Conference Records 2024-06-30:

Interesting sessions

Session name:

Details and Comments

• Color qualia structure (Kawakita et al., 2023)
• Empirical: measure the similarity of the structure
  → One of most interesting work is in the session below

Session name:

Details and Comments

• the definition of Qualia structure seems like a semantic vector space

• Does GWD capture the global feature of the point cloud space

• Persistence Homology might help?

• Why Wasserstein distance? Think of the curse of dimensionality, when dimensionality is large, the error of Wasserstein distance might become larger.

• Optimal transport seems functioned as algorithm that find the project function which costs less to transport the input points to output ones.

• Indeed, optimal transport could serve as a good function finder to capture the feature of a space, but is this feature really reflex the topological features of the vector space?

Answers from Prof. Oizumi

• Optimal transport is just one of the methods to capture the feature of the structures
• Indeed, anyone could test other distance
• → It's okay to reanalysis the open access data, let's do it

Session name:

Details and Comments

• Quality is Structure
• Cause-effect, Intrinsically, Information, Exclusion, Composition

On Space

• The way to capture "Space": reflexivity, Inclusion, Connection, Fusion
• Space is gridded

On Time

• Temporal flow: directed grid

Daily Summary

What's new

• The descriptive trial of measuring the semantic structure of "color"

What should I check

• OSF of Oizumi's work: https://osf.io/9xwr2/
• Hans and Tononi's work: https://www.mdpi.com/1099-4300/21/12/1160
• The usage of Pyphi: https://github.com/wmayner/pyphi/tree/feature/iit-4.0

References