Search

This article explores the criteria used to ascertain whether or not, and how, ancient Chinese bronze bells might form sets of musical instruments, thus clarifying their original intended use by ancient musicians. The focus is on ten bells unearthed from Changsha city in Hunan province in China, which date to the twelfth century BCE. A range of diverse relationships between these bells provide valuable insights that can be explored. Debate is ongoing among music historians regarding the nature of the bells. Some argue that the ten bells do not constitute a set of musical instruments because their designs and acoustic properties lack sufficient similarity and correlation. Conversely, another group of historians suggests that nine of the ten bells could indeed be seen as a set, but with the tenth bell being an outlier due to its distinct design. The third group of historians contends that the ten bells should be considered a musical set because of their organized sequence of consecutive semi-tones, while the design differences of the bells are less significant and can be overlooked if we focus on their musical functions. In this study, we scrutinize the assumptions made by these music historians, including their definitions of design similarity and the sequence of progressive pitches. Further previously overlooked criteria, such as the geographical proximity of these ten bells and others found in neighboring areas, and the incremental changes in the bell sizes, are also considered. Through the use of these criteria we aim to explore a wide range of relationships between ancient Chinese bells and to reassess what other new evidence the bells may offer to the study of ancient Chinese musical concepts. The concept of the chromatic scale discovered by ancient Chinese musicians was embodied on the casting of bronze bells. [ABSTRACT FROM AUTHOR]

Brain network analysis plays an increasingly important role in studying brain function and the exploring of disease mechanisms. However, existing brain network construction tools have some limitations, including dependency on empirical users, weak consistency in repeated experiments and time-consuming processes. In this work, a diffusion-based brain network pipeline, DGCL is designed for end-to-end construction of brain networks. Initially, the brain region-aware module (BRAM) precisely determines the spatial locations of brain regions by the diffusion process, avoiding subjective parameter selection. Subsequently, DGCL employs graph contrastive learning to optimize brain connections by eliminating individual differences in redundant connections unrelated to diseases, thereby enhancing the consistency of brain networks within the same group. Finally, the node-graph contrastive loss and classification loss jointly constrain the learning process of the model to obtain the reconstructed brain network, which is then used to analyze important brain connections. Validation on two datasets, ADNI and ABIDE, demonstrates that DGCL surpasses traditional methods and other deep learning models in predicting disease development stages. Significantly, the proposed model improves the efficiency and generalization of brain network construction. In summary, the proposed DGCL can be served as a universal brain network construction scheme, which can effectively identify important brain connections through generative paradigms and has the potential to provide disease interpretability support for neuroscience research.