PrompTHis: Visualizing the Process and Influence of Prompt Editing during Text-to-Image Creation.

Generative text-to-image models, which allow users to create appealing images through a text prompt, have seen a dramatic increase in popularity in recent years. However, most users have a limited understanding of how such models work and often rely on trial and error strategies to achieve satisfactory results. The prompt history contains a wealth of information that could provide users with insights into what has been explored and how the prompt changes impact the output image, yet little research attention has been paid to the visual analysis of such process to support users. We propose the Image Variant Graph, a novel visual representation designed to support comparing prompt-image pairs and exploring the editing history. The Image Variant Graph models prompt differences as edges between corresponding images and presents the distances between images through projection. Based on the graph, we developed the PrompTHis system through co-design with artists. Based on the review and analysis of the prompting history, users can better understand the impact of prompt changes and have a more effective control of image generation. A quantitative user study and qualitative interviews demonstrate that PrompTHis can help users review the prompt history, make sense of the model, and plan their creative process.

PM-Vis: A Visual Analytics System for Tracing and Analyzing the Evolution of Pottery Motifs.

In Chinese archaeological research, analyzing the evolution of motifs in ancient pottery is crucial for studying the spread and growth of cultures across various eras and regions. However, such analyses are often challenging due to the complexities of identifying motifs with evolutionary connections that may manifest concurrent changes in appearance, space, and time, compounded by ineffective documentation. We propose PM-Vis, a visual analytics system for tracing and analyzing the evolution of pottery motifs. PM-Vis is anchored in a "selection-organization-documentation" workflow. In the selection stage, we design a three-fold projection paired with a motif-based search mechanism, displaying the appearance similarity and temporal and spatial proximities of all motifs or a specific motif, aiding users in selecting motifs with evolutionary connections. The organization stage helps users establish the evolutionary sequence and segment the selected motifs into distinct evolutionary phases. Finally, the documentation stage enables users to record their observations and insights through various forms of annotation. We demonstrate the usefulness and effectiveness of PM-Vis through two case studies, expert feedback, and a user study.

Seeing Your Stories: Visualization for Narrative Medicine.

Importance: Narrative medicine (NM), in which patient stories play a crucial role in their diagnosis and treatment, can potentially support a more holistic approach to patient care than traditional scientific ones. However, there are some challenges in the implementation of narrative medicine, for example, differences in understanding illnesses between physicians and patients and physicians' increased workloads and overloaded schedules. This paper first presents a review to explore previous visualization research for narrative medicine to bridge the gap between visualization researchers and narrative medicine experts and explore further visualization opportunities. Highlights: The review is conducted from 2 perspectives: (a) the contexts and domains in which visualization has been explored for narrative medicine and (b) the forms and solutions applied in these studies. Four applied domains are defined, including understanding patients from narrative records, medical communication, medical conversation training in education, and psychotherapy and emotional wellness enhancement. Conclusions: A future work framework illustrates some opportunities for future research, including groups of specific directions and future points for the 4 domains and 3 technological exploration opportunities (combination of narrative and medical data visualization, task-audience-based visual storytelling, and user-centered interactive visualization). Specifically, 3 directions of future work in medical communication (asynchronous online physician-patient communication, synchronous face-to-face medical conversation, and medical knowledge dissemination) were concluded.

Subspace-Map: Interactive Visual Analysis for Subspace Data with a Map Metaphor.

Subspace analysis of high-dimensional data is extremely challenging due to the huge exploration space. We propose Subspace-Map, a novel approach with a map metaphor for interactive exploration of various subspaces. We utilize a subspace search algorithm to identify a moderate number of potentially valuable subspaces, each visualized as a city on the map. Similar cities are clustered into provinces and countries, highlighting common data and dimensional patterns that can guide users in constructing desired subspaces. With the map, users can grasp an overview of the exploration space and explore different subspaces via recommended tour routes in more detail. We demonstrate the effectiveness of Subspace-Map through cases with real-world data, experiments with user feedback, and a comparison with state-of-the-art subspace data visualizations.

LiberRoad: Probing into the Journey of Chinese Classics Through Visual Analytics.

