Dupo: A Mixed-Initiative Authoring Tool for Responsive Visualization.

Designing responsive visualizations for various screen types can be tedious as authors must manage multiple chart versions across design iterations. Automated approaches for responsive visualization must take into account the user's need for agency in exploring possible design ideas and applying customizations based on their own goals. We design and implement Dupo, a mixedinitiative approach to creating responsive visualizations that combines the agency afforded by a manual interface with automation provided by a recommender system. Given an initial design, users can browse automated design suggestions for a different screen type and make edits to a chosen design, thereby supporting quick prototyping and customizability. Dupo employs a two-step recommender pipeline that first suggests significant design changes (Exploration) followed by more subtle changes (Alteration). We evaluated Dupo with six expert responsive visualization authors. While creating responsive versions of a source design in Dupo, participants could reason about different design suggestions without having to manually prototype them, and thus avoid prematurely fixating on a particular design. This process led participants to create designs that they were satisfied with but which they had previously overlooked.

Socrates: Data Story Generation via Adaptive Machine-Guided Elicitation of User Feedback.

Visual data stories can effectively convey insights from data, yet their creation often necessitates intricate data exploration, insight discovery, narrative organization, and customization to meet the communication objectives of the storyteller. Existing automated data storytelling techniques, however, tend to overlook the importance of user customization during the data story authoring process, limiting the system's ability to create tailored narratives that reflect the user's intentions. We present a novel data story generation workflow that leverages adaptive machine-guided elicitation of user feedback to customize the story. Our approach employs an adaptive plug-in module for existing story generation systems, which incorporates user feedback through interactive questioning based on the conversation history and dataset. This adaptability refines the system's understanding of the user's intentions, ensuring the final narrative aligns with their goals. We demonstrate the feasibility of our approach through the implementation of an interactive prototype: Socrates. Through a quantitative user study with 18 participants that compares our method to a state-of-the-art data story generation algorithm, we show that Socrates produces more relevant stories with a larger overlap of insights compared to human-generated stories. We also demonstrate the usability of Socrates via interviews with three data analysts and highlight areas of future work.

Factors Associated With Tobacco Cessation Advice Recall and Quit Rates in Vascular Surgery Patients. A Single Center Study.

OBJECTIVES: Smoking is an important modifiable risk factor in all vascular diseases and verbal advice from providers has been shown to increase rates of tobacco cessation. We sought to identify factors that will improve tobacco cessation and recall of receiving verbal cessation advice in vascular surgery patients at a single institution. METHODS: The study is a retrospective cohort study. Patients seen in outpatient vascular surgery clinic who triggered a tobacco Best Practice Advisory (BPA) during their office visits over a 10-month period were contacted post-clinic and administered surveys detailing smoking status, cessation advice recall, and validated scales for nicotine dependence and willingness to quit smoking. This BPA is a "hard stop" that requires providers to document actions taken. Charts were reviewed for tobacco cessation documentation. Nine-digit zip-codes identified the area deprivation index, a measure of socioeconomic status. Univariate analysis was used to identify factors associated with cessation and advice recall. RESULTS: One hundred out of 318 (31.4%) patients responded to the survey. Epic Slicer Dicer found 97 BPA responses. To dismiss the BPA, 89 providers (91.8%) selected "advised tobacco cessation" and "Unable to Advise" otherwise. Of the 318 patients, 115 (36.1%) had cessation intervention documented in their provider notes and 151 (47.5%) received written tobacco cessation advice. Of survey respondents, 70 recalled receiving verbal advice, 27 recalled receiving written advice, 28 reported receiving offers of medication/therapy for cessation. 55 patients reported having tobacco cessation plans, and among those 17 reported having quit tobacco. Recall of receiving written advice (P < .001) and recall of receiving medication/therapy (P = .008) were associated with recall of receiving verbal cessation advice. CONCLUSIONS: Providing patients with tobacco cessation medication/therapy and written tobacco cessation education during office visits is associated with increased patients' recall of tobacco cessation advice. Vascular surgeons should continue to provide directed tobacco cessation advice.

Evaluating the Use of Uncertainty Visualisations for Imputations of Data Missing At Random in Scatterplots.

Most real-world datasets contain missing values yet most exploratory data analysis (EDA) systems only support visualising data points with complete cases. This omission may potentially lead the user to biased analyses and insights. Imputation techniques can help estimate the value of a missing data point, but introduces additional uncertainty. In this work, we investigate the effects of visualising imputed values in charts using different ways of representing data imputations and imputation uncertainty-no imputation, mean, 95% confidence intervals, probability density plots, gradient intervals, and hypothetical outcome plots. We focus on scatterplots, which is a commonly used chart type, and conduct a crowdsourced study with 202 participants. We measure users' bias and precision in performing two tasks-estimating average and detecting trend-and their self-reported confidence in performing these tasks. Our results suggest that, when estimating averages, uncertainty representations may reduce bias but at the cost of decreasing precision. When estimating trend, only hypothetical outcome plots may lead to a small probability of reducing bias while increasing precision. Participants in every uncertainty representation were less certain about their response when compared to the baseline. The findings point towards potential trade-offs in using uncertainty encodings for datasets with a large number of missing values. This paper and the associated analysis materials are available at: https://osf.io/q4y5r/.

