arXiv subjects

Ke Wan

Publications and source records attributed to Ke Wan.

21 records · Page 2Linked to original sources

arXiv2023

A General Model of Vehicle Routing Guidance Systems based on Distributive Learning Scheme

Dynamic traffic assignment and vehicle route guidance have been important problems in ITS for some time. This paper proposes a new model for VRGS, which takes into consideration of the information propagation, user selection and information reaction. Parameter p is then defined as the updating weight for computing cost of traffic based on a distributive learning scheme. p is calculated through a function which denotes information propagation over time and space and the function needs further optimization. Comparison to static traffic assignment, DTA and feasible strategies are given, and future work is also stated.

cs.NI↗

arXiv2023

Hybrid Quantum Algorithms integrating QAOA, Penalty Dephasing and Zeno Effect for Solving Binary Optimization Problems with Multiple Constraints

When tackling binary optimization problems using quantum algorithms, the conventional Ising representation and Quantum Approximate Optimization Algorithm (QAOA) encounter difficulties in efficiently handling errors for large-scale problems involving multiple constraints. To address these challenges, this paper presents a hybrid framework that combines the use of standard Ising Hamiltonians to solve a subset of the constraints, while employing non-Ising formulations to represent and address the remaining constraints. The resolution of these non-Ising constraints is achieved through either penalty dephasing or the quantum Zeno effect. This innovative approach leads to a collection of quantum circuits with adaptable structures, depending on the chosen representation for each constraint. Furthermore, this paper introduces a novel technique that utilizes the quantum Zeno effect by frequently measuring the constraint flag, enabling the resolution of any optimization constraint. Theoretical properties of these algorithms are discussed, and their performance in addressing practical aircraft loading problems is highly promising, showcasing significant potential for a wide range of industrial applications.

quant-ph↗

arXiv2022

Rules Ensemble Method with Group Lasso for Heterogeneous Treatment Effect Estimation

The increasing scientific attention given to precision medicine based on real-world data has led many recent studies to clarify the relationships between treatment effects and patient characteristics. However, this is challenging because of ubiquitous heterogeneity in the treatment effect for individuals and the real-world data on their background being complex and noisy. Because of their flexibility, various heterogeneous treatment effect (HTE) machine learning (ML) estimation methods have been proposed. However, most ML methods incorporate black-box models that hamper direct interpretation of the interrelationships between individuals' characteristics and the treatments' effects. This study proposes an ML method for estimating HTE based on the rule ensemble method termed RuleFit. The main advantage of RuleFit are interpretability and accuracy. However, HTEs are always defined in the potential outcome framework, and RuleFit cannot be applied directly. Thus, we modified RuleFit and proposed a method to estimate HTEs that directly interpret the interrelationships among the individuals' features from the model.

stat.ME↗