Books act as a crucial carrier of cultural dissemination in ancient times. This work involves joint efforts between visualization and humanities researchers, aiming at building a holistic view of the cultural exchange and integration between China and Japan brought about by the overseas circulation of Chinese classics. Book circulation data consist of uncertain spatiotemporal trajectories, with multiple dimensions, and movement across hierarchical spaces forms a compound network. LiberRoad visualizes the circulation of books collected in the Imperial Household Agency of Japan, and can be generalized to other book movement data. The LiberRoad system enables a smooth transition between three views (Location Graph, map, and timeline) according to the desired perspectives (spatial or temporal), as well as flexible filtering and selection. The Location Graph is a novel uncertainty-aware visualization method that employs improved circle packing to represent spatial hierarchy. The map view intuitively shows the overall circulation by clustering and allows zooming into single book trajectory with lenses magnifying local movements. The timeline view ranks dynamically in response to user interaction to facilitate the discovery of temporal events. The evaluation and feedback from the expert users demonstrate that LiberRoad is helpful in revealing movement patterns and comparing circulation characteristics of different times and spaces.

OldVisOnline: Curating a Dataset of Historical Visualizations.

With the increasing adoption of digitization, more and more historical visualizations created hundreds of years ago are accessible in digital libraries online. It provides a unique opportunity for visualization and history research. Meanwhile, there is no large-scale digital collection dedicated to historical visualizations. The visualizations are scattered in various collections, which hinders retrieval. In this study, we curate the first large-scale dataset dedicated to historical visualizations. Our dataset comprises 13K historical visualization images with corresponding processed metadata from seven digital libraries. In curating the dataset, we propose a workflow to scrape and process heterogeneous metadata. We develop a semi-automatic labeling approach to distinguish visualizations from other artifacts. Our dataset can be accessed with OldVisOnline, a system we have built to browse and label historical visualizations. We discuss our vision of usage scenarios and research opportunities with our dataset, such as textual criticism for historical visualizations. Drawing upon our experience, we summarize recommendations for future efforts to improve our dataset.

The value of 3D T2-weighted SPACE sequence in the differential diagnosis of spinal arteriovenous fistula and acute transverse myelitis.

OBJECTIVE: Spinal arteriovenous fistulas (SAVF) was often neglected and misdiagnosed as acute transverse myelitis (ATM) due to its insidious onset and non-specific clinical symptoms. This study aims to investigate the differential diagnostic value of high-resolution T2-weighted volumetric sequence (3D sampling perfection with application-optimized contrasts using different flip-angle evolutions [SPACE]) in patients with SAVF and ATM. METHODS: Retrospectively analyzed the clinical and radiological findings of 32 SDAVF patients and 32 ATM patients treated at our institutions from May 2018 to January 2023. They all underwent conventional spinal MRI and T2-SPACE examination, compared their performance in identifying lesions, to estimate the value of T2 SPACE sequence in the diagnosis of SAVF and ATM patients. RESULTS: The clue of cauda equina area change (CEAC) in conventional MRI and T2-SPACE sequences is specific for the diagnosis of SAVF. The diagnostic model composed of perimedullary flow voids (PFV) and CEAC has good diagnostic performance (AUCMRI = 0.95; AUCSPACE = 0.935). Compared with conventional MRI, the T2-SPACE sequence has a higher detection rate, sensitivity, and negative predictive value for PFV and CEAC in SAVF patients, but lower specificity and positive predictive value. In T2-SPACE images, there are significant differences in the distribution range, quadrant, and maximum diameter of PFV vessels between SAVF and ATM patients. Moreover, T2-SPACE sequence can determine the site of fistula in most SAVF patients preferably, and the inter-rater agreement was good in the assessment of the fistula. CONCLUSION: The CEAC is a new and useful clue for the diagnosis of thoracolumbar SAVF. And T2-SPACE sequence can more intuitively observe the lesions of SAVF, has good differential diagnostic value for SAVF and ATM patients.

SpectrumVA: Visual Analysis of Astronomical Spectra for Facilitating Classification Inspection.

