Score normalization is a key back-end technique in speaker verification for improving score comparability across trials. As a convenient and widely used normalization method, Adaptive Symmetric Normalization (AS-Norm) standardizes raw scores using mean-and-standard-deviation normalization parameters estimated from an adaptive cohort comprising the most similar impostor scores. However, since the adaptive cohort retains only the top-ranked impostor scores, these central-moment statistics may not optimally characterize the empirical distribution of these scores, resulting in suboptimal speaker verification performance. In this letter, we propose Adaptive Symmetric Extreme Value Normalization (AS-EVNorm), which treats the adaptive cohort as upper-tail samples from the impostor-score distribution and models them under extreme-value theory for more accurate normalization parameters. Experiments on VoxCeleb and CN-Celeb show that AS-EVNorm consistently reduces both EER and minDCF compared with AS-Norm across a broad range of adaptive cohort configurations, while maintaining competitive normalization time.

Buffer management is critical for DBMSs but often suffers from scalability bottlenecks and poor cache locality, which stems from centralized reference counting in page access and intensive locking in page-to-buffer translation. However, prior radical approaches like pointer swizzling or optimistic lock can hardly be adopted in production-grade DBMSs due to its inherent complexity and incompatibility. This paper proposes ScaleCache , a scalable, highly-efficient and production-grade buffer management system with three key designs. ScaleCache first incorporates a novel compact per-group buffer reference counting technique, which enables scalable buffer pinning and unpinning by concurrent threads on many-core servers. It then devised a novel read-write lock based on copy-on-write and per-group reference counting, which is suitable for B-link tree. At last, we propose an optimistic, CPU-cache friendly and SIMD-accelerated hash table for fast and scalable page-to-buffer translation, which eliminates most contention on modern many-core hardware. ScaleCache has been adopted in Huawei GaussDB , a commercial high-performance DBMS. Evaluation on a 128-core server demonstrates that ScaleCache exhibits near-linear scalability and can significantly improve index query throughput of both classic B-link tree index and complex graph-based vector index.

With the increasing adoption of digitization, more and more historical visualizations created hundreds of years ago are accessible in digital libraries online. It provides a unique opportunity for visualization and history research. Meanwhile, there is no large-scale digital collection dedicated to historical visualizations. The visualizations are scattered in various collections, which hinders retrieval. In this study, we curate the first large-scale dataset dedicated to historical visualizations. Our dataset comprises 13K historical visualization images with corresponding processed metadata from seven digital libraries. In curating the dataset, we propose a workflow to scrape and process heterogeneous metadata. We develop a semi-automatic labeling approach to distinguish visualizations from other artifacts. Our dataset can be accessed with OldVisOnline, a system we have built to browse and label historical visualizations. We discuss our vision of usage scenarios and research opportunities with our dataset, such as textual criticism for historical visualizations. Drawing upon our experience, we summarize recommendations for future efforts to improve our dataset.