Search Results for author: Ziyue Li

Found 49 papers, 23 papers with code

Runway capacity expansion planning for public airports under demand uncertainty

no code implementations4 Feb 2025 Ziyue Li, Joseph Y. J. Chow, Qianwen Guo

Furthermore, the relationship between optimal investment timing and expansion size is complex: if the expansion size remains unchanged, the trigger demand decreases as the demand growth rate increases; if the expansion size experiences a jump, the trigger demand also exhibits a sharp rise.

Decision Making

Vehicle occupancy estimation in Automated Guideway Transit via deep learning with Wi-Fi probe requests

no code implementations28 Jan 2025 Ziyue Li, Qianwen Guo

This study contributes to the advancement of vehicle occupancy estimation in Automated Guideway Transit (AGT) systems using Wi-Fi probe requests and deep learning models.

LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting

no code implementations24 Nov 2024 Lingzheng Zhang, Lifeng Shen, Yimin Zheng, Shiyuan Piao, Ziyue Li, Fugee Tsung

Building on recent advancements in using linear models for time series, this paper introduces an LLM-enhanced mixture of linear experts for precise and efficient time series forecasting.

Computational Efficiency Natural Language Understanding +3

MCCoder: Streamlining Motion Control with LLM-Assisted Code Generation and Rigorous Verification

1 code implementation19 Oct 2024 Yin Li, Liangwei Wang, Shiyuan Piao, Boo-Ho Yang, Ziyue Li, Wei Zeng, Fugee Tsung

To address these challenges, we introduce MCCoder, an LLM-powered system designed to generate code that addresses complex motion control tasks, with integrated soft-motion data verification.

Code Generation RAG

Your Mixture-of-Experts LLM Is Secretly an Embedding Model For Free

1 code implementation14 Oct 2024 Ziyue Li, Tianyi Zhou

Moreover, our extensive analysis shows that the MoE routing weights (RW) is complementary to the hidden state (HS) of LLMs, a widely-used embedding.

Enabling Tensor Decomposition for Time-Series Classification via A Simple Pseudo-Laplacian Contrast

no code implementations23 Sep 2024 Man Li, Ziyue Li, Lijun Sun, Fugee Tsung

Tensor decomposition has emerged as a prominent technique to learn low-dimensional representation under the supervision of reconstruction error, primarily benefiting data inference tasks like completion and imputation, but not classification task.

Data Augmentation Imputation +3

XTraffic: A Dataset Where Traffic Meets Incidents with Explainability and More

no code implementations16 Jul 2024 Xiaochuan Gou, Ziyue Li, Tian Lan, Junpeng Lin, Zhishuai Li, Bingyu Zhao, Chen Zhang, Di Wang, Xiangliang Zhang

Our data can revolutionalize traditional traffic-related tasks towards higher interpretability and practice: instead of traditional prediction or classification tasks, we conduct: (1) post-incident traffic forecasting to quantify the impact of different incidents on traffic indexes; (2) incident classification using traffic indexes to determine the incidents types for precautions measures; (3) global causal analysis among the traffic indexes, meta-attributes, and incidents to give high-level guidance of the interrelations of various factors; (4) local causal analysis within road nodes to examine how different incidents affect the road segments' relations.

Deep Causal Learning to Explain and Quantify The Geo-Tension's Impact on Natural Gas Market

no code implementations15 Jul 2024 Philipp Kai Peter, Yulin Li, Ziyue Li, Wolfgang Ketter

Natural gas demand is a crucial factor for predicting natural gas prices and thus has a direct influence on the power system.

counterfactual

Eliminating Feature Ambiguity for Few-Shot Segmentation

1 code implementation13 Jul 2024 Qianxiong Xu, Guosheng Lin, Chen Change Loy, Cheng Long, Ziyue Li, Rui Zhao

Recent advancements in few-shot segmentation (FSS) have exploited pixel-by-pixel matching between query and support features, typically based on cross attention, which selectively activate query foreground (FG) features that correspond to the same-class support FG features.

