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Yu Meng

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20 papers

2 author rows

EAAI Journal 2026 Journal Article

Text-guided class-incremental point cloud semantic segmentation with category distribution constraint

Chao Zheng
Yan Xu
Xiaorui Peng
Meijun Wang
Yu Meng

Class-incremental light detection and ranging (LiDAR) semantic segmentation refers to maintaining recognition performance on previously learned categories while gradually introducing new categories during learning. This task is particularly challenging due to the sparse, irregular nature of point clouds and the severe class imbalance in large-scale scenes. Existing methods mainly rely on single-modality point-cloud features, which provide limited semantic priors and are vulnerable to category distribution drift across incremental steps. Moreover, its global knowledge distillation tends to make the features of old and novel classes overlap, resulting in confusion between similar categories. In this paper, we propose a novel incremental learning method that effectively integrates multimodal information to enhance the distinction between categories. We introduce textual information and deeply couple the incremental point cloud with text features through a cross-modal attention mechanism, effectively achieving the semantic complementarity across modalities. To further enhance the discriminability of previously learned category boundaries, we propose a historical multi-prototype distribution consistency constraint. In addition, we propose an orthogonality constraint between novel and previous knowledge to mitigate the performance degradation caused by conflicts introduced when learning new categories incrementally. Extensive experiments on the autonomous driving dataset show that our proposed method achieves the state-of-the-art results.

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AAAI Conference 2026 Conference Paper

Towards Aligned and Efficient Large Language Models

Yu Meng

Large language models (LLMs) have rapidly transformed the landscape of AI, demonstrating remarkable capabilities across reasoning, communication, and problem-solving. Yet, realizing their full potential requires addressing two critical challenges. First, their behavior must be steered and refined after training to ensure reliability, safety, and alignment with human values and intentions. Second, their large scale comes with substantial costs in training and deployment, necessitating research into more efficient methods. My research centers on advancing both of these fronts—making LLMs both aligned and efficient. On one side, I investigate post-training techniques that allow models to better reflect human preferences, demonstrate strong reasoning capabilities, and mitigate hallucination. On the other side, I study methods for improving data efficiency in training and inference efficiency in deployment. Together, these thrusts highlight a broader vision of enabling LLMs that are not only powerful, but also trustworthy and accessible at scale.

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EAAI Journal 2025 Journal Article

Integrating convolutional guidance and Transformer fusion with Markov Random Fields smoothing for monocular depth estimation

Xiaorui Peng
Yu Meng
Boqiang Shi
Chao Zheng
Meijun Wang

Monocular depth estimation is a challenging and prominent problem in current computer vision research and is widely used in intelligent transportation like environment perception, navigation and localization. Accurately delineating object boundaries and ensuring smooth transitions in estimated depth images from a single image remain significant challenges. These issues place higher demands on the network’s global and local feature extraction capabilities. In response, we proposed a depth estimation framework, designed to address detection accuracy and the global smooth transition of predicted depth maps. Our method introduces a novel feature decoding structure named Convolutional Guided Fusion (CoGF), which utilizes local features extracted by a convolutional neural network as a guide and fuses them with long-range dependent features extracted by a Transformer. This approach enables the model to retain both local details and global contextual information during the decoding process. To ensure global smoothness in the depth estimation results, we incorporate a smoothing strategy based on Markov Random Fields (MRF), enhancing pixel-to-pixel continuity and ensuring robust spatial consistency in the generated depth maps. Our proposed method is evaluated on current mainstream benchmarks. Experimental results demonstrate that our depth estimation method outperforms previous approaches. The code is available at https: //github. com/pxrw/CGTF-Depth. git.

