Effective patient-provider communication is often hindered by disparities in medical knowledge and the use of technical jargon. Although analogies and metaphors can help bridge this gap, physicians struggle to generate them under clinical time pressure, highlighting a need for supportive design. Through formative interviews with patients and physicians, we identified requirements for explanatory tools that are clear, accurate, and context-sensitive. In response, we designed MediMate, an interactive system that allows physicians to rehearse and iteratively refine patient-friendly explanations using LLM-generated analogies in a low-stakes setting. The interface of MediMate is designed to scaffold the creative process, helping physicians balance clarity with medical accuracy. In a user study involving both physicians and patients, we found that explanations developed with MediMate enhanced communication efficiency by providing such scaffolding. Physicians reported increased self-efficacy and perceived value in using the system as a practice tool for developing their communication skills. Our work demonstrates how interactive AI-powered tools can support clinical communication rehearsal and offers insights for the design of future clinical decision-support and educational tools.

Visual Analytics (VA) researchers frequently collaborate closely with domain experts to derive requirements and select appropriate solutions to fulfill these requirements. Despite strides made in exploring requirement and solution spaces, challenges persist due to the absence of guidance in the initial consideration space and the lack of shared problem-solving knowledge, often resulting in suboptimal solutions. To address these issues, we conducted an empirical study of VA research, with a focus on mapping the relations between requirement and solution spaces. Analyzing 220 VA papers, we formulate refined topologies for data, requirements, and solutions. We propose conceptualizing the connections between requirements, data, and solutions through knowledge graphs and utilizing solution paths to encapsulate fundamental problem-solving knowledge in visual analytics research. Through the integration of solution paths into a graph and analyzing their interconnections, we identified a subset of problem-driven design patterns that demonstrated the efficacy of our approach. By externalizing problem-solving knowledge and formulating problem-driven design patterns, our aim is to streamline the exploration of consideration space, facilitating the inclusion of "good" solutions, and establish a benchmark for shared design decisions among researchers and readers.

Underwater acoustic target recognition (UATR) is an important supporting technology for underwater information acquisition and countermeasure. Usually, ship radiated noise is covered by the underwater acoustic background and previous deep learning methods for this task rely on clear and effective acoustic features. We propose a novel network called AMNet to alleviate the problem in this letter. It consists of a multibranch backbone network coupled with a convolutional attention network. The proposed network is able to obtain the internal features of radiated noise from the time-frequency map of the original data. The convolutional attention network adaptively selects the effective features by weighting them against the global information of the time-frequency map to assist the multibranch backbone network in classification recognition. Experimental results demonstrate that our model achieves an overall accuracy of 99.4% (2.4% improvement) on the ShipsEar database.