Deep learning-based log anomaly detection demands extensive labeled data, posing significant challenges for emerging systems with limited logs. Transfer learning mitigates this issue by leveraging knowledge from data-rich source domains, enabling effective adaptation to data-scarce target domains. The challenge in recent cross-domain research lies in the joint optimization of knowledge transfer efficacy and model generalization capability. To address these limitations, we propose LogCADA, a novel logarithmic anomaly detection framework based on transfer learning, which can obtain effective common features through double-layer adversarial training, and distinguish common features and unique features between different domains through multi-source domain contrast alignment to achieve better knowledge transfer.The results demonstrate that our method can be adapted from dual source to single target domain and effectively overcome the inherent limitations of traditional cross-domain anomaly detection methods, yielding significant practical value for real-world log analysis scenarios.

GW approximation is a powerful approach to accurately describe the excited-state of semiconductors. However, GW incurs high computational cost $\mathcal{O}\left(N^{4}\right)$ and large memory usage $\mathcal{O}\left(N^{3}\right)$, limiting its applications to thousands of (2,742) atoms even on leadership supercomputers. Herein we present a massively parallel implementation of accurate and efficient cubic-scaling plane-wave GW calculations by using low-rank approximations and high-performance computing on leadership supercomputers. By using a series of low rank approximations, we can reduce the expensive GW calculations to the cubic-scaling computational cost $\mathcal{O}\left(N^{3}\right)$ and quadratic memory usage $\mathcal{O}\left(N^{2}\right)$. With the help of parallel and communication optimization, the plane-wave GW calculations gain an overall speedup of over 70x and efficiently scale up to 13,824 atoms within a few minutes using 449,280 cores on new Sunway supercomputer. This accomplishment paves the way for excited-state quantum mechanical material simulations at mesoscopic scale (10K atoms) and for the design of next-generation semiconductor devices.