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.

Contribution statements are an increasingly common way to make research labor visible, reduce academic malfeasance, and provide broader transparency. Despite this potential value, they remain uncommon in visualization and HCI. To explore this gap, we conducted an online study with (N=21) visualization and HCI researchers. We find a range of differing opinions about the utility of contribution statements, which are set against a background of tensions relating to contribution frameworks that inadequately fit contribution types in HCI and especially visualization, power dynamics between authors, bias in authorship perceptions, and the tedium of providing yet another form of documentation. From these factors, we offer a modest recommendation to authors: consider contribution statements. There are contexts when they may usefully explicate work, and others where they can cause author-team conflict or become a burdensome chore. Regardless of whether they are used by readers or not, we suggest that scaffolded mechanisms for reflecting on contribution roles are valuable both for public accountability and internal alignment. To institutions, we recommend that contribution statements be exempt from page or word limits, and that flexible templates and examples be provided to authors, but that they continue to not be required. By surfacing these perspectives, we seek to open a dialogue about what constitutes authorship, how our community might move toward more equitable and transparent attribution practices, and where the visualization and HCI communities might be uniquely equipped to help.

High-quality Late Gadolinium Enhancement (LGE) MRI can be helpful for atrial fibrillation management, yet scan quality is frequently compromised by patient motion, irregular breathing, and suboptimal image acquisition timing. While Multiple Instance Learning (MIL) has emerged as a powerful tool for automated quality assessment under weak supervision, current state-of-the-art methods map localized visual evidence to a single, opaque global feature vector. This black box approach fails to provide actionable feedback on specific failure modes, obscuring whether a scan degrades due to motion blur, inadequate contrast, or a lack of anatomical context. In this paper, we propose Adversarial Concept-MIL (AC-MIL), a weakly supervised framework that decomposes global image quality into clinically defined radiological concepts using only volume-level supervision. To capture latent quality variations without entangling predefined concepts, our framework incorporates an unsupervised residual branch guided by an adversarial erasure mechanism to strictly prevent information leakage. Furthermore, we introduce a spatial diversity constraint that penalizes overlap between distinct concept attention maps, ensuring localized and interpretable feature extraction. Extensive experiments on a clinical dataset of atrial LGE-MRI volumes demonstrate that AC-MIL successfully opens the MIL black box, providing highly localized spatial concept maps that allow clinicians to pinpoint the specific causes of non-diagnostic scans. Crucially, our framework achieves this deep clinical transparency while maintaining highly competitive ordinal grading performance against existing baselines. Code to be released on acceptance.

Heavy ball momentum accelerates gradient descent optimization methods, especially within the realm of machine learning. One standard way of employing momentum is with a fixed hyperparameter, which needs much tuning. Relying on a one-size-fits-all hyper-parameter is likely to keep the method from reaching optimum performance. This paper advances a new adaptive momentum to resolve this problem and take the burden of parameter tuning away from the user, inspired by the ideal setting of heavy-ball momentum for quadratics. Our momentum can fully maintain the stability of SGD and Adam with large learning rates so that it converges faster and generalizes better compared to the standard SGD and Adam. We demonstrate the efficacy of the method on various machine learning benchmark tasks, including image classification, language modeling, machine translation, tabular learning, and time-series classification. We also provide convergence guarantees for SGD and Adam with our adaptive momentum.

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. 

Transcatheter arterial embolization (TAE) is a widely used therapy for treating tumors and vascular abnormalities. A major limitation is the reflux of embolic particles, which can damage healthy tissue and reduce treatment efficacy. Despite technological advances, optimizing particle delivery while minimizing reflux remains a significant challenge. In this work, a novel dual-lumen catheter embolization strategy is proposed. A four-way coupled and multiphase computational fluid dynamics and Lagrangian particle tracking framework in OpenFOAM is used to investigate particle transport and embolization dynamics in idealized hepatic artery geometries. Multiple catheter configurations were evaluated under pulsatile blood flow: the Standard End-Hole Microcatheter (SEHM), catheters with one and three sets of side holes, and a dual-lumen design. An equivalent electrical circuit model was implemented to represent embolization-induced resistance, enabling outlet-specific flow redistribution with progressive occlusion. Results highlight the hemodynamic interactions between blood and saline during embolization and demonstrate that reflux primarily occurs in later stages, as target vessel occlusion intensifies. Side holes on the catheter improve delivery only when injection flow rates are increased to offset leakage, whereas the dual-lumen design enhances delivery efficiency at lower flow rates. This study demonstrates the utility of advanced computational models to assist in the design of microcatheters to minimize or eliminate particle reflux and off-target embolization.

