In particular, we apply this approach to build CUBE (CUltural BEnchmark forText-to-Image models), afirst-of-its-kind benchmark toevaluate cultural competence of T2I models.2 CUBE covers cultural artifacts associated with 8 countries across different geo-cultural regions and along 3 concepts: cuisine, landmarks, and art. CUBE consists of 1) CUBE-1K, a set of high-quality prompts thatenable theevaluation ofcultural awareness, and2)CUBE-CSpace, a larger dataset of cultural artifacts that serves as grounding to evaluate cultural diversity.

Scikit - learn: Machinelearninginpython.the Journalofmachine Learningresearch, 12: 2825-2830, 2011.

Such diversity hampers knowledge sharing, learning, and generalization across tasks and environments.

It also tends towards high capacity storage of all pieces of information which may be relevant for future reasoning [42, 3, 4]. By contrast, longterm memory changes slowly [45, 41], is highly selective and involves repeated consolidation. It contains a set of memories that summarize the entire past, only storing details about observations whicharemostrelevant[28,6]. Deep Learning has seen a variety of architectures for processing sequential data [36, 57, 18].

Intheexamplein Figure 2, the queryqi is "Loadalltablesfromdataset". Conferenceon Reverse Engineering (WCRE 2002), 28 October - 1 November 2002, VA, USAIEEE Computer Society, 2002.