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1. A Gallery of Gaussian Periods

   Eischen, Ellen; Garcia, Stephan (July 2020, Proceedings of Bridges 2020: Mathematics, Art, Music, Architecture, Education, Culture)

   Gaussian periods are certain sums of roots of unity whose study dates back to Gauss’s seminal work in algebra and number theory. Recently, large scale plots of Gaussian periods have been revealed to exhibit striking visual patterns, some of which have been explored in the second named author’s prior work. In 2020, the first named author produced a new app, Gaussian Periods, which allows anyone to create these plots much more efficiently and at a larger scale than before. In this paper, we introduce Gaussian periods, present illustrations created with the new app, and summarize how mathematics controls some visual features, including colorings left unexplained in earlier work. 

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2. QPV: An Input Control Component For Progressive Visualization Analytics [Work-in-progress]

   Zhang, Xin; Eldawy, Ahmed (August 2024, The VLDB Endowment)

   Progressive visual analytics enable data scientists to efficiently explore large datasets and examine progressive results with low latency. Most progressive visualization frameworks use a progressive query processing module that controls the quality of the results and then feeds these results into a visualization module. The goal is to avoid poor-quality progressive results which could mislead data scientists. This method misses some optimization opportunities as it improves the quality of the intermediate result while ignoring how this result affects the final visualization. This work presents a work-in-progress quality-aware progressive visualization input control component, named QPV. The key idea of the proposed framework is to integrate the visualization module into the progressive query results so that the quality control takes into account the final visualization. With limited computational resources, QPV solves an optimization problem to allocate resources and alleviate the misleading effects in the progressive plots. 

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3. Kiviat Defense: An Empirical Evaluation of Visual Encoding Effectiveness in Multivariate Data Similarity Detection

   https://doi.org/10.2352/J.ImagingSci.Technol.2023.67.6.060406  

   Mantovani, Mirko; Wentzel, Andrew; Trabucco, Juan Trelles; Michaelis, Joseph; Elisabeta_Marai, G (November 2023, Journal of Imaging Science and Technology)

   Similarity detection seeks to identify similar, but distinct items over multivariate datasets. Often, similarity cannot be defined computationally, leading to a need for visual analysis, such as in cases with ensemble, computational, patient cohort, or geospatial data. In this work, we empirically evaluate the effectiveness of common visual encodings for multivariate data in the context of visual similarity detection. We conducted a user study with 40 participants to measure similarity detection performance and response time under moderate scale (16 items) and large scale (36 items). Our analysis shows that there are significant differences in performance between encodings, especially as the number of items increases. Surprisingly, we found that juxtaposed star plots outperformed superposed parallel coordinate plots. Furthermore, color-cues significantly improved response time, and attenuated error at larger scales. In contrast to existing guidelines, we found that filled star plots (Kiviats) outperformed other encodings in terms of scalability and error. 

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4. Usability Assessment of Voice-Enabled Technologies for Users with Visual Disabilities

   https://doi.org/10.1177/10711813241260395  

   Kim, Hyung_Nam (August 2024, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   Voice-enabled technologies such as VoiceOver (screen reader) and the Seeing AI app (image recognition) have revolutionized daily tasks for people with visual disabilities, fostering greater independence and information access. However, a gap remains in understanding the user experience (UX) of these technologies. This study investigated how those with visual disabilities interacted with VoiceOver and the Seeing AI app. A convenience sample of eight participants with visual disabilities engaged in direct observations while using these technologies. The study utilized the System Usability Scale (SUS) to assess perceived usability and analyzed findings using descriptive statistics. Results indicated a poorer UX with VoiceOver compared to the Seeing AI app, with challenges identified in graphical user interfaces (GUIs), voice and gesture commands. Relevant recommendations were made to enhance usability. The study emphasizes the need for more intuitive GUIs and optimized voice/gesture interactions for users with visual disabilities. 

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5. Magnetic shielding paints an accurate and easy-to-visualize portrait of aromaticity

   https://doi.org/10.1039/D1CC03701C  

   Karadakov, Peter B.; VanVeller, Brett (September 2021, Chemical Communications)

   Chemists are trained to recognize aromaticity semi-intuitively, using pictures of resonance structures and Frost-Musulin diagrams, or simple electron-counting rules such as Hückel's 4 n + 2/4 n rule. To quantify aromaticity one can use various aromaticity indices, each of which is a number reflecting some experimentally measured or calculated molecular property, or some feature of the molecular wavefunction, which often has no visual interpretation or may not have direct chemical relevance. We show that computed isotropic magnetic shielding isosurfaces and contour plots provide a feature-rich picture of aromaticity and chemical bonding which is both quantitative and easy-to-visualize and interpret. These isosurfaces and contour plots make good chemical sense as at atomic positions they are pinned to the nuclear shieldings which are experimentally measurable through chemical shifts. As examples we discuss the archetypal aromatic and antiaromatic molecules of benzene and square cyclobutadiene, followed by modern visual interpretations of Clar's aromatic sextet theory, the aromaticity of corannulene and heteroaromaticity. 

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