A large body of research shows that the built environment significantly shapes household vehicle miles traveled (VMT), yet little is known about how these effects may differ between electric vehicles (EVs) and conventional vehicles. As EV adoption accelerates, understanding these dynamics is increasingly important for sustainable transportation planning. This study uses data from the 2023 Utah Household Travel Survey to compare how built environmental characteristics influence VMT across vehicle types. A two-stage hurdle framework, implemented with Gradient Boosting Decision Trees (GBDT), is applied to address zero-inflated outcomes and capture nonlinear relationships. Results show that built environment attributes play a larger role in EV adoption and usage, whereas socioeconomic factors remain more influential for conventional vehicles. In particular, income strongly affects EV adoption but has little effect on EV usage, while it remains an important determinant of both adoption and usage for conventional vehicles. The analysis also reveals notable nonlinear effects of the built environment on VMT. For example, the marginal influence of land-use entropy on EV adoption plateaus at a threshold of 0.85, and the effect of transit stop density on EV usage declines sharply before stabilizing at approximately 20 stops per square mile. For planning and policy implementation, these findings highlight the importance of aligning EV infrastructure with local land use conditions and expanding adoption incentives to reach broader households. Overall, the study provides empirical evidence to inform integrated land use and transportation strategies that facilitate sustainable transportation systems.

Scalar fields derived from 3D X-ray CT scans of samples undergoing ex situ processes, such as thermal aging, chemical etching, or mechanical stress, pose unique challenges for characterizing similarities and differences across acquisitions. Typically, a sample A (source) is imaged, removed, and subjected to experimental conditions that alter its microstructure, and then re-imaged as sample B (target) to study the resulting changes. Direct comparison between A and B is rendered impractical if not impossible for current techniques because the challenges of physical and morphological changes are compounded by the effects of geometric misalignment, differences in reconstruction parameters, discretization artifacts, and changes in acquisition settings such as position, beam intensity, or exposure time. To overcome these challenges, we introduce a geometry-rich topological representation that uses the hierarchical Morse complex to capture the structural relationships among regions segmented within each sample and shape descriptors to characterize their metric properties. With this data structure, we cast the similarity problem as a sequence of optimizations, each minimizing differences in structure and geometry at a given resolution. The sequence of optimizations begins by aligning the fine-scale segmentations of the two samples. Following optimization minimizes differences across incrementally coarser levels, producing a fully synchronized hierarchical representation of the two samples. In addition, we introduce a visualization framework that enables interactive exploration and manual editing of the matched hierarchies, thereby allowing an expert user to further improve the quality of the comparison. We apply our workflow to characterize changes in grain structure for energetic materials undergoing aging, match segmentations for materials under different stress conditions, and perform image registration that outperforms state-of-the-art techniques.

Camera-only 3D object detection has emerged as a cost-effective and scalable alternative to LiDAR for autonomous driving, yet existing methods primarily prioritize overall performance while overlooking the severe long-tail imbalance inherent in real-world datasets. In practice, many rare but safety-critical categories such as children, strollers, or emergency vehicles are heavily underrepresented, leading to biased learning and degraded performance. This challenge is further exacerbated by pronounced inter-class ambiguity (e.g., visually similar subclasses) and substantial intra-class diversity (e.g., objects varying widely in appearance, scale, pose, or context), which together hinder reliable long-tail recognition. In this work, we introduce SemLT3D, a Semantic-Guided Expert Distillation framework designed to enrich the representation space for underrepresented classes through semantic priors. SemLT3D consists of: (1) a language-guided mixture-of-experts module that routes 3D queries to specialized experts according to their semantic affinity, enabling the model to better disentangle confusing classes and specialize on tail distributions; and (2) a semantic projection distillation pipeline that aligns 3D queries with CLIP-informed 2D semantics, producing more coherent and discriminative features across diverse visual manifestations. Although motivated by long-tail imbalance, the semantically structured learning in SemLT3D also improves robustness under broader appearance variations and challenging corner cases, offering a principled step toward more reliable camera-only 3D perception.

