Tootfinder

Spatiotemporal Prediction of Electric Vehicle Charging Load Based on Large Language Models
Hang Fan, Mingxuan Li, Jingshi Cui, Zuhan Zhang, Wencai Run, Dunnan Liu
https://arxiv.org/abs/2506.03728

Spatiotemporal Prediction of Electric Vehicle Charging Load Based on Large Language Models
The rapid growth of EVs and the subsequent increase in charging demand pose significant challenges for load grid scheduling and the operation of EV charging stations. Effectively harnessing the spatiotemporal correlations among EV charging stations to improve forecasting accuracy is complex. To tackle these challenges, we propose EV-LLM for EV charging loads based on LLMs in this paper. EV-LLM integrates the strengths of Graph Convolutional Networks (GCNs) in spatiotemporal feature extraction w…

Monolithically Integrated Optical Convolutional Processors on Thin Film Lithium Niobate
Ruixue Liu, Rongbo Wu, Yong Zheng, Yuan Ren, Boyang Nan, Min Wang, Yunpeng Song, Ya Cheng
https://arxiv.org/abs/2507.20552

Monolithically Integrated Optical Convolutional Processors on Thin Film Lithium Niobate
Photonic neural networks (PNNs) of sufficiently large physical dimensions and high operation accuracies are envisaged as an ideal candidate for breaking the major bottlenecks in the current artificial intelligence architectures in terms of latency, energy efficiency and computational power. To achieve this vision, it is of vital importance to scale up the PNNs and in the meantime reduce the high demand on the dimensions required by the PNNs. The underlying cause of this strategy is the enormous…