Unified Spatio-Temporal Tokens are Bases for Generalizable Traffic Forecasting
Yujun Chen*, Shihao Tu*, Wenyue Ding, Yicheng Lu, Qingkai Ren, Yangjie Zheng, Yang Yang (*: equal contribution)
STUNet (Spatio-Temporal Unified Network) explicitly encodes spatial structure into unified tokens and integrates them with temporal representations for generalizable traffic forecasting. It tokenizes the adjacency matrix into spatial patches and uses query-aggregate attention to capture upstream–downstream dependencies across traffic networks.STUNet 面向交通流预测中的跨路网泛化问题，把空间结构显式编码为统一表征，再与时间信息结合。模型将邻接矩阵划分成空间 token，并用查询–聚合注意力刻画上下游依赖，从而在不同交通网络上保持较好的预测能力。

ASTNet: Asynchronous Spatio-Temporal Network for Large-Scale Chemical Sensor Forecasting
Shihao Tu, Yang Yang, Wenyue Ding, Yicheng Lu, Qingkai Ren, Yupeng Zhang, Yin Zhang
ASTNet proposes a novel approach for real-time spatiotemporal forecasting in chemical sensor networks, addressing computational latency and complex spatial dependencies. It features asynchronous modeling to reduce latency and dynamic graph fusion to enhance robustness.ASTNet 面向大规模化学传感器网络的实时时空预测：用异步建模降低计算时延，并用动态图融合更好地刻画复杂的空间依赖。

PowerPM: Foundation Model for Power Systems
Shihao Tu*, Yupeng Zhang*, Jing Zhang, Zhendong Fu, Yin Zhang, Yang Yang (*: equal contribution)
PowerPM is an advanced model for electricity time series (ETS) analysis, designed to address the complexities of hierarchical and temporal data for power systems applications, focusing on electricity usage forecasting, grid stability and consumer behaviour analysis.PowerPM 是面向电力系统电量时间序列的基础模型，同时建模层次结构与时间依赖，可用于负荷预测、电网稳定评估和用户行为分析。

DMNet: Self-comparison Driven Model for Subject-independent Seizure Detection
Shihao Tu, Linfeng Cao, Daoze Zhang, Junru Chen, Lvbin Ma, Yin Zhang, Yang Yang
Difference Matrix-based Neural Network (DMNet) addresses the domain shift in iEEG signals across different subjects by leveraging a self-comparison mechanism for subject-independent automatic seizure detection.DMNet 针对不同患者颅内脑电信号的分布差异，借助自比较机制缓解域偏移，从而实现跨受试者的癫痫发作自动检测。