Researchers have proposed that the culture in which we are raised shapes the way that we attend to the objects and events that surround us. What remains unclear, however, is how early any such culturally-inflected differences emerge in development. Here, we address this issue directly, asking how 24-month-old infants from the US and China deploy their attention to objects and actions in dynamic scenes. By analyzing infants' eye movements while they observed dynamic scenes, the current experiment revealed striking convergences, overall, in infants' patterns of visual attention in the two communities, but also pinpointed a brief period during which their attention reliably diverged. This divergence, though modest, suggested that infants from the US devoted relatively more attention to the objects and those from China devoted relatively more attention to the actions in which they were engaged. This provides the earliest evidence for strong overlap in infants' attention to objects and events in dynamic scenes, but also raises the possibility that by 24 months, infants' attention may also be shaped subtly by the culturally-inflected attentional proclivities characteristic of adults in their cultural communities.

We present JueWu-SL, the first supervisedlearning-based artificial intelligence (AI) program that achieves human-level performance in playing multiplayer online battle arena (MOBA) games. Unlike prior attempts, we integrate the macro-strategy and the micromanagement of MOBA-game-playing into neural networks in a supervised and end-to-end manner. Tested on Honor of Kings, the most popular MOBA at present, our AI performs competitively at the level of High King players in standard 5v5 games. Index Terms-Game artificial intelligence (AI), learning systems, macro-strategy, micromanagement, multiplayer online battle arena (MOBA), neural networks. I. INTRODUCTION M ULTIPLAYER online battle arena (MOBA) games, e.g., Dota, Honor of Kings, and League of Legends, have been considered as an important and suitable testbed for artificial intelligence (AI) research due to their considerable complexity and varied playing mechanics [1]-[4]. The standard game mode of MOBA is 5v5, where two opposing teams of five players each compete against each other. In this mode, each individual in a team has to control the actions of one hero in real time based on both the situation dynamics and the team strategy. During the game, a hero can grow stronger by killing enemy heroes, pushing turrets, killing creeps and monsters, and so on. The goal for players in a team is to destroy their enemy's main structure while protecting their own.

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The thermodynamics and overcharging problem in RN-AdS black holes with a cloud of strings and quintessence are investigated by the absorption of scalar particles and fermions in extended phase space. The cosmological constant is treated as the pressure of the black hole. The parameters related to quintessence and the cloud of strings are treated as thermodynamic variables. We find that the first law of thermodynamics is satisfied and the second law of thermodynamics is indefinite. Furthermore, we find that near-extremal and extremal black holes cannot be overcharged.