Multiple-choice questions (MCQs) are a widely used educational tool, particularly in domains such as visualization literacy that require broad conceptual coverage and support diverse real-world applications. However, designing high-quality visualization literacy MCQs remains challenging, as instructors must coordinate multimodal elements (e.g., charts, question stems, and distractors), address diverse visualization tasks, and accommodate learners with heterogeneous backgrounds. Existing visualization literacy assessments primarily rely on standardized, fixed item banks, offering limited support for iterative question design that adapts to differences in learners' abilities, backgrounds, and reasoning strategies. To address these challenges, we present VizQStudio, a visual analytics system that supports instructors in iteratively designing and refining visualization literacy MCQs using MLLM-powered simulated students. Instructors can specify diverse student profiles spanning demographics, knowledge levels, and learning-related traits. The system then visualizes how simulated students reason about and respond to different question components, helping instructors explore potential misconceptions, difficulty calibration, and design trade-offs prior to classroom deployment. We investigate VizQStudio through a mixed-method evaluation, including expert interviews, case studies, a classroom deployment, and a large-scale online study. Our results indicate that MCQs designed with VizQStudio can support measurable learning gains and, within our exploratory online sample, yielded observed post-test outcomes similar to established benchmark questions, while enabling greater flexibility and scalability during the design process. Overall, this work reframes MLLM-based student simulation in assessment authoring as a design-time, exploratory aid. By examining both its value and limitations in realistic instructional settings, we surface design insights that inform how future systems can support instructor-centered, iterative, and responsible uses of AI for multimodal assessment design in visualization literacy and related domains.

Vector processing has evolved from early systems like the CDC STAR-100 and Cray-1 to modern ISAs like ARM's Scalable Vector Extension (SVE) and RISC-V Vector (RVV) extensions.However, scaling vector processing for contemporary workloads presents challenges due to overheads in traditional architectures.We introduce Titan-I (T1), an out-of-order (OoO) RVV architecture designed

For the joint communication and radar (JCR) system, how to alleviate the interference from the transmitted communication signal to the target echo is of crucial importance for radar detection. Concentrating on this, in this paper, we design a single-carrier spreading based joint communication and radar (SCS-JCR) system, which can significantly reduce the radio frequency (RF) chain isolation requirement between the transmitter and receiver. In our designed SCS-JCR system, we utilize the unique-word (UW) sequence for both pulse-Doppler radar detection and the channel equalization for communication. To alleviate the interference from the communication signal to the received target echo, we exploit the direct-sequence spreading method to reduce the communication transmission power. By optimizing the channel estimation and equalization for communication as well as the pulse compression and constant false alarm rate (CFAR) detection for radar detection, we can provide a high bit recovery performance in the multi-path fading channel, and maintain high target detection accuracy and multi-target resolution performance. Simulation results demonstrate that our designed SCS-JCR system can both maintain a low bit error rate (BER) in the multi-path fading channel, and in the meanwhile, can preserve an excellent multi-target detection performance given a low RF chain isolation between transmitter and receiver.

Regular screening is critical for individuals at risk of neurocognitive disorders (NCDs) to receive early intervention. Conversational agents (CAs) have been adopted to administer dialog-based NCD screening tests for their scalability compared to human-administered tests. However, unique communication skills are required for CAs during NCD screening, e.g., clinicians often apply scaffolding to ensure subjects’ understanding of and engagement in screening tests. Based on scaffolding theories and analysis of clinicians’ practices from human-administered test recordings, we designed a scaffolding framework for the CA. In an exploratory wizard-of-Oz study, the CA empowered by ChatGPT administered tasks in the Grocery Shopping Dialog Task with 15 participants (10 diagnosed with NCDs). Clinical experts verified the quality of the CA’s scaffolding and we explored its effects on task understanding of the participants. Moreover, we proposed implications for the future design of CAs that enable scaffolding for scalable NCD screening.