105 citations · 478 across the 66 of their papers we have counts for
27 papers · 1 filter
CasEm: A Cascade Architecture for Long-Horizon Neural Emulation
Zhaoyi Li, Jingtao Ding, Shihua Li
Autoregressive neural emulators can drift or diverge over long rollouts despite accurate short-term predictions. We introduce Cascaded Emulation (CasEm), a one-way rollout architec…
BER-PEF: Unified Human Mobility Predictability Evaluation via Bayes Error Rate Estimation
En Xu, Jingtao Ding, Zhiwen Yu +1
Human mobility predictability concerns the best prediction performance attainable from a given target and input information, but its ground truth is not directly observable on real…
Fusing Urban Structure and Semantics: A Conditional Diffusion Model for Cross-City OD Matrix Generation
Bin Chen, Zhuoya Meng, Fang Yang +5
Accurate modeling of commuting flows is important for urban governance, traffic planning, and resource allocation. However, the combined influence of individual intentions, geograp…
PID-controlled Langevin Dynamics for Faster Sampling of Generative Models
Hongyi Chen, Jianhai Shu, Jingtao Ding +2
Langevin dynamics sampling suffers from extremely low generation speed, fundamentally limited by numerous fine-grained iterations to converge to the target distribution. We introdu…
Fine-Tuning Flow Matching via Maximum Likelihood Estimation of Reconstructions
Zhaoyi Li, Jingtao Ding, Yong Li +1
Flow Matching (FM) models achieve remarkable results in generative tasks. Building upon diffusion models, FM's simulation-free training paradigm enables simplicity and efficiency b…
Beyond Model Ranking: Predictability-Aligned Evaluation for Time Series Forecasting
Wanjin Feng, Yuan Yuan, Jingtao Ding +1
In the era of increasingly complex AI models for time series forecasting, progress is often measured by marginal improvements on benchmark leaderboards. However, this approach suff…
MoveFM-R: Advancing Mobility Foundation Models via Language-driven Semantic Reasoning
Fanjin Meng, Yuan Yuan, Jingtao Ding +3
Mobility Foundation Models (MFMs) have advanced the modeling of human movement patterns, yet they face a ceiling due to limitations in data scale and semantic understanding. While…
ChaosNexus: A Foundation Model for ODE-based Chaotic System Forecasting with Hierarchical Multi-scale Awareness
Chang Liu, Bohao Zhao, Jingtao Ding +1
Foundation models have shown great promise in achieving zero-shot or few-shot forecasting for ODE-based chaotic systems via large-scale pretraining. However, existing architectures…
Beyond Accuracy and Complexity: The Effective Information Criterion for Structurally Stable Symbolic Regression
Zihan Yu, Guanren Wang, Jingtao Ding +2
Symbolic regression (SR) traditionally balances accuracy and complexity, implicitly assuming that simpler formulas are structurally more rational. We argue that this assumption is…
UniMove: A Unified Model for Multi-city Human Mobility Prediction
Chonghua Han, Yuan Yuan, Yukun Liu +3
Human mobility prediction is vital for urban planning, transportation optimization, and personalized services. However, the inherent randomness, non-uniform time intervals, and com…
Large language model as user daily behavior data generator: balancing population diversity and individual personality
Haoxin Li, Jingtao Ding, Jiahui Gong +1
Predicting human daily behavior is challenging due to the complexity of routine patterns and short-term fluctuations. While data-driven models have improved behavior prediction by…
PhyxMamba: Chaotic System Reconstruction from Short Context Observations with Generative State-Space Models
Chang Liu, Bohao Zhao, Jingtao Ding +2
Understanding chaotic dynamics is a fundamental problem across scientific disciplines, including climate science, neuroscience, and fluid dynamics, yet direct experimentation and i…
Collaborative Deterministic-Probabilistic Forecasting for Diverse Spatiotemporal Systems
Zhi Sheng, Yuan Yuan, Yudi Zhang +2
Probabilistic forecasting is crucial for real-world spatiotemporal systems, such as climate, energy, and urban environments, where quantifying uncertainty is essential for informed…
Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction
Zhi Sheng, Daisy Yuan, Jingtao Ding +1
Accurate prediction of mobile traffic, i.e., network traffic from cellular base stations, is crucial for optimizing network performance and supporting urban development. However, t…
Noise Matters: Diffusion Model-based Urban Mobility Generation with Collaborative Noise Priors
Yuheng Zhang, Yuan Yuan, Jingtao Ding +2
With global urbanization, the focus on sustainable cities has largely grown, driving research into equity, resilience, and urban planning, which often relies on mobility data. The…
UniFlow: A Foundation Model for Unified Urban Spatio-Temporal Flow Prediction
Yuan Yuan, Jingtao Ding, Chonghua Han +3
Urban spatio-temporal flow prediction, encompassing traffic flows and crowd flows, is crucial for optimizing city infrastructure and managing traffic and emergency responses. Tradi…
Diffusion Transformers as Open-World Spatiotemporal Foundation Models
Yuan Yuan, Chonghua Han, Jingtao Ding +3
The urban environment is characterized by complex spatio-temporal dynamics arising from diverse human activities and interactions. Effectively modeling these dynamics is essential…
Symbolic regression via MDLformer-guided search: from minimizing prediction error to minimizing description length
Zihan Yu, Jingtao Ding, Yong Li +1
Symbolic regression, a task discovering the formula best fitting the given data, is typically based on the heuristical search. These methods usually update candidate formulas to ob…
Large-scale Urban Facility Location Selection with Knowledge-informed Reinforcement Learning
Hongyuan Su, Yu Zheng, Jingtao Ding +2
The facility location problem (FLP) is a classical combinatorial optimization challenge aimed at strategically laying out facilities to maximize their accessibility. In this paper,…
A Population-to-individual Tuning Framework for Adapting Pretrained LM to On-device User Intent Prediction
Jiahui Gong, Jingtao Ding, Fanjin Meng +5
Mobile devices, especially smartphones, can support rich functions and have developed into indispensable tools in daily life. With the rise of generative AI services, smartphones c…
Spatio-Temporal Few-Shot Learning via Diffusive Neural Network Generation
Yuan Yuan, Chenyang Shao, Jingtao Ding +2
Spatio-temporal modeling is foundational for smart city applications, yet it is often hindered by data scarcity in many cities and regions. To bridge this gap, we propose a novel g…
Estimating On-road Transportation Carbon Emissions from Open Data of Road Network and Origin-destination Flow Data
Jinwei Zeng, Yu Liu, Jingtao Ding +2
Accounting for over 20% of the total carbon emissions, the precise estimation of on-road transportation carbon emissions is crucial for carbon emission monitoring and efficient mit…
UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction
Yuan Yuan, Jingtao Ding, Jie Feng +2
Urban spatio-temporal prediction is crucial for informed decision-making, such as traffic management, resource optimization, and emergence response. Despite remarkable breakthrough…
Spatio-temporal Diffusion Point Processes
Yuan Yuan, Jingtao Ding, Chenyang Shao +2
Spatio-temporal point process (STPP) is a stochastic collection of events accompanied with time and space. Due to computational complexities, existing solutions for STPPs compromis…