Generative AI has demonstrated significant potential in creative design, enabling the rapid generation of visual content and imaginative concepts. Although deep AI models achieve effective featurization in the latent space, navigating the space remains a challenge. Current techniques, such as GANSlider and SliderSpace, use multiple sliders to generate high-dimensional vectors in generative AI's latent space. Despite applying (global) PCA to reduce the number of sliders, these approaches struggle with scalability and usability as the number of control dimensions increases. In this paper, we introduce LatentGandr, a visual analytics technique that facilitates latent space exploration by extracting locally linear dimensions from embeddings in high-dimensional latent spaces. By analyzing the topology and local curvature of the embeddings, LatentGandr automatically identifies local neighborhoods and computes their principal components using localized PCA. These local principal components are visualized as interactive image grids, allowing users to efficiently explore and control the generative process, providing an intuitive means to refine the generation of novel content and concepts. To evaluate the effectiveness of LatentGandr, we conducted a study comparing it to GANSlider, the current state-of-the-art visualization interface for generative AI models. The results offer insights into how localized exploration techniques can enhance user interaction with these models.

Neural networks are used as generative surrogate models for scientific discovery, which are trainable approximations of scientific simulations. These models enable users to replace time-consuming numerical simulations with learned alternatives, providing quick solutions. However, high-fidelity generative surrogate models require massive training datasets, which can create storage and I/O challenges. Lossy compression is a promising way to reduce this burden, but compression errors may affect the model quality in subtle ways, making it challenging to quantify their impact. In this work, we examine how lossy compression of training data impacts the quality of generative surrogate models. We begin by characterizing the uncertainty inherent in training neural networks, showing that identical training configurations can produce different models. By exploiting this variability, we propose a method to estimate how much compression-induced error a surrogate model can tolerate without affecting its accuracy. Evaluation of two application simulations demonstrates that our approach significantly reduces memory/storage requirements and speeds up training while producing high-quality surrogate models. These results show that lossy compression saves data storage up to 23.7x and 39x with negligible impact on the quality of the surrogate model. Meanwhile, reducing the size of the training data set also enhances the data loading speed and reduces the training time by up to 3x.

Idling Vehicle Detection (IVD) seeks to determine, at the final frame of a video clip, whether any vehicle is idling, meaning the vehicle is stationary with its engine running, using synchronized video from a remote surveillance camera and multichannel audio captured by spatially distributed wireless microphones along the roadside. Prior full-image, clip-level fusion approaches tend to overfit scene background and full-frame context, produce unstable temporal decisions, and lack an explicit spatial prior to align vehicles with microphones, which makes them brittle under domain shift and data inefficient. Instead, we introduce TAVR-IVD, an audio-visual framework guided by multi-object tracking. Our method detects vehicles, links detections into tracklets, and classifies each vehicle by operating on its tracklet. This design raises the effective signal-to-noise ratio, stabilizes temporal decisions through tracklets, enforces an explicit spatial prior to align vehicles with microphones, and adapts across domains with limited calibration annotations while remaining detector agnostic and efficient. To evaluate deployment robustness, we further curate two evaluation extensions, AVIVD-LT and AVIVD-M, covering inter-day and cross-site shifts.

Computational biomechanics increasingly requires models that combine mechanics, transport, chemistry, and biological regulation across different spatial and temporal scales. The FEBio simulation software (Finite Elements for Biomechanics and Biophysics) provides extensive open-source capabilities for modeling these processes using monolithic approaches. However, assembling independently developed physics models into reproducible coupled workflows remains challenging. Existing approaches often require custom scripts or external software pipelines, which can limit model reuse and complicate development. We present FUSE, the FEBio Unified Simulation and Exchange framework, a partitioned coupling plugin that enables separately defined FEBio models to communicate through structured field exchange. FUSE is designed for problems that are best solved independently, particularly when fast mechanical responses influence slower biological or chemical evolution. The framework uses a time-decoupled strategy in which a primary model advances on the longer time scale, while one or more secondary models are repeatedly initialized, supplied with updated fields, solved over shorter time horizons, with results returned to the primary model. Field exchange utilizes existing FEBio data maps, output fields, and user-specified filters, allowing coupled workflows to be constructed without modifying the underlying solvers. The framework was able to reproduce reference coupled solutions while handling bidirectional transfer, spatial field mapping, and filtered exchange of model variables. Example applications demonstrated coupling between mechanical loading and chemical degradation in injured cartilage and interaction between biological tissue formation and mechanical feedback during bone healing. By separating coupling logic from physics implementation, FUSE provides a practical mechanism for building maintainable multiphysics workflows within FEBio.

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.

