HALo uses smartglasses IMU head orientation to localize conversation partners' acoustic zones, achieving 21% better performance with known partner count, while CoCo classifies partner numbers at 0.74 accuracy using only IMU data.
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MAP4TS combines global, local, statistical, and temporal prompts derived from classical time-series analysis with raw embeddings via cross-modality alignment to improve LLM forecasting performance across eight datasets.
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Towards Localizing Conversation Partners using Head Motion
HALo uses smartglasses IMU head orientation to localize conversation partners' acoustic zones, achieving 21% better performance with known partner count, while CoCo classifies partner numbers at 0.74 accuracy using only IMU data.
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MAP4TS: A Multi-Aspect Prompting Framework for Time-Series Forecasting with Large Language Models
MAP4TS combines global, local, statistical, and temporal prompts derived from classical time-series analysis with raw embeddings via cross-modality alignment to improve LLM forecasting performance across eight datasets.