Few-Shot Semantic Segmentation

DualTime: A Dual-Adapter Multimodal Language Model for Time Series Representation

no code implementations7 Jun 2024 Weiqi Zhang, Jiexia Ye, Ziyue Li, Jia Li, Fugee Tsung

In this study, based on the complementary information mining of time series multimodal data, we propose DualTime, a Dual-adapter multimodal language model for Time series representation implementing temporal-primary and textual-primary modeling simultaneously.

Language Modeling Language Modelling +1

TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignment

2 code implementations3 Jun 2024 Chenxi Liu, Qianxiong Xu, Hao Miao, Sun Yang, Lingzheng Zhang, Cheng Long, Ziyue Li, Rui Zhao

As another key design, to reduce the computational costs from time series with their length textual prompts, we design an effective prompt to encourage the most essential temporal information to be encapsulated in the last token: only the last token is passed to downstream prediction.

Multivariate Time Series Forecasting Time Series

SQL-to-Schema Enhances Schema Linking in Text-to-SQL

no code implementations15 May 2024 Sun Yang, Qiong Su, Zhishuai Li, Ziyue Li, Hangyu Mao, Chenxi Liu, Rui Zhao

Consequently, there is a critical need to filter out unnecessary tables and columns, directing the language models attention to relevant tables and columns with schema-linking, to reduce errors during SQL generation.

Text-To-SQL

A Survey of Time Series Foundation Models: Generalizing Time Series Representation with Large Language Model

1 code implementation3 May 2024 Jiexia Ye, Weiqi Zhang, Ke Yi, Yongzi Yu, Ziyue Li, Jia Li, Fugee Tsung

There are two main research lines, namely pre-training foundation models from scratch for time series and adapting large language foundation models for time series.

Decision Making Few-Shot Learning +5

X-Light: Cross-City Traffic Signal Control Using Transformer on Transformer as Meta Multi-Agent Reinforcement Learner

1 code implementation18 Apr 2024 Haoyuan Jiang, Ziyue Li, Hua Wei, Xuantang Xiong, Jingqing Ruan, Jiaming Lu, Hangyu Mao, Rui Zhao

The effectiveness of traffic light control has been significantly improved by current reinforcement learning-based approaches via better cooperation among multiple traffic lights.

Traffic Signal Control

Low-Rank Robust Subspace Tensor Clustering for Metro Passenger Flow Modeling

no code implementations5 Apr 2024 Jiuyun Hu, Ziyue Li, Chen Zhang, Fugee Tsung, Hao Yan

Moreover, a case study in the station clustering based on real passenger flow data is conducted, with quite valuable insights discovered.

Clustering Dimensionality Reduction

PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency

1 code implementation13 Mar 2024 Zhishuai Li, Xiang Wang, Jingjing Zhao, Sun Yang, Guoqing Du, Xiaoru Hu, Bin Zhang, Yuxiao Ye, Ziyue Li, Rui Zhao, Hangyu Mao

Then, in the first stage, question-SQL pairs are retrieved as few-shot demonstrations, prompting the LLM to generate a preliminary SQL (PreSQL).

In-Context Learning Text-To-SQL

Many-Objective Multi-Solution Transport

no code implementations6 Mar 2024 Ziyue Li, Tian Li, Virginia Smith, Jeff Bilmes, Tianyi Zhou

Optimizing the performance of many objectives (instantiated by tasks or clients) jointly with a few Pareto stationary solutions (models) is critical in machine learning.

Federated Learning Multi-Task Learning

Benchmarking the Text-to-SQL Capability of Large Language Models: A Comprehensive Evaluation

no code implementations5 Mar 2024 Bin Zhang, Yuxiao Ye, Guoqing Du, Xiaoru Hu, Zhishuai Li, Sun Yang, Chi Harold Liu, Rui Zhao, Ziyue Li, Hangyu Mao

Then we formulate five evaluation tasks to comprehensively assess the performance of diverse methods across various LLMs throughout the Text-to-SQL process. Our study highlights the performance disparities among LLMs and proposes optimal in-context learning solutions tailored to each task.

