Recent developments in multimodal large language models (MLLM) have equipped language models to reason about vision and language jointly. This permits MLLMs to both perceive and answer questions about data visualization across a variety of designs and tasks. Applying MLLMs to a broad range of visualization tasks requires us to properly evaluate their capabilities, and the most common way to conduct evaluation is through measuring a model’s visualization reasoning capability, analogous to how we would evaluate human understanding of visualizations (e.g., visualization literacy). However, we found that in the context of visualization question answering (VisQA), how an MLLM perceives and reasons about visualizations can be fundamentally different from how humans approach the same problem. During the evaluation, even without visualization, the model could correctly answer a substantial portion of the visualization test questions, regardless of whether any selection options were provided. We hypothesize that the vast amount of knowledge encoded in the language model permits factual recall that supersedes the need to seek information from the visual signal. It raises concerns that the current VisQA evaluation may not fully capture the models’ visualization reasoning capabilities. To address this, we propose a comprehensive sanity check framework that integrates a rule-based decision tree and a sanity check table to disentangle the effects of ”seeing” (visual processing) and ”recall” (reliance on prior knowledge). This validates VisQA datasets for evaluation, highlighting where models are truly ”seeing”, positively or negatively affected by the factual recall, or relying on inductive biases for question answering. Our study underscores the need for careful consideration in designing future visualization understanding studies when utilizing MLLMs.

Idling vehicle detection (IVD) uses surveillance video and multichannel audio to localize and classify vehicles in the last frame as moving, idling, or engine-off in pick-up zones. IVD faces three challenges: (i) modality heterogeneity between visual cues and audio patterns; (ii) large box scale variation requiring multi-resolution detection; and (iii) training instability due to coupled detection heads. The previous end-to-end (E2E) model [1] with simple CBAM-based [2] bi-modal attention fails to handle these issues and often misses vehicles. We propose HAVT-IVD, a heterogeneity-aware network with a visual feature pyramid and decoupled heads. Experiments show HAVT-IVD improves mAP by 7.66 over the disjoint baseline and 9.42 over the E2E baseline.

Predicting particle trajectories with neural networks (NNs) has substantially enhanced many scientific and engineering domains. However, effectively quantifying and visualizing the inherent uncertainty in predictions remains challenging. Without an understanding of the uncertainty, the reliability of NN models in applications where trustworthiness is paramount is significantly compromised. This paper introduces the uncertainty tube, a novel, computationally efficient visualization method designed to represent this uncertainty in NN-derived particle paths. Our key innovation is the design and implementation of a superelliptical tube that accurately captures and intuitively conveys nonsymmetric uncertainty. By integrating well-established uncertainty quantification techniques, such as Deep Ensembles, Monte Carlo Dropout (MC Dropout), and Stochastic Weight Averaging-Gaussian (SWAG), we demonstrate the practical utility of the uncertainty tube, showcasing its application on both synthetic and simulation datasets.

Idling vehicle detection (IVD) uses surveillance video and multichannel audio to localize and classify vehicles in the last frame as moving, idling, or engine-off in pick-up zones. IVD faces three challenges: (i) modality heterogeneity between visual cues and audio patterns; (ii) large box scale variation requiring multi-resolution detection; and (iii) training instability due to coupled detection heads. The previous end-to-end (E2E) model with simple CBAM-based bi-modal attention fails to handle these issues and often misses vehicles. We propose HAVT-IVD, a heterogeneity-aware network with a visual feature pyramid and decoupled heads. Experiments show HAVT-IVD improves mAP by 7.66 over the disjoint baseline and 9.42 over the E2E baseline. 

While many of recent electoral reforms in the U.S. states have been centered on more restrictive legislation such as a higher standard of voter ID law, the call for expansive legal reforms has also drawn increasing attention. Among the expansive legislative proposals, arguably the most controversial one is to make voting compulsory. As Massachusetts learned from Australia and adopted the secret ballot in 1888, the Australian compulsory voting system which was enacted in 1924 has also been proposed recently by some U.S. legislators at the state level. The major supporters of compulsory voting in the U.S. have mainly come from the political left whose agendas have met vigorous opposition from the political right. Many criticisms are based on the assumptions of its beneficial effects on the voters at the margins whose voting participation has been historically lower than those of more well-to-do, informed voters, which would suggest a larger advantage for the Democratic Party. No empirical research, however, has shown such a possible Democratic advantage if compulsory voting were adopted. The main reason for no prior empirical support was because compulsory voting has never been implemented in the U.S. at the national level, and no previous studies have had comprehensive tools at hand to handle many layers and complexities in predicting the outcomes of compulsory voting at both the national and state levels. This article takes advantage of the most recent machine learning tools to narrow this empirical gap significantly. The ANES data spanning presidential elections from 1948 to 2020 provide the most comprehensive and unprecedented big data sets to test whether it is indeed the Democratic Party that would reap more benefits in compulsory voting at both the national and state levels. Our starting point is to use a state-of-the-art adaptation of the classic logit regression – elastic-net regression – for both our data-driven feature selection and modeling tasks. Furthermore, based on an innovative Transfer Component Analysis (TCA) approach added to our modeling pipeline, our machine learning prediction model shows that the effect of compulsory voting does benefit overall the Democratic Party at the national level. At the state level, however, the Republican Party can also achieve many positive electoral impacts within the context of compulsory voting.

