Efficient processing of streaming graphs is crucial to improve system performance. Due to the highly irregular and frequent access to data in streaming graph processing, existing cache management methods are difficult to accurately predict cache behavior, resulting in serious cache misses. To address the issues, we propose CEGraph, an efficient cache management approach for streaming graph processing. Specifically, for graph data, we propose a cache replacement policy based on vertex importance. This policy accurately evaluates the importance of vertices in the incremental processing of streaming graphs from our proposed three factors: the association degree of affected state of a vertex, the path distance of a vertex, and whether a vertex will be updated. Vertices with high importance are identified and kept in the cache to reduce cache thrashing. Experimental results reveal that compared with LRU, DRRIP and Grasp, CEGraph reduces the LLC misses by an average of 22.93% (maximum 34.27%), 20.87% and 11.91%, respectively. Compared with the state-of-the-art cache management method P-OPT, CEGraph reduces the LLC misses by 6.46% on average, therefore demonstrating the effectiveness of CEGraph.

In recent years, advancements in large language models have led to significant innovation and critical progress in AI. However, some of these innovations are raising privacy and security concerns. Machine unlearning has therefore emerged as a potential solution to mitigate such risks. Yet, while erasing data records from traditional models is relatively straightforward, making a large language model “forget” what it has learned is often very challenging. This is not just because they include so many parameters, it is also because the knowledge they possess is intricately entangled. Further, the privacy risk of unlearned data remains neglected in most unlearning solutions. To overcome these limitations, we took advantage of information retrieval and developed an efficient privacy-preserving unlearning mechanism. Our solution eliminates the impact of targeted information by removing high-risk semantic meanings from the model’s output. It also incorporates differentially-private randomization to make the unlearned information statistically indiscernible. Most importantly, the algorithm requires neither parametric fine-tuning nor in-context prompt calibration. A theoretical analysis demonstrates that this method satisfies rigorous privacy and unlearning guarantees. Additionally, experiments on real-world datasets prove that the method is both effective and has the capacity to handle practical unlearning tasks for large language model applications.

Nanopore selective sequencing allows the targeted sequencing of DNA of interest using computational approaches rather than experimental methods such as targeted multiplex polymerase chain reaction or hybridization capture. Compared to sequence-alignment strategies, deep learning (DL) models for classifying target and nontarget DNA provide large speed advantages. However, the relatively low accuracy of these DL-based tools hinders their application in nanopore selective sequencing. Here, we present a DL-based tool named ReadCurrent for nanopore selective sequencing, which takes electric currents as inputs. ReadCurrent employs a modified very deep convolutional neural network (VDCNN) architecture, enabling significantly lower computational costs for training and quicker inference compared to conventional VDCNN. We evaluated the performance of ReadCurrent across 10 nanopore sequencing datasets spanning human, yeasts, bacteria, and viruses. We observed that ReadCurrent achieved a mean accuracy of 98.57% for classification, outperforming four other DL-based selective sequencing methods. In experimental validation that selectively sequenced microbial DNA from human DNA, ReadCurrent achieved an enrichment ratio of 2.85, which was higher than the 2.7 ratio achieved by MinKNOW using the sequence-alignment strategy. In summary, ReadCurrent can rapidly classify target and nontarget DNA with high accuracy, providing an alternative in the toolbox for nanopore selective sequencing. ReadCurrent is available at https://github.com/Ming-Ni-Group/ReadCurrent.