Benchmarking In-Context Learning +1

Non-Neighbors Also Matter to Kriging: A New Contrastive-Prototypical Learning

1 code implementation23 Jan 2024 Zhishuai Li, Yunhao Nie, Ziyue Li, Lei Bai, Yisheng Lv, Rui Zhao

As a pre-trained paradigm, we conduct the Kriging task from a new perspective of representation: we aim to first learn robust and general representations and then recover attributes from representations.

Attribute Self-Supervised Learning

Spatial-Temporal Large Language Model for Traffic Prediction

2 code implementations18 Jan 2024 Chenxi Liu, Sun Yang, Qianxiong Xu, Zhishuai Li, Cheng Long, Ziyue Li, Rui Zhao

In the ST-LLM, we define timesteps at each location as tokens and design a spatial-temporal embedding to learn the spatial location and global temporal patterns of these tokens.

Language Modeling Language Modelling +5

Online Test-Time Adaptation of Spatial-Temporal Traffic Flow Forecasting

1 code implementation8 Jan 2024 Pengxin Guo, Pengrong Jin, Ziyue Li, Lei Bai, Yu Zhang

To make the model trained on historical data better adapt to future data in a fully online manner, this paper conducts the first study of the online test-time adaptation techniques for spatial-temporal traffic flow forecasting problems.

Test-time Adaptation Traffic Prediction

DuaLight: Enhancing Traffic Signal Control by Leveraging Scenario-Specific and Scenario-Shared Knowledge

1 code implementation22 Dec 2023 Jiaming Lu, Jingqing Ruan, Haoyuan Jiang, Ziyue Li, Hangyu Mao, Rui Zhao

Furthermore, we implement a scenario-shared Co-Train module to facilitate the learning of generalizable dynamics information across different scenarios.

Decision Making Traffic Signal Control

VisionTraj: A Noise-Robust Trajectory Recovery Framework based on Large-scale Camera Network

1 code implementation11 Dec 2023 Zhishuai Li, Ziyue Li, Xiaoru Hu, Guoqing Du, Yunhao Nie, Feng Zhu, Lei Bai, Rui Zhao

Trajectory recovery based on the snapshots from the city-wide multi-camera network facilitates urban mobility sensing and driveway optimization.

Clustering Denoising +1

Controlling Large Language Model-based Agents for Large-Scale Decision-Making: An Actor-Critic Approach

no code implementations23 Nov 2023 Bin Zhang, Hangyu Mao, Jingqing Ruan, Ying Wen, Yang Li, Shao Zhang, Zhiwei Xu, Dapeng Li, Ziyue Li, Rui Zhao, Lijuan Li, Guoliang Fan

The remarkable progress in Large Language Models (LLMs) opens up new avenues for addressing planning and decision-making problems in Multi-Agent Systems (MAS).

Decision Making Hallucination +4

TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Systems

no code implementations19 Nov 2023 Yilun Kong, Jingqing Ruan, Yihong Chen, Bin Zhang, Tianpeng Bao, Shiwei Shi, Guoqing Du, Xiaoru Hu, Hangyu Mao, Ziyue Li, Xingyu Zeng, Rui Zhao

Large Language Models (LLMs) have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools that require a blend of task planning and the utilization of external tools, such as APIs.

In-Context Learning Language Modeling +3

A Critical Perceptual Pre-trained Model for Complex Trajectory Recovery

no code implementations5 Nov 2023 Dedong Li, Ziyue Li, Zhishuai Li, Lei Bai, Qingyuan Gong, Lijun Sun, Wolfgang Ketter, Rui Zhao

Then, we propose a Multi-view Graph and Complexity Aware Transformer (MGCAT) model to encode these semantics in trajectory pre-training from two aspects: 1) adaptively aggregate the multi-view graph features considering trajectory pattern, and 2) higher attention to critical nodes in a complex trajectory.