Digital twins are developed to model the behavior of a specific physical asset (or twin), and they can consist of high-fidelity physics-based models or surrogates. A highly accurate surrogate is often preferred over multi-physics models as they enable forecasting the physical twin future state in real-time. To adapt to a specific physical twin, the digital twin model must be updated using in-service data from that physical twin. Here, we extend Gaussian process (GP) models to include derivative data, for improved accuracy, with dynamic updating to ingest physical twin data during service. Including derivative data, however, comes at a prohibitive cost of increased covariance matrix dimension. We circumvent this issue by using a sparse GP approximation, for which we develop extensions to incorporate derivatives. Numerical experiments demonstrate that the prediction accuracy of the derivative-enhanced sparse GP method produces improved models upon dynamic data additions. Lastly, we apply the developed algorithm within a DT framework to model fatigue crack growth in an aerospace vehicle. 

Air quality impacts on human health are an increasing concern globally. Vehicle pollution is a particular concern because of its multiple adverse health effects, and discretionary vehicle idling contributes significantly to local-scale poor air quality. This study introduces a novel approach to traditional static (unchanging) anti-idling signage. Here, we demonstrate a system, called SmartAir, that provides dynamic social-norm messages to drivers coupled with information about idling status or vehicle emissions in the area. A machine learning algorithm with audio and video inputs determines vehicle idling status. Vehicle emissions are measured using a suite of low-cost air quality nodes. In this study, we show that the SmartAir system reduces idling time by 28.0% and local CO2 concentrations by 29.5% compared with background.

Numerical methods for radiative transfer play a key role in modern-day astrophysics and cosmology, including study of the inhomogeneous reionization process. In this context, ray tracing methods are well-regarded for accuracy but notorious for high computational cost. In this work, we extend the capabilities of the Nyx N-body / hydrodynamics code, coupling radiation to gravitational and gas dynamics. We formulate adaptive ray tracing as a novel series of filters and transformations that can be used with AMReX particle abstractions, simplifying implementation and enabling portability across Exascale GPU architectures. To address computational cost, we present a new algorithm for merging sources, which significantly accelerates computation once reionization is well underway. Furthermore, we develop a novel prescription for geometric overlap correction with low-density neighbor cells. We perform verification and validation against standard analytic and numerical test problems. Finally, we demonstrate scaling to up to 1024 nodes and 4096 GPUs running multiphysics cosmological simulations, with 4096^3 Eulerian gas cells, 4096^3 dark matter particles, and ray tracing on a 1024^3 coarse grid. For these full cosmological simulations, we demonstrate convergence in terms of reionization history and post-ionization Lyman-alpha forest flux. 

Data visualizations are typically not accessible to blind and low-vision (BLV) users. Automatically generating text descriptions offers an enticing mechanism for democratizing access to the information held in complex scientific charts, yet appropriate procedures for generating those texts remain elusive. Pursuing this issue, we study a single complex chart form: UpSet plots. UpSet Plots are a common way to analyze set data, an area largely unexplored by prior accessibility literature. By analyzing the patterns present in real-world examples, we develop a system for automatically captioning any UpSet plot. We evaluated the utility of our captions via semi-structured interviews with (N=11) BLV users and found that BLV users find them informative. In extensions, we find that sighted users can use our texts similarly to UpSet plots and that they are better than naive LLM usage.

Given the constantly increasing number of newly published vulnerabilities, manually assessing their scores (e.g., under the Common Vulnerability Scoring System) has become unfeasible. Recently, learning-based systems have been proposed to automatically predict vulnerability scores. Such systems use vulnerability indexing databases to train deep learning algorithms. However, their practical applicability has important limitations, including a high dependency on the quality and diversity of training data, and high computational requirements. In addition, vulnerability descriptions often do not follow the standard templates and are not rich enough with respect to the expected features. In this paper, we propose a novel architecture that takes advantage of both generative artificial intelligence and lightweight deep learning techniques to provide an efficient and effective solution for automated vulnerability scoring. Data extracted from the National Vulnerability Dataset is fed into a large language model layer, whose output (i.e., an augmented dataset) is then used in a lightweight fine-tuned BERTsmall layer. We provide the results of an extensive experimental assessment of the effect of both each layer of the architecture and end-to-end performances. The results suggest that the combination of GPT3.5-Turbo and BERTsmall provides the most effective accuracy-time trade-off. We also compare the performance of the proposed architecture with other LLMs, BERT models, and cutting-edge approaches. The results show good improvements in prediction quality also when compared to a recent technique that incorporates data from 66 different sources, including the NVD.

