Understanding how dynamic patterns vary across large spatiotemporal ensembles is essential in many scientific domains. In fluid dynamics, for instance, researchers analyze how splash patterns in droplet impact experiments change with physical parameters such as fluid type or impact velocity. These experiments produce large volumes of data where patterns differ in size, shape, and duration, making manual analysis tedious and error-prone. Recently, interactive visualization approaches have been developed to assist analysis using learned similarity models for pattern-based querying. However, they assume fixed-size inputs and only support single-pattern queries, thus limiting their effectiveness for multiscale, multi-pattern analysis and exploration of ensembles. In this paper, we present a visual analysis approach for the interactive exploration of spatiotemporal ensembles through multiscale pattern querying. Our approach extends an existing similarity model to support variable-sized patterns, allowing users to define queries by selecting examples directly on visualized data. Coordinated views enable interactive querying, comparison, and analysis of pattern occurrences and relate pattern occurrences to ensemble parameters. A guidance mechanism supports the user in finding underexplored regions. We demonstrate the utility of our approach on synthetic and real-world datasets. Domain expert feedback confirms that the approach is intuitive, easy to use, and effective for revealing parameter-pattern relationships.

Visual cluster analysis is a central task to explore multidimensional data. Dimensionality Reduction (DR) techniques support this task by spatializing multidimensional (MD) data similarities as point patterns in scatterplots. However, unavoidable false and missing neighbor distortions limit their accuracy. For instance, false neighbors make truly separated data clusters appear to overlap in the layout, while missing neighbors split true clusters into falsely separated groups. In general, both types of distortions exist in DR layouts except for orthogonal linear projections (OLP) that only generate false neighbors. In this work, we propose Isometric Seriation-based Dimensionality Reductions (ISilDR) that provably generate at most missing neighbors. We study how ISilDR and OLP together could be leveraged to discover true MD clusters. An ISilDR first creates a seriation of the MD data points, i.e., an ordering along a one-dimensional projection axis, and then each pair of consecutive points along this axis is spaced by their MD distance. An $m$D ISilDR can be obtained by combining $m$ 1D ISilDRs. We study the theoretical and empirical characteristics of different variants of ISilDRs and OLPs and propose a systematic and formal analysis based on ε-neighborhood graphs. From there, we derive rules to discover cluster patterns in MD data from interactive linking of ISilDR and OLP coordinated layouts. We then conduct case studies and illustrate scenarios for trustworthy visual cluster analysis using a combination of ISilDR and other classical DR techniques.

The computation of a potentially visible set (PVS) can accelerate many computer graphics algorithms, such as framerate upsampling, streaming rendering, global illumination, and multi-fragment effects. Algorithms for from-region PVS have an inherently high complexity. Previous from-region PVS algorithms propagate occlusion through the scene in a front-to-back manner and are order-dependent, which places bounds on parallelism and restricts execution speed. We introduce the disocclusion buffer, which operates on a sparse, layered representation of the scene with quantized depth. In this representation, we invert the traditional PVS problem formulation and explicitly compute disocclusion rather than occlusion. Disocclusion can be computed in parallel in an order-independent manner, overcoming the main bottleneck in traditional PVS computation. Our PVS algorithm is over six times faster than the previous state of the art at the same level of accuracy in a direct comparison. It runs in shaders on the GPU without requiring any hardware extensions. We demonstrate how our work outperforms previous PVS algorithms in the range of supported camera motion without compromising quality.

Abstract Varying the input parameters of simulations or experiments often leads to different classes of results. Parameter sensitivity analysis in this context includes estimating the sensitivity to the individual parameters, that is, to understand which parameters contribute most to changes in output classifications and for which parameter ranges these occur. We propose a novel visual parameter sensitivity analysis approach based on Voronoi cell interfaces between the sample points in the parameter space to tackle the problem. The Voronoi diagram of the sample points in the parameter space is first calculated. We then extract Voronoi cell interfaces which we use to quantify the sensitivity to parameters, considering the class label information of each sample's corresponding output. Multiple visual encodings are then utilized to represent the cell interface transitions and class label distribution, including stacked graphs for local parameter sensitivity. We evaluate the approach's expressiveness and usefulness with case studies for synthetic and real‐world datasets.

