Adaptive signal control using approximate dynamic programming

This paper presents a concise summary of a study on adaptive traffic signal controller for real time operation. The adaptive controller is designed to achieve three operational objectives: first, the controller adopts a dual control principle to achieve a balanced influence between immediate cost and long-term cost in operation; second, controller switches signals without referring to a preset plan and is acyclic; third, controller adjusts its parameters online to adapt new environment. Not all of these features are available in existing operational controllers. Although dynamic programming (DP) is the only exact solution for achieving the operational objectives, it is usually impractical for real time operation because of demand in computation and information. To circumvent the difficulties, we use approximate dynamic programming (ADP) in conjunction with online learning techniques. This approach can substantially reduce computational burden by replacing the exact value function of DP with a continuous linear approximation function, which is then updated progressively by online learning techniques. Two online learning techniques, which are reinforcement learning and monotonicity approximation respectively, are investigated. We find in computer simulation that the ADP controller leads to substantial savings in vehicle delays in comparison with optimised fixed-time plans. The implications of this study to traffic control are: the ADP controller meet all of the three operational objectives with competitive results, and can be readily implemented for operations at both isolated intersection and traffic networks; the ADP algorithm is computationally efficient, and the ADP controller is an evolving system that requires minimum human intervention; the ADP technique offers a flexible theoretical framework in which a range of functional forms and learning techniques can be further studied

Analytical investigation for multiplicity difference correlators under QGP phase transition

It is suggested that the study of multiplicity difference correlators between two well-separated bins in high-energy heavy-ion collisions can be used as a means to detect evidence of a quark-hadron phase transition. Analytical expressions for the scaled factorial moments of multiplicity difference distribution are obtained in a kinetical region ${\bar s}\le 0.3$ within Ginzburg-Landau description. It is shown that the scaling behaviors between the moments are still valid, though the behaviors of the moments with respect to the bin size are completely different from the so-called intermittency patterns. A universal exponent $\gamma$ is given to describe the dynamical fluctuations in the phase transition.Comment: 5 pages, RevTeX, three figures in EPS forma

Chiron: A Robust Recommendation System with Graph Regularizer

Recommendation systems have been widely used by commercial service providers for giving suggestions to users. Collaborative filtering (CF) systems, one of the most popular recommendation systems, utilize the history of behaviors of the aggregate user-base to provide individual recommendations and are effective when almost all users faithfully express their opinions. However, they are vulnerable to malicious users biasing their inputs in order to change the overall ratings of a specific group of items. CF systems largely fall into two categories - neighborhood-based and (matrix) factorization-based - and the presence of adversarial input can influence recommendations in both categories, leading to instabilities in estimation and prediction. Although the robustness of different collaborative filtering algorithms has been extensively studied, designing an efficient system that is immune to manipulation remains a significant challenge. In this work we propose a novel "hybrid" recommendation system with an adaptive graph-based user/item similarity-regularization - "Chiron". Chiron ties the performance benefits of dimensionality reduction (through factorization) with the advantage of neighborhood clustering (through regularization). We demonstrate, using extensive comparative experiments, that Chiron is resistant to manipulation by large and lethal attacks