TacPrint: Visualizing the Biomechanical Fingerprint in Table Tennis.

Table tennis is a sport that demands high levels of technical proficiency and body coordination from players. Biomechanical fingerprints can provide valuable insights into players' habitual movement patterns and characteristics, allowing them to identify and improve technical weaknesses. Despite the potential, few studies have developed effective methods for generating such fingerprints. To address this gap, we propose TacPrint, a framework for generating a biomechanical fingerprint for each player. TacPrint leverages machine learning techniques to extract comprehensive features from biomechanics data collected by inertial measurement units (IMU) and employs the attention mechanism to enhance model interpretability. After generating fingerprints, TacPrint provides a visualization system to facilitate the exploration and investigation of these fingerprints. In order to validate the effectiveness of the framework, we designed an experiment to evaluate the model's performance and conducted a case study with the system. The results of our experiment demonstrated the high accuracy and effectiveness of the model. Additionally, we discussed the potential of TacPrint to be extended to other sports.

JsonCurer: Data Quality Management for JSON Based on an Aggregated Schema.

High-quality data is critical to deriving useful and reliable information. However, real-world data often contains quality issues undermining the value of the derived information. Most existing research on data quality management focuses on tabular data, leaving semi-structured data under-exploited. Due to the schema-less and hierarchical features of semi-structured data, discovering and fixing quality issues is challenging and time-consuming. To address the challenge, this paper presents JsonCurer, an interactive visualization system to assist with data quality management in the context of JSON data. To have an overview of quality issues, we first construct a taxonomy based on interviews with data practitioners and a review of 119 real-world JSON files. Then we highlight a schema visualization that presents structural information, statistical features, and quality issues of JSON data. Based on a similarity-based aggregation technique, the visualization depicts the entire JSON data with a concise tree, where summary visualizations are given above each node, and quality issues are illustrated using Bubble Sets across nodes. We evaluate the effectiveness and usability of JsonCurer with two case studies. One is in the domain of data analysis while the other concerns quality assurance in MongoDB documents. The source code of JsonCurer is available under the Apache License 2.0 at https://github.com/changevis/JsonCurer.

SNIL: Generating Sports News From Insights With Large Language Models.

To enhance the appeal and informativeness of data news, there is an increasing reliance on data analysis techniques and visualizations, which poses a high demand for journalists' abilities. While numerous visual analytics systems have been developed for deriving insights, few tools specifically support and disseminate viewpoints for journalism. Thus, this work aims to facilitate the automatic creation of sports news from natural language insights. To achieve this, we conducted an extensive preliminary study on the published sports articles. Based on our findings, we propose a workflow - 1) exploring the data space behind insights, 2) generating narrative structures, 3) progressively generating each episode, and 4) mapping data spaces into communicative visualizations. We have implemented a human-AI interaction system called SNIL, which incorporates user input in conjunction with large language models (LLMs). It supports the modification of textual and graphical content within the episode-based structure by adjusting the description. We conduct user studies to demonstrate the usability of SNIL and the benefit of bridging the gap between analysis tasks and communicative tasks through expert and fan feedback.

Effects of postoperative environmental noise on surgery induced pain: Evidence based on a prospective observational study.

CONTEXT: Many patients recovering from surgery in wards are disturbed by environmental noise. However, the effects of environmental noise on postoperative pain are unclear. OBJECTIVES: This study aimed to assess the association between postoperative noise and pain. METHODS: This prospective study included 182 women who underwent cesarean sections. Postoperative noise was continuously recorded, and pain intensity at rest was assessed using a numerical rating scale (NRS) for 0-6, 6-12, 12-18, and 18-24 h after the patients were returned to the ward. Cumulative pain scores were calculated by summing the NRS scores at each time point and comprised the primary outcome. The maximum pain NRS score and analgesic consumption during the 24 h after surgery were also recorded. RESULTS: Mean environmental noise intensity during the daytime was an independent factor for cumulative pain scores, maximum pain scores, and analgesic use during the first postoperative 24 h (ß, 0.37; 95% CI, 0.21-0.53 and ß, 0.12; 95% CI, 0.07-0.17; P < 0.001 for both; ß, 0.86; 95% CI, 0.25-1.46; P = 0.006). Cumulative and maximum NRS pain scores as well as the incidence of NRS ≥ 4 were significantly higher in patients under mean daytime environmental noise of ≥58, than <58 decibels (dB) (8.0 [6.0-11.3] vs. 6.0 (5.0-7.0); 3.0 [2.0-4.0] vs. 2.0 [2.0-2.0, and 25.6% vs. 11.0%; RR, 2.32; 95% CI, 1.19-4.54, respectively; P < 0.001 for all). CONCLUSIONS: Higher-level postoperative noise exposure was associated with more severe postoperative pain and increased analgesic needs, as well as a higher incidence of moderate-to-severe pain in patients recovering from cesarean delivery. Our findings indicate that reducing environmental ward noise might benefit for postoperative pain management.

