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Man Xu

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5 papers

2 author rows

ICML Conference 2025 Conference Paper

LangTime: A Language-Guided Unified Model for Time Series Forecasting with Proximal Policy Optimization

Wenzhe Niu
Zongxia Xie
Yanru Sun
Wei He
Man Xu
Chao Hao

Recent research has shown an increasing interest in utilizing pre-trained large language models (LLMs) for a variety of time series applications. However, there are three main challenges when using LLMs as foundational models for time series forecasting: (1) Cross-domain generalization. (2) Cross-modality alignment. (3) Error accumulation in autoregressive frameworks. To address these challenges, we proposed LangTime, a lan guage- g uided unified model for time series forecasting that incorporates cross-domain pre-training with reinforcement learning-based fine-tuning. Specifically, LangTime constructs Temporal Comprehension Prompts (TCPs), which include dataset-wise and channel-wise instructions, to facilitate domain adaptation and condense time series into a single token, enabling LLMs to understand better and align temporal data. To improve autoregressive forecasting, we introduce TimePPO, a reinforcement learning-based fine-tuning algorithm. TimePPO mitigates error accumulation by leveraging a multidimensional rewards function tailored for time series and a repeat-based value estimation strategy. Extensive experiments demonstrate that LangTime achieves state-of-the-art cross-domain forecasting performance, while TimePPO fine-tuning effectively enhances the stability and accuracy of autoregressive forecasting.

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EAAI Journal 2024 Journal Article

An adaptive spatial–temporal prediction model for landslide displacement based on decomposition architecture

Man Xu
Dongmei Zhang
Jiang Li
Yiping Wu

Landslide displacement forecasting is a core issue in geohazard research, it is particularly challenging for accumulation-type landslides with complex geological patterns. Traditional landslide displacement prediction methods use single-point modeling and often fail to consider the spatial correlation characteristics of each deformation point on the surface of a landslide. On the other hand, they have difficulty in learning the changes caused by rainfall and reservoir water level. To tackle these obstacles, we introduce an adaptive spatial–temporal landslide displacement prediction model based on a decomposition architecture, named Self-Adaptive Unet with Decomposed Temporal Attention Encoder(SAU-DTAE). To effectively separate the features of different scales in time series changes and model them separately, we employ a progressive decomposition architecture based on a Lightweight Temporal Attention Encoder(LTAE). Furthermore, we design a gating mechanism with Sample Entropy (SampEn) to adaptively extract global and local spatial features at multiple scales. By quantifying the spatial complexity, we can achieve adaptive extraction of spatial correlation features. Relevant experiments were conducted with the 2016-2023 Interferometry Synthetic Aperture Radar (InSAR) landslide displacement dataset of the Three Gorges area. The new proposed algorithm was compared and validated against several classical time-series prediction models: Back Propagation(BP) neural network, Long Short Term Memory(LSTM) neural network, Gated Recurrent Unit(GRU), Convolutional LSTM(ConvLSTM), Informer, and Autoformer. The findings from the experiment indicated that our model surpassed the benchmark models, achieving superior prediction results on the test set. The Mean Absolute Error (MAE) was 5. 516 millimeters(mm), the Root Mean Square Error (RMSE) was 3. 856 mm, and the R-Square( R 2 ) was 0. 896.

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YNICL Journal 2021 Journal Article

Brain iron assessment in patients with First-episode schizophrenia using quantitative susceptibility mapping

Man Xu
Yihao Guo
Junying Cheng
Kangkang Xue
Meng Yang
Xueqin Song
Yanqiu Feng
Jingliang Cheng

PURPOSE: Decreased serum ferritin level was recently found in schizophrenia. Whether the brain iron concentration in schizophrenia exists abnormality is of research significance. Quantitative susceptibility mapping (QSM) was used in this study to assess brain iron changes in the grey matter nuclei of patients with first-episode schizophrenia. METHODS: * was evaluated using receiver operating characteristic curve. The correlations between regional iron variations and clinical PANSS (Positive and Negative Syndrome Scale) scores were assessed using partial correlation analysis. RESULTS: * values did not show significant correlations with PANSS scores (p > 0.05). CONCLUSION: * in the evaluation of schizophrenia-related brain iron changes. It demonstrated that QSM may be a potential biomarker for further understanding the pathophysiological mechanism of first-episode schizophrenia.

