Xinrui Zhang(张心睿)

Xinrui Zhang(张心睿)

Ph.D. student, School of Computer Science and Engineering，focusing on causal inference, graph neural network.

Sitemap

A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

图因果效应估计

less than 1 minute read

Published:

该论文发表在 AAAI 2022，介绍了近年来图因果效应估计的代表性方法，以及未来研究方向。

基于稀疏注意力机制的多智能体强化学习框架

7 minute read

Published:

本文所介绍的基于稀疏注意力机制的多智能体强化学习框架，来自于发表在KDD 2022上的论文”S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning?”，作者来自于浙江大学和华为诺亚方舟实验室。

近似算法介绍

5 minute read

Published:

《研究生算法设计》课程报告

VScode插件开发

3 minute read

Published:

本文将介绍如何快速构建一个自己的VSCode插件，主要使用语言为TypeScript。

信息检索基础概述

1 minute read

Published:

本文将根据 elastic search 的原理，按照信息检索的一般流程对信息检索进行简要的介绍，旨在从较高层面了解大致流程，不会深入细节和具体实现。

永远的三毛

less than 1 minute read

Published:

《思想道德与法治修养》结课论文，《撒哈拉的故事》读书笔记

publications

Multi-scale Temporal Feature Fusion for Time-Limited Order Prediction

Published in China Conference on Wireless Sensor Networks, 2022

The time-limited order is a new type of real-time delivery service that platforms need to complete the order delivery with different time granularity (e.g. one day, two days, or three days). Predicting the number of time-limited orders plays an important role for real-time order delivery allocation and anomaly detection in logistics IoT scenarios. However, the impact between orders of different time granularity is complex and the contribution of the static order features is unknown. Previous order predicting methods are not suitable for time-limited orders because they do not fully consider the dependencies among orders with different time granularity. In this paper, we propose a spatial-temporal framework based on stacked long short-term memory networks (LSTM) and deep & cross network (DCN) to take the dependencies of multi-scale temporal features into account and fuse cross-domain static features effectively. In addition, we utilize a multi-head attention mechanism to model the heterogeneous strengths of different dependencies. We evaluate our model on a real-world dataset with about 400,000 orders from one of the largest logistics companies in China. The evaluation results show that the Mean Absolute Error and R2 score of our method achieve 9.407 and 0.948, outperforming state-of-the-art solutions. Download paper here

Recommended citation: Wang, J., Zhou, X., Liu, Y., Zhang, X., & Wang, S. (2022, November). Multi-scale Temporal Feature Fusion for Time-Limited Order Prediction. In China Conference on Wireless Sensor Networks (pp. 132-144). Singapore: Springer Nature Singapore. https://link.springer.com/chapter/10.1007/978-981-19-8350-4_11

PATR: Periodicity-Aware Trajectory Recovery for Express System via Seq2Seq Model

Published in IEEE Globecom, 2022

The analysis of express trajectory is essential for delivery service optimization and courier management. Express trajectory consists of a large amount of community paths which is different from the trajectory of transportation tools such as taxis which run in the main road of a city. Due to the complexity of the community road networks and the energy constraint of devices, trajectories are collected under a low sampling rate and accuracy. These trajectories need to be recovered and mapped to the road networks for further analysis. Current recovery approaches utilize two-stage or end-to-end models to generate the upsampled trajectories. These methods fail to fully exploit the periodicity relation in historical data, which leads to accuracy losses for complex community road networks. In this paper, we design PATR, a periodicity-aware Seq2Seq model, to recover the couriers’ trajectories by leveraging the periodical patterns of the historical data. We conduct experimental evaluations based on real-world GPS data of JD logistics consisting of 836,959 points and 25,726 road segments, and our model outperforms the state-of-the-art baselines by reducing recovery error by 2.4%-21.9%.

Recommended citation: Zhang, X., Liang, X., Wang, H., Wang, S., & He, T. (2022, December). PATR: Periodicity-Aware Trajectory Recovery for Express System via Seq2Seq Model. In GLOBECOM 2022-2022 IEEE Global Communications Conference (pp. 486-491). IEEE. https://ieeexplore.ieee.org/abstract/document/10001476