ChartGPT: Leveraging LLMs to Generate Charts from Abstract Natural Language.

The use of natural language interfaces (NLIs) to create charts is becoming increasingly popular due to the intuitiveness of natural language interactions. One key challenge in this approach is to accurately capture user intents and transform them to proper chart specifications. This obstructs the wide use of NLI in chart generation, as users' natural language inputs are generally abstract (i.e., ambiguous or under-specified), without a clear specification of visual encodings. Recently, pre-trained large language models (LLMs) have exhibited superior performance in understanding and generating natural language, demonstrating great potential for downstream tasks. Inspired by this major trend, we propose ChartGPT, generating charts from abstract natural language inputs. However, LLMs are struggling to address complex logic problems. To enable the model to accurately specify the complex parameters and perform operations in chart generation, we decompose the generation process into a step-by-step reasoning pipeline, so that the model only needs to reason a single and specific sub-task during each run. Moreover, LLMs are pre-trained on general datasets, which might be biased for the task of chart generation. To provide adequate visualization knowledge, we create a dataset consisting of abstract utterances and charts and improve model performance through fine-tuning. We further design an interactive interface for ChartGPT that allows users to check and modify the intermediate outputs of each step. The effectiveness of the proposed system is evaluated through quantitative evaluations and a user study.

A Comparative Study on Fixed-order Event Sequence Visualizations: Gantt, Extended Gantt, and Stringline Charts.

We conduct two in-lab experiments (N=93) to evaluate the effectiveness of Gantt charts, extended Gantt charts, and stringline charts for visualizing fixed-order event sequence data. We first formulate five types of event sequences and define three types of sequence elements: point events, interval events, and the temporal gaps between them. Our two experiments focus on event sequences with a pre-defined, fixed order, and measure task error rates and completion time. The first experiment shows single sequences and assesses the three charts' performance in comparing event duration or gap. The second experiment shows multiple sequences and evaluates how well the charts reveal temporal patterns. The results suggest that when visualizing single fixed-order event sequences, 1) Gantt and extended Gantt charts lead to comparable error rates in the duration-comparing task; 2) Gantt charts exhibit either shorter or equal completion time than extended Gantt charts; 3) both Gantt and extended Gantt charts demonstrate shorter completion times than stringline charts; 4) however, stringline charts outperform the other two charts with fewer errors in the comparing task when event type counts are high. Additionally, when visualizing multiple point-based fixed-order event sequences, stringline charts require less time than Gantt charts for people to find temporal patterns. Based on these findings, we discuss design opportunities for visualizing fixed-order event sequences and discuss future avenues for optimizing these charts.

Causality-Based Visual Analysis of Questionnaire Responses.

As the final stage of questionnaire analysis, causal reasoning is the key to turning responses into valuable insights and actionable items for decision-makers. During the questionnaire analysis, classical statistical methods (e.g., Differences-in-Differences) have been widely exploited to evaluate causality between questions. However, due to the huge search space and complex causal structure in data, causal reasoning is still extremely challenging and time-consuming, and often conducted in a trial-and-error manner. On the other hand, existing visual methods of causal reasoning face the challenge of bringing scalability and expert knowledge together and can hardly be used in the questionnaire scenario. In this work, we present a systematic solution to help analysts effectively and efficiently explore questionnaire data and derive causality. Based on the association mining algorithm, we dig question combinations with potential inner causality and help analysts interactively explore the causal sub-graph of each question combination. Furthermore, leveraging the requirements collected from the experts, we built a visualization tool and conducted a comparative study with the state-of-the-art system to show the usability and efficiency of our system.

Action-Evaluator: A Visualization Approach for Player Action Evaluation in Soccer.

