Conference record: 20240630_preASSC_workshop
Conference Records 2024-06-30:
Conference Name: pre-ASSC
Location: Tokyo
Interesting sessions
Session name:
Topic: Introduction to Qualia Structure
Time: 9:00
Speaker: Naotsugu Tsuchiya
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:
Topic: Unsupervised alignment of qualia structures
Time: 10:30
Speaker: Masafumi Oizumi
Details and Comments
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the definition of Qualia structure seems like a semantic vector space
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Does GWD capture the global feature of the point cloud space
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Persistence Homology might help?
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Why Wasserstein distance? Think of the curse of dimensionality, when dimensionality is large, the error of Wasserstein distance might become larger.
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Optimal transport seems functioned as algorithm that find the project function which costs less to transport the input points to output ones.
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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:
Topic: introduction to IIT 4.0
Time: 11:30
Speaker: Giulio Tononi
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