a design probe, LintMe, we explore how linting can be used to improve READMEs given these diverse contexts, aiding style and content issues while preserving authorial agency. Users create context-specific checks using a lightweight DSL that uses a novel combination of programmatic operations (e.g., for broken links) with LLM-based content evaluation (e.g., for detecting jargon), yielding checks that would be challenging for prior linters. Through a user study (N=11), comparison with naive LLM usage, and an extensibility case study, we find that our design is approachable, flexible, and well matched with the needs of this domain. This work opens the door for linting more complex documentation and other culturally mediated text-based documents.

Synchrotron facilities like the Cornell High Energy Synchrotron Source (CHESS) generate massive data volumes from complex beamline experiments, but face challenges such as limited access time, the need for on-site experiment monitoring, and managing terabytes of data per user group. We present the design, deployment, and evaluation of a framework that addresses CHESS's data acquisition and management issues. Deployed on a secure CHESS server, our system provides real time, web-based tools for remote experiment monitoring and data quality assessment, improving operational efficiency. Implemented across three beamlines (ID3A, ID3B, ID4B), the framework managed 50-100 TB of data and over 10 million files in late 2024. Testing with 43 research groups and 86 dashboards showed reduced overhead, improved accessibility, and streamlined data workflows. Our paper highlights the development, deployment, and evaluation of our framework and its transformative impact on synchrotron data acquisition.

Computing is an indispensable component of nearly all technologies and is ubiquitous for vast segments of society. It is also essential to discoveries and innovations in most disciplines. However, while past grand challenges in science have involved computing as one of the tools to address the challenge, these challenges have not been principally about computing. Why has the computing community not yet produced challenges at the scale of grandeur that we see in disciplines such as physics, astronomy, or engineering? How might we go about identifying similarly grand challenges? What are the grand challenges of computing that transcend our discipline's traditional boundaries and have the potential to dramatically improve our understanding of the world and positively shape the future of our society?

There is a significant benefit in us, as a field, taking a more intentional approach to "grand challenges." We are seeking challenge problems that are sufficiently compelling as to both ignite the imagination of computer scientists and draw researchers from other disciplines to computational challenges.

This paper emphasizes the importance, now more than ever, of defining and pursuing grand challenges in computing as a field, and being intentional about translation and realizing its impacts on science and society. Building on lessons from prior grand challenges, the paper explores the nature of a grand challenge today emphasizing both scale and impact, and how the community may tackle such a grand challenge, given a rapidly changing innovation ecosystem in computing. The paper concludes with a call to action for our community to come together to define grand challenges in computing for the next decade and beyond. 

Serverless computing is observing rapid adoption in cloud computing platforms. Prior works have extensively focused on improving the performance of serverless computing platforms via “keeping alive” functions in memory proactively to lower the function execution latency, but the potential environmental sustainability aspects of such performance-enhancing strategies remain underexplored. This work highlights that serverless computing introduces unique carbon footprint sources and trade-offs between performance and sustainability. We present LowCarb, a novel reinforcement learning-based solution that co-optimizes serverless function performance and carbon footprint. LowCarb effectively quantifies and resolves the inherent conflict between performance and sustainability to achieve results within 15% of optimality.

Volume rendering techniques for scientific visualization has recently shifted toward Monte Carlo (MC) methods for their flexibility and robustness, but their use in multi-channel visualization remains underexplored. Traditional multi-channel volume rendering often relies on arbitrary, non-physically-based color blending functions that hinder interpretation. We introduce multi-density Woodcock tracking, a simple extension of Woodcock tracking that leverages an MC method to produce high-fidelity, physically grounded multi-channel renderings without arbitrary blending. By generalizing Woodcock's distance tracking, we provide a unified blending modality that also integrates blending functions from prior works. We further implement effects that enhance boundary and feature recognition. By accumulating frames in real-time, our approach delivers high-quality visualizations with perceptual benefits, demonstrated on diverse datasets. 

A substantial body of health research demonstrates a strong link between neighborhood environments and health outcomes. Recently, there has been increasing interest in leveraging advances in computer vision to enable large-scale, systematic characterization of neighborhood built environments. However, the generalizability of vision models across fundamentally different domains remains uncertain, for example, transferring knowledge from ImageNet to the distinct visual characteristics of Google Street View (GSV) imagery. In applied fields such as social health research, several critical questions arise: which models are most appropriate, whether to adopt unsupervised training strategies, what training scale is feasible under computational constraints, and how much such strategies benefit downstream performance. These decisions are often costly and require specialized expertise.

In this paper, we answer these questions through empirical analysis and provide practical insights into how to select and adapt foundation models for datasets with limited size and labels, while leveraging larger, unlabeled datasets through unsupervised training. Our study includes comprehensive quantitative and visual analyses comparing model performance before and after unsupervised adaptation.