In soccer, player action evaluation provides a fine-grained method to analyze player performance and plays an important role in improving winning chances in future matches. However, previous studies on action evaluation only provide a score for each action, and hardly support inspecting and comparing player actions integrated with complex match context information such as team tactics and player locations. In this work, we collaborate with soccer analysts and coaches to characterize the domain problems of evaluating player performance based on action scores. We design a tailored visualization of soccer player actions that places the action choice together with the tactic it belongs to as well as the player locations in the same view. Based on the design, we introduce a visual analytics system, Action-Evaluator, to facilitate a comprehensive player action evaluation through player navigation, action investigation, and action explanation. With the system, analysts can find players to be analyzed efficiently, learn how they performed under various match situations, and obtain valuable insights to improve their action choices. The usefulness and effectiveness of this work are demonstrated by two case studies on a real-world dataset and an expert interview.

Visualizing Large-Scale Spatial Time Series with GeoChron.

In geo-related fields such as urban informatics, atmospheric science, and geography, large-scale spatial time (ST) series (i.e., geo-referred time series) are collected for monitoring and understanding important spatiotemporal phenomena. ST series visualization is an effective means of understanding the data and reviewing spatiotemporal phenomena, which is a prerequisite for in-depth data analysis. However, visualizing these series is challenging due to their large scales, inherent dynamics, and spatiotemporal nature. In this study, we introduce the notion of patterns of evolution in ST series. Each evolution pattern is characterized by 1) a set of ST series that are close in space and 2) a time period when the trends of these ST series are correlated. We then leverage Storyline techniques by considering an analogy between evolution patterns and sessions, and finally design a novel visualization called GeoChron, which is capable of visualizing large-scale ST series in an evolution pattern-aware and narrative-preserving manner. GeoChron includes a mining framework to extract evolution patterns and two-level visualizations to enhance its visual scalability. We evaluate GeoChron with two case studies, an informal user study, an ablation study, parameter analysis, and running time analysis.

Effect of noise isolation using noise-cancelling headphones during laparoscopic surgery for postoperative pain reduction: A randomized clinical trial.

STUDY OBJECTIVE: Most laparoscopic surgeries under general anesthesia are performed in noisy environments, although the effect of intraoperative noise reduction on postoperative pain remains uncertain. This study aimed to explore whether postoperative pain could be reduced through the intraoperative use of noise-cancelling headphones. DESIGN: This study was conducted as a prospective parallel-group randomized clinical trial. SETTING: Operating room and surgery room. PATIENTS: Ninety patients who underwent laparoscopic surgery under general anesthesia. INTERVENTIONS: In the intervention group, noise-cancelling headphones were used to reduce noise intensity during laparoscopic surgery under general anesthesia. MEASUREMENTS: The primary outcome was the maximum movement-evoked pain intensity within 24 h post-surgery, measured using a 10-point numeric rating scale. Secondary outcomes included the maximum resting pain score and total opioid consumption during the 24-h period post-surgery. Mean intraoperative noise and the proportion of intraoperative time with noise intensity ≥70 dB were recorded. MAIN RESULTS: The maximum movement-evoked pain score was significantly lower in the intervention group than in the control group (mean score [SD], 2.7 [1.0] and 4.0[1.0], respectively; P < 0.001). The intervention group required significantly fewer opioids than the control group (mean [SD], 44.2 [12.8] and 51.3[17.5] mg, respectively; P = 0.032). In the control group, but not the intervention group, all postoperative pain scores were significantly associated with the proportion of intraoperative time with noise intensity ≥70 dB, which was an independent risk factor for postoperative pain. CONCLUSION: During laparoscopic surgery under general anesthesia, intraoperative noise isolation using noise-cancelling headphones is a safe and effective strategy for relieving postoperative pain and decreasing total opioid analgesic consumption.

Interactive Table Synthesis with Natural Language.

Tables are a ubiquitous data format for insight communication. However, transforming data into consumable tabular views remains a challenging and time-consuming task. To lower the barrier of such a task, research efforts have been devoted to developing interactive approaches for data transformation, but many approaches still presume that their users have considerable knowledge of various data transformation concepts and functions. In this study, we leverage natural language (NL) as the primary interaction modality to improve the accessibility of average users to performing complex data transformation and facilitate intuitive table generation and editing. Designing an NL-driven data transformation approach introduces two challenges: a) NL-driven synthesis of interpretable pipelines and b) incremental refinement of synthesized tables. To address these challenges, we present NL2Rigel, an interactive tool that assists users in synthesizing and improving tables from semi-structured text with NL instructions. Based on a large language model and prompting techniques, NL2Rigel can interpret the given NL instructions into a table synthesis pipeline corresponding to Rigel specifications, a declarative language for tabular data transformation. An intuitive interface is designed to visualize the synthesis pipeline and the generated tables, helping users understand the transformation process and refine the results efficiently with targeted NL instructions. The comprehensiveness of NL2Rigel is demonstrated with an example gallery, and we further confirmed NL2Rigel's usability with a comparative user study by showing that the task completion time with NL2Rigel is significantly shorter than that with the original version of Rigel with comparable completion rates.

