Jonathan Karin @jonathankarin.bsky.social · 08/12/20259/9 Our generative model discovered the "Kite" configuration that breaks the established trade-off and maximizes the number of surviving agents. 000
Jonathan Karin @jonathankarin.bsky.social · 08/12/20258/9 To solve this, we designed SwaGen: A GNN-based generative model that optimizes agents positions using a multi objective loss function that minimizes both DOD and Diffusion simultaneously. 100
Jonathan Karin @jonathankarin.bsky.social · 08/12/20257/9 Simulations validated this trade-off where the V-formation was frequently detected (58%) but rarely went extinct (5.6%). Conversely, the compact Rectangle was harder to detect (32.4%) but suffered a significantly higher extinction rate (32.0%). 100
Jonathan Karin @jonathankarin.bsky.social · 08/12/20255/9 Diffusion: How fast the predator diffuses on the swarm’s graph. Compact swarms are easy to diffuse (bad for surviving). 100
Jonathan Karin @jonathankarin.bsky.social · 08/12/20254/9 Those risks can be quantified : Domain of Danger (DOD): The area where the predator can detect the prey. Compact swarms have low DOD (good for hiding). 100
Jonathan Karin @jonathankarin.bsky.social · 08/12/20253/9 We model this problem as a graph problem, where each agent in the warm is a node, and the predator is a signal diffusing through the graph. 100
Jonathan Karin @jonathankarin.bsky.social · 08/12/20242/3 Explore how deep learning training dynamics reveal insights into single-cell and spatial omics data—It can identify erroneous annotations, ambiguous cell states, infer continuous trajectories from binary labels. 110
Jonathan Karin @jonathankarin.bsky.social · 05/12/20242/3 Explore how deep learning training dynamics reveal insights into single-cell and spatial omics data—It can identify erroneous annotations, ambiguous cell states states, infer continuous trajectories from binary labels. 110
