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Xiaodong Chen

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4 papers

2 author rows

YNICL Journal 2025 Journal Article

Alterations of long-range association fibers in patients with anti-N-methyl-D-aspartate receptor encephalitis

Xiaodong Chen
Ling Fang
Yiying Huang
Yu Huang
Yi Lu
Jinhui Wang
Chunxin Liu
Huanquan Liao

BACKGROUND: Patients with anti-NMDAR encephalitis typically exhibit impaired cognitive integration, which relies on the integrity of long-range association fibers connecting diverse brain regions. However, the microstructural integrity of long-range association fibers in this population remains unknown. METHODS: Diffusion tensor imaging (DTI) data were collected from 32 patients with anti-NMDAR encephalitis and 30 healthy controls. Patients were further categorized into early and delayed immunotherapy subgroups based on a 2-week threshold for immunotherapy initiation. The diffusion properties of major long-range association fibers were quantified at both the bundle and node levels. RESULTS: Compared with healthy controls, patients exhibited widespread microstructural damage within long-range association fibers, with more severe alterations in the delayed immunotherapy subgroup (FDR-corrected p < 0.05). In this subgroup(n = 14), radial diffusivity (RD) of left inferior fronto-occipital fasciculus (IFOF), left inferior longitudinal fasciculus (ILF), left superior longitudinal fascicles (SLF), and bilateral arcuate fascicles correlated significantly with global cognition (MMSE, FDR-corrected p < 0.05). Notably, RD also strongly correlated with working memory in the delayed immunotherapy subgroup, showing bundle-wise associations for IFOF (left: r = -0.8315, p = 0.0112; right: r = -0.7044, p = 0.0295), ILF (left: r = -0.7473, p = 0.0243), SLF (left: r = -0.7562, p = 0.0243; right: r = -0.6599, p = 0.0391), and arcuate fasciculus (left: r = -0.7240, p = 0.0272; right: r = -0.6835, p = 0.0333), with left-hemisphere predominance confirmed by node-wise analyses of IFOF, ILF, SLF, and arcuate fasciculus (FDR-corrected p < 0.05). CONCLUSIONS: Our findings highlight widespread microstructural damage in long-range association fibers in patients with anti-NMDAR encephalitis, particularly in those with delayed immunotherapy. This damage may serve as the neurophysiological basis for cognitive impairments, with working memory being most affected.

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ICRA Conference 2025 Conference Paper

Deep Learning Based Topography Aware Gas Source Localization with Mobile Robot

Changhao Tian
Annan Wang
Han Fan
Thomas Wiedemann 0002
Yifei Luo
Le Yang 0007
Weisi Lin
Achim J. Lilienthal

Gas source localization in complex environments is critical for applications such as environmental monitoring, industrial safety, and disaster response. Traditional methods often struggle with the challenges posed by a lack of environmental topography integration, especially when interactions between wind and obstacles distort gas dispersion patterns. In this paper, we propose a deep learning-based approach, which leverages spatial context and environmental mapping to enhance gas source localization. By integrating Simultaneous Localization and Mapping (SLAM) with a U-Net-based model, our method predicts the likelihood of gas source locations by analyzing gas sensor data, wind flow, and topography of the environment represented by a 2D occupancy map. We demonstrate the efficacy of our approach using a wheeled robot equipped with a photoionization detector, a LIDAR, and an anemometer, in various scenarios with dynamic wind fields and multiple obstacles. The results show that our approach can robustly locate gas sources, even in challenging environments with fluctuating wind directions, outperforming conventional methods by utilizing topography contextual information. This study underscores the importance of topographical context in gas source localization and offers a flexible and robust solution for real-world applications. Data and code are publicly available.

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ICLR Conference 2025 Conference Paper

Streamlining Redundant Layers to Compress Large Language Models

Xiaodong Chen
Yuxuan Hu
Jing Zhang 0001
Yanling Wang
Cuiping Li 0001
Hong Chen 0001

This paper introduces LLM-Streamline, a pioneer work on layer pruning for large language models (LLMs). It is based on the observation that different layers have varying impacts on hidden states, enabling the identification of less important layers to be pruned. LLM-Streamline comprises two parts: layer pruning, which removes consecutive layers with the lowest importance based on target sparsity, and layer replacement, a novel module that trains a lightweight network to replace the pruned layers to mitigate performance loss. Additionally, a new metric called stability is proposed to address the limitations of the widely used accuracy metric in evaluating model compression. Experiments show that LLM-Streamline outperforms both previous and concurrent state-of-the-art pruning methods in terms of both performance and training efficiency. Our code is available at \href{https://github.com/RUCKBReasoning/LLM-Streamline}{this repository}.

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ICRA Conference 2024 Conference Paper

Sensorized Soft Skin for Dexterous Robotic Hands

Jana Egli
Benedek Forrai
Thomas Buchner
Jiangtao Su
Xiaodong Chen
Robert K. Katzschmann

Conventional industrial robots often use two-fingered grippers or suction cups to manipulate objects or interact with the world. Because of their simplified design, they are unable to reproduce the dexterity of human hands when manipulating a wide range of objects. While the control of humanoid hands evolved greatly, hardware platforms still lack capabilities, particularly in tactile sensing and providing soft contact surfaces. In this work, we present a method that equips the skeleton of a tendon-driven humanoid hand with a soft and sensorized tactile skin. Multi-material 3D printing allows us to iteratively approach a cast skin design which preserves the robot’s dexterity in terms of range of motion and speed. We demonstrate that a soft skin enables firmer grasps and piezoresistive sensor integration enhances the hand’s tactile sensing capabilities.

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