Abstract for Karin Dahmen's talk
Simple statistical models can be used to describe the statistics of domino-effect like avalanches in many systems, ranging from magnets to earthquakes to outbreaks of epidemics, to cascades supply chain interruptions. The models can be used to predict how the sizes of the largest avalanches can be kept small, and how to transfer scaling results from one system to another. The models predict qualitative phase diagrams and universal (i.e. detail-independent) scaling properties of the statistical distributions of the avalanches. These universal properties can be compared with experiments, simulations, and observations in the real world for a large variety of problems on a wide range of scales. Systems of particular interest include outbreaks in epidemics and supply chain breakdowns.
Abstract for Yuxiong Wang's talk
The visual world which artificial intelligent agents live in and perceive is intrinsically open, streaming, and dynamic. However, despite impressive advances in visual learning and perception, state-of- the-art systems are still narrowly applicable, operating within a closed, static world of fixed datasets. In this talk, I will discuss our efforts towards developing generalizable and adaptive open-world perception and learning systems. Our key insight is to introduce a mental model with hallucination ability – creating internal imaginations of scenes, objects, and their variations and dynamics not actually present to the senses. I will focus on how to integrate such an intrinsic mental model with extrinsic task-oriented models and construct a corresponding closed-loop feedback system. I will demonstrate the potential of this framework for scaling up open-world, in-the-wild perception in application domains such as transportation, robotics, geospatial intelligence, and healthcare.