Search Results for author: Yan Lin

Found 10 papers, 2 papers with code

OverleafCopilot: Empowering Academic Writing in Overleaf with Large Language Models

1 code implementation13 Mar 2024 Haomin Wen, Zhenjie Wei, Yan Lin, Jiyuan Wang, Yuxuan Liang, Huaiyu Wan

In this technical report, we explore the integration of LLMs and the popular academic writing tool, Overleaf, to enhance the efficiency and quality of academic writing.

Diff-RNTraj: A Structure-aware Diffusion Model for Road Network-constrained Trajectory Generation

no code implementations12 Feb 2024 Tonglong Wei, Youfang Lin, Shengnan Guo, Yan Lin, Yiheng Huang, Chenyang Xiang, Yuqing Bai, Menglu Ya, Huaiyu Wan

In this paper, we propose a new problem to meet the practical application need, \emph{i. e.}, road network-constrained trajectory (RNTraj) generation, which can directly generate trajectories on the road network with road-related information.

GTM: General Trajectory Modeling with Auto-regressive Generation of Feature Domains

no code implementations11 Feb 2024 Yan Lin, Jilin Hu, Shengnan Guo, Bin Yang, Christian S. Jensen, Youfang Lin, Huaiyu Wan

Experiments involving three representative trajectory-related tasks on two real-world trajectory datasets provide insight into the intended properties performance of GTM and offer evidence that GTM is capable of meeting its objectives.

Trajectory Modeling Trajectory Prediction +1

Origin-Destination Travel Time Oracle for Map-based Services

no code implementations6 Jul 2023 Yan Lin, Huaiyu Wan, Jilin Hu, Shengnan Guo, Bin Yang, Youfang Lin, Christian S. Jensen

Given an origin (O), a destination (D), and a departure time (T), an Origin-Destination (OD) travel time oracle~(ODT-Oracle) returns an estimate of the time it takes to travel from O to D when departing at T. ODT-Oracles serve important purposes in map-based services.

Travel Time Estimation

Privacy-Preserving Joint Edge Association and Power Optimization for the Internet of Vehicles via Federated Multi-Agent Reinforcement Learning

no code implementations26 Jan 2023 Yan Lin, Jinming Bao, Yijin Zhang, Jun Li, Feng Shu, Lajos Hanzo

Proactive edge association is capable of improving wireless connectivity at the cost of increased handover (HO) frequency and energy consumption, while relying on a large amount of private information sharing required for decision making.

Decision Making Multi-agent Reinforcement Learning +1

Pre-training General Trajectory Embeddings with Maximum Multi-view Entropy Coding

no code implementations29 Jul 2022 Yan Lin, Huaiyu Wan, Shengnan Guo, Jilin Hu, Christian S. Jensen, Youfang Lin

Spatio-temporal trajectories provide valuable information about movement and travel behavior, enabling various downstream tasks that in turn power real-world applications.

Contrastive Learning Data Augmentation

Multi-RIS Aided 3D Secure Precise Wireless Transmission

no code implementations23 Nov 2020 Tong Shen, Wenlong Cai, Yan Lin, Shuo Zhang, Jinyong Lin, Feng Shu, Jiangzhou Wang

Then, multiple RISs are utilized to achieve SPWT through the reflection path among transmitter, RISs and receivers in order to enhance the communication performance and energy efficiency simultaneously.

Vehicle Tracking in Wireless Sensor Networks via Deep Reinforcement Learning

no code implementations22 Feb 2020 Jun Li, Zhichao Xing, Weibin Zhang, Yan Lin, Feng Shu

Vehicle tracking has become one of the key applications of wireless sensor networks (WSNs) in the fields of rescue, surveillance, traffic monitoring, etc.

reinforcement-learning Reinforcement Learning (RL)

A novel tree-structured point cloud dataset for skeletonization algorithm evaluation

1 code implementation9 Jan 2020 Yan Lin, Ji Liu, Jianlin Zhou

Since the implicit surface is sufficiently expressive to retain the edges and details of the complex branches model, we use the implicit surface to model the triangular mesh.

Regional Robust Secure Precise Wireless Transmission Design for Multi-user UAV Broadcasting System

no code implementations9 Apr 2019 Tong Shen, Tingting Liu, Yan Lin, Yongpeng Wu, Feng Shu, Zhu Han

Proposed regional robust schemes are designed for optimizing the secrecy performance in the whole error region around the estimated location.

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