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Now showing 1 - 5 of 5
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    Rendering and Extracting Extremal Features in 3D Fields
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Kindlmann, Gordon L.; Chiw, Charisee; Huynh, Tri; Gyulassy, Attila; Reppy, John; Bremer, Peer-Timo; Jeffrey Heer and Heike Leitte and Timo Ropinski
    Visualizing and extracting three-dimensional features is important for many computational science applications, each with their own feature definitions and data types. While some are simple to state and implement (e.g. isosurfaces), others require more complicated mathematics (e.g. multiple derivatives, curvature, eigenvectors, etc.). Correctly implementing mathematical definitions is difficult, so experimenting with new features requires substantial investments. Furthermore, traditional interpolants rarely support the necessary derivatives, and approximations can reduce numerical stability. Our new approach directly translates mathematical notation into practical visualization and feature extraction, with minimal mental and implementation overhead. Using a mathematically expressive domain-specific language, Diderot, we compute direct volume renderings and particlebased feature samplings for a range of mathematical features. Non-expert users can experiment with feature definitions without any exposure to meshes, interpolants, derivative computation, etc. We demonstrate high-quality results on notoriously difficult features, such as ridges and vortex cores, using working code simple enough to be presented in its entirety.
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    AVA: Towards Autonomous Visualization Agents through Visual Perception-Driven Decision-Making
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Liu, Shusen; Miao, Haichao; Li, Zhimin; Olson, Matthew; Pascucci, Valerio; Bremer, Peer-Timo; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
    With recent advances in multi-modal foundation models, the previously text-only large language models (LLM) have evolved to incorporate visual input, opening up unprecedented opportunities for various applications in visualization. Compared to existing work on LLM-based visualization works that generate and control visualization with textual input and output only, the proposed approach explores the utilization of the visual processing ability of multi-modal LLMs to develop Autonomous Visualization Agents (AVAs) that can evaluate the generated visualization and iterate on the result to accomplish user-defined objectives defined through natural language. We propose the first framework for the design of AVAs and present several usage scenarios intended to demonstrate the general applicability of the proposed paradigm. Our preliminary exploration and proof-of-concept agents suggest that this approach can be widely applicable whenever the choices of appropriate visualization parameters require the interpretation of previous visual output. Our study indicates that AVAs represent a general paradigm for designing intelligent visualization systems that can achieve high-level visualization goals, which pave the way for developing expert-level visualization agents in the future.
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    Exploring High-Dimensional Structure via Axis-Aligned Decomposition of Linear Projections
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Thiagarajan, Jayaraman J.; Liu, Shusen; Ramamurthy, Karthikeyan Natesan; Bremer, Peer-Timo; Jeffrey Heer and Heike Leitte and Timo Ropinski
    Two-dimensional embeddings remain the dominant approach to visualize high dimensional data. The choice of embeddings ranges from highly non-linear ones, which can capture complex relationships but are difficult to interpret quantitatively, to axis-aligned projections, which are easy to interpret but are limited to bivariate relationships. Linear project can be considered as a compromise between complexity and interpretability, as they allow explicit axes labels, yet provide significantly more degrees of freedom compared to axis-aligned projections. Nevertheless, interpreting the axes directions, which are often linear combinations of many non-trivial components, remains difficult. To address this problem we introduce a structure aware decomposition of (multiple) linear projections into sparse sets of axis-aligned projections, which jointly capture all information of the original linear ones. In particular, we use tools from Dempster-Shafer theory to formally define how relevant a given axis-aligned project is to explain the neighborhood relations displayed in some linear projection. Furthermore, we introduce a new approach to discover a diverse set of high quality linear projections and show that in practice the information of k linear projections is often jointly encoded in ~ k axis-aligned plots. We have integrated these ideas into an interactive visualization system that allows users to jointly browse both linear projections and their axis-aligned representatives. Using a number of case studies we show how the resulting plots lead to more intuitive visualizations and new insights.
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    Leveraging Topological Events in Tracking Graphs for Understanding Particle Diffusion
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) McDonald, Torin; Shrestha, Rebika; Yi, Xiyu; Bhatia, Harsh; Chen, De; Goswami, Debanjan; Pascucci, Valerio; Turbyville, Thomas; Bremer, Peer-Timo; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
    Single particle tracking (SPT) of fluorescent molecules provides significant insights into the diffusion and relative motion of tagged proteins and other structures of interest in biology. However, despite the latest advances in high-resolution microscopy, individual particles are typically not distinguished from clusters of particles. This lack of resolution obscures potential evidence for how merging and splitting of particles affect their diffusion and any implications on the biological environment. The particle tracks are typically decomposed into individual segments at observed merge and split events, and analysis is performed without knowing the true count of particles in the resulting segments. Here, we address the challenges in analyzing particle tracks in the context of cancer biology. In particular, we study the tracks of KRAS protein, which is implicated in nearly 20% of all human cancers, and whose clustering and aggregation have been linked to the signaling pathway leading to uncontrolled cell growth. We present a new analysis approach for particle tracks by representing them as tracking graphs and using topological events –- merging and splitting, to disambiguate the tracks. Using this analysis, we infer a lower bound on the count of particles as they cluster and create conditional distributions of diffusion speeds before and after merge and split events. Using thousands of time-steps of simulated and in-vitro SPT data, we demonstrate the efficacy of our method, as it offers the biologists a new, detailed look into the relationship between KRAS clustering and diffusion speeds.
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    Interactive Investigation of Traffic Congestion on Fat-Tree Networks Using TREESCOPE
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Bhatia, Harsh; Jain, Nikhil; Bhatele, Abhinav; Livnat, Yarden; Domke, Jens; Pascucci, Valerio; Bremer, Peer-Timo; Jeffrey Heer and Heike Leitte and Timo Ropinski
    Parallel simulation codes often suffer from performance bottlenecks due to network congestion, leaving millions of dollars of investments underutilized. Given a network topology, it is critical to understand how different applications, job placements, routing schemes, etc., are affected by and contribute to network congestion, especially for large and complex networks. Understanding and optimizing communication on large-scale networks is an active area of research. Domain experts often use exploratory tools to develop both intuitive and formal metrics for network health and performance. This paper presents TREESCOPE, an interactive, web-based visualization tool for exploring network traffic on large-scale fat-tree networks. TREESCOPE encodes the network topology using a tailored matrix-based representation and provides detailed visualization of all traffic in the network. We report on the design process of TREESCOPE, which has been received positively by network researchers as well as system administrators. Through case studies of real and simulated data, we demonstrate how TREESCOPE's visual design and interactive support for complex queries on network traffic can provide experts with new insights into the occurrences and causes of congestion in the network.