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EAAI Journal 2025 Journal Article

Open-pit mine occlusion object detection for unmanned transport vehicles

Chao Zheng
Guoxing Bai
Yu Meng
Lu Wang
Xianyao Jiang
Li Liu

Accurate object recognition in open-pit mine environments is crucial for the safety of autonomous transport vehicles. Existing autonomous driving perception mostly focuses on urban structured road traffic, and it is hard to adapt to the challenging open-pit mine environment. Lacking of datasets further limits the development of the specific work. In this paper, we propose an object detection dataset for open-pit mine autonomous driving applications. This dataset encompasses data from several mines and includes different periods such as day, dusk, and night. It provides detailed annotations for diverse objects in the open-pit mines and incorporates additional attributes for evaluating occlusion detection. In addition, to address the multi-scale changes of objects in open-pit mines and the occlusion problems caused by dust, we propose a novel occlusion mine object general distribution detection method, utilizing soft labels and vehicle attribute location to reduce the positioning ambiguity in difficult backgrounds and achieve specific object detection in harsh open-pit mine environments. Our work explores the benchmark for open-pit mine object recognition involving occlusion. Comparison with mainstream techniques on the benchmark demonstrates that our approach outperforms existing state-of-the-art methods and can achieve 82. 2%, 81. 7%, and 76. 7% average precision in easy, moderate, and hard modes, respectively.

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NeurIPS Conference 2025 Conference Paper

RAST: Reasoning Activation in LLMs via Small-model Transfer

Siru Ouyang
Xinyu Zhu
Zilin Xiao
Minhao Jiang
Yu Meng
Jiawei Han

Reinforcement learning (RL) has become a powerful approach for improving the reasoning capabilities of large language models (LLMs), as evidenced by recent successes such as OpenAI's o1 and Deepseek-R1. However, applying RL at scale remains intimidatingly resource-intensive, requiring multiple model copies and extensive GPU workloads. On the other hand, while being powerful, recent studies suggest that RL does not fundamentally endow models with new knowledge; rather, it primarily reshapes the model's output distribution to activate reasoning capabilities latent in the base model. Building on this insight, we hypothesize that the changes in output probabilities induced by RL are largely model-size invariant, opening the door to a more efficient paradigm: training a small model with RL and transferring its induced probability shifts to larger base models. To verify our hypothesis, we conduct a token-level analysis of decoding trajectories and find high alignment in RL-induced output distributions across model scales, validating our hypothesis. Motivated by this, we propose RAST, a simple yet effective method that transfers reasoning behaviors by injecting RL-induced probability adjustments from a small RL-trained model into larger models. Experiments across multiple mathematical reasoning benchmarks show that RAST substantially and consistently enhances the reasoning capabilities of base models while requiring significantly lower GPU memory than direct RL training, sometimes even yielding better performance than the RL-trained counterparts. Our findings offer new insights into the nature of RL-driven reasoning and practical strategies for scaling its benefits without incurring its full computational cost. The project page of RAST is available at https: //ozyyshr. github. io/RAST/.

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NeurIPS Conference 2025 Conference Paper

The Surprising Effectiveness of Negative Reinforcement in LLM Reasoning

Xinyu Zhu
Mengzhou Xia
Zhepei Wei
Wei-Lin Chen
Danqi Chen
Yu Meng

Reinforcement learning with verifiable rewards (RLVR) is a promising approach for training language models (LMs) on reasoning tasks that elicit emergent long chains of thought (CoTs). Unlike supervised learning, it updates the model using both correct and incorrect samples via policy gradients. To better understand its mechanism, we decompose the learning signal into reinforcing correct responses and penalizing incorrect ones, referred to as **P**ositive and **N**egative **S**ample **R**einforcement (**PSR** and **NSR**), respectively. We train `Qwen2. 5-Math-7B`, `Qwen3-4B` and `Llama-3. 1-8B-Instruct` on a mathematical reasoning dataset and uncover a surprising result: training with only negative samples — without reinforcing correct responses — can be highly effective: it consistently improves performance over the base model across the entire Pass@$k$ spectrum $k$ up to 256), often matching or surpassing PPO and GRPO. In contrast, reinforcing only correct responses improves Pass@1 but degrades performance at higher $k$, due to reduced diversity. These inference-scaling trends highlight that solely penalizing incorrect responses may contribute more to performance than previously recognized. Through gradient analysis, we show that NSR works by suppressing incorrect generations and redistributing probability mass toward other plausible candidates, guided by the model's prior beliefs. It refines the model's existing knowledge rather than introducing entirely new behaviors. Building on this insight, we propose a simple variant of the RL objective that upweights NSR, and show that it consistently improves overall Pass@$k$ performance on MATH, AIME 2025, and AMC23. Our code is available at [`https: //github. com/TianHongZXY/RLVR-Decomposed`](https: //github. com/TianHongZXY/RLVR-Decomposed).