An Automated Approach to Reasoning About Task-Oriented Insights in Responsive Visualization.

Authors often transform a large screen visualization for smaller displays through rescaling, aggregation and other techniques when creating visualizations for both desktop and mobile devices (i.e., responsive visualization). However, transformations can alter relationships or patterns implied by the large screen view, requiring authors to reason carefully about what information to preserve while adjusting their design for the smaller display. We propose an automated approach to approximating the loss of support for task-oriented visualization insights (identification, comparison, and trend) in responsive transformation of a source visualization. We operationalize identification, comparison, and trend loss as objective functions calculated by comparing properties of the rendered source visualization to each realized target (small screen) visualization. To evaluate the utility of our approach, we train machine learning models on human ranked small screen alternative visualizations across a set of source visualizations. We find that our approach achieves an accuracy of 84% (random forest model) in ranking visualizations. We demonstrate this approach in a prototype responsive visualization recommender that enumerates responsive transformations using Answer Set Programming and evaluates the preservation of task-oriented insights using our loss measures. We discuss implications of our approach for the development of automated and semi-automated responsive visualization recommendation.

Estimation of Graphlet Counts in Massive Networks.

Graphlets are induced subgraphs of a large network and are important for understanding and modeling complex networks. Despite their practical importance, graphlets have been severely limited to applications and domains with relatively small graphs. Most previous work has focused on exact algorithms; however, it is often too expensive to compute graphlets exactly in massive networks with billions of edges, and finding an approximate count is usually sufficient for many applications. In this paper, we propose an unbiased graphlet estimation framework that is: (a) fast with large speedups compared to the state of the art; (b) parallel with nearly linear speedups; (c) accurate with less than 1% relative error; (d) scalable and space efficient for massive networks with billions of edges; and (e) effective for a variety of real-world settings as well as estimating global and local graphlet statistics (e.g., counts). On 300 networks from 20 domains, we obtain <1% relative error for all graphlets. This is vastly more accurate than the existing methods while using less data. Moreover, it takes a few seconds on billion edge graphs (as opposed to days/weeks). These are by far the largest graphlet computations to date.

The Orphan C2orf40 Gene is a Neuroimmune Factor in Alzheimer's Disease.

Expression of the orphan C2orf40 gene is associated with the aggregation of the neurofibrillary tangle-protein tau in transgenic mice, tumor suppression, the induction of senescence in CNS, and the activation of microglia and peripheral mononuclear leukocytes. This gene also encodes several secreted pro- and anti-inflammatory neuropeptide-like cytokines, suggesting they might be implicated in the inflammatory component(s) of Alzheimer's disease (AD). Accordingly, we evaluated human AD and control brains for expression changes by RT-qPCR, Western blot, and histological changes by immunolabeling. RT-qPCR demonstrated increased cortical gene expression in AD. The molecular form of Ecrg4 detected in cortex was 8-10 kDa, which was shown previously to interact with the innate immunity receptor complex. Immunocytochemical studies showed intensely stained microglia and intravascular blood-borne monocytes within cerebral cortical white matter of AD patients. Staining was diminished within cortical neurons, except for prominent staining in neurofibrillary tangles. Choroid plexuses showed a decreasing trend. These findings support our hypothesis that c2orf40 participates in the neuroimmune response in AD.

Esophageal cancer related gene-4 is a choroid plexus-derived injury response gene: evidence for a biphasic response in early and late brain injury.

By virtue of its ability to regulate the composition of cerebrospinal fluid (CSF), the choroid plexus (CP) is ideally suited to instigate a rapid response to traumatic brain injury (TBI) by producing growth regulatory proteins. For example, Esophageal Cancer Related Gene-4 (Ecrg4) is a tumor suppressor gene that encodes a hormone-like peptide called augurin that is present in large concentrations in CP epithelia (CPe). Because augurin is thought to regulate senescence, neuroprogenitor cell growth and differentiation in the CNS, we evaluated the kinetics of Ecrg4 expression and augurin immunoreactivity in CPe after CNS injury. Adult rats were injured with a penetrating cortical lesion and alterations in augurin immunoreactivity were examined by immunohistochemistry. Ecrg4 gene expression was characterized by in situ hybridization. Cell surface augurin was identified histologically by confocal microscopy and biochemically by sub-cellular fractionation. Both Ecrg4 gene expression and augurin protein levels were decreased 24-72 hrs post-injury but restored to uninjured levels by day 7 post-injury. Protein staining in the supraoptic nucleus of the hypothalamus, used as a control brain region, did not show a decrease of auguin immunoreactivity. Ecrg4 gene expression localized to CPe cells, and augurin protein to the CPe ventricular face. Extracellular cell surface tethering of 14 kDa augurin was confirmed by cell surface fractionation of primary human CPe cells in vitro while a 6-8 kDa fragment of augurin was detected in conditioned media, indicating release from the cell surface by proteolytic processing. In rat CSF however, 14 kDa augurin was detected. We hypothesize the initial release and proteolytic processing of augurin participates in the activation phase of injury while sustained Ecrg4 down-regulation is dysinhibitory during the proliferative phase. Accordingly, augurin would play a constitutive inhibitory function in normal CNS while down regulation of Ecrg4 gene expression in injury, like in cancer, dysinhibits proliferation.