Data wrangling - the process of cleaning, transforming, and preparing data for analysis - is a well-known bottleneck in data science workflows. Existing tools either rely on manual scripting, which is error-prone and hard to debug, or automate cleaning through opaque black-box pipelines that offer limited control. We present Buckaroo, a scalable visual data wrangling system that restructures data preparation as a direct manipulation task over visualizations. Buckaroo enables users to explore and repair data anomalies - such as missing values, outliers, and type mismatches - by interacting directly with coordinated data visualizations. The system extensibly supports user-defined error detectors and wranglers, tracks provenance for undo/redo, and generates reproducible scripts for downstream tasks. Buckaroo maintains efficient indexing data structures and differential storage to localize anomaly detection and minimize recomputation. To demonstrate the applicability of our model, Buckaroo is integrated with the \textitHopara pan-and-zoom engine, which enables multi-layered navigation over large datasets without sacrificing interactivity. Through empirical evaluation and an expert review, we show that Buckaroo makes visual data wrangling scalable - bridging the gap between visual inspection and programmable repairs. 

Deep neural networks (DNNs) and Kolmogorov-Arnold networks (KANs) are popular methods for function approximation due to their flexibility and expressivity. However, they typically require a large number of trainable parameters to produce a suitable approximation. Beyond making the resulting network less transparent, overparameterization creates a large optimization space, likely producing local minima in training that have quite different generalization errors. In this case, network initialization can have an outsize impact on the model's out-of-sample accuracy. For these reasons, we propose shallow universal polynomial networks (SUPNs). These networks replace all but the last hidden layer with a single layer of polynomials with learnable coefficients, leveraging the strengths of DNNs and polynomials to achieve sufficient expressivity with far fewer parameters. We prove that SUPNs converge at the same rate as the best polynomial approximation of the same degree, and we derive explicit formulas for quasi-optimal SUPN parameters. We complement theory with an extensive suite of numerical experiments involving SUPNs, DNNs, KANs, and polynomial projection in one, two, and ten dimensions, consisting of over 13,000 trained models. On the target functions we numerically studied, for a given number of trainable parameters, the approximation error and variability are often lower for SUPNs than for DNNs and KANs by an order of magnitude. In our examples, SUPNs even outperform polynomial projection on non-smooth functions. 

Background: Google Street View (GSV) images offer a unique and scalable alternative to in-person audits for examining neighborhood built environment characteristics. Additionally, most prior neighborhood studies have relied on cross-sectional designs.

Objective: This study aimed to use GSV images and computer vision to examine longitudinal changes in the built environment, demographic shifts, and health outcomes in Washington, DC, from 2014 to 2019.

Methods: In total, 434,115 GSV images were systematically sampled at 100 m intervals along primary and secondary road segments. Convolutional neural networks, a type of deep learning algorithm, were used to extract built environment features from images. Census tract summaries of the neighborhood built environment were created. Multilevel mixed-effects linear models with random intercepts for years and census tracts were used to assess associations between built environment changes and health outcomes, adjusting for covariates, including median age, percentage male, percentage Hispanic, percentage African American, percentage college educated, percentage owner-occupied housing, and median household income.

Results: Washington, DC, experienced a shift toward higher-density housing, with non-single-family homes rising from 66% to 72% of the housing stock. Single-lane roads increased from 37% to 42%, suggesting a shift toward more sustainable and compact urban forms. Gentrification trends were reflected in a rise in college-educated residents (16%-41%), a US $17,490 increase in the median household income, and a US $159,600 increase in property values. Longitudinal analyses revealed that increased construction activity was associated with lower rates of obesity, diabetes, high cholesterol, and cancer, while growth in non-single-family housing was correlated with reductions in the prevalence of obesity and diabetes. However, neighborhoods with higher proportions of African American residents experienced reduced construction activity.

Conclusions: Washington, DC, has experienced significant urban transformation, marked by substantial changes in neighborhood built environments and demographic shifts. Urban development is associated with reduced prevalence of chronic conditions. These findings highlight the complex interplay between urban development, demographic changes, and health, underscoring the need for future research to explore the broader impacts of neighborhood built environment changes on community composition and health outcomes. GSV imagery, along with advances in computer vision, can aid in the acceleration of neighborhood studies.

