Over the past few years, various domain-specific pretrained language models (PLMs) have been proposed and have outperformed general-domain PLMs in specialized areas such as biomedical, scientific, and clinical domains. In addition, financial PLMs have been studied because of the high economic impact of financial data analysis. However, we found that financial PLMs were not pretrained on sufficiently diverse financial data. This lack of diverse training data leads to a subpar generalization performance, resulting in general-purpose PLMs, including BERT, often outperforming financial PLMs on many downstream tasks. To address this issue, we collected a broad range of financial corpus and trained the Financial Language Model (FiLM) on these diverse datasets. Our experimental results confirm that FiLM outperforms not only existing financial PLMs but also general domain PLMs. Furthermore, we provide empirical evidence that this improvement can be achieved even for unseen corpus groups.

Modern DRAM cells are known to be vulnerable to RowHammer attacks because they are sensitive to electrical interference between adjacent rows. Most existing approaches to mitigate Rowhammer require complicated hardware structures. This paper proposes an effective protection scheme called Alert Refresh System. The proposed scheme has a low implementation overhead because it utilizes a pin called ALERT_n, which is a part of the standard DRAM interface. In addition, it applies hardware-friendly algorithms to track complicated RowHammer attacks. Experimental results show that the proposed method achieves a high average RowHammer protection rate of 99.81% against maliciously crafted RowHammer attacks, which is 16.94% higher than PRoHIT, one of the state-of-the-art probabilistic RowHammer mitigation methods.