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The computational notebook serves as a versatile tool for data analysis. However, its conventional user interface falls short of keeping pace with the ever-growing data-related tasks, signaling the need for novel approaches. With the rapid development of interaction techniques and computing environments, there is a growing interest in integrating emerging technologies in data-driven workflows. Virtual reality, in particular, has demonstrated its potential in interactive data visualizations. In this work, we aimed to experiment with adapting computational notebooks into VR and verify the potential benefits VR can bring. We focus on the navigation and comparison aspects as they are primitive components in analysts' workflow. To further improve comparison, we have designed and implemented a Branching&Merging functionality. We tested computational notebooks on the desktop and in VR, both with and without the added Branching&Merging capability. We found VR significantly facilitated navigation compared to desktop, and the ability to create branches enhanced comparison. 

Interactive dimensionality reduction helps analysts explore the high dimensional data based on their personal needs and domain-specific problems. Recently, expressive nonlinear models are employed to support these tasks. However, the interpretation of these human steered nonlinear models during human-in-the-loop analysis has not been explored. To address this problem, we present a new visual explanation design called semantic explanation. Semantic explanation visualizes model behaviors in a manner that is similar to users’ direct projection manipulations. This design conforms to the spatial analytic process and enables analysts better understand the updated model in response to their interactions. We propose a pipeline to empower interactive dimensionality reduction with semantic explanation using counterfactuals. Based on the pipeline, we implement a visual text analytics system with nonlinear dimensionality reduction powered by deep learning via the BERT model. We demonstrate the efficacy of semantic explanation with two case studies of academic article exploration and intelligence analysis. 

Current techniques for characterizing cybersickness (visually induced motion sickness) in virtual environments rely on qualitative questionnaires. For interactive graphics to create visual experiences that enhance the illusion of presence while mitigating cybersickness, interactive measures are needed to characterize cybersickness. In this paper, we acquire EEG signals from participants as they experience vection-induced cybersickness and compare those signals to a baseline. Our study shows that there is a correlation between the participant-reported cybersickness (as measured by movements of a joystick) and brain EEG signals. Through independent component analysis, we separate those signals which are a result of cybersickness from other sources (such as eye blinks). Our user study finds that there is a highly correlative and statistically significant Delta- (1.0–4.0 Hz), Theta- (4.0–7.0 Hz), and Alpha-wave (7.0–13.0 Hz) increase associated with cybersickness in immersive virtual environments across participants. Establishing a strong correlation between cybersickness and EEG-measured brain activity provides us with the first step toward interactively characterizing and mitigating cybersickness in virtual environments.