We introduce an adaptive cardio-respiratory biofeedback system implemented on ubiquitous hand-worn devices such as smart watches and rings, enabling accessible and real-time physiological training outside clinical settings. Users place a hand on their abdomen to promote embodied awareness of breathing rhythms, while PPG and IMU sensors continuously capture cardio-respiratory signals. Unlike conventional open-loop biofeedback that delivers fixed breathing guidance irrespective of user response, our system employs a closed-loop adaptation: real-time physiological signals adjust breathing cues to optimize cardio-respiratory coupling, ensuring personalized training trajectories. This shift from static to adaptive guidance markedly improves user engagement and training efficacy. A user performance evaluation study further showed that adaptive biofeedback significantly boosts HRV, prolongs high-HRV states, and enhances user experience, demonstrating clear advantages over non-adaptive methods. Together, these findings position adaptive, hand-worn biofeedback as a promising approach for ubiquitous, user-centered mental health interventions.

As social robots take on increasingly complex roles like game masters (GMs) in multi-party games, the expectation that physicality universally enhances user experience remains debated. This study challenges the "one-size-fits-all" view of tangible interaction by identifying a critical boundary condition: users' Negative Attitudes towards Robots (NARS). In a between-subjects experiment (N = 67), a custom-built robot GM facilitated a multi-party murder mystery game (MMG) by delivering clues either through direct tangible interaction or a digitally mediated interface. Baseline multivariate analysis (MANOVA) showed no significant main effect of delivery modality, confirming that tangibility alone does not guarantee superior engagement. However, primary analysis using multilevel linear models (MLM) revealed a reliable moderation: participants high in NARS experienced markedly lower narrative immersion under tangible delivery, whereas those with low NARS scores showed no such decrement. Qualitative findings further illuminate this divergence: tangibility provides novelty and engagement for some but imposes excessive proxemic friction for anxious users, for whom the digital interface acts as a protective social buffer. These results advance a conditional model of HRI and emphasize the necessity for adaptive systems that can tailor interaction modalities to user predispositions.

Trust mechanisms diverge between centralized and decentralized exchanges, representing distinct sociotechnical governance paradigms. However, quantifying trust dynamics and their redistribution between these architectures remains empirically challenging, limiting understanding of how institutional shocks affect market behavior. The FTX collapse offers a natural experiment to bridge this gap. Through an interdisciplinary approach combining causal inference and computational text analysis, we find significant price declines and capital reallocation from centralized to decentralized exchanges following the event. While sentiment metrics showed no sharp discontinuities, topic modeling and network analysis of Discord communities reveal that seasonal holiday discourse obscured underlying trust concerns in centralized exchange forums. These findings underscore the fragility of institutional trust architectures and demonstrate how mixed methods can illuminate behavioral patterns during systemic crises, offering insights for exchange risk management and regulatory assessment.

This paper presents AccessiBot, a tactile paving detection robot developed to address urban accessibility challenges. Beyond collecting real-time data for evidence-based governance, this study explores AccessiBot's potential to encourage citizen engagement. Using the Wizard-of-Oz methodology, we conducted an in-the-wild study to simulate real-world interactions and observe how people respond to the robot. Our observation findings show that people do help robots, and their engagement with the robot varied significantly, ranging from passive acknowledgement to active assistance. Besides, post-hoc interviews indicated that participants recognized the robot's social value.

Low-batch large language model (LLM) inference has been extensively applied to edge-side generative tasks, such as personal chat helper, virtual assistant, reception bot, private edge server, etc.To efficiently handle both prefill and decoding stages in LLM inference, near-memory processing (NMP) enabled heterogeneous computation paradigm has been proposed.However, existing NMP designs typically embed processing engines into DRAM dies, resulting in limited computation capacity, which in turn restricts their ability to accelerate edge-side low-batch LLM inference.To tackle this problem, we propose H 2 -LLM, a Hybrid-bondingbased Heterogeneous accelerator for edge-side low-batch LLM inference.To balance the trade-off between computation capacity and bandwidth intrinsic to hybrid-bonding technology, we propose * Co-corresponding authors.