Immersive analytics is gaining attention across multiple domains due to its capability to facilitate intuitive data analysis in expansive environments through user interaction with data. However, creating immersive analytics systems for specific tasks is challenging due to the need for programming expertise and significant development effort. Despite the introduction of various immersive visualization authoring toolkits, domain experts still face hurdles in adopting immersive analytics into their workflow, particularly when faced with dynamically changing tasks and data in real time. To lower such technical barriers, we introduce XROps, a web-based authoring system that allows users to create immersive analytics applications through interactive visual programming, without the need for low-level scripting or coding. XROps enables dynamic immersive analytics authoring by allowing users to modify each step of the data visualization process with immediate feedback, enabling them to build visualizations on-the-fly and adapt to changing environments. It also supports the integration and visualization of real-time sensor data from XR devices-a key feature of immersive analytics-facilitating the creation of various analysis scenarios. We evaluated the usability of XROps through a user study and demonstrate its efficacy and usefulness in several example scenarios.

In the rapidly evolving field of deep learning, traditional methodologies for designing models predominantly rely on code-based frameworks. While these approaches provide flexibility, they create a significant barrier to entry for non-experts and obscure the immediate impact of architectural decisions on model performance. In response to this challenge, recent no-code approaches have been developed with the aim of enabling easy model development through graphical interfaces. However, both traditional and no-code methodologies share a common limitation that the inability to predict model outcomes or identify issues without executing the model. To address this limitation, we introduce an intuitive visual feedback-based no-code approach to visualize and analyze deep learning models during the design phase. This approach utilizes dataflow-based visual programming with dynamic visual encoding of model architecture. A user study was conducted with deep learning developers to demonstrate the effectiveness of our approach in enhancing the model design process, improving model understanding, and facilitating a more intuitive development experience. The findings of this study suggest that real-time architectural visualization significantly contributes to more efficient model development and a deeper understanding of model behaviors.

Neurons have a polarized structure, with dendrites and axons, and compartment-specific functions can be affected by the dwelling mitochondria. Recent studies have shown that the morphology of mitochondria is closely related to the functions of neurons and neurodegenerative diseases. However, the conventional mitochondria analysis workflow mainly relies on manual annotations and generic image-processing software. Moreover, even though there have been recent developments in automatic mitochondria analysis using deep learning, the application of existing methods in a daily analysis remains challenging because the performance of a pretrained deep learning model can vary depending on the target data, and there are always errors in inference time, requiring human proofreading. To address these issues, we introduce MitoVis, a novel visualization system for end-to-end data processing and an interactive analysis of the morphology of neuronal mitochondria. MitoVis introduces a novel active learning framework based on recent contrastive learning, which allows accurate fine-tuning of the neural network model. MitoVis also provides novel visual guides for interactive proofreading so that users can quickly identify and correct errors in the result with minimal effort. We demonstrate the usefulness and efficacy of the system via case studies conducted by neuroscientists. The results show that MitoVis achieved up to 13.3× faster total analysis time in the case study compared to the conventional manual analysis workflow.

Dendritic spines are dynamic, submicron-scale protrusions on neuronal dendrites that receive neuronal inputs. Morphological changes in the dendritic spine often reflect alterations in physiological conditions and are indicators of various neuropsychiatric conditions. However, owing to the highly dynamic and heterogeneous nature of spines, accurate measurement and objective analysis of spine morphology are major challenges in neuroscience research. Most conventional approaches for analyzing dendritic spines are based on two-dimensional (2D) images, which barely reflect the actual three-dimensional (3D) shapes. Although some recent studies have attempted to analyze spines with various 3D-based features, it is still difficult to objectively categorize and analyze spines based on 3D morphology. Here, we propose a unified visualization framework for an interactive 3D dendritic spine analysis system, DXplorer, that displays 3D rendering of spines and plots the high-dimensional features extracted from the 3D mesh of spines. With this system, users can perform the clustering of spines interactively and explore and analyze dendritic spines based on high-dimensional features. We propose a series of high-dimensional morphological features extracted from a 3D mesh of dendritic spines. In addition, an interactive machine learning classifier with visual exploration and user feedback using an interactive 3D mesh grid view ensures a more precise classification based on the spine phenotype. A user study and two case studies were conducted to quantitatively verify the performance and usability of the DXplorer. We demonstrate that the system performs the entire analytic process effectively and provides high-quality, accurate, and objective analysis.

