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CURE is proposed, a confounder-aware framework for multi-view urban region representation learning that improves predictive performance, remains robust under missing and noisy input views, and provides reliable cross-view integration through shared component separation and context-dependent view weighting.
This work revisits trajectory similarity learning from a lower-bound representation perspective and proposes LB-TrajRep, a unified lower-bound representation framework independent of deep neural embeddings that is able to consistently outperform state-of-the-art neural trajectory embeddings across diverse distance meas...
The results show that tool failures can be recognized at the return boundary, but reliable diagnosis requires combining multiple signals, and likelihood-based signals clearly capture incomplete returns and some direct inconsistencies, while action-based signals reveal how strongly a failure changes the next decision.
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