Ventricular tachycardia is a life-threatening rhythm disorder and a major cause of sudden cardiac death. Pace-mapping is a clinical procedure for identifying the intervention target during catheter ablation of VT. It requires clinicians to pace different sites in the ventricles and rapidly interpret the resulting electrocardiograms to determine where to pace next or whether a target site has been identified. Active learning AI models have been proposed to guide clinicians to the next pacing site, showing promise in reducing the number of pacing sites and improving the efficiency of pace-mapping. Existing methods require retraining each target without the ability to transfer knowledge across multiple VTs within the same patient or across patients. We introduce cAPM for continuous AI-assisted pace-mapping to capture and transfer knowledge accumulated from past pace-mapping data to reduce the number of pace-mapping data needed for future target VTs. This is made possible by a task-agnostic surrogate neural network that learns the mapping from pacing sites to 12-lead ECG morphology, an active-learning strategy that refines this surrogate model by selecting the most informative pacing site for each target, and a continual learning strategy to do so sequentially while retaining knowledge from prior targets. Evaluated on an in-silico testbed consisting of sequentially-presented localization tasks across different physiological conditions and ventricular geometries, cAPM with and without replay of past data samples achieved an 81% probability of localizing within clinical tolerance (5 mm accuracy) using 4.5 pace-mapping sites, compared to the state-of-the-art active-learning method achieving 38% probability using 13.7 pacing sites. These results provide a strong basis for preparing cAPM towards in-vivo preclinical and clinical studies where it can be used to guide pace-mapping.

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.

We are witnessing urgent events, including extreme weather events, wildfires, natural disasters, cyberattacks, pandemics, and infrastructure failures, with increasing frequency and impact. At the same time, technological advances and strategic investments have resulted in the pervasive availability of a continuum of unparalleled computational and data capabilities, services, and expertise, which, if harnessed, make it possible to move beyond ad hoc judgments to respond to such events and enable rigorous, data-driven decision making to save lives, protect property, and safeguard public health. Urgent science refers to science that supports rigorous, data-driven decision making in response to emergencies while effectively managing inherent uncertainties. Urgent computing refers to computing that supports urgent science. It can be defined as computing under strict time and quality constraints to support decision making with the desired confidence, within a specified time interval. The goal is to effectively leverage a diverse set of computing and data resources distributed across the end-to-high-performance computing (HPC)/cloud digital continuum to detect events, develop responses, and trigger actions. Several computational science and engineering research challenges underlie the vision of urgent science and the realization of urgent computing. This special issue of Computing in Science & Engineering focuses on articles that address various aspects of urgent computing and urgent science, including use cases, mathematical foundations, computational frameworks and services, policy and governance structures, and end-to-end experiences.

Predicting the risk of distant metastasis from primary tumor tissue histology is a critical yet challenging task in computational pathology. Multiple Instance Learning (MIL) approaches can attend to subdomains in tumor regions that harbor features of metastatic cancer progression. However MIL models treat tissue patches as unordered bags, discarding the spatial layout that defines the metastatic potential. We propose that metastatic risk is inherently dictated by the geometric arrangement of the tumor microenvironment at the interface with tumor cells. Our model is designed to explicitly capture the spatial relationships between tumor cells, tumor associated fibroblasts and infiltrating lymphocytes. For this purpose, we propose Distance aware Tissue Modeling for Multiple Instance Learning(DTMf-MIL), a novel method that reinforces visual features with explicit spatial priors. By computing signed distance functions (SDF) relative to tissue phenotypes, our model learns to recognize structural signatures of metastatic risk. This geometric awareness translates directly to superior clinical performance as DTMf-MIL significantly outperforms state-of-the-art methods that ignore spatial layout on metastasis prediction from tissue in the primary tumor. We further validate our approach on public benchmarks, demonstrating that spatial awareness consistently improves diagnostic accuracy across diverse clinical tasks.

A new release of ChemNetworks (Journal of Computational Chemistry, 2013, 35, 495-505), is described. Updates have made the software performant in supercomputing environments, capable of real-time graph construction and analysis with running simulations, and incorporate graph theory libraries for diverse and customizable analysis workflows. Here, we describe newly implemented algorithms (and examples) in ChemNetworks that include a recursive Z-matrix algorithm for structure identification and graph construction, the incorporation of the DataSpaces data staging framework as one of its optional I/O engines, and user-contributed graph theory analyses that leverage the igraph library. The new release of ChemNetworks better enables ongoing development through community contributions.

