Revitalizing Japan's remote areas has become a crucial task, and Matsue City exemplifies this effort in its temporary event spaces, created through collective efforts to foster urban vibrancy and bring together residents and visitors. This research examines the relationship between data-driven in-sights using generative AI and visual attractiveness by evaluating tempo-rary events in Matsue City, particularly considering the cognitive-cultural differences in processing visual information of the participants. The first phase employs semantic keyword extraction from interviews, categorizing responses into physical elements, activities, and atmosphere. The second phase analyzes spatial perception through three categories: layout hierar-chy, product visibility, and visual attention. The correlation indicates that successful event design requires a balance between spatial efficiency and diverse needs, with a spatial organization that optimizes visitor flow and visibility strategies considering cultural and demographic diversity. These findings contribute to understanding the urban quality of temporary event spaces and offer a replicable framework for enhancing the visual appeal of events in remote areas throughout Japan.

We introduce EV3, a novel meta-optimization framework designed to efficiently train scalable machine learning models through an intuitive explore-assess-adapt protocol. In each iteration of EV3, we explore various model parameter updates, assess them using pertinent evaluation methods, and then adapt the model based on the optimal updates and previous progress history. EV3 offers substantial flexibility without imposing stringent constraints like differentiability on the key objectives relevant to the tasks of interest, allowing for exploratory updates with intentionally-biased gradients and through a diversity of losses and optimizers. Additionally, the assessment phase provides reliable safety controls to ensure robust generalization, and can dynamically prioritize tasks in scenarios with multiple objectives. With inspiration drawn from evolutionary algorithms, meta-learning, and neural architecture search, we investigate an application of EV3 to knowledge distillation. Our experimental results illustrate EV3's capability to safely explore the modeling landscape, while hinting at its potential applicability across numerous domains due to its inherent flexibility and adaptability.

Causal learning is an inductive process and causal knowledge about the world is of paramount importance for intelligent systems, natural or artificial. Given an observation of events happening in the world, how does an intelligent system establish the causalities between them? The issue is further complicated by intervening noisy events. Psychologists have proposed a contingency model of causal induction but it does not incorporate computational means of addressing the issues of intervening noise to recover the causalities between events. In this paper we propose an inductive causal learning method that is able to establish causalities between events in the presence of intervening noisy events, and we apply the method to real-world data to investigate its viability. We demonstrate that the learning method works well in uncovering valid causalities, and relatively non-noisy, opportunistic situations provide the best confirmation of the causalities involved. Causal knowledge is the foundation of problem solving and the ability to learn causal knowledge enables the intelligent system to be maximally adaptive.