Efficient workload forecasting is a key enabler of modern AIOps (Artificial Intelligence for IT Operations), supporting proactive and autonomous resource management across the computing continuum, from edge environments to large-scale cloud infrastructures. In this paper, we propose a Temporal Transformer architecture for CPU utilization prediction, designed to capture both short-term fluctuations and long-range temporal dependencies in workload dynamics. The model is first pretrained on a large-scale Microsoft Azure VM dataset and subsequently fine-tuned on the Alibaba container dataset, enabling effective transfer learning across heterogeneous virtualization environments. Experimental results demonstrate that the proposed approach achieves high predictive accuracy while maintaining a compact model size and inference times compatible with real-time operation. Qualitative analyses further highlight the model’s ability to reproduce workload patterns with high fidelity. These findings indicate that the proposed Temporal Transformer constitutes a lightweight and accurate forecasting component for next-generation AIOps pipelines, suitable for deployment across both cloud and edge intelligence scenarios.

This work focuses on visualizing uncertainty of local divergence of two-dimensional vector fields. Divergence is one of the fundamental attributes of fluid flows, as it can help domain scientists analyze potential positions of sources (positive divergence) and sinks (negative divergence) in the flow. However, uncertainty inherent in vector field data can lead to erroneous divergence computations, adversely impacting downstream analysis. While Monte Carlo (MC) sampling is a classical approach for estimating divergence uncertainty, it suffers from slow convergence and poor scalability with increasing data size and sample counts. Thus, we present a two-fold contribution that tackles the challenges of slow convergence and limited scalability of the MC approach. (1) We derive a closed-form approach for highly efficient and accurate uncertainty visualization of local divergence, assuming independently Gaussian-distributed vector uncertainties. (2) We further integrate our approach into Viskores, a platform-portable parallel library, to accelerate uncertainty visualization. In our results, we demonstrate significantly enhanced efficiency and accuracy of our serial analytical (speed-up up to $1946 \times$) and parallel Viskores (speed-up up to 19698X) algorithms over the classical serial MC approach. We also demonstrate qualitative improvements of our probabilistic divergence visualizations over traditional mean-field visualization, which disregards uncertainty. We validate the accuracy and efficiency of our methods on wind forecast and ocean simulation datasets.

The growing resolution and volume of climate data from remote sensing and simulations pose significant storage, processing, and computational challenges. Traditional compression or subsampling methods often compromise data fidelity, limiting scientific insights. We introduce a scalable ecosystem that integrates hierarchical multiresolution data management, intelligent transmission, and ML-assisted reconstruction to balance accuracy and efficiency. Our approach reduces storage and computational costs by 99%, lowering expenses from 100,00 to 24 while maintaining a Root Mean Square (RMS) error of 1.46 degrees Celsius. Our experimental results confirm that even with significant data reduction, essential features required for accurate climate analysis are preserved. Validated on petascale NASA climate datasets, this solution enables cost-effective, high-fidelity climate analysis for research and decision-making.

The massive data generated by scientists daily serve as both a major catalyst for new discoveries and innovations, as well as a significant roadblock that restricts access to the data. Our paper introduces a new approach to removing big data barriers and democratizing access to petascale data for the broader scientific community. Our novel data fabric abstraction layer allows user-friendly querying of scientific information while hiding the complexities of dealing with file systems or cloud services. We enable FAIR (Findable, Accessible, Interoperable, and Reusable) access to datasets such as NASA's petascale climate datasets. Our paper presents an approach to managing, visualizing, and analyzing petabytes of data within a browser on equipment ranging from the top NASA supercomputer to commodity hardware like a laptop. Our novel data fabric abstraction utilizes state-of-the art progressive compression algorithms and machinelearning insights to power scalable visualization dashboards for petascale data. The result provides users with the ability to identify extreme events or trends dynamically, expanding access to scientific data and further enabling discoveries. We validate our approach by improving the ability of climate scientists to visually explore their data via three fully interactive dashboards. We further validate our approach by deploying the dashboards and simplified training materials in the classroom at a minorityserving institution. These dashboards, released in simplified form to the general public, contribute significantly to a broader push to democratize the access and use of climate data. 

Technological advances and rapid service deployments have resulted in a pervasive and interconnected computing continuum, which is enabling new classes of application formulations and workflows, delivering novel services to consumers, and is becoming a core engine for discovery, innovation, and economic growth. The computing continuum is also unleashing new system and application management challenges, which must be addressed before its potential and promises are truly realized. Autonomic computing can provide the abstractions and mechanisms essential to effectively harnessing the computing continuum, but must evolve to address these new challenges. This paper is a call to action for rebooting autonomics to enable us to harness the computing continuum.