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In time-domain astronomy, STLF (Short-Timescale and Large Field-of-view) sky survey is the latest way of sky observation. Compared to traditional sky survey who can only find astronomical phenomena, STLF sky survey can even reveal how short astronomical phenomena evolve. The difference does not only lead the new survey data but also the new analysis style. It requires that database behind STLF sky survey should support continuous analysis on data streaming, real-time analysis on short-term data and complex analysis on long-term historical data. In addition, both insertion and query latencies have strict requirements to ensure that scientific phenomena can be discovered. However, the existing databases cannot support our scenario. In this paper, we propose AstroServ, a distributed system for analysis and management of largescale and full life-cycle astronomical data. AstroServ's core components include three data service layers and a query engine. Each data service layer serves for a specific time period of data and query engine can provide the uniform analysis interface on different data. In addition, we also provide many applications including interactive analysis interface and data mining tool to help scientists efficiently use data. The experimental results show that AstroServ can meet the strict performance requirements and the good recognition accuracy.

Unsupervised domain adaptation endeavors to learn a desirable classifier for a target domain by transferring knowledge learned from a related (source) domain. Existing approaches focus on deriving domain-invariant feature representations by aligning the domain distributions. However, those works often require an extra classifier. In contrast, this paper proposes a classifier adaptation method based on modified label propagation (CAMLP) for unsupervised domain adaptation. Inspired by pseudolabeling, CAMLP proposes a simple but effective measurement for relationships among cross-domain samples. Thus, samples from distinct domains are constructed in a same graph. The true labels can then propagate from the source domain to the target one along the graph. We also propose a consistency-aware pseudolabel annotation to alleviate the problem of negative transfer caused by unreliable pseudo labels. Extensive experiments on several benchmark datasets confirm that the proposed method performs favorably against the state-of-the-art approaches.