YJ
Yuting Jin
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3 records found
1
MIRA
A Human-AI Co-Creation Agent for Self-reflection through Squiggle Game
Conference paper
(2026)
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Yuting Jin, Tingting Wang, Dantong Qin, Zhibin Zhou, Mengkun Bi, Min Hua, Pan Wang
AI agents are increasingly explored for supporting reflection and well-being. However, we know little about how AI agents participate in reflective practices, particularly through co-creation. We presented MIRA, a co-creative AI agent that engages users in transforming abstract squiggles into concrete drawings while offering reflective feedback. Through a three-group comparative study, we examine how AI-mediated co-creation shapes reflective experience. We found that MIRA operates as a scaffold that introduces external reflective perspectives, helping users reinterpret experiences and surface emotions through visual expression. These findings highlight how AI agents can foster engaging co-creative experiences that encourage reflection among university students, while providing insights for designing AI-human co-creation that can extend to broader populations.
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AI agents are increasingly explored for supporting reflection and well-being. However, we know little about how AI agents participate in reflective practices, particularly through co-creation. We presented MIRA, a co-creative AI agent that engages users in transforming abstract squiggles into concrete drawings while offering reflective feedback. Through a three-group comparative study, we examine how AI-mediated co-creation shapes reflective experience. We found that MIRA operates as a scaffold that introduces external reflective perspectives, helping users reinterpret experiences and surface emotions through visual expression. These findings highlight how AI agents can foster engaging co-creative experiences that encourage reflection among university students, while providing insights for designing AI-human co-creation that can extend to broader populations.
Towards stereoscopic vision
Attention-guided gaze estimation with EEG in 3D space
Since traditional gaze-tracking methods rely on line-of-sight estimation, spatial attention modeling from neural activity offers an alternative perspective to gaze estimation. This paper presents a proof-of-concept study on attention-guided gaze estimation with Electroencephalography (EEG), investigating whether brain signals can be leveraged to estimate attentional focus within a controlled 3D environment. We first conducted a preliminary survey to gather public opinions, revealing a generally positive attitude towards EEG-driven gaze tracking. Building on this insight, we collected an EEG dataset in VR, where participants engaged with stimuli presented at predefined spatial locations. We introduce a deep learning model that estimates the relative saliency of candidate positions, enabling gaze estimation through optimization within the learned representation. Our results demonstrate that attentional focus was successfully mapped in a 3D coordinate space from 5 participants, and low-frequency oscillations contributed more significantly to predictive performance. The model achieved robust accuracy in distinguishing gaze locations, highlighting the potential of EEG-based gaze estimation for attention tracking in 3D environments.
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Since traditional gaze-tracking methods rely on line-of-sight estimation, spatial attention modeling from neural activity offers an alternative perspective to gaze estimation. This paper presents a proof-of-concept study on attention-guided gaze estimation with Electroencephalography (EEG), investigating whether brain signals can be leveraged to estimate attentional focus within a controlled 3D environment. We first conducted a preliminary survey to gather public opinions, revealing a generally positive attitude towards EEG-driven gaze tracking. Building on this insight, we collected an EEG dataset in VR, where participants engaged with stimuli presented at predefined spatial locations. We introduce a deep learning model that estimates the relative saliency of candidate positions, enabling gaze estimation through optimization within the learned representation. Our results demonstrate that attentional focus was successfully mapped in a 3D coordinate space from 5 participants, and low-frequency oscillations contributed more significantly to predictive performance. The model achieved robust accuracy in distinguishing gaze locations, highlighting the potential of EEG-based gaze estimation for attention tracking in 3D environments.
Journal article
(2024)
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Jiaqi Li, Yuting Jin, Jiahang Li, Zhong Li, Mingxing Zhang, Dake Xu, Arjan Mol, Fuhui Wang
The influence of dissolved oxygen (DO) and Shewanella algae on the corrosion behavior of pure titanium were systematically studied in this work. The formation of S. algae biofilms on titanium surface was facilitated by the anaerobic environment, which accelerated titanium corrosion. Upon the breakdown of passive film, S. algae acquired electrons from the titanium base substrate through extracellular electron transfer (EET) processes. Various EET-related genes were overexpressed by the low DO condition, leading to enhanced EET kinetics. High concentration DO increased TiO2 content and the thickness of passive film, which enhanced the protective effect and mitigated microbiologically influenced corrosion.
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The influence of dissolved oxygen (DO) and Shewanella algae on the corrosion behavior of pure titanium were systematically studied in this work. The formation of S. algae biofilms on titanium surface was facilitated by the anaerobic environment, which accelerated titanium corrosion. Upon the breakdown of passive film, S. algae acquired electrons from the titanium base substrate through extracellular electron transfer (EET) processes. Various EET-related genes were overexpressed by the low DO condition, leading to enhanced EET kinetics. High concentration DO increased TiO2 content and the thickness of passive film, which enhanced the protective effect and mitigated microbiologically influenced corrosion.