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Kexin Xiang

Publications and source records attributed to Kexin Xiang.

3 recordsLinked to original sources

CRAFT: Exploring Wearable Creative AI on Smart Glasses for Fiction Writing in Real-World Contexts

Creative writing increasingly integrates AI assistance, yet current tools miss in-situ moments when writers draw inspiration from real-world experiences. We envision Context-aware Reality-Fiction Transformation (CRAFT), an approach for AI glasses that translates daily experiences into fiction narratives. We explored its desirability, feasibility, and potential viability through three studies. Interviews with nine writers yielded desires and three design goals: 1) augmenting in-situ perception to bridge reality-fiction gaps, 2) promoting authenticity grounded in real-world experiences while maintaining fictionalization, and 3) preserving creative agency, enjoyment, and life-art boundaries. Co-design workshops with 16 writers and researchers operationalized these goals into concrete interaction mechanisms using a technology probe. We then conducted supported field trials with eight writers across 24 sessions using a refined probe, revealing writer-perceived benefits (e.g., enriched fictional ideas from serendipitous encounters), emergent practices (e.g., micro-creation), and design considerations for future sustained use. We contribute design explorations for the CRAFT approach, offering design implications and empirical insights on ubiquitous human-AI creative collaboration in everyday life.

cs.HC

Realization of Topology-controlled Photonic Cavities in a Valley Photonic Crystal

We report an experimental realization of a new type of topology-controlled photonic cavities in valley photonic crystals by adopting judiciously oriented mirrors to localize the valley-polarized edge states along their propagation path. By using microwave frequency- and time-domain measurements, we directly observe the strong confinement of electromagnetic energy at the mirror surface due to the extended time delay required for the valley index flipping. Moreover, we experimentally demonstrate that both the degree of energy localization and quality factors of the topology-controlled photonic cavities are determined by the valley-flipping time which is controlled by the topology of the mirror. These results extend and complement the current design paradigm of topological photonic cavities.

physics.optics

Method of diagnosing heart disease based on deep learning ECG signal

The traditional method of diagnosing heart disease on ECG signal is artificial observation. Some have tried to combine expertise and signal processing to classify ECG signal by heart disease type. However, the currency is not so sufficient that it can be used in medical applications. We develop an algorithm that combines signal processing and deep learning to classify ECG signals into Normal AF other rhythm and noise, which help us solve this problem. It is demonstrated that we can obtain the time-frequency diagram of ECG signal by wavelet transform, and use DNN to classify the time-frequency diagram to find out the heart disease that the signal collector may have. Overall, an accuracy of 94 percent is achieved on the validation set. According to the evaluation criteria of PhysioNet/Computing in Cardiology (CinC) in 2017, the F1 score of this method is 0.957, which is higher than the first place in the competition in 2017.

eess.SP