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YNICL Journal 2017 Journal Article

Alterations of white matter structural networks in patients with non-neuropsychiatric systemic lupus erythematosus identified by probabilistic tractography and connectivity-based analyses

Man Xu
Xiangliang Tan
Xinyuan Zhang
Yihao Guo
Yingjie Mei
Qianjin Feng
Yikai Xu
Yanqiu Feng

PURPOSE: Systemic lupus erythematosus (SLE) is a chronic inflammatory female-predominant autoimmune disease that can affect the central nervous system and exhibit neuropsychiatric symptoms. In SLE patients without neuropsychiatric symptoms (non-NPSLE), recent diffusion tensor imaging studies showed white matter abnormalities in their brains. The present study investigated the entire brain white matter structural connectivity in non-NPSLE patients by using probabilistic tractography and connectivity-based analyses. METHODS: Whole-brain structural networks of 29 non-NPSLE patients and 29 healthy controls (HCs) were examined. The structural networks were constructed with interregional probabilistic connectivity. Graph theory analysis was performed to investigate the topological properties, and network-based statistic was employed to assess the alterations of the interregional connections among non-NPSLE patients and controls. RESULTS: Compared with HCs, non-NPSLE patients demonstrated significantly decreased global and local network efficiencies and showed increased characteristic path length. This finding suggests that the global integration and local specialization were impaired. Moreover, the regional properties (nodal efficiency and degree) in the frontal, occipital, and cingulum regions of the non-NPSLE patients were significantly changed and negatively correlated with the disease activity index. The distribution pattern of the hubs measured by nodal degree was altered in the patient group. Finally, the non-NPSLE group exhibited decreased structural connectivity in the left median cingulate-centered component and increased connectivity in the left precuneus-centered component and right middle temporal lobe-centered component. CONCLUSION: This study reveals an altered topological organization of white matter networks in non-NPSLE patients. Furthermore, this research provides new insights into the structural disruptions underlying the functional and neurocognitive deficits in non-NPSLE patients.

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YNIMG Journal 2017 Journal Article

Denoise diffusion-weighted images using higher-order singular value decomposition

Xinyuan Zhang
Jie Peng
Man Xu
Wei Yang
Zhe Zhang
Hua Guo
Wufan Chen
Qianjin Feng

Noise usually affects the reliability of quantitative analysis in diffusion-weighted (DW) magnetic resonance imaging (MRI), especially at high b-values and/or high spatial resolution. Higher-order singular value decomposition (HOSVD) has recently emerged as a simple, effective, and adaptive transform to exploit sparseness within multidimensional data. In particular, the patch-based HOSVD denoising has demonstrated superb performance when applied to T1-, T2-, and proton density-weighted MRI data. In this study, we aim to investigate the feasibility of denoising DW data using the HOSVD transform. With the low signal-to-noise ratio in typical DW data, the patch-based HOSVD denoising suffers from stripe artifacts in homogeneous regions because of the HOSVD bases learned from the noisy patches. To address this problem, we propose a novel denoising method. It first introduces a global HOSVD-based denoising as a prefiltering stage to guide the subsequent patch-based HOSVD denoising stage. The HOSVD bases from the patch groups in prefiltered images are then used to transform the noisy patch groups in original DW data. Experiments were performed using simulated and in vivo DW data. Results show that the proposed method significantly reduces stripe artifacts compared with conventional patch-based HOSVD denoising methods, and outperforms two state-of-the-art denoising methods in terms of denoising quality and diffusion parameters estimation.

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