1 citations · 1 across the 12 of their papers we have counts for
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SemPSG: A Semantic Channel-Aware Foundation Model for Polysomnography Analysis
Junyu Chen, Chenxi Liu, Shiqin Tang +5
Polysomnography (PSG) integrates multiple physiological signals to provide a comprehensive characterization of human sleep, yet its heterogeneous channel configurations across cent…
Tracing the Evidence Behind Zero-Shot Time-Series Forecasting: A Source-First Taxonomy and Audit Framework
Delun Kong, Wanyun Ling, Chenxi Liu +1
Zero-shot time-series forecasting (TSF) is often described as forecasting without target-specific parameter updates, but that training-status condition does not specify what eviden…
UHI-Bench: Benchmarking Dual-Source Urban Heat Island Modeling Across Cities in Diverse Climate Regimes
Wanyun Ling, Chenxi Liu, Yi Xie +3
Urban heat islands (UHIs) are intensifying under climate change, exacerbating thermal exposure risks. Their two primary observations, land surface temperature UHI (LST-UHI) and nea…
Decoupling Perception from Description: Computation-Grounded Representation Alignment between Multivariate Time Series and Language
Xinran Feng, Yi Xie, Chao Zhang +4
Training multimodal models to align time series with language runs into a self-supervision trap. The usual recipe asks an LLM to read a series and write a description, so label qua…
Adaptive Patching Is Harder Than It Looks For Time-Series Forecasting
Federico Zucchi, Yi Xie, Chao Zhang +3
Adaptive patching has been proposed for time-series forecasting: allocate finer patches where the sequence looks locally informative. We compare three adaptive patching methods wit…
MedSpaformer: a Transferable Transformer with Multi-granularity Token Sparsification for Medical Time Series Classification
Jiexia Ye, Weiqi Zhang, Ziyue Li +2
Accurate medical time series (MedTS) classification is essential for effective clinical diagnosis, yet remains challenging due to complex multi-channel temporal dependencies, infor…
LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting
Lingzheng Zhang, Lifeng Shen, Yimin Zheng +3
Recent research has shown that large language models (LLMs) can be effectively used for real-world time series forecasting due to their strong natural language understanding capabi…
Enabling Tensor Decomposition for Time-Series Classification via A Simple Pseudo-Laplacian Contrast
Man Li, Ziyue Li, Lijun Sun +1
Tensor decomposition has emerged as a prominent technique to learn low-dimensional representation under the supervision of reconstruction error, primarily benefiting data inference…
MedualTime: A Dual-Adapter Language Model for Medical Time Series-Text Multimodal Learning
Jiexia Ye, Weiqi Zhang, Ziyue Li +3
The recent rapid advancements in language models (LMs) have garnered attention in medical time series-text multimodal learning. However, existing contrastive learning-based and pro…