Urban visual querying leverages visual representations and interactions to depict the domain of interest and express related requests for exploring complex datasets, which is usually an iterative process. One main challenge of this process is the vast search space in terms of identifying querying conditions, observing querying results, and making the subsequent queries. This paper proposes a novel acceleration scheme that intelligently recommends a small set of querying results subject to previous queries. Central to our approach is a reinforcement learning based approach that trains a recommendation agent by simulating user behavior and characterizing the search space. We propose a mixed-initiative urban visual query scheme to enhance the exploration process additionally. We evaluate our approach by performing qualitative and quantitative experiments on a real-world scenario. The experimental results demonstrate the capability of reducing user workload, achieving optimized querying, and improving analysis efficiency.

Specifying visual encodings and interactions is exhausting but essential for authoring interactive visualizations. In this paper, we present G2, a customizable framework designed to support rapid generation of interactive visualizations with uniform specifications. G2 employs a data-driven grammar of graphics and defines a uniform set of interaction specifications. We discuss the design and implementation of G2 with rich examples, and a user interview to demonstrate its effectiveness. Since its first release in March 2016, G2 has undergone 360 iterations, received 12,000 stars, and supported over 22,600 related projects on Github.

Node-link diagrams are the most popular form for graph visualization. Yet, salient information of a node-link diagram cannot be fully depicted by solely presenting the visualization. We propose to augment node-link diagrams by creating textual descriptions for interested information. We conduct an expert review and a user interview to identify six requirements of generated interpretations, including three requirements for connection extraction and three requirements for visual expression. Our solution, GraphDescriptor, generates textual descriptions with two stages: feature extraction and description generation. The first one identifies and extracts features of node-link diagrams, like node connections, visual designs, and types of graph layouts. The second stage creates a group of hierarchical sentences based on a pre-defined schema. To the best of our knowledge, our approach is the first attempt to generate textual descriptions automatically. Three use cases and the in-lab user study confirm the superiority of our approach.

Collusive fraud, in which multiple fraudsters collude to defraud health insurance funds, threatens the operation of the healthcare system. However, existing statistical and machine learning-based methods have limited ability to detect fraud in the scenario of health insurance due to the high similarity of fraudulent behaviors to normal medical visits and the lack of labeled data. To ensure the accuracy of the detection results, expert knowledge needs to be integrated with the fraud detection process. By working closely with health insurance audit experts, we propose FraudAuditor, a three-stage visual analytics approach to collusive fraud detection in health insurance. Specifically, we first allow users to interactively construct a co-visit network to holistically model the visit relationships of different patients. Second, an improved community detection algorithm that considers the strength of fraud likelihood is designed to detect suspicious fraudulent groups. Finally, through our visual interface, users can compare, investigate, and verify suspicious patient behavior with tailored visualizations that support different time scales. We conducted case studies in a real-world healthcare scenario, i.e., to help locate the actual fraud group and exclude the false positive group. The results and expert feedback proved the effectiveness and usability of the approach.

Modeling multivariate time series (MTS) is critical in modern intelligent systems. The accurate forecast of MTS data is still challenging due to the complicated latent variable correlation. Recent works apply the Graph Neural Networks (GNNs) to the task, with the basic idea of representing the correlation as a static graph. However, predicting with a static graph causes significant bias because the correlation is time-varying in the real-world MTS data. Besides, there is no gap analysis between the actual correlation and the learned one in their works to validate the effectiveness. This paper proposes a temporal polynomial graph neural network (TPGNN) for accurate MTS forecasting, which represents the dynamic variable correlation as a temporal matrix polynomial in two steps. First, we capture the overall correlation with a static matrix basis. Then, we use a set of time-varying coefficients and the matrix basis to construct a matrix polynomial for each time step. The constructed result empirically captures the precise dynamic correlation of six synthetic MTS datasets generated by a non-repeating random walk model. Moreover, the theoretical analysis shows that TPGNN can achieve perfect approximation under a commutative condition. We conduct extensive experiments on two traffic datasets with prior structure and four benchmark datasets. The results indicate that TPGNN achieves the state-of-the-art on both short-term and long-term MTS forecastings.

Use of synthetic data has provided a potential solution for addressing unavailable or insufficient training samples in deep learning-based magnetic resonance imaging (MRI). However, the challenge brought by domain gap between synthetic and real data is usually encountered, especially under complex experimental conditions. In this study, by combining Bloch simulation and general MRI models, we propose a framework for addressing the lack of training data in supervised learning scenarios, termed MOST-DL. A challenging application is demonstrated to verify the proposed framework and achieve motion-robust [Formula: see text] mapping using single-shot overlapping-echo acquisition. We decompose the process into two main steps: (1) calibrationless parallel reconstruction for ultra-fast pulse sequence and (2) intra-shot motion correction for [Formula: see text] mapping. To bridge the domain gap, realistic textures from a public database and various imperfection simulations were explored. The neural network was first trained with pure synthetic data and then evaluated with in vivo human brain. Both simulation and in vivo experiments show that the MOST-DL method significantly reduces ghosting and motion artifacts in [Formula: see text] maps in the presence of unpredictable subject movement and has the potential to be applied to motion-prone patients in the clinic. Our code is available at https://github.com/qinqinyang/MOST-DL.