Designing immersive technologies for older adults often overlooks the situated nature of community-based activities. While Virtual Reality (VR) offers promising affordances for meaningful enrichment, its integration remains underexplored in community musicking. We conducted a two-month field study at a Chinese Continuing Care Retirement Community (CCRC), collaborating with 15 older adults and 3 music therapists through observations, interviews, and envisioning workshops. Our findings identify how older adults and therapists perceive VR through the lens of established musical values, including self-cultivation, social connectedness, and emotional and aesthetic resonance. We identified polarized responses to immersion shaped by personal history, and a recognized potential for VR to support musical imagination and reinterpretation beyond mere functionality. We discuss design considerations that emphasize therapists’ roles as gatekeepers, the need for culturally meaningful content, and responsive interactions to support the inclusive and sustainable deployment of VR in managed care settings.

This paper investigates the robust complete synchronization of coupled Boolean networks (BNs) subject to stuck-at fault. When stuck-at fault occurs, certain nodes become permanently fixed and the original synchronization conditions may no longer be applicable. To address this issue, we propose a new concept of fault-preserving subset to characterize the admissible invariant state evolution induced by stuck-at fault. Based on this concept, necessary and sufficient conditions are derived to determine whether complete synchronization remains valid without reconstructing the faulty network model. Furthermore, for coupled Boolean control networks (BCNs), a robust feedback complete synchronization scheme is developed by designing state feedback controllers based on maximal control fault-preserving subset. Finally, several numerical examples are provided to demonstrate the effectiveness of the proposed results.

The widespread adoption of digital technology has ushered in a new era of digital transformation across all aspects of our lives. Online learning, social, and work activities, such as distance education, videoconferencing, interviews, and talks, have led to a dramatic increase in speech-rich video content. In contrast to other video types, such as surveillance footage, which typically contain abundant visual cues, speech-rich videos convey most of their meaningful information through the audio channel. This poses challenges for improving content consumption using existing visual-based video summarization, navigation, and exploration systems. In this paper, we present VisAug, a novel interactive system designed to enhance speech-rich video navigation and engagement by automatically generating informative and expressive visual augmentations based on the speech content of videos. Our findings suggest that this system has the potential to significantly enhance the consumption and engagement of information in an increasingly video-driven digital landscape.

Standing at the intersection of science and art, artistic data visualization has gained popularity in recent years and emerged as a significant domain. Despite more than a decade since the field’s conceptualization, a noticeable gap remains in research concerning the design features of artistic data visualizations, the aesthetic goals they pursue, and their potential to inspire our community. To address these gaps, we analyzed 220 data artworks to understand their design paradigms and intents, and construct a design taxonomy to characterize their design techniques (e.g., sensation, interaction, narrative, physicality). We also conducted in-depth interviews with twelve data artists to explore their practical perspectives, such as their understanding of artistic data visualization and the challenges they encounter. In brief, we found that artistic data visualization is deeply rooted in art discourse, with its own distinctive characteristics in both inner pursuits and outer presentations. Based on our research, we outline seven prospective paths for future work.

Video question answering has become a cornerstone task for evaluating vision language models. However, existing models often fail to ground their answers in relevant visual evidence or incorrectly model distributions during localization. To address this limitation, we propose MGQA, which models videos as a sequence of discrete events using mixture Gaussian distributions, with each Gaussian characterized by its center, range, and weight. MGQA leverages question-answering accuracy as a weak supervision signal and incorporates two additional Gaussian-related loss functions. The method can be easily integrated into existing models with negligible parameter overhead. Experiments conducted on the NExT-GQA and ReX-Time datasets demonstrate the effectiveness of our proposed method.