Visualization Authoring for Data-driven Storytelling

Data-driven storytelling is the process of communicating insights and findings that are supported by data, forming a visualization-based narrative. However, most current visualization creation tools either only support fixed sets of designs or require an in-depth understanding of programming concepts. To enable non-programmers to create custom visualizations for data-driven storytelling, we design interactions and implement user interfaces for visualization authoring. In the first part of this dissertation, we introduce and evaluate a series of three visualization authoring tools using traditional user interfaces: (1) iVisDesigner, which uses a data-flow model and enables users to author visualizations by specifying mappings from data to graphics interactively; (2) ChartAccent, a tool for annotating a given visualization; and (3) Charticulator, which allows users to design custom layouts interactively. We then reflect on the evaluation of visualization authoring user interfaces. In the second part of the dissertation, we extend our approach to multiple presentation media or display environments, including traditional 2-dimensional screens, large projection-based virtual-reality (VR) systems, and head-mounted virtual/augmented reality displays (HMDs). To leverage such immersive visualization environments, we ported and extended the iVisDesigner authoring approach to projection-based virtual reality. To facilitate the development of immersive visualizations, we built a visualization library called Stardust, which provides a familiar API to utilize GPU processing power in a cross-platform way. Finally, we present Idyll-MR, a system for authoring data-driven stories in virtual and augmented reality. We evaluated these authoring tools and libraries, and demonstrated high expressiveness, usability, and performance, as well as portability across platforms. In summary, our contributions enable larger audiences to create visual data-driven stories using different presentation media, leading to an overall enriched diversity of visualization designs

An on-line sample pretreatment technique for the HPLC analysis of plant samples.

A continuous-flow, on-line sample pretreatment technique using a silica gel microsyringe extractor has been developed. All steps including extraction, separation, clean-up, and concentration occur in the microsyringe. The overall sample pretreatment process takes 0.99. Complex plant samples of Sambucus Mandshurica Kitag have been tested using this method. Fluorene, phenanthrene, pyrene, and plant hormones were detected in all the samples, and concentrations ranged from 24.2–34.9, 43.8–67.1, 25.9–29.2, and 14.5∼110.8 ng/g, respectively

mage: Fluid Moves Between Code and Graphical Work in Computational Notebooks

We aim to increase the flexibility at which a data worker can choose the right tool for the job, regardless of whether the tool is a code library or an interactive graphical user interface (GUI). To achieve this flexibility, we extend computational notebooks with a new API mage, which supports tools that can represent themselves as both code and GUI as needed. We discuss the design of mage as well as design opportunities in the space of flexible code/GUI tools for data work. To understand tooling needs, we conduct a study with nine professional practitioners and elicit their feedback on mage and potential areas for flexible code/GUI tooling. We then implement six client tools for mage that illustrate the main themes of our study findings. Finally, we discuss open challenges in providing flexible code/GUI interactions for data workers

WeiboEvents: A Crowd Sourcing Weibo Visual Analytic System

In this work, we propose a visual analytic system for analyzing events of Weibo, a Chinese-version microblog service. We build a system which consists of two interfaces: a web-based online visualization interface for public users and an offline expert visual analytic system which wraps the online one and provides additional analysis functions. The online interface provides an intuitive and powerful retweet tree visualization which inspires users' creativity. The expert system adopts public users' analysis results collected from the web interface, and can visualize and analyzeWeibo events to a deeper extent.CPCI-S(ISTP)[email protected]; [email protected]; [email protected]; [email protected]; [email protected]

Prognosis Risk Model Based on Necroptosis-Related Signature for Bladder Cancer

Background: Bladder cancer(BLCA) is the ninth most common cancer. In recent years, necroptosis was found to be related to the occurrence and development of tumors. In this study, we aimed to construct a model based on a necroptosis-related signature to evaluate the potential prognostic application in BLCA. Methods: A total of 67 necroptosis-related genes were used to select the ideal cluster numbers, and it was found that there were four necroptosis-related patterns. Then, we compared the gene expression levels among all of the groups and established a necroptosis-related prognostic model. We made the following enrichment analysis of function and built a novel scoring system, the NEC score, to evaluate the state of necroptosis according to the expression level of necroptosis-related genes. Results: A total of 67 necroptosis-related genes were used to define four distinct necroptosis-related patterns: NEC cluster1–4. Each NEC cluster exhibited different patterns of survival and immune infiltration. Based on univariate Cox regression analyses and least absolute shrinkage and selection operator (Lasso) regression, 14 necroptosis-related genes were established to develop the NEC score. Patients were divided into two groups based on the NEC score. Patients in the high NEC score group had a significantly poorer overall survival than those in the low NEC score group. We further confirmed the correlation of clinical characteristics, as well as the immunotherapy outcome, with the NEC score, and confirmed the potential of the NEC score to be an independent prognostic factor. Furthermore, we compared the expression levels of eight potential biomarker genes between our own BLCA tissues and para-carcinoma tissue. Conclusion: We developed a novel NEC score that has a potential prognostic value in BLCA patients and may help personalized immunotherapy counselling

High-resolution Iterative Feedback Network for Camouflaged Object Detection

Spotting camouflaged objects that are visually assimilated into the background is tricky for both object detection algorithms and humans who are usually confused or cheated by the perfectly intrinsic similarities between the foreground objects and the background surroundings. To tackle this challenge, we aim to extract the high-resolution texture details to avoid the detail degradation that causes blurred vision in edges and boundaries. We introduce a novel HitNet to refine the low-resolution representations by high-resolution features in an iterative feedback manner, essentially a global loop-based connection among the multi-scale resolutions. In addition, an iterative feedback loss is proposed to impose more constraints on each feedback connection. Extensive experiments on four challenging datasets demonstrate that our \ourmodel~breaks the performance bottleneck and achieves significant improvements compared with 29 state-of-the-art methods. To address the data scarcity in camouflaged scenarios, we provide an application example by employing cross-domain learning to extract the features that can reflect the camouflaged object properties and embed the features into salient objects, thereby generating more camouflaged training samples from the diverse salient object datasets The code will be available at https://github.com/HUuxiaobin/HitNet

Critical Reflections on Visualization Authoring Systems

© 1995-2012 IEEE. An emerging generation of visualization authoring systems support expressive information visualization without textual programming. As they vary in their visualization models, system architectures, and user interfaces, it is challenging to directly compare these systems using traditional evaluative methods. Recognizing the value of contextualizing our decisions in the broader design space, we present critical reflections on three systems we developed-Lyra, Data Illustrator, and Charticulator. This paper surfaces knowledge that would have been daunting within the constituent papers of these three systems. We compare and contrast their (previously unmentioned) limitations and trade-offs between expressivity and learnability. We also reflect on common assumptions that we made during the development of our systems, thereby informing future research directions in visualization authoring systems