Matrix preconditioning is a critical technique to accelerate the solution of linear systems, where performance heavily depends on the selection of preconditioning parameters. Traditional parameter selection approaches often define fixed constants for specific scenarios. However, they rely on domain expertise and fail to consider the instance-wise features for individual problems, limiting their performance. In contrast, machine learning (ML) approaches, though promising, are hindered by high inference costs and limited interpretability. To combine the strengths of both approaches, we propose a symbolic discovery framework—namely, **Sym**bolic **Ma**trix **P**reconditioning (**SymMaP**)—to learn efficient symbolic expressions for preconditioning parameters. Specifically, we employ a neural network to search the high-dimensional discrete space for expressions that can accurately predict the optimal parameters. The learned expression allows for high inference efficiency and excellent interpretability (expressed in concise symbolic formulas), making it simple and reliable for deployment. Experimental results show that SymMaP consistently outperforms traditional strategies across various benchmarks.

Aspect Sentiment Triplet Extraction (ASTE) aims to co-extract the sentiment triplets in a given corpus.Existing approaches within the pretraining-finetuning paradigm tend to either meticulously craft complex tagging schemes and classification heads, or incorporate external semantic augmentation to enhance performance.In this study, we, for the first time, re-evaluate the redundancy in tagging schemes and the internal enhancement in pretrained representations.We propose a method to improve and utilize pretrained representations by integrating a minimalist tagging scheme and a novel token-level contrastive learning strategy.The proposed approach demonstrates comparable or superior performance compared to stateof-the-art techniques while featuring a more compact design and reduced computational overhead.Additionally, we are the first to formally evaluate GPT-4's performance in fewshot learning and Chain-of-Thought scenarios for this task.The results demonstrate that the pretraining-finetuning paradigm remains highly effective even in the era of large language models.

In recent years, encrypted traffic classification has been found widespread applications in the field of cybersecurity. Its main challenge lies in accurately represent traffic when features are obscured due to encryption. To address this, researchers utilize fingerprint construction methods based on statistical information or employ Deep Learning (DL) for traffic representation. However, in previous methods of feature selection, flat key-value pair features, or raw packet bytes are often used, ignoring the structured information embedded in packets and flows. Therefore, We propose a novel structured encrypted traffic fingerprint generation method called SHTree. It constructs traffic fingerprints using a set of tree-based structures to represent traffic, encapsulating structural features from the traffic, enhancing the representation of traffic. This enables it to adapt to various classification tasks through general feature selection. The experiments demonstrate that our method achieves comparable accuracy to state-of-the-art Large Language Models (LLMs), with an F1 score higher by 0.5% on specific tasks. Meanwhile, it outperforms by three orders of magnitude in classification speed. In unsupervised abnormal detection tasks, the True Positive Rate (TPR) exceeds 99%, while maintaining a False Positive Rate (FPR) of 0.5%.