Hsa_circRNA_001676 accelerates the proliferation, migration and stemness in colorectal cancer through regulating miR-556-3p/G3BP2 axis.

Circular RNAs (circRNAs) play key roles in colorectal cancer (CRC) progression, but little is known about the biological functions of hsa_circRNA_001676 in CRC. Therefore, we explored the potential role of hsa_circRNA_001676 in CRC development. RT-qPCR was performed to determine hsa_circRNA_001676, miR-556-3p and Ras-GTPase-activating SH3 domain-binding-proteins 2 (G3BP2) levels in CRC tissues. Meanwhile, to evaluate the roles of hsa_circRNA_001676, miR-556-3p and G3BP2 on CRC, functional analysis of cell proliferation, migration and stemness were then performed. Our results showed that compared to normal tissues, hsa_circRNA_001676 and G3BP2 level was elevated, but miR-556-3p level was reduced in CRC tissues. Additionally, luciferase reporter results showed that hsa_circRNA_001676 was shown to target miR-556-3p, and G3BP2 was targeted by miR-556-3p. Hsa_circRNA_001676 or G3BP2 overexpression promoted CRC cell proliferation and migration. Conversely, miR-556-3p overexpression suppressed CRC cell proliferation and migration. Moreover, deficiency of hsa_circRNA_001676 or G3BP2 repressed the CRC cell proliferation, migration and stemness. Meanwhile, hsa_circRNA_001676 deficiency obviously reduced tumor growth and stemness in a CRC mouse xenograft model. Furthermore, hsa_circRNA_001676 deficiency notably reduced G3BP2 level, but elevated miR-556-3p level in tumor tissues from tumor-bearing mice. Mechanistically, hsa_circRNA_001676 targeted miR-556-3p to increase G3BP2 level, contributing to the progression of CRC. Collectively, hsa_circRNA_001676 was able to accelerate proliferation, migration and stemness in CRC through regulating miR-556-3p/G3BP2 axis, suggesting that hsa_circRNA_001676 may become a potential therapeutic target in treating CRC.

Opioid infusions at different times of the day produce varying degrees of opioid-induced hyperalgesia.

BACKGROUND: Opioids are metabolised by enzymes the activities of which vary with the circadian rhythm. We examined whether opioid infusions administered at different times of the day produce varying degrees of opioid-induced hyperalgesia (OIH) in animal experiments and clinical studies. METHODS: Male Sprague-Dawley rats received remifentanil infusions (1 µg kg-1·min-1 for 1 h) at Zeitgeber times (ZT) 0, 4, 8, 12, 16, or 20 h. Rhythmicity of mechanical hypersensitivity was assayed after the infusion. Mechanical hypersensitivity, drug concentration, and metabolic enzyme activity of Wistar rats that received sufentanil (10 µg kg-1; four consecutive i.p. injections at 15-min intervals) or remifentanil infusion at ZT0 or ZT8 were assayed. Sixty patients who underwent abdominal laparoscopic surgery under general anaesthesia received remifentanil infusion (0.15 µg kg-1 min-1) and sufentanil injection (0.2 µg kg-1) at induction and skin incision, respectively. Postoperative pressure pain sensitivity, pain Numeric Rating Scale (NRS), drug concentrations, and nonspecific esterase activity were assessed. RESULTS: Sprague-Dawley rats that received remifentanil infusion exhibited a robust rhythmic paw withdrawal threshold (JTK_CYCLE: P=0.001, Q=0.001, Phase=26). Wistar rats infused with remifentanil or sufentanil at ZT8 exhibited greater OIH (P<0.001) than those infused at ZT0, with higher blood concentrations (P<0.001) and lower metabolic enzyme activities (P=0.026 and P=0.028, respectively). Patients in the afternoon group exhibited higher pressure pain sensitivity at forearm (P=0.002), higher NRS (P<0.05), higher drug concentrations (sufentanil: P=0.037, remifentanil: P=0.005), and lower nonspecific esterase activity (P=0.024) than the morning group. CONCLUSIONS: Opioid infusions administered at different times of day produced varying degrees of OIH, possibly related to circadian rhythms of metabolic enzyme activities. CLINICAL TRIAL REGISTRATION: NCT05234697.

