AdaVis: Adaptive and Explainable Visualization Recommendation for Tabular Data

Automated visualization recommendation facilitates the rapid creation of effective visualizations, which is especially beneficial for users with limited time and limited knowledge of data visualization. There is an increasing trend in leveraging machine learning (ML) techniques to achieve an end-to-end visualization recommendation. However, existing ML-based approaches implicitly assume that there is only one appropriate visualization for a specific dataset, which is often not true for real applications. Also, they often work like a black box, and are difficult for users to understand the reasons for recommending specific visualizations. To fill the research gap, we propose AdaVis, an adaptive and explainable approach to recommend one or multiple appropriate visualizations for a tabular dataset. It leverages a box embedding-based knowledge graph to well model the possible one-to-many mapping relations among different entities (i.e., data features, dataset columns, datasets, and visualization choices). The embeddings of the entities and relations can be learned from dataset-visualization pairs. Also, AdaVis incorporates the attention mechanism into the inference framework. Attention can indicate the relative importance of data features for a dataset and provide fine-grained explainability. Our extensive evaluations through quantitative metric evaluations, case studies, and user interviews demonstrate the effectiveness of AdaVis

AdaVis: Adaptive and explainable visualization recommendation for tabular data

Ministry of Education, Singapore under its Academic Research Funding Tier

Multi-Channel Blind Restoration of Mixed Noise Images under Atmospheric Turbulence

The imaging quality of astronomical or space objects is significantly degraded by atmospheric turbulence, photon noise, image sensor noise, and other factors. A multi-channel alternating minimization (MCAM) method is proposed to restore degraded images, in which multiple blurred images at different times are selected, and the imaging object and the point spread function are reconstructed alternately. Results show that the restoration index can converge rapidly after two iterations of the MCAM method when six different images are adopted. According to the analysis of the structure similarity index, the stronger the influence of turbulence and mixed noise, the higher the degree of image improvement. The above results can provide a reference for blind restoration of images degraded by atmospheric turbulence and mixed noises

KG4Vis: A knowledge graph-based approach for visualization recommendation

Visualization recommendation or automatic visualization generation can significantly lower the barriers for general users to rapidly create effective data visualizations, especially for those users without a background in data visualizations. However, existing rule-based approaches require tedious manual specifications of visualization rules by visualization experts. Other machine learning-based approaches often work like black-box and are difficult to understand why a specific visualization is recommended, limiting the wider adoption of these approaches. This paper fills the gap by presenting KG4Vis, a knowledge graph (KG)-based approach for visualization recommendation. It does not require manual specifications of visualization rules and can also guarantee good explainability. Specifically, we propose a framework for building knowledge graphs, consisting of three types of entities (i.e., data features, data columns and visualization design choices) and the relations between them, to model the mapping rules between data and effective visualizations. A TransE-based embedding technique is employed to learn the embeddings of both entities and relations of the knowledge graph from existing dataset-visualization pairs. Such embeddings intrinsically model the desirable visualization rules. Then, given a new dataset, effective visualizations can be inferred from the knowledge graph with semantically meaningful rules. We conducted extensive evaluations to assess the proposed approach, including quantitative comparisons, case studies and expert interviews. The results demonstrate the effectiveness of our approach.Comment: 11 pages, 8 figures. IEEE Transactions on Visualization and Computer Graphics (Proceedings of IEEE VIS 2021

Effect of Simulated Organic–Inorganic N Deposition on Leaf Stoichiometry, Chlorophyll Content, and Chlorophyll Fluorescence in <i>Torreya grandis</i>

Atmospheric nitrogen (N) deposition is coupled with organic nitrogen (ON) and inorganic nitrogen (IN); however, little is known about plant growth and the balance of elements in Torreya grandis growing under different ON/IN ratios. Here, we investigated the effects of ON/IN ratios (1/9, 3/7, 7/3, and 9/1) on leaf stoichiometry (LF), chlorophyll content, and chlorophyll fluorescence of T. grandis. We used ammonium nitrate as the IN source and an equal proportion of urea and glycine as the ON source. The different ON/IN ratios altered the stoichiometry and photochemical efficiency in T. grandis. Although the leaf P content increased significantly after treatment, leaf N and N:P maintained a certain homeostasis. Torreya grandis plants performed best at an ON/IN ratio of 3/7, with the highest values of chlorophyll-a, total chlorophyll, maximum photochemical efficiency, and photosynthetic performance index. Thus, both ON and IN types should be considered when assessing the responses of plant growth to increasing N deposition in the future. Our results also indicated that the leaf P concentration was positively correlated with Chl, Fv/Fm, and PIabs. This result further indicates the importance of the P element for plant growth against the background of nitrogen deposition. Overall, these results indicate that T. grandis might cope with changes in the environment by maintaining the homeostasis of element stoichiometry and the plasticity of PSII activity

Comprehensive Genomic Survey, Structural Classification, and Expression Analysis of WRKY Transcription Factor&nbsp; Family in Rhododendron simsii

(1) Rhododendron is one of the top ten traditional flowers in China, with both high ornamental and economic values. However, with the change of the environment, Rhododendron suffers from various biological stresses. The WRKY transcription factor is a member of the most crucial transcription factor families, which plays an essential regulatory role in a variety of physiological processes and developmental stresses. (2) In this study, 57 RsWRKYs were identified using genome data and found to be randomly distributed on 13 chromosomes. Based on gene structure and phylogenetic relationships, 57 proteins were divided into three groups: I, II, and III. Multiple alignments of RsWRKYs with Arabidopsis thaliana homologous genes revealed that WRKY domains in different groups had different conserved sites. RsWRKYs have a highly conserved domain, WRKYGQK, with three variants, WRKYGKK, WRKYGEK, and WRKYGRK. Furthermore, cis-acting elements analysis revealed that all of the RsWRKYs had stress and plant hormone cis-elements, with figures varying by group. Finally, the expression patterns of nine WRKY genes treated with gibberellin acid (GA), methyl jasmonate (MeJA), heat, and drought in Rhododendron were also measured using quantitative real-time PCR (qRT-PCR). The results showed that the expression levels of the majority of RsWRKY genes changed in response to multiple phytohormones and abiotic stressors. (3) This current study establishes a theoretical basis for future studies on the response of RsWRKY transcription factors to various hormone and abiotic stresses as well as a significant foundation for the breeding of new stress-tolerant Rhododendron varieties