Distributed databases have been widely researched and developed in recent years due to their scalability, availability, and consistency guarantees. The write-ahead logging (WAL) system is one of the most vital components in a database. It is still a non-trivial problem to design a replicated logging system as the foundation of a distributed database with the power of ACID transactions. This paper proposes PALF, a Paxos-backed Append-only Log File System, to address these challenges. The basic idea behind PALF is to co-design the logging system with the entire database for supporting database-specific functions and to abstract the functions as PALF primitives to power other distributed systems. Many database functions, including transaction processing, database restore, and physical standby databases, have been built based on PALF primitives. Evaluation shows that PALF greatly outperforms well-known implementations of consensus protocols and is fully competent for distributed database workloads. PALF has been deployed as a component of the OceanBase 4.0 database and has been made open-source along with it.

Image saliency detection has attracted much attention in recent years, while several challenging problems are still unsolved, such as inaccurate saliency detection in complex scenes and suppressing salient objects near image borders. In this paper, a novel algorithm is proposed to solve these problems. Firstly, we collect background seeds from image borders by boundary information and construct a background-based saliency map via low level features. Then, a novel propagation mechanism named global-object-seed-guided Cellular Automata model is builded. Cellular Automata exploits the intrinsic relevance of similar regions through interactions with neighbors, and global object seeds reduce the difference between dissimilar adjacent regions in the whole salient object. Experimental results on public benchmark datasets demonstrate the superiority of the proposed algorithm over ten state-of-the-art saliency models.