Effect of intraoperative noise on postoperative pain in surgery patients under general anesthesia: evidence from a prospective study and mouse model.

BACKGROUND: Most patients are in a noisy environment during abdominal surgery under general anesthesia. This study included patients who underwent abdominal surgery under general anesthesia and established an animal model to determine whether intraoperative noise affects postoperative pain. MATERIALS AND METHODS: This prospective study included 200 patients who underwent abdominal surgery under general anesthesia. Intraoperative noise and electroencephalograms were continuously recorded, and the mean level and time proportion of noise intensity of greater than 70 dB were calculated. Maximum postoperative pain was assessed using a numerical rating scale at 0-12 h and 12-24 h after surgery, and postoperative analgesia consumption in patients receiving patient-controlled intravenous analgesia was recorded. Postoperative pain intensity and electroencephalogram amplitude were compared between patients with high-noise exposure (time proportion of noise intensity greater than 70 dB ≥40%) and low-noise exposure (<40%). Mechanical pain sensitivity was tested in two groups of mice with plantar incisions exposed to 40 dB or 70-100 dB. RESULTS: The time proportion of noise intensity greater than 70 dB was identified as an independent risk factor for postoperative pain intensity ( P <0.001). P ain numerical rating scale 0-12 h (4.5±1.5 vs. 3.7±1.3, P =0.001) and 12-24 h (3.9±1.5 vs. 3.2±1.1, P =0.004) after surgery in patients with high-noise exposure was significantly higher than in patients with low-noise exposure. The electroencephalogram amplitude of patients with high-noise exposure was significantly lower than that of patients with low-noise exposure ( P <0.05). In the mouse model, mechanical hyperalgesia in the 70-100 dB group was significantly greater than that in the 40 dB group ( P <0.001). CONCLUSION: High-level intraoperative noise exposure aggravates the degree of postoperative pain and analgesic needs of patients undergoing abdominal surgery, which may be related to the impact of noise on the neurophysiological activity of the brain and postoperative hyperalgesia.

MediVizor: Visual Mediation Analysis of Nominal Variables.

Mediation analysis is crucial for diagnosing indirect causal relations in many scientific fields. However, mediation analysis of nominal variables requires examining and comparing multiple total effects and their corresponding direct/indirect causal effects derived from mediation models. This process is tedious and challenging to achieve with classical analysis tools such as Excel tables. In this study, we worked closely with experts from two scientific domains to design MediVizor, a visualization system that enables experts to conduct visual mediation analysis of nominal variables. The visualization design allows users to browse and compare multiple total effects together with the direct/indirect effects that compose them. The design also allows users to examine to what extent the positive and negative direct/indirect effects contribute to and reduce the total effects, respectively. We conducted two case studies separately with the experts from the two domains, sports and communication science,and a user study with common users to evaluate the system and design.The positive feedback from experts and common users demonstrates the effectiveness and generalizability of the system.

Pre-ictal fluctuation of EEG functional connectivity discriminates seizure phenotypes in mesial temporal lobe epilepsy.

OBJECTIVE: We explored whether quantifiable differences between clinical seizures (CSs) and subclinical seizures (SCSs) occur in the pre-ictal state. METHODS: We analyzed pre-ictal stereo-electroencephalography (SEEG) retrospectively across mesial temporal lobe epilepsy patients with recorded CSs and SCSs. Power spectral density and functional connectivity (FC) were quantified within and between the seizure onset zone (SOZ) and the early propagation zone (PZ), respectively. To evaluate the fluctuation of neural connectivity, FC variability was computed. Measures were further verified by a logistic regression model to evaluate their classification potentiality through the area under the receiver-operating-characteristics curve (AUC). RESULTS: Fifty-four pre-ictal SEEG epochs (27 CSs and 27 SCSs) were selected among 14 patients. Within the SOZ, pre-ictal FC variability of CSs was larger than SCSs in 1-45 Hz during 30 seconds before seizure onset. Pre-ictal FC variability between the SOZ and PZ was larger in SCSs than CSs in 55-80 Hz within 1 minute before onset. Using these two variables, the logistic regression model achieved an AUC of 0.79 when classifying CSs and SCSs. CONCLUSIONS: Pre-ictal FC variability within/between epileptic zones, not signal power or FC value, distinguished SCSs from CSs. SIGNIFICANCE: Pre-ictal epileptic network stability possibly marks seizure phenotypes, contributing insights into ictogenesis and potentially helping seizure prediction.