Trajectory Recovery

KITS: Inductive Spatio-Temporal Kriging with Increment Training Strategy

1 code implementation5 Nov 2023 Qianxiong Xu, Cheng Long, Ziyue Li, Sijie Ruan, Rui Zhao, Zhishuai Li

To address this issue, we first present a novel Increment training strategy: instead of masking nodes (and reconstructing them), we add virtual nodes into the training graph so as to mitigate the graph gap issue naturally.

Choose A Table: Tensor Dirichlet Process Multinomial Mixture Model with Graphs for Passenger Trajectory Clustering

no code implementations31 Oct 2023 Ziyue Li, Hao Yan, Chen Zhang, Lijun Sun, Wolfgang Ketter, Fugee Tsung

In this paper, we propose a novel tensor Dirichlet Process Multinomial Mixture model with graphs, which can preserve the hierarchical structure of the multi-dimensional trip information and cluster them in a unified one-step manner with the ability to determine the number of clusters automatically.

Clustering Community Detection +1

Reboost Large Language Model-based Text-to-SQL, Text-to-Python, and Text-to-Function -- with Real Applications in Traffic Domain

no code implementations28 Oct 2023 Guanghu Sui, Zhishuai Li, Ziyue Li, Sun Yang, Jingqing Ruan, Hangyu Mao, Rui Zhao

Our experiments with Large Language Models (LLMs) illustrate the significant performance improvement on the business dataset and prove the substantial potential of our method.

Language Modeling Language Modelling +2

TPTU: Large Language Model-based AI Agents for Task Planning and Tool Usage

no code implementations7 Aug 2023 Jingqing Ruan, Yihong Chen, Bin Zhang, Zhiwei Xu, Tianpeng Bao, Guoqing Du, Shiwei Shi, Hangyu Mao, Ziyue Li, Xingyu Zeng, Rui Zhao

With recent advancements in natural language processing, Large Language Models (LLMs) have emerged as powerful tools for various real-world applications.

Language Modeling Language Modelling +2

Relation-Aware Distribution Representation Network for Person Clustering with Multiple Modalities

no code implementations1 Aug 2023 Kaijian Liu, Shixiang Tang, Ziyue Li, Zhishuai Li, Lei Bai, Feng Zhu, Rui Zhao

The distribution representation of a clue is a vector consisting of the relation between this clue and all other clues from all modalities, thus being modality agnostic and good for person clustering.

Clustering Relation

Protecting the Future: Neonatal Seizure Detection with Spatial-Temporal Modeling

no code implementations2 Jul 2023 Ziyue Li, Yuchen Fang, You Li, Kan Ren, Yansen Wang, Xufang Luo, Juanyong Duan, Congrui Huang, Dongsheng Li, Lili Qiu

A timely detection of seizures for newborn infants with electroencephalogram (EEG) has been a common yet life-saving practice in the Neonatal Intensive Care Unit (NICU).

EEG Seizure Detection

Tensor Dirichlet Process Multinomial Mixture Model for Passenger Trajectory Clustering

no code implementations23 Jun 2023 Ziyue Li, Hao Yan, Chen Zhang, Andi Wang, Wolfgang Ketter, Lijun Sun, Fugee Tsung

In this paper, we propose a novel Tensor Dirichlet Process Multinomial Mixture model (Tensor-DPMM), which is designed to preserve the multi-mode and hierarchical structure of the multi-dimensional trip information via tensor, and cluster them in a unified one-step manner.

