Journal

The other group, which included me, worked on creating an adjacency matrix through triangle queries. We got 150 queries in the Airtable database. The dataset was processed on Matlab making adjacency matrices for 30, 60, 90, 120, and 150 queries. Below is a visualization of the adjacency matrix of the dataset after 150 queries. Yellow represents an edge, teal represents no edge, and blue represents an edge not observed.


Matthew did spectral clustering on the five adjacency matrices. Below is a plot of the graph of 30 queries. From the graph, we derive two clusters and 4 outliers. Matthew notes that spectral did so well that he started to become suspicious of the integrity of the dataset. After reviewing all the results, he notes that there is some positive correlation with performance and queries. Our goal for the next meeting is to get another dataset and compare its results with the strawberry one.


For me, my idea for the next few weeks is to create and improve a convex program for the large dataset. A convex optimization problem is a problem where all of the constraints are convex functions with the goal (in our specific case) to minimize the number of missing edges in clusters. When proposed to Dr. Hassibi, he mentioned that it would obviously be more difficult to implement than spectral. But he also liked the idea which is something I will try to pursue for the next few weeks. Below is a video of our meeting with Dr. Hassibi:


On an unrelated note, today I watched a youtube video from one of my favorite people on the platform, Michael Reeves. Michael builds bad-idea-fueled-robots with an unbelievably high level of skill. This week he dropped a video about a robot that is supposed to mimic the famous "da Vinci Surgical System". What I loved the most about the video is the VR camera he bought to track his hand for coordinate values. It's super amazing to know that there is AI there to trace everything down to the knuckles and joints. Below is the video I am referencing (profanity warning):


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