The Ball is in Our Court: Conducting Visualization Research With Sports Experts.

Most sports visualizations rely on a combination of spatial, highly temporal, and user-centric data, making sports a challenging target for visualization. Emerging technologies, such as augmented and mixed reality (AR/XR), have brought exciting opportunities along with new challenges for sports visualization. We share our experience working with sports domain experts and present lessons learned from conducting visualization research in SportsXR. In our previous work, we have targeted different types of users in sports, including athletes, game analysts, and fans. Each user group has unique design constraints and requirements, such as obtaining real-time visual feedback in training, automating the low-level video analysis workflow, or personalizing embedded visualizations for live game data analysis. In this article, we synthesize our best practices and pitfalls we identified while working on SportsXR. We highlight lessons learned in working with sports domain experts in designing and evaluating sports visualizations and in working with emerging AR/XR technologies. We envision that sports visualization research will benefit the larger visualization community through its unique challenges and opportunities for immersive and situated analytics.

Ictal EEG desynchronization during low-voltage fast activity for prediction of surgical outcomes in focal epilepsy.

OBJECTIVE: The authors investigated alterations in functional connectivity (FC) and EEG power during ictal onset patterns of low-voltage fast activity (LVFA) in drug-resistant focal epilepsy. They hypothesized that such changes would be useful to classify epilepsy surgical outcomes. METHODS: In a cohort of 79 patients with drug-resistant focal epilepsy who underwent stereoelectroencephalography (SEEG) evaluation as well as resective surgery, FC changes during the peri-LVFA period were measured using nonlinear regression (h2) and power spectral properties within/between three regions: the seizure onset zone (SOZ), early propagation zone (PZ), and noninvolved zone (NIZ). Desynchronization and power desynchronization h2 indices were calculated to assess the degree of EEG desynchronization during LVFA. Multivariate logistic regression was employed to control for confounding factors. Finally, receiver operating characteristic curves were generated to evaluate the performance of desynchronization indices in predicting surgical outcome. RESULTS: Fifty-three patients showed ictal LVFA and distinct zones of the SOZ, PZ, and NIZ. Among them, 39 patients (73.6%) achieved seizure freedom by the final follow-up. EEG desynchronization, measured by h2 analysis, was found in the seizure-free group during LVFA: FC decreased within the SOZ and between regions compared with the pre-LVFA and post-LVFA periods. In contrast, the non-seizure-free group showed no prominent EEG desynchronization. The h2 desynchronization index, but not the power desynchronization index, enabled classification of seizure-free versus non-seizure-free patients after resective surgery. CONCLUSIONS: EEG desynchronization during the peri-LVFA period, measured by within-zone and between-zone h2 analysis, may be helpful for identifying patients with favorable postsurgical outcomes and also may potentially improve epileptogenic zone identification in the future.

Team-Builder: Toward More Effective Lineup Selection in Soccer.

Lineup selection is an essential and important task in soccer matches. To win a match, coaches must consider various factors and select appropriate players for a planned formation. Computation-based tools have been proposed to help coaches on this complex task, but they are usually based on over-simplified models on player performances, do not support interactive analysis, and overlook the inputs by coaches. In this article, we propose a method for visual analytics of soccer lineup selection by tackling two challenges: characterizing essential factors involved in generating optimal lineup, and supporting coach-driven visual analytics of lineup selection. We develop a lineup selection model that integrates such important factors, such as spatial regions of player actions and defensive interactions with opponent players. A visualization system, Team-Builder, is developed to help coaches control the process of lineup generation, explanation, and comparison through multiple coordinated views. The usefulness and effectiveness of our system are demonstrated by two case studies on a real-world soccer event dataset.

VisImages: A Fine-Grained Expert-Annotated Visualization Dataset.

Images in visualization publications contain rich information, e.g., novel visualization designs and implicit design patterns of visualizations. A systematic collection of these images can contribute to the community in many aspects, such as literature analysis and automated tasks for visualization. In this paper, we build and make public a dataset, VisImages, which collects 12,267 images with captions from 1,397 papers in IEEE InfoVis and VAST. Built upon a comprehensive visualization taxonomy, the dataset includes 35,096 visualizations and their bounding boxes in the images. We demonstrate the usefulness of VisImages through three use cases: 1) investigating the use of visualizations in the publications with VisImages Explorer, 2) training and benchmarking models for visualization classification, and 3) localizing visualizations in the visual analytics systems automatically.