Clustering Trajectory Clustering

Adaptive Hierarchical SpatioTemporal Network for Traffic Forecasting

no code implementations15 Jun 2023 YiRong Chen, Ziyue Li, Wanli Ouyang, Michael Lepech

In this work, we propose an Adaptive Hierarchical SpatioTemporal Network (AHSTN) to promote traffic forecasting by exploiting the spatial hierarchy and modeling multi-scale spatial correlations.

Dynamic Causal Graph Convolutional Network for Traffic Prediction

1 code implementation12 Jun 2023 Junpeng Lin, Ziyue Li, Zhishuai Li, Lei Bai, Rui Zhao, Chen Zhang

In this work, we propose a novel approach for traffic prediction that embeds time-varying dynamic Bayesian network to capture the fine spatiotemporal topology of traffic data.

Prediction Traffic Prediction

Correlated Time Series Self-Supervised Representation Learning via Spatiotemporal Bootstrapping

1 code implementation12 Jun 2023 Luxuan Wang, Lei Bai, Ziyue Li, Rui Zhao, Fugee Tsung

We evaluated the effectiveness and flexibility of our representation learning framework on correlated time series forecasting and cold-start transferring the forecasting model to new instances with limited data.

Correlated Time Series Forecasting Representation Learning +1

MM-DAG: Multi-task DAG Learning for Multi-modal Data -- with Application for Traffic Congestion Analysis

1 code implementation5 Jun 2023 Tian Lan, Ziyue Li, Zhishuai Li, Lei Bai, Man Li, Fugee Tsung, Wolfgang Ketter, Rui Zhao, Chen Zhang

This encourages the multi-task design: with each DAG as a task, the MM-DAG tries to learn the multiple DAGs jointly so that their consensus and consistency are maximized.

SIMPLE: Specialized Model-Sample Matching for Domain Generalization

1 code implementation International Conference on Learning Representations 2023 Ziyue Li, Kan Ren, Xinyang Jiang, Yifei Shen, Haipeng Zhang, Dongsheng Li

Moreover, our method is highly efficient and achieves more than 1000 times training speedup compared to the conventional DG methods with fine-tuning a pretrained model.

Domain Generalization model

Towards Inference Efficient Deep Ensemble Learning

no code implementations29 Jan 2023 Ziyue Li, Kan Ren, Yifan Yang, Xinyang Jiang, Yuqing Yang, Dongsheng Li

Ensemble methods can deliver surprising performance gains but also bring significantly higher computational costs, e. g., can be up to 2048X in large-scale ensemble tasks.

Ensemble Learning

Jointly Contrastive Representation Learning on Road Network and Trajectory

1 code implementation14 Sep 2022 Zhenyu Mao, Ziyue Li, Dedong Li, Lei Bai, Rui Zhao

Unlike the existing cross-scale contrastive learning methods on graphs that only contrast a graph and its belonging nodes, the contrast between road segment and trajectory is elaborately tailored via novel positive sampling and adaptive weighting strategies.

Contrastive Learning Representation Learning +1

SANE: Specialization-Aware Neural Network Ensemble

no code implementations29 Sep 2021 Ziyue Li, Kan Ren, Xinyang Jiang, Mingzhe Han, Haipeng Zhang, Dongsheng Li

Real-world data is often generated by some complex distribution, which can be approximated by a composition of multiple simpler distributions.

Diversity Ensemble Learning

Long-Short Term Spatiotemporal Tensor Prediction for Passenger Flow Profile

no code implementations23 Apr 2020 Ziyue Li, Hao Yan, Chen Zhang, Fugee Tsung

Spatiotemporal data is very common in many applications, such as manufacturing systems and transportation systems.

Clustering Prediction +1

Tensor Completion for Weakly-dependent Data on Graph for Metro Passenger Flow Prediction

1 code implementation11 Dec 2019 Ziyue Li, Nurettin Dorukhan Sergin, Hao Yan, Chen Zhang, Fugee Tsung

Low-rank tensor decomposition and completion have attracted significant interest from academia given the ubiquity of tensor data.

Tensor Decomposition

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