Given the complicated label hierarchy, hierarchical text classification (HTC) has emerged as a challenging subtask in the realm of multi-label text classification. Existing methods enhance the quality of text representations by contrastive learning, but this supervised contrastive learning is designed for single-label setting and has two main limitations. On one hand, sample pairs with completely identical labels which should be treated as positive pairs are ignored. On the other hand, a simple pair is deemed as an absolutely positive or negative pair, which lacks consideration about the situation where sample pairs share some labels while having labels unique to each sample. Therefore, we propose a method combining multi-label contrastive learning with KNN (MLCL-KNN) for HTC. The proposed multi-label contrastive learning method can make text representations of sample pairs having more shared labels closer and separate those with no labels in common. During inference, we employ KNN to retrieve several neighbor samples and regard their labels as additional prediction, which is interpolated into the model output to further improve the performance of MLCL-KNN. Compared with the strongest baseline, MLCL-KNN achieves average improvements of 0.31%, 0.76%, 0.83%, and 0.43% on Micro-F1, Macro-F1, accuracy, and HiF respectively, which demonstrates its effectiveness.

Non-Uniform Cache Architecture (NUCA) is a viable solution to mitigate the problem of large on-chip wire delay due to the rapid increase in the cache capacity of chip multiprocessors (CMPs). Through partitioning the last-level cache (LLC) into smaller banks connected by on-chip network, the access latency will exhibit non-uniform distribution. Various works have well explored the NUCA design, including block migration, block replication and block searching. However, all of the previous mechanisms designed for NUCA are thread-oblivious when multi-threaded applications are deployed on CMP systems. Due to the interference on shared resources, threads often demonstrate unbalanced progress wherein the lagging threads with slow progress are more critical to overall performance. In this paper, we propose a novel NUCA design called thread Criticality Assisted Replication and Migration (CARM). CARM exploits the runtime thread criticality information as hints to adjust the block replication and migration in NUCA. Specifically, CARM aims at boosting parallel application execution through prioritizing block replication and migration for critical threads. Full-system experimental results show that CARM reduces the execution time of a set of PARSEC workloads by 13.7 and 6.8 percent on average compared with the tradition D-NUCA and Re-NUCA respectively. Moreover, CARM also consumes much less energy compared with the evaluated schemes.