In astronomical spectral analysis, class recognition is essential and fundamental for subsequent scientific research. The experts often perform the visual inspection after automatic classification to deal with low-quality spectra to improve accuracy. However, given the enormous spectral volume and inadequacy of the current inspection practice, such inspection is tedious and time-consuming. This paper presents a visual analytics system named SpectrumVA to promote the efficiency of visual inspection while guaranteeing accuracy. We abstract inspection as a visual parameter space analysis process, using redshifts and spectral lines as parameters. Different navigation strategies are employed in the "selection-inspection-promotion" workflow. At the selection stage, we help the experts identify a spectrum of interest through spectral representations and auxiliary information. Several possible redshifts and corresponding important spectral lines are also recommended through a global-to-local strategy to provide an appropriate entry point for the inspection. The inspection stage adopts a variety of instant visual feedback to help the experts adjust the redshift and select spectral lines in an informed trial-and-error manner. Similar spectra to the inspected one rather than different ones are visualized at the promotion stage, making the inspection process more fluent. We demonstrate the effectiveness of SpectrumVA through a quantitative algorithmic assessment, a case study, interviews with domain experts, and a user study.

AutoTitle: An Interactive Title Generator for Visualizations.

We propose AutoTitle, an interactive visualization title generator satisfying multifarious user requirements. Factors making a good title, namely, the feature importance, coverage, preciseness, general information richness, conciseness, and non-technicality, are summarized based on the feedback from user interviews. Visualization authors need to trade off among these factors to fit specific scenarios, resulting in a wide design space of visualization titles. AutoTitle generates various titles through the process of visualization facts traversing, deep learning-based fact-to-title generation, and quantitative evaluation of the six factors. AutoTitle also provides users with an interactive interface to explore the desired titles by filtering the metrics. We conduct a user study to validate the quality of generated titles as well as the rationality and helpfulness of these metrics.

GoTreeScape: Navigate and Explore the Tree Visualization Design Space.

Declarative grammar is becoming an increasingly important technique for understanding visualization design spaces. The GoTreeScape system presented in the paper allows users to navigate and explore the vast design space implied by GoTree, a declarative grammar for visualizing tree structures. To provide an overview of the design space, GoTreeScape, which is based on an encoder-decoder architecture, projects the tree visualizations onto a 2D landscape. Significantly, this landscape takes the relationships between different design features into account. GoTreeScape also includes an exploratory framework that allows top-down, bottom-up, and hybrid modes of exploration to support the inherently undirected nature of exploratory searches. Two case studies demonstrate the diversity with which GoTreeScape expands the universe of designed tree visualizations for users. The source code associated with GoTreeScape is available at https://github.com/bitvis2021/gotreescape.

EnConVis: A Unified Framework for Ensemble Contour Visualization.

Ensemble simulation is a crucial method to handle potential uncertainty in modern simulation and has been widely applied in many disciplines. Many ensemble contour visualization methods have been introduced to facilitate ensemble data analysis. On the basis of deep exploration and summarization of existing techniques and domain requirements, we propose a unified framework of ensemble contour visualization, EnConVis (Ensemble Contour Visualization), which systematically combines state-of-the-art methods. We model ensemble contour visualization as a four-step pipeline consisting of four essential procedures: member filtering, point-wise modeling, uncertainty band extraction, and visual mapping. For each of the four essential procedures, we compare different methods they use, analyze their pros and cons, highlight research gaps, and attempt to fill them. Specifically, we add Kernel Density Estimation in the point-wise modeling procedure and multi-layer extraction in the uncertainty band extraction procedure. This step shows the ensemble data's details accurately and provides abstract levels. We also analyze existing methods from a global perspective. We investigate their mechanisms and compare their effects, on the basis of which, we offer selection guidelines for them. From the overall perspective of this framework, we find choices and combinations that have not been tried before, which can be well compensated by our method. Synthetic data and real-world data are leveraged to verify the efficacy of our method. Domain experts' feedback suggests that our approach helps them better understand ensemble data analysis.

A Framework for Multiclass Contour Visualization.

Multiclass contour visualization is often used to interpret complex data attributes in such fields as weather forecasting, computational fluid dynamics, and artificial intelligence. However, effective and accurate representations of underlying data patterns and correlations can be challenging in multiclass contour visualization, primarily due to the inevitable visual cluttering and occlusions when the number of classes is significant. To address this issue, visualization design must carefully choose design parameters to make visualization more comprehensible. With this goal in mind, we proposed a framework for multiclass contour visualization. The framework has two components: a set of four visualization design parameters, which are developed based on an extensive review of literature on contour visualization, and a declarative domain-specific language (DSL) for creating multiclass contour rendering, which enables a fast exploration of those design parameters. A task-oriented user study was conducted to assess how those design parameters affect users' interpretations of real-world data. The study results offered some suggestions on the value choices of design parameters in multiclass contour visualization.