In the early days of perceptual image quality research more than 30 years ago, the multidimensionality of distortions in perceptual space was considered important. However, research focused on scalar quality as measured by mean opinion scores. With our work, we intend to revive interest in this relevant area by presenting a first pilot dataset of annotated triplet comparisons for image quality assessment. It contains one source stimulus together with distorted versions derived from 7 distortion types at 12 levels each. Our crowdsourced and curated dataset contains roughly 50,000 responses to 7,000 triplet comparisons. We show that the multidimensional embedding of the dataset poses a challenge for many established triplet embedding algorithms. Finally, we propose a new reconstruction algorithm, dubbed logistic triplet embedding (LTE) with Tikhonov regularization. It shows promising performance. This study helps researchers to create larger datasets and better embedding techniques for multidimensional image quality. The dataset includes images and ratings and can be accessed at https://github.com/jenadeleh/multidimensionalIQA-dataset/tree/main.

We investigate reading strategies for node-link diagrams that wrap around the boundaries in a flattened torus topology by examining eye tracking data recorded in a previous controlled study. Prior work showed that torus drawing affords greater flexibility in clutter reduction than traditional node-link representations, but impedes link-and-path exploration tasks, while repeating tiles around boundaries aids comprehension. However, it remains unclear what strategies users apply in different wrapping settings. This is important for design implications for future work on more effective wrapped visualizations for network applications, and cyclic data that could benefit from wrapping. We perform visual-exploratory data analysis of gaze data, and conduct statistical tests derived from the patterns identified. Results show distinguishable gaze behaviors, with more visual glances and transitions between areas of interest in the non-replicated layout. Full-context has more successful visual searches than partial-context, but the gaze allocation indicates that the layout could be more space-efficient.

The development of new pseudo-random number generators (PRNGs) has steadily increased over the years. Commonly, PRNGs' randomness is "measured" by using statistical pass/fail suite tests, but the question remains, which PRNG is the best when compared to others. Existing randomness tests lack means for comparisons between PRNGs, since they are not quantitatively analysing. It is, therefore, an important task to analyze the quality of randomness for each PRNG, or, in general, comparing the randomness property among PRNGs. In this paper, we propose a novel visual approach to analyze PRNGs randomness allowing for a ranking comparison concerning the PRNGs' quality. Our analysis approach is applied to ensembles of time series which are outcomes of different PRNG runs. The ensembles are generated by using a single PRNG method with different parameter settings or by using different PRNG methods. We propose a similarity metric for PRNG time series for randomness and apply it within an interactive visual approach for analyzing similarities of PRNG time series and relating them to an optimal result of perfect randomness. The interactive analysis leads to an unsupervised classification, from which respective conclusions about the impact of the PRNGs' parameters or rankings of PRNGs on randomness are derived. We report new findings using our approach in a study of randomness for state-of-the-art numerical PRNGs such as LCG, PCG, SplitMix, Mersenne Twister, and RANDU as well as chaos-based PRNG families such as K-Logistic map and K-Tent map with varying parameter K.

Abstract To understand how topology shapes the dynamics in excitable networks is one of the fundamental problems in network science when applied to computational systems biology and neuroscience. Recent advances in the field discovered the influential role of two macroscopic topological structures, namely hubs and modules. We propose a visual analytics approach that allows for a systematic exploration of the role of those macroscopic topological structures on the dynamics in excitable networks. Dynamical patterns are discovered using the dynamical features of excitation ratio and co‐activation. Our approach is based on the interactive analysis of the correlation of topological and dynamical features using coordinated views. We designed suitable visual encodings for both the topological and the dynamical features. A degree map and an adjacency matrix visualization allow for the interaction with hubs and modules, respectively. A barycentric‐coordinates layout and a multi‐dimensional scaling approach allow for the analysis of excitation ratio and co‐activation, respectively. We demonstrate how the interplay of the visual encodings allows us to quickly reconstruct recent findings in the field within an interactive analysis and even discovered new patterns. We apply our approach to network models of commonly investigated topologies as well as to the structural networks representing the connectomes of different species. We evaluate our approach with domain experts in terms of its intuitiveness, expressiveness, and usefulness.