Sexual dimorphism exists widely in animals, manifesting in different forms, such as body size, color, shape, unique characteristics, behavior, and sound. Of these, body mass dimorphism is the most obvious. Studies of evolutionary and ontogenetic development and adaptation mechanisms of animals’ sexual dimorphism in body mass (SDBM), allow us to understand how environment, social group size, diet, and other external factors have driven the selection of sexual dimorphism. There are fewer reports of the ontogenetic development of sexual dimorphism in body mass in Rhinopithecus. This study explores the ontogenetic development pattern of SDBM in wild black-and-white snub-nosed monkeys (R. bieti), and the causes resulting in extreme sexual dimorphism compared to other colobines. A significant dimorphism with a ratio of 1.27 (p < 0.001) appears when females enter the reproductive period around six years old, reaching a peak (1.85, p < 0.001) when males become sexually mature. After the age of eight, the SDBM falls to 1.78, but is still significant (p < 0.001). The results also indicate that males had a longer body mass growth period than females (8 years vs. 5 years); females in larger breeding units had a significantly higher SDBM than those in smaller ones (2.12 vs. 1.93, p < 0.01). A comparative analysis with other colobines further clarifies that Rhinopithecus and Nasalis, which both have multilevel social organization, have the highest degree of SDBM among all colobines. The large SDBM in R. bieti can be explained through Bergman’s and Rensch’s rules. Overall, environmental adaptation, a distinctive alimentary system, and a complex social structure contribute to R. bieti having such a remarkable SDBM compared to other colobines. In addition, we found that females’ choice for males may not be significantly related to the development of SDBM.

Indexing structures are widely used in modern data-processing applications to support high-performance queries, and there are a variety of recent designs specifically optimized for the newly available persistent memory (PM). The primary focus of previous PM indexes is on reducing the expensive PM writes for persisting data. However, we find that in tree-based PM indexes, because of the smaller performance gap between writes and random reads on real PM devices, the read-intensive tree traversal phase dominates the overall latency. This observation calls for further optimizations on existing indexing structures for PM. In this paper, we propose Extendible Radix Tree (ERT), an efficient indexing structure for PM that significantly reduces tree heights to minimize random reads, while still maintaining fast in-node search speed. The key idea is to use extendible hashing for each node in a radix tree. This design allows us to have a relatively large fanout of the radix tree to keep the tree height small, and also to realize constant-time lookups within a node. Using extendible hashing also allows for incremental node modification without excessive writes during inserts and updates. Range queries are efficiently and robustly handled by enforcing partial ordering among the keys in the hash table of each node without introducing more hash collisions. Our experiments on both synthetic and real-world data sets demonstrate that ERT achieves up to 2.65×, 4.41×, and 2.43× speedups for search, insert, and range queries over the respectively state-of-the-art PM index.

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