Assessment of Morphological Features and Imaging Characteristics of Patients with Intracranial Artery Dissection: A High-Resolution MRI Study.

BACKGROUND: Intracranial artery dissection (IAD) is a pathological dissection of the arterial wall. .However, the morphological features and imaging characteristics of patients with intracranial artery dissection (IAD) remain poorly understood. METHODS: The study reports on 70 IAD patients (30 culprit and 40 non-culprit). All participants underwent high-resolution magnetic resonance imaging (HR-MRI) scans. The morphological features and imaging characteristics of artery dissection were carefully investigated. Demographics and clinical characteristics of culprit and non-culprit patients were also collected. Apparent differences between the two groups, which could be used as biomarkers for ischemic event caused by the culprit dissection, were identified by receiver operating characteristic (ROC) curve analysis. RESULTS: The IAD patients studied could be classified into five different types on the basis of morphological features: classical dissection (n = 31), fusiform aneurysm (n = 2), long dissected aneurysm (n = 9), dolichoectatic dissecting aneurysm (n = 6), and saccular aneurysm (n = 22). The direct sites of artery dissection (double lumen and intimal flap) can be seen in most IAD patients on HR-MRI. Additionally, the presence of hypertension, double lumen and intimal flap were associated with culprit lesions and might be considered biomarkers for the ischemic event caused by the culprit dissection. CONCLUSIONS: Analysis showed that HR-MRI allowed easy visualization of abnormal morphology of artery dissection lesions. This was of great significance for the diagnosis of IAD and gave a better understanding of its pathophysiological mechanism.

A Probability Density-Based Visual Analytics Approach to Forecast Bias Calibration.

Biases inevitably occur in numerical weather prediction (NWP) due to an idealized numerical assumption for modeling chaotic atmospheric systems. Therefore, the rapid and accurate identification and calibration of biases is crucial for NWP in weather forecasting. Conventional approaches, such as various analog post-processing forecast methods, have been designed to aid in bias calibration. However, these approaches fail to consider the spatiotemporal correlations of forecast bias, which can considerably affect calibration efficacy. In this article, we propose a novel bias pattern extraction approach based on forecasting-observation probability density by merging historical forecasting and observation datasets. Given a spatiotemporal scope, our approach extracts and fuses bias patterns and automatically divides regions with similar bias patterns. Termed BicaVis, our spatiotemporal bias pattern visual analytics system is proposed to assist experts in drafting calibration curves on the basis of these bias patterns. To verify the effectiveness of our approach, we conduct two case studies with real-world reanalysis datasets. The feedback collected from domain experts confirms the efficacy of our approach.

Contribution of Temperature Increase to Restrain the Transmission of COVID-19.

The COVID-19 outbreak has already become a global pandemic and containing this rapid worldwide transmission is of great challenge. The impacts of temperature and humidity on the COVID-19 transmission rate are still under discussion. Here, we elucidated these relationships by utilizing two unique scenarios, repeated measurement and natural experiment, using the COVID-19 cases reported from January 23 - February 21, 2020, in China. The modeling results revealed that higher temperature was most strongly associated with decreased COVID-19 transmission at a lag time of 8 days. Relative humidity (RH) appeared to have only a slight effect. These findings were verified by assessing SARS-CoV-2 infectivity under the relevant conditions of temperature (4°C-37°C) and RH (> 40%). We concluded that temperature increase made an important, but not determined, contribution to restrain the COVID-19 outbreak in China. It suggests that the emphasis of other effective controlling polices should be strictly implemented to restrain COVID-19 transmission in cold seasons.

Uncertainty-Oriented Ensemble Data Visualization and Exploration using Variable Spatial Spreading.

