This paper presents a novel motion localization approach for recognizing actions and events in real videos. Examples include StandUp and Kiss in Hollywood movies. The challenge can be attributed to the large visual and motion variations imposed by realistic action poses. Previous works mainly focus on learning from descriptors of cuboids around space time interest points (STIP) to characterize actions. The size, shape and space-time position of cuboids are fixed without considering the underlying motion dynamics. This often results in large set of fragmentized cuboids which fail to capture long-term dynamic properties of realistic actions. This paper proposes the detection of spatio-temporal motion volumes (namely Volume of Interest, VOI) of scale and position adaptive to localize actions. First, motions are described as bags of point trajectories by tracking keypoints along the time dimension. VOIs are then adaptively extracted by clustering trajectory on the motion mainfold. The resulting VOIs, of varying scales and centering at arbitrary positions depending on motion dynamics, are eventually described by SIFT and 3D gradient features for action recognition. Comparing with fixed-size cuboids, VOI allows comprehensive modeling of long-term motion and shows better capability in capturing contextual information associated with motion dynamics. Experiments on a realistic Hollywood movie dataset show that the proposed approach can achieve 20% relative improvement compared to the state-ofthe-art STIP based algorithm. Categories and Subject Descriptors KeywordsHuman action recognition, Realistic videos, Motion subspace learning, Keypoint trajectory, Mean-shift clustering Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.

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Though ferric iron indisputably exists on the highly reduced Moon, its formation mechanism and evolution have yet to be disclosed. Here we show that micrometeorite impact-induced ferrous disproportionation could produce a large amount of ferric iron (average Fe 3+ /∑Fe > 0.4) in agglutinate melts returned by Chang'e-5 mission. The disproportionation reaction synchronously generated nanophase metallic iron (npFe 0 ), a dominant formation pathway of npFe 0 within the lunar agglutinate glass. The discovery of the disproportionation reaction in the agglutinates suggests that much more Fe 3+ could be ubiquitously present on the Moon than previously thought, and its abundance is progressively increasing with micrometeoroid impacts.