Turner College economist Frank Mixon and Steven Caudill of Auburn University recently inked a deal with Nova Science Publishers out of Hauppauge, New York, to produce a new scholarly book titled Mixture Models: Statistical Foundations, Estimation Strategies, and Applications . Mixture models, and their latent class model representatives, are powerful statistical techniques used to uncover hidden, unobserved subgroups (classes) within a broader population. Both models assume that the overall population is a mixture of multiple distinct subpopulations. Because class membership is unobserved, the models estimate the probability that an individual belongs to each class, effectively making them probabilistic, model-based clustering methods. Mixon has published several books over his career, with the most recent being a 2025 book with Turner College accounting professor Jasmine Bordere titled The Beauty Premium in Academe: An Economic Approach . In 2020, Mixon and Laura Ahlstrom of the Uni...
The rapid growth of unstructured data has driven the widespread adoption of LSM-tree-based key-value stores (KV stores). The write amplification resulting from compaction in LSM-trees causes a performance bottleneck. Existing solutions attempt to address this issue through key-value separation strategies. However, these studies fail to optimize the memory components of LSM-trees or provide efficient garbage collection (GC) strategies that achieve high performance while minimizing CPU overhead. These limitations motivated TSYS School computer scientist Yi Zhou and researchers from Anhui University, Zhongguancun Laboratory and Auburn University to propose a GPGPU-empowered gradient data hierarchy and key-value separation for optimizing KV stores, named GDH+. In a study set to appear in a forthcoming issue of ACM Transactions on Architecture and Code Optimization , Zhou et al. utilize GPGPU acceleration for sorting and lushing operations, op...