From Idle to Urgent: A Resource-Harvested HPC Workflow for High-Fidelity Seismic Estimation
提出了一种高效计算工作流，利用闲置计算资源和神经网络加速大地震时的高精度地震估计，大幅减少紧急情况下的计算成本。
0 citationsRead paper
Selected work
Representative Papers
An Efficient Out-of-Core Tomographic Imaging Framework for Edge Devices
本文提出了一种名为edgeFBP的框架，通过采用混合精度策略加速反投影内核，解决了边缘设备上因计算能力和内存限制而难以进行CT成像的问题。
0 citationsRead paper
Fine-tuning LLMs for Tourist Trajectory Prediction using Field Experiment Data
该研究通过使用实地实验数据微调大型语言模型来预测游客轨迹，解决了传统方法难以泛化到未观察场景的问题。
0 citationsRead paper
Distilling Aggregate Mobility Statistics into a Language Model Policy for Post-Event Crowd Simulation
研究通过微调语言模型来模拟人群行为，使用迭代比例拟合方法调整目的地分布以匹配观察数据，解决了个体行为不确定问题。
0 citationsRead paper
ZIPBrain: Can EEG Foundation Models Be Faster, Locally Deployable, but Accurate?
This work addresses the quadratic computational overhead and redundancy in EEG foundation models caused by long input sequences and low signal-to-noise ratios. To this end, the authors propose ZIPBrain, a training-free, plug-and-play perceptual redundancy-aware token pooling module. ZIPBrain partitions EEG tokens into redundant and distinctive groups, then merges each redundant token with its most similar distinctive counterpart, substantially compressing sequence length. The method seamlessly integrates into standard Transformer encoders without requiring fine-tuning and leverages CUDA Graph acceleration for efficient inference. Evaluated across multiple EEG foundation models, ZIPBrain consistently improves average accuracy by 1.3%–10.5% while reducing inference time by 32.7% on average—up to 41.8%—achieving a favorable trade-off between efficiency and performance.
0 citationsRead paper
Recent publications
Latest Papers
From Idle to Urgent: A Resource-Harvested HPC Workflow for High-Fidelity Seismic Estimation
提出了一种高效计算工作流，利用闲置计算资源和神经网络加速大地震时的高精度地震估计，大幅减少紧急情况下的计算成本。
0 citationsRead paper
An Efficient Out-of-Core Tomographic Imaging Framework for Edge Devices
本文提出了一种名为edgeFBP的框架，通过采用混合精度策略加速反投影内核，解决了边缘设备上因计算能力和内存限制而难以进行CT成像的问题。
0 citationsRead paper
Fine-tuning LLMs for Tourist Trajectory Prediction using Field Experiment Data
该研究通过使用实地实验数据微调大型语言模型来预测游客轨迹，解决了传统方法难以泛化到未观察场景的问题。
0 citationsRead paper
Distilling Aggregate Mobility Statistics into a Language Model Policy for Post-Event Crowd Simulation
研究通过微调语言模型来模拟人群行为，使用迭代比例拟合方法调整目的地分布以匹配观察数据，解决了个体行为不确定问题。
0 citationsRead paper
ZIPBrain: Can EEG Foundation Models Be Faster, Locally Deployable, but Accurate?
This work addresses the quadratic computational overhead and redundancy in EEG foundation models caused by long input sequences and low signal-to-noise ratios. To this end, the authors propose ZIPBrain, a training-free, plug-and-play perceptual redundancy-aware token pooling module. ZIPBrain partitions EEG tokens into redundant and distinctive groups, then merges each redundant token with its most similar distinctive counterpart, substantially compressing sequence length. The method seamlessly integrates into standard Transformer encoders without requiring fine-tuning and leverages CUDA Graph acceleration for efficient inference. Evaluated across multiple EEG foundation models, ZIPBrain consistently improves average accuracy by 1.3%–10.5% while reducing inference time by 32.7% on average—up to 41.8%—achieving a favorable trade-off between efficiency and performance.
0 citationsRead paper