In cross-modal music processing, translation between visual, auditory, and semantic content opens up new possibilities as well as challenges. The construction of such a transformative scheme depends upon a benchmark corpus with a comprehensive data infrastructure. In particular, the assembly of a large-scale cross-modal dataset presents major challenges. In this paper, we present the MOSA (Music mOtion with Semantic Annotation) dataset, which contains high quality 3-D motion capture data, aligned audio recordings, and note-by-note semantic annotations of pitch, beat, phrase, dynamic, articulation, and harmony for 742 professional music performances by 23 professional musicians, comprising more than 30 hours and 570 K notes of data. To our knowledge, this is the largest cross-modal music dataset with note-level annotations to date. To demonstrate the usage of the MOSA dataset, we present several innovative cross-modal music information retrieval (MIR) and musical content generation tasks, including the detection of beats, downbeats, phrases, and expressive contents from audio, video and motion data, and the generation of musicians' body motion from given music audio. The dataset and codes are available alongside this publication (https://github.com/yufenhuang/MOSA-Music-mOtion-and-Semantic-Annotation-dataset).

Currently, on the Chinese internet, the term “involution” refers to intense but meaningless competition for limited resources, also known as “the inflation of effort.” Involution is a prevalent phenomenon in various social competition scenarios and can lead to significant social contradictions. On the other hand, “lying flat” refers to a conscious choice by individuals to withdraw from intense competition and pursue a self-centered and hollow existence. The propagation of lying flat culture may lead to nihilism or irrationalism. However, existing literature on involution and lying flat has two main limitations. First, previous studies have separately examined involution and lying flat as two opposite yet closely related outcomes of intense social competition. This approach fails to provide a comprehensive and in-depth understanding of involution and lying flat in the context of increasingly fierce social competition. Second, existing literature predominantly adopts value-based payoff allocation methods, disregarding the prevalence of rank-based payoff allocation methods. Consequently, the other prevalent rank-based allocation (RA) method remains understudied. To address these limitations, we propose an evolutionary game model on a square lattice to abstract the fierce social competition between involution, cooperation, and lying flat. Our experimental results demonstrate that various factors, including social resources, the cost of involution, the relative utility of the involution strategy, and the variance of agents’ competition abilities, play distinct yet significant roles in social competition. Furthermore, these factors exhibit interaction effects on the levels of cooperation, involution, and lying flat. The rank threshold ($T$) positively affects the level of cooperation, exhibits a U-shaped effect on the level of lying flat, and an inverted U-shaped effect on the level of involution. The RA rule tends to result in a higher level of involution, whereas the value-based rule tends to result in a higher level of lying flat. These findings remain robust across different network sizes. Our study provides insights into strategies for suppressing the levels of involution and lying flat in social competitions.