AMS-Net: Modeling Adaptive Multi-Granularity Spatio-Temporal Cues for Video Action Recognition.

Effective spatio-temporal modeling as a core of video representation learning is challenged by complex scale variations in spatio-temporal cues in videos, especially different visual tempos of actions and varying spatial sizes of moving objects. Most of the existing works handle complex spatio-temporal scale variations based on input-level or feature-level pyramid mechanisms, which, however, rely on expensive multistream architectures or explore multiscale spatio-temporal features in a fixed manner. To effectively capture complex scale dynamics of spatio-temporal cues in an efficient way, this article proposes a single-stream architecture (SS-Arch.

House dust mite exposure attenuates influenza A infection in a mouse model of pulmonary allergic inflammation.

Environmental allergens elicit complex immune responses in the lungs that can promote the development of asthma or exacerbate preexisting asthma in susceptible individuals. House dust mites are one of the most common indoor allergens and are a significant driver of allergic disease. Respiratory infections are known factors in acute exacerbations of asthma but the impact of allergen on the pathogen is not well understood.