As an important method of handling potential uncertainties in numerical simulations, ensemble simulation has been widely applied in many disciplines. Visualization is a promising and powerful ensemble simulation analysis method. However, conventional visualization methods mainly aim at data simplification and highlighting important information based on domain expertise instead of providing a flexible data exploration and intervention mechanism. Trial-and-error procedures have to be repeatedly conducted by such approaches. To resolve this issue, we propose a new perspective of ensemble data analysis using the attribute variable dimension as the primary analysis dimension. Particularly, we propose a variable uncertainty calculation method based on variable spatial spreading. Based on this method, we design an interactive ensemble analysis framework that provides a flexible interactive exploration of the ensemble data. Particularly, the proposed spreading curve view, the region stability heat map view, and the temporal analysis view, together with the commonly used 2D map view, jointly support uncertainty distribution perception, region selection, and temporal analysis, as well as other analysis requirements. We verify our approach by analyzing a real-world ensemble simulation dataset. Feedback collected from domain experts confirms the efficacy of our framework.

Co-Bridges: Pair-wise Visual Connection and Comparison for Multi-item Data Streams.

In various domains, there are abundant streams or sequences of multi-item data of various kinds, e.g. streams of news and social media texts, sequences of genes and sports events, etc. Comparison is an important and general task in data analysis. For comparing data streams involving multiple items (e.g., words in texts, actors or action types in action sequences, visited places in itineraries, etc.), we propose Co-Bridges, a visual design involving connection and comparison techniques that reveal similarities and differences between two streams. Co-Bridges use river and bridge metaphors, where two sides of a river represent data streams, and bridges connect temporally or sequentially aligned segments of streams. Commonalities and differences between these segments in terms of involvement of various items are shown on the bridges. Interactive query tools support the selection of particular stream subsets for focused exploration. The visualization supports both qualitative (common and distinct items) and quantitative (stream volume, amount of item involvement) comparisons. We further propose Comparison-of-Comparisons, in which two or more Co-Bridges corresponding to different selections are juxtaposed. We test the applicability of the Co-Bridges in different domains, including social media text streams and sports event sequences. We perform an evaluation of the users' capability to understand and use Co-Bridges. The results confirm that Co-Bridges is effective for supporting pair-wise visual comparisons in a wide range of applications.

BarcodeTree: Scalable Comparison of Multiple Hierarchies.

We propose BarcodeTree (BCT), a novel visualization technique for comparing topological structures and node attribute values of multiple trees. BCT can provide an overview of one hundred shallow and stable trees simultaneously, without aggregating individual nodes. Each BCT is shown within a single row using a style similar to a barcode, allowing trees to be stacked vertically with matching nodes aligned horizontally to ease comparison and maintain space efficiency. We design several visual cues and interactive techniques to help users understand the topological structure and compare trees. In an experiment comparing two variants of BCT with icicle plots, the results suggest that BCTs make it easier to visually compare trees by reducing the vertical distance between different trees. We also present two case studies involving a dataset of hundreds of trees to demonstrate BCT's utility.

R-Map: A Map Metaphor for Visualizing Information Reposting Process in Social Media.

We propose R-Map (Reposting Map), a visual analytical approach with a map metaphor to support interactive exploration and analysis of the information reposting process in social media. A single original social media post can cause large cascades of repostings (i.e., retweets) on online networks, involving thousands, even millions of people with different opinions. Such reposting behaviors form the reposting tree, in which a node represents a message and a link represents the reposting relation. In R-Map, the reposting tree structure can be spatialized with highlighted key players and tiled nodes. The important reposting behaviors, the following relations and the semantics relations are represented as rivers, routes and bridges, respectively, in a virtual geographical space. R-Map supports a scalable overview of a large number of information repostings with semantics. Additional interactions on the map are provided to support the investigation of temporal patterns and user behaviors in the information diffusion process. We evaluate the usability and effectiveness of our system with two use cases and a formal user study.

SmartCube: An Adaptive Data Management Architecture for the Real-Time Visualization of Spatiotemporal Datasets.

Interactive visualization and exploration of large spatiotemporal data sets is difficult without carefully-designed data pre-processing and management tools. We propose a novel architecture for spatiotemporal data management. The architecture can dynamically update itself based on user queries. Datasets is stored in a tree-like structure to support memory sharing among cuboids in a logical structure of data cubes. An update mechanism is designed to create or remove cuboids on it, according to the analysis of the user queries, with the consideration of memory size limitation. Data structure is dynamically optimized according to different user queries. During a query process, user queries are recorded to predict the performance increment of the new cuboid. The creation or deletion of a cuboid is determined by performance increment. Experiment results show that our prototype system deliveries good performance towards user queries on different spatiotemporal datasets, which costing small memory size with comparable performance compared with other state-of-the-art algorithms.