Qiang Ni

Reinforcing Edge-DASH: Deep Learning for Multi-Objective Streaming Optimization

Arash Bozorgchenani, David Naseh, Daniele Tarchi, Sergio A. Salinas Monroy, Farshad Mashhadi, Qiang Ni

TL;DR: This work considers an Edge-DASH scenario and forms a joint optimization problem that involves four critical aspects: bitrate allocation, user-to-server assignment, caching, and bandwidth allocation, and employs deep reinforcement learning for three of them and develops a heuristic solution for the fourth.

With the growing demand for multimedia services, Dynamic Adaptive Streaming over HTTP (DASH) has become a key solution for delivering high-quality video content. In this work, we consider an Edge-DASH scenario and formulate a joint optimization problem that involves four critical…Read more

XAI2Brain: A Perspective on Mechanistic Interpretability for Brain–AI Alignment

Richard Jiang, Yongchen Zhou, Boyuan Wang, Plamen Angelov, Qiang Ni

The convergence of artificial intelligence (AI), explainable AI (XAI), and neuroscience is fostering new opportunities for understanding both machine and biological intelligence through interpretable and human-centered learning paradigms. In this Perspective, we introduce XAI2Bra…Read more