Annotation is a central mechanism in visualization design that enables people to communicate key insights. Prior research has provided essential accounts of the visual forms annotations take, but less attention has been paid to the decisions behind them. This paper examines how annotations are designed in practice and how educators reflect on those practices. We conducted a two-phase qualitative study: interviews with ten practitioners from diverse backgrounds revealed the heuristics they draw on when creating annotations, and interviews with seven visualization educators offered complementary perspectives situated within broader concerns of clarity, guidance, and viewer agency. These studies provide a systematic account of annotation design knowledge in professional settings, highlighting the considerations, trade-offs, and contextual judgments that shape the use of annotations. By making this tacit expertise explicit, our work complements prior form-focused studies, strengthens understanding of annotation as a design activity, and points to opportunities for improved tool and guideline support.

Annotation is among the most demanding visualization tasks to automate, as it simultaneously requires correctly navigating visual, semantic, and stylistic constraints. Failure to meet any of these conditions severely undermines the utility of an annotation, rendering it challenging to read, inaccurate, or visually discordant. Despite a growing body of annotation tools and automations, no existing benchmark or evaluation framework tests whether these conditions are met because of their scope and annotation not being the focus of their studies. We introduce AnnoBench, a benchmark for visualization annotation that materializes the inherent challenges of this domain in a structured and testable manner. AnnoBench pairs visualizations from professional data journalism and visualization galleries with annotation tasks, spanning four representation formats, five chart description conditions, and two prompt specification levels. The benchmark is executed via VLM-as-a-judge, using models aligned with manual human assessment. We evaluate the benchmark via four one-factor-at-a-time experiments, exploring the effects of input representation, semantic context, and prompt specificity, and model selection on annotation quality. This work provides a foundation for advancing annotation automation, tooling, and visualization-generation pipelines.

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. 

Time-varying implicit neural representations (INRs) provide a compact representation of scientific volumes and, for modalities such as dynamic X-ray computed tomography (CT), are often the only practical way to represent the data. However, interactive volume rendering of INRs is challenging, as cheap memory lookups are replaced by expensive neural inferences, hindering the performance. Therefore, conventional volume rendering methods such as ray marching with dense sampling are often impractical. While resampling, caching, and retraining can mitigate this cost, they compromise convenience and accuracy and become impractical for time-varying data. We tackle these challenges using a query-efficient stochastic volume rendering framework based on delta tracking. Our system employs a four-stage pipeline that exploits heterogeneous parallelism, using ray tracing cores for traversal and tensor cores for batched neural evaluation. Furthermore, we present strategies to reduce INR queries via ray budgeting and query pruning, thereby increasing per-frame performance. Using our renderer, many time-varying INRs can be rendered directly from their original representation. The system achieves ~30-40 FPS at 1024x1024 resolution on an RTX 4090 GPU and converges to high-fidelity images. Moreover, the system enables interactive temporal exploration of the continuous domain, with timestep updates taking approximately 1-2 ms.

Asynchronous many-task (AMT) runtime systems have become an important paradigm for expressing fine-grained parallelism and managing asynchrony in high-performance computing (HPC). Originating from early dataflow concepts, AMTs have evolved to enable dynamic task generation, explicit dependency management, and asynchronous execution, facilitating the overlap of computation and communication. These capabilities address the limitations of traditional bulk-synchronous models, such as those employed in MPI+X, which can struggle with irregular, adaptive, or data-driven workloads. This survey provides a comprehensive overview of representative distributed AMT systems—including Charm++, HPX, Legion, PaRSEC, Uintah, Chapel, and StarPU—focusing on their design principles, execution models, and runtime mechanisms for scheduling, communication, and synchronization. We examine how these systems tackle key challenges such as load imbalance, runtime overheads, programmability, and performance portability. In addition, the paper discusses application domains where AMTs have demonstrated tangible benefits and highlights the conditions under which their use is most advantageous. The goal of this survey is to equip researchers and practitioners with a clear understanding of distributed AMT models and to provide guidance for selecting and applying the most suitable runtime system for specific computational objectives.

Deep learning has achieved remarkable success in deformable image registration, yet the visual information that drives deformation estimation remains poorly understood. Rather than pursuing incremental performance improvements, this work investigates the fundamental source of robustness in deep registration models. Using diverse, domain-agnostic synthetic datasets, we decouple deformation learning from application-specific appearance and show that domain-shift immunity is an inherent, largely architecture-agnostic property of deep deformable registration when trained with a robust pipeline. To identify the mechanism underlying this immunity, we compare models trained directly on raw image intensities with models operating exclusively on local feature representations extracted by a fixed, pre-defined feature extractor. The comparable performance of these models provides strong empirical evidence that deformation estimation is governed primarily by local structural features, rather than global, domain-specific appearance cues. These findings offer a principled explanation for the cross-domain generalizability of deep registration networks and point toward feature-centric designs for domain-independent registration.