Selected work

Representative Papers

GeoPrior-Mamba: Structured Process Priors with Mamba for Fine-Resolution XCO2 Reconstruction

Oct 07, 2026

This study addresses the challenge of learning heterogeneous source-sink relationships in high-resolution XCO2 reconstruction from sparse satellite observations by proposing the GeoPrior-Mamba framework. This framework pioneers the use of large language models to offline organize carbon cycle process knowledge, generating deterministic structured priors rather than direct numerical predictions. These priors are injected into a multi-directional Mamba backbone via lightweight adapters, enabling adaptive spatial instantiation of geo-ecological knowledge. Experimental results demonstrate that the proposed method achieves an RMSE of 0.81 ppm (R² = 0.93), reducing errors by 48.2% compared to CAMS. Furthermore, it exhibits significantly faster convergence than prior-free baselines and is validated against TCCON ground-based measurements, establishing a new paradigm for high-fidelity carbon field reconstruction.

0 citationsRead paper

CerebroSim: Scalable Whole-Brain Simulator at 100-Trillion-Synapse Scale on the LineShine Supercomputer

Sep 23, 2026

CerebroSim通过创新通信、多线程处理和存储压缩技术，在LineShine超级计算机上实现了860亿神经元和100万亿突触规模的全脑模拟，以支持脑疾病机制研究。

0 citationsRead paper

Topology-Aware Neighborhood Learning for Source-Free Cross-Scene Hyperspectral Image Classification

Aug 06, 2026

This work addresses the challenge of source-data-unavailable cross-scenario hyperspectral image classification by proposing a topology-aware unsupervised domain adaptation framework. The method uniquely integrates global collaborative representation with local nearest-neighbor search to construct a contextual neighborhood topology that comprehensively captures the intrinsic manifold structure of the target domain. To enhance pseudo-label quality, it introduces entropy-momentum-based pseudo-label refinement, complemented by a log-inner-product topological consistency constraint and an information maximization regularizer. Extensive experiments on three standard cross-scenario hyperspectral datasets demonstrate that the proposed approach significantly outperforms current state-of-the-art methods. Ablation studies further validate the effectiveness of each component, underscoring the critical role of topological modeling in source-free hyperspectral domain adaptation.

0 citationsRead paper

Recent publications

Latest Papers

GeoPrior-Mamba: Structured Process Priors with Mamba for Fine-Resolution XCO2 Reconstruction

Oct 07, 2026

This study addresses the challenge of learning heterogeneous source-sink relationships in high-resolution XCO2 reconstruction from sparse satellite observations by proposing the GeoPrior-Mamba framework. This framework pioneers the use of large language models to offline organize carbon cycle process knowledge, generating deterministic structured priors rather than direct numerical predictions. These priors are injected into a multi-directional Mamba backbone via lightweight adapters, enabling adaptive spatial instantiation of geo-ecological knowledge. Experimental results demonstrate that the proposed method achieves an RMSE of 0.81 ppm (R² = 0.93), reducing errors by 48.2% compared to CAMS. Furthermore, it exhibits significantly faster convergence than prior-free baselines and is validated against TCCON ground-based measurements, establishing a new paradigm for high-fidelity carbon field reconstruction.

0 citationsRead paper

CerebroSim: Scalable Whole-Brain Simulator at 100-Trillion-Synapse Scale on the LineShine Supercomputer

Sep 23, 2026

CerebroSim通过创新通信、多线程处理和存储压缩技术，在LineShine超级计算机上实现了860亿神经元和100万亿突触规模的全脑模拟，以支持脑疾病机制研究。

0 citationsRead paper

Topology-Aware Neighborhood Learning for Source-Free Cross-Scene Hyperspectral Image Classification

Aug 06, 2026

This work addresses the challenge of source-data-unavailable cross-scenario hyperspectral image classification by proposing a topology-aware unsupervised domain adaptation framework. The method uniquely integrates global collaborative representation with local nearest-neighbor search to construct a contextual neighborhood topology that comprehensively captures the intrinsic manifold structure of the target domain. To enhance pseudo-label quality, it introduces entropy-momentum-based pseudo-label refinement, complemented by a log-inner-product topological consistency constraint and an information maximization regularizer. Extensive experiments on three standard cross-scenario hyperspectral datasets demonstrate that the proposed approach significantly outperforms current state-of-the-art methods. Ablation studies further validate the effectiveness of each component, underscoring the critical role of topological modeling in source-free hyperspectral domain adaptation.

0 citationsRead paper