We present RaFI, a CUDA and MPI based software framework that simplifies the task of building GPU-enabled data-parallel software where rays or similar work items need to migrate between different GPUs. RaFI provides a simple interface for CUDA kernels to forward such work items to other GPUs, while under the hood managing all the CUDA and MPI related work required to make this happen. We describe RaFI's motivation and implementation, and show its potential in several example applications.

Generative models that transport a simple source distribution to a complex data distribution—such as diffusion and flow-based models—are central to high-fidelity data generation. Test-time guidance can further steer pretrained models toward user-specified high-reward regions without costly retraining. However, existing guidance methods face critical limitations: they struggle with non-differentiable rewards, fail to navigate complex landscapes, and often lack theoretical guarantees on generation performance. We propose Source Parallel Tempering (SPT), a gradient-free test-time guidance framework that operates entirely in source space, leveraging its simpler geometry to avoid the complexities of the data manifold. SPT couples a local exploration kernel with parallel tempering, enabling efficient barrier crossing and robust discovery of high-reward modes. Theoretically, we provide a new error bound linking training-time approximation error to test-time guidance performance. Empirically, SPT significantly improves over state-of-the-art methods on benchmark tasks in conditional image synthesis and dynamical system trajectory sampling.

Purpose: Valvular heart disease is prevalent and a major contributor to heart failure. Valve leaflet strain is a promising metric for evaluating the mechanics underlying the initiation and progression of valvular pathology. However, generalizable methods for noninvasively quantifying valvular strain from clinically acquired patient images remain limited. This study aims to develop a robust feature tracking framework that enables accurate shape matching across variable valve morphologies and quantification of in vivo atrioventricular leaflet strain from three-dimensional echocardiographic (3DE) images in pediatric and adult patients. Methods We developed a geometric feature tracking framework to quantify in vivo leaflet strain from 3DE images and to assess anatomical deformation across the cardiac cycle. Our approach integrates a cohort-derived geometric reference atlas to establish geometric correspondence and introduces a novel distance-weighted coherent point drift algorithm within a Gaussian mixture model framework for non-rigid registration. We evaluated performance against a finite element benchmark model and compared the approach with conventional point-based tracking methods. The framework was applied to pediatric and adult patient datasets (N = 31) to assess robustness across variable valve morphologies. Results The proposed method demonstrated greater accuracy in quantifying anatomical alignment and leaflet strain than conventional point-based approaches. Validation against the finite element benchmark confirmed improved strain estimation. The framework achieved reliable inter-phase tracking of valve deformation across diverse morphologies in pediatric and adult patients. Analysis identified a consistent distribution pattern of the principal strain associated with leaflet billow (prolapse). Conclusion This feature tracking framework provides a generalizable method for noninvasive quantification of atrioventricular valve leaflet strain from clinical 3DE images. Characterization of biomechanical strain patterns may improve prognostic assessment and support longitudinal evaluation of valvular heart disease. Further investigation of the biomechanical signatures of heart valve disease has the potential to enhance prognostic assessment and longitudinal evaluation of valvular heart disease.

We extend earlier international efforts to optimise hexahedral-based spectral element methods on GPUs and vectorised CPUs to mixed element meshes additionally involving prismatic, pyramidic, and tetrahedral shapes using tensorial expansions. We demonstrate that common finite element operators (such as the mass and Helmholtz matrices) benefit from alternative implementation strategies depending on the element shape, choice of polynomial order, and system architecture in order to achieve optimal performance. In addition, we introduce a new approach/interpretation to efficiently evaluate more complex operations involving inner products with the derivative of the expansions as part of the integrand such as the stiffness matrix. This approach seeks to maximise operations using the collocation properties of the nodal tensorial expansion associated with classical quadrature rules. Our GPU performance tests demonstrate that the throughput of the Helmholtz operator on tetrahedral elements is at most 2.5 times slower than on hexahedral elements, despite tetrahedra having a factor of six greater floating-point operations.