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Knowledge graph is becoming an indispensable resource that offers structured information for numerous AI applications. However, the knowledge graph often suffers from its incompleteness. Building a complete, high-quality knowledge graph is time-consuming and requires significant human annotation efforts. In this paper, we study the Knowledge Graph Population task, which aims at extending the scale of structured knowledge, with a special focus on reducing data preparation and annotation efforts. Previous works mainly based on discriminative methods build classifiers and verify candidate triplets that are extracted from texts, which heavily rely on the quality of data collection and co-occurrance of entities in the text. However, such methods fail to generalize on entity pairs that are not highly co-occurred, and fail to discover entity pairs that are not co-occurred at all in the given text corpus. We introduce a generative perspective to approach this task and define each relationship by learning the data distribution that embodies the core common properties for relational reasoning. A generative model KGGen is proposed, which samples from the learned data distribution for each relation and can generate triplets regardless of entity pair co-occurrence in the text corpus. To further improve the generation quality while alleviate human annotation efforts, adversarial learning is adopted to not only encourage generating high quality triplets, but also give model the ability to automatically assess the generation quality. Quantitative and qualitative experimental results conducted on two real-world generic knowledge graphs show that the proposed model KGGen generates novel and meaningful triplets with improved efficiency and less human annotation comparing with the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In recent years, NAND flash memory has been widely used in mobile devices, laptops, desktops, and data center storage systems due to its low-power consumption, high performance, high density, lightweight, shock resistance, and high-reliability natures. However, as the stacked layers and the storage density increase, flash memory also suffers from a higher raw bit error rate (RBER) and shorter lifetime. Observing that reliable cell states suffer from less data retention errors and program disturbance, we propose a differential evolution coding scheme to increase the probability of storing data in more reliable cell states, thereby reducing the RBER. We conducted the experiments over a development platform of SSD storage device with 3D-TLC charge trap (CT) NAND flash memory. The experimental results showed that the proposed differential evolution algorithm with asymmetric coding scheme could averagely reduce RBER by 48.88%, 65.45%, 52.61%, 61.99%, 80.19%, and 33.18% compared with baseline, asymmetric coding algorithm, asymmetric coding scheme with stripe-pattern elimination algorithm, UAC- $n$ LC, word-line batch score modulation programming, and SCB schemes. [ABSTRACT FROM AUTHOR]