Events

ARISE Program – Validate

Duality of Research: The Academic and Commercial Perspective

Create4Good 2023/2024 Showcase Day

Catholic Junior College – Python Programming with the Raspberry Pi Workshop

It has long been suggested that the biological brain operates at some critical point between two different phases, possibly order and chaos, to maximize the information processing power. Investigating the same hypothesis on the ‘artificial’ brains, i.e. the modern computer vision models, we find that they exhibits the same pattern, i.e. highest test accuracy or lowest test loss at the edge of chaos. A theoretical investigation demonstrates that, the best performance is attributed to the maximal metastable states/periodic cycle length near the edge of chaos, where each metastable state can represent an information point. Applied on a very simple network equivalent of the SK spin glass model and Fashion MNIST dataset, we illustrate a simple and principled training method that can achieve both high accuracy and prevent fitting noisy labels automatically.

VIE Connects

Decentralized Gap Funding 2nd Call for Applications

DH Asia Webinar Series: “The Diaries of the Soviet Ambassador in Pyongyang: Data-Specific Network Approaches to North Korean History Studies” by Dr. Donghyun Woo

We consider the diffusion of two alternatives in social networks using a game-theoretic approach. Each individual plays a coordination game with its neighbors repeatedly and decides which to adopt. As products are used in conjunction with others and through repeated interactions, individuals are more interested in their long-term benefits and tend to show trust to others to maximize their long-term utility by choosing a suboptimal option with respect to instantaneous payoff. To capture such trust behavior, we deploy limited-trust equilibrium (LTE) in diffusion process. We analyze the convergence of emerging dynamics to equilibrium points using mean-field approximation and study the equilibrium state and the convergence rate of diffusion using absorption probability and expected absorption time of a reduced-size absorbing Markov chain. We also show that the diffusion model on LTE under the best-response strategy can be converted to the well-known linear threshold model. Simulation results show that when agents behave trustworthy, their long-term utility will increase significantly compared to the case when they are solely self-interested. Moreover, the Markov chain analysis provides a good estimate of convergence properties over random networks.

Vincent Leon (University of Illinois at Urbana-Champaign) – Limited-Trust in Diffusion of Competing Alternatives Over Social Networks & Apurv Shukla (Texas A&M University) – Differentially Private Online Resource Allocation