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   https://doi.org/10.1145/3447818.3460356  

   Ren, Jie ; Luo, Jiaolin ; Peng, Ivy ; Wu, Kai ; Li, Dong ( June 2021 , ACM International Conference on Supercomputing)

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2. Improving memory via automated targeted memory reactivation during sleep

   https://doi.org/10.1111/jsr.13731  

   Whitmore, Nathan W. ; Harris, Jasmine C. ; Kovach, Torin ; Paller, Ken A. ( December 2022 , Journal of Sleep Research)
3. Enhanced Memory as a Common Effect of Active Learning: Enhanced Memory Through Active Learning

   https://doi.org/10.1111/mbe.12117  

   Markant, Douglas B. ; Ruggeri, Azzurra ; Gureckis, Todd M. ; Xu, Fei ( September 2016 , Mind, Brain, and Education)
4. ‘Sleep-dependent’ memory consolidation? Brief periods of post-training rest and sleep provide an equivalent benefit for both declarative and procedural memory

   https://doi.org/10.1101/lm.053330.120  

   Wang, Serene Y. ; Baker, Kirsten C. ; Culbreth, Jessica L. ; Tracy, Olivia ; Arora, Madison ; Liu, Tingtong ; Morris, Sydney ; Collins, Megan B. ; Wamsley, Erin J. ( May 2021 , Learning & Memory)

   Sleep following learning facilitates the consolidation of memories. This effect has often been attributed to sleep-specific factors, such as the presence of sleep spindles or slow waves in the electroencephalogram (EEG). However, recent studies suggest that simply resting quietly while awake could confer a similar memory benefit. In the current study, we examined the effects of sleep, quiet rest, and active wakefulness on the consolidation of declarative and procedural memory. We hypothesized that sleep and eyes-closed quiet rest would both benefit memory compared with a period of active wakefulness. After completing a declarative and a procedural memory task, participants began a 30-min retention period with PSG (polysomnographic) monitoring, in which they either slept ( n = 24), quietly rested with their eyes closed ( n = 22), or completed a distractor task ( n = 29). Following the retention period, participants were again tested on their memory for the two learning tasks. As hypothesized, sleep and quiet rest both led to better performance on the declarative and procedural memory tasks than did the distractor task. Moreover, the performance advantages conferred by rest were indistinguishable from those of sleep. These data suggest that neurobiology specific to sleep might not be necessary to induce the consolidation of memory, at least across very short retention intervals. Instead, offline memory consolidation may function opportunistically, occurring during either sleep or stimulus-free rest, provided a favorable neurobiological milieu and sufficient reduction of new encoding. 

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5. Breaking down memory walls: adaptive memory management in LSM-based storage systems

   https://doi.org/10.14778/3430915.3430916  

   Luo, Chen ; Carey, Michael J. ( November 2020 , Proceedings of the VLDB Endowment)

   Log-Structured Merge-trees (LSM-trees) have been widely used in modern NoSQL systems. Due to their out-of-place update design, LSM-trees have introduced memory walls among the memory components of multiple LSM-trees and between the write memory and the buffer cache. Optimal memory allocation among these regions is non-trivial because it is highly workload-dependent. Existing LSM-tree implementations instead adopt static memory allocation schemes due to their simplicity and robustness, sacrificing performance. In this paper, we attempt to break down these memory walls in LSM-based storage systems. We first present a memory management architecture that enables adaptive memory management. We then present a partitioned memory component structure with new flush policies to better exploit the write memory to minimize the write cost. To break down the memory wall between the write memory and the buffer cache, we further introduce a memory tuner that tunes the memory allocation between these two regions. We have conducted extensive experiments in the context of Apache AsterixDB using the YCSB and TPC-C benchmarks and we present the results here. 

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