This paper presents a mobile AR system for visualizing airflow and temperature change made by virtual air conditioners. Even though there have been efforts to integrate the results of airflow/temperature simulation into the real world via AR, they support neither interactive modeling of the environments nor real-time simulation. This paper presents an AR system, where 3D mapping and air conditioner installation are made interactively, and then airflow/temperature simulation and visualization are made at real time. The proposed system is designed in a client-server architecture, where the server is in charge of simulation and the rest is taken by the client.

With the advent of advances in imaging and computing technologies, large-scale data acquisition and processing have become commonplace in many science and engineering disciplines. Conventional workflows for large-scale data processing usually rely on in-house or commercial software that are designed for domain-specific computing tasks. Recent advances in MapReduce, which was originally developed for batch processing textual data via a simplified programming model of the map and reduce functions, have expanded its applications to more general tasks in big-data processing, such as scientific computing, and biomedical image processing. However, as shown in previous work, volume rendering and visualization using MapReduce is still considered challenging and impractical owing to the disk-based, batch-processing nature of its computing model. In this article, contrary to this common belief, we show that the MapReduce computing model can be effectively used for interactive visualization. Our proposed system is a novel extension of Spark, one of the most popular open-source MapReduce frameworks, which offers GPU-accelerated MapReduce computing. To minimize CPU-GPU communication and overcome slow, disk-based shuffle performance, the proposed system supports GPU in-memory caching and MPI-based direct communication between compute nodes. To allow for GPU-accelerated in-situ visualization using raster graphics in Spark, we leveraged the CUDA-OpenGL interoperability, resulting in faster processing speeds by several orders of magnitude compared to conventional MapReduce systems. We demonstrate the performance of our system via several volume processing and visualization tasks, such as direct volume rendering, iso-surface extraction, and numerical simulations with in-situ visualization.

This paper presents DXR, a toolkit for building immersive data visualizations based on the Unity development platform. Over the past years, immersive data visualizations in augmented and virtual reality (AR, VR) have been emerging as a promising medium for data sense-making beyond the desktop. However, creating immersive visualizations remains challenging, and often require complex low-level programming and tedious manual encoding of data attributes to geometric and visual properties. These can hinder the iterative idea-to-prototype process, especially for developers without experience in 3D graphics, AR, and VR programming. With DXR, developers can efficiently specify visualization designs using a concise declarative visualization grammar inspired by Vega-Lite. DXR further provides a GUI for easy and quick edits and previews of visualization designs in-situ, i.e., while immersed in the virtual world. DXR also provides reusable templates and customizable graphical marks, enabling unique and engaging visualizations. We demonstrate the flexibility of DXR through several examples spanning a wide range of applications.

In this paper, we propose a novel machine learning-based voxel classification method for highly-accurate volume rendering. Unlike conventional voxel classification methods that incorporate intensity-based features, the proposed method employs dictionary based features learned directly from the input data using hierarchical multi-scale 3D convolutional sparse coding, a novel extension of the state-of-the-art learning-based sparse feature representation method. The proposed approach automatically generates high-dimensional feature vectors in up to 75 dimensions, which are then fed into an intelligent system built on a random forest classifier for accurately classifying voxels from only a handful of selection scribbles made directly on the input data by the user. We apply the probabilistic transfer function to further customize and refine the rendered result. The proposed method is more intuitive to use and more robust to noise in comparison with conventional intensity-based classification methods. We evaluate the proposed method using several synthetic and real-world volume datasets, and demonstrate the methods usability through a user study.