Smart Cities

UrbanKGent: A unified LLM agent framework revolutionizes urban knowledge graph construction, achieving significantly improved accuracy and efficiency.

TEdit: a novel diffusion model edits existing time series to meet specified attribute targets, preserving other properties, solving limitations of prior synthesis methods.

LoTNext framework tackles human mobility prediction’s long-tail problem by using graph and loss adjustments to improve the accuracy of predicting less-visited locations.

Garner, a novel framework, enhances road network representation learning by incorporating the Third Law of Geography, significantly boosting performance in downstream tasks.

PowerPM: A foundation model revolutionizing power system analysis by mastering complex ETS data through a novel self-supervised pre-training approach, achieving state-of-the-art performance.

Model-Based Transfer Learning (MBTL) boosts deep RL sample efficiency by strategically selecting training tasks, achieving up to 50x improvement over traditional methods.

LLM agents effectively generate realistic personal mobility patterns using semantically rich data.

EpoD, a novel dynamic graph learning model, significantly improves generalization via a self-prompted learning mechanism for environment inference and a structural causal model utilizing dynamic subgr…

CMuST: a novel continuous multi-task spatiotemporal learning framework tackles urban data limitations by enabling cross-interactions and task-level cooperation for enhanced generalization and adaptabi…

GeNSIT: a neural framework efficiently generates origin-destination matrices for agent-based models, outperforming existing methods in accuracy and scalability by directly operating on the discrete sp…

DiffLight: a novel conditional diffusion model for traffic signal control effectively addresses data-missing scenarios by unifying traffic data imputation and decision-making, demonstrating superior p…

CITRUS: a novel continuous graph neural network efficiently processes multidomain data on multiple graphs, achieving superior performance in spatiotemporal forecasting.

Active Sequential Neural Posterior Estimation (ASNPE) boosts simulation-based inference efficiency by actively selecting informative simulation parameters, significantly outperforming existing methods…