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.

Understanding and interpreting a script is essential for effective acting. Existing visualization methods, however, primarily focus on general narrative comprehension and often neglect actors' specific cognitive and expressive needs. To address this gap, we propose an actor-oriented visual analytics approach that automatically extracts key script features, including implicit emotions, causal relationships, and critical events, to support rapid comprehension of coherent storylines. These extracted features are integrated into a coordinated multi-view visualization, enabling actors to analyze and explore scripts comprehensively and gain deeper insights into the narrative. Furthermore, we introduce a method for structuring performance guidance, converting script events into interpretable, multi-stage recommendations that adapt to individual acting styles. Actor feedback is incorporated to iteratively refine these recommendations, ensuring alignment with personal performance traits. To demonstrate the practicality of our approach, we implement ActorVis, a system that operationalizes these methods and provides actors with interactive visualizations and adaptive performance guidance. Case studies and user evaluations confirm the approach's effectiveness in facilitating rapid script understanding and enhancing personalized performance support.

Integrating pathological image and multimodal information such as patient metadata can help improve the segmentation performance. Most existing segmentation methods overlook the importance to exploit patient metadata, and suffer from suboptimal segmentation performance. To tackle these challenges, a novel multimodal feature fusion module (MFFM) is proposed. By incorporating a cross-modal attention mechanism and a self-attention mechanism, MFFM effectively and efficiently integrates information from different modalities. Experiments are conducted on GlaS, a glandular segmentation dataset, and the experimental results demonstrate that the method outperforms the state-of-the-art segmentation networks.

Medical image segmentation often suffers from performance degradation when applied to images from different domains. To address this, we propose DiMA-Seg (Diffusion Model Adaptation for Segmentation), a novel framework for unsupervised domain adaptation in medical image segmentation. DiMA-Seg combines GAN-based image translation with diffusion model-based feature extraction, leveraging the strengths of both approaches. Our method utilizes the hierarchical nature of diffusion models to extract multi-scale features for accurate segmentation in the target domain. Experiment on MMWHS dataset demonstrates that DiMA-Seg outperforms existing methods in segmentation accuracy.

The daily passage of vehicles generates a huge amount of location-aware social data, which provides a rich source of data for analyzing vehicle travel behavior. Being able to accurately predict the future destinations of vehicle travel has great economic value and social impact. The presence of larger sparsity, fewer features and error information in the real dataset led to difficulties in convergence of previous models. Therefore, we propose a Novel Vehicle Destination Prediction Model with Expandable Features Using Attention Mechanism and Variational Autoencoder (EFAMVA). The EFAMVA model combines the autoencoder model and the attention mechanism has overcome the above mentioned problems. The variational autoencoder model obtains the hidden features conforming to the characteristics of the data from the structured vehicle driving data. And the attention mechanism can learn the appropriate combination of weight parameters. The comprehensive experimental results with other comparison models show that the EFAMVA model achieved the best index score, with the MSE value of 0.750, the RMSE value of 1.215, and the MAE value of 0.955. Therefore, it can be shown that the EFAMVA model has a better predictive effect on the future destination of the vehicle.