Quantum sensing with solid-state electron spin systems finds broad applications in diverse areas ranging from material and biomedical sciences to fundamental physics. Exploiting collective behavior of noninteracting spins holds the promise of pushing the detection limit to even lower levels, while to date, those levels are scarcely reached because of the broadened linewidth and inefficient readout of solid-state spin ensembles. Here, we experimentally demonstrate that such drawbacks can be overcome by a reborn maser technology at room temperature in the solid state. Owing to maser action, we observe a fourfold reduction in the electron paramagnetic resonance linewidth of an inhomogeneously broadened molecular spin ensemble, which is narrower than the same measured from single spins at cryogenic temperatures. The maser-based readout applied to near zero-field magnetometry showcases the measurement signal-to-noise ratio of 133 for single shots. This technique would be an important addition to the toolbox for boosting the sensitivity of solid-state ensemble spin sensors.

Language-driven action localization in videos is a challenging task that involves not only visual-linguistic matching but also action boundary prediction. Recent progress has been achieved through aligning language queries to video segments, but estimating precise boundaries is still under-explored. In this paper, we propose entity-aware and motion-aware Transformers that progressively localize actions in videos by first coarsely locating clips with entity queries and then finely predicting exact boundaries in a shrunken temporal region with motion queries. The entity-aware Transformer incorporates the textual entities into visual representation learning via cross-modal and cross-frame attentions to facilitate attending action-related video clips. The motion-aware Transformer captures fine-grained motion changes at multiple temporal scales via integrating long short-term memory into the self-attention module to further improve the precision of action boundary prediction. Extensive experiments on the Charades-STA and TACoS datasets demonstrate that our method achieves better performance than existing methods.