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EAAI Journal 2024 Journal Article

Passable area segmentation for open-pit mine road from vehicle perspective

Chao Zheng
Li Liu
Yu Meng
Meijun Wang
Xianyao Jiang

Recognition of passable areas of mine roads based on vehicle perspective is crucial for autonomous vehicles to drive in unmanned open-pit mine scenes. In the past few years, deep learning-based passable area recognition methods have been proven feasible. However, these works have mainly focused on structured urban road environments. Few works designed for mine road region detection due to dataset scarcity. In this article, we propose the mine road scapes segmentation dataset, which is collected from open-pit mine road environments. The dataset is collected from different mine areas, covers different daytime and dusk periods, and annotates the road and non-road areas in detail. Besides, a novel transformer-based road segmentation network is proposed, which is designed for the mine road characteristics of texture and distribution. The segmentation network is composed of an improved transformer feature extraction backbone, which can simultaneously aggregate global context and local detail information, and realize pixel-level recognition. We solve the problem of mine road segmentation benchmark under vehicle perspective. Moreover, by comparing the results of current mainstream methods on the mine road dataset, our method obtains state-of-the-art performance.

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NeurIPS Conference 2024 Conference Paper

Pedestrian-Centric 3D Pre-collision Pose and Shape Estimation from Dashcam Perspective

Meijun Wang
Yu Meng
Zhongwei Qiu
Chao Zheng
Yan Xu
Xiaorui Peng
Jian Gao

Pedestrian pre-collision pose is one of the key factors to determine the degree of pedestrian-vehicle injury in collision. Human pose estimation algorithm is an effective method to estimate pedestrian emergency pose from accident video. However, the pose estimation model trained by the existing daily human pose datasets has poor robustness under specific poses such as pedestrian pre-collision pose, and it is difficult to obtain human pose datasets in the wild scenes, especially lacking scarce data such as pedestrian pre-collision pose in traffic scenes. In this paper, we collect pedestrian-vehicle collision pose from the dashcam perspective of dashcam and construct the first Pedestrian-Vehicle Collision Pose dataset (PVCP) in a semi-automatic way, including 40k+ accident frames and 20K+ pedestrian pre-collision pose annotation (2D, 3D, Mesh). Further, we construct a Pedestrian Pre-collision Pose Estimation Network (PPSENet) to estimate the collision pose and shape sequence of pedestrians from pedestrian-vehicle accident videos. The PPSENet first estimates the 2D pose from the image (Image to Pose, ITP) and then lifts the 2D pose to 3D mesh (Pose to Mesh, PTM). Due to the small size of the dataset, we introduce a pre-training model that learns the human pose prior on a large number of pose datasets, and use iterative regression to estimate the pre-collision pose and shape of pedestrians. Further, we classify the pre-collision pose sequence and introduce pose class loss, which achieves the best accuracy compared with the existing relevant \textit{state-of-the-art} methods. Code and data are available for research at https: //github. com/wmj142326/PVCP.

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NeurIPS Conference 2024 Conference Paper

SimPO: Simple Preference Optimization with a Reference-Free Reward

Yu Meng
Mengzhou Xia
Danqi Chen

Direct Preference Optimization (DPO) is a widely used offline preference optimization algorithm that reparameterizes reward functions in reinforcement learning from human feedback (RLHF) to enhance simplicity and training stability. In this work, we propose SimPO, a simpler yet more effective approach. The effectiveness of SimPO is attributed to a key design: using the _average_ log probability of a sequence as the implicit reward. This reward formulation better aligns with model generation and eliminates the need for a reference model, making it more compute and memory efficient. Additionally, we introduce a target reward margin to the Bradley-Terry objective to encourage a larger margin between the winning and losing responses, further improving the algorithm's performance. We compare SimPO to DPO and its latest variants across various state-of-the-art training setups, including both base and instruction-tuned models such as Mistral, Llama 3, and Gemma 2. We evaluate on extensive chat-based evaluation benchmarks, including AlpacaEval 2, MT-Bench, and Arena-Hard. Our results demonstrate that SimPO consistently and significantly outperforms existing approaches without substantially increasing response length. Specifically, SimPO outperforms DPO by up to 6. 4 points on AlpacaEval 2 and by up to 7. 5 points on Arena-Hard. Our top-performing model, built on Gemma-2-9B-it, achieves a 72. 4\% length-controlled win rate on AlpacaEval 2, a 59. 1\% win rate on Arena-Hard, and ranks 1st on Chatbot Arena among $<$10B models with real user votes.

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NeurIPS Conference 2024 Conference Paper

Unchosen Experts Can Contribute Too: Unleashing MoE Models’ Power by Self-Contrast

Chufan Shi
Cheng Yang
Xinyu Zhu
Jiahao Wang
Taiqiang Wu
Siheng Li
Deng Cai
Yujiu Yang

Mixture-of-Experts (MoE) has emerged as a prominent architecture for scaling model size while maintaining computational efficiency. In MoE, each token in the input sequence activates a different subset of experts determined by a routing mechanism. However, the unchosen experts in MoE models do not contribute to the output, potentially leading to underutilization of the model's capacity. In this work, we first conduct exploratory studies to demonstrate that increasing the number of activated experts does not necessarily improve and can even degrade the output quality. Then, we show that output distributions from an MoE model using different routing strategies substantially differ, indicating that different experts do not always act synergistically. Motivated by these findings, we propose S elf- C ontrast M ixture- o f- E xperts (SCMoE), a training-free strategy that utilizes unchosen experts in a self-contrast manner during inference. In SCMoE, the next-token probabilities are determined by contrasting the outputs from strong and weak activation using the same MoE model. Our method is conceptually simple and computationally lightweight, as it incurs minimal latency compared to greedy decoding. Experiments on several benchmarks (GSM8K, StrategyQA, MBPP and HumanEval) demonstrate that SCMoE can consistently enhance Mixtral 8x7B’s reasoning capability across various domains. For example, it improves the accuracy on GSM8K from 61. 79 to 66. 94. Moreover, combining SCMoE with self-consistency yields additional gains, increasing major@20 accuracy from 75. 59 to 78. 31.

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NeurIPS Conference 2023 Conference Paper

Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias

Yue Yu
Yuchen Zhuang
Jieyu Zhang
Yu Meng
Alexander J. Ratner
Ranjay Krishna
Jiaming Shen
Chao Zhang

Large language models (LLMs) have been recently leveraged as training data generators for various natural language processing (NLP) tasks. While previous research has explored different approaches to training models using generated data, they generally rely on simple class-conditional prompts, which may limit the diversity of the generated data and inherit systematic biases of LLM. Thus, we investigate training data generation with diversely attributed prompts (e. g. , specifying attributes like length and style), which have the potential to yield diverse and attributed generated data. Our investigation focuses on datasets with high cardinality and diverse domains, wherein we demonstrate that attributed prompts outperform simple class-conditional prompts in terms of the resulting model's performance. Additionally, we present a comprehensive empirical study on data generation encompassing vital aspects like bias, diversity, and efficiency, and highlight three key observations: firstly, synthetic datasets generated by simple prompts exhibit significant biases, such as regional bias; secondly, attribute diversity plays a pivotal role in enhancing model performance; lastly, attributed prompts achieve the performance of simple class-conditional prompts while utilizing only 5\% of the querying cost of ChatGPT associated with the latter. The data and code are available on {\url{https: //github. com/yueyu1030/AttrPrompt}}.

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NeurIPS Conference 2022 Conference Paper

Generating Training Data with Language Models: Towards Zero-Shot Language Understanding

Yu Meng
Jiaxin Huang
Yu Zhang
Jiawei Han

Pretrained language models (PLMs) have demonstrated remarkable performance in various natural language processing tasks: Unidirectional PLMs (e. g. , GPT) are well known for their superior text generation capabilities; bidirectional PLMs (e. g. , BERT) have been the prominent choice for natural language understanding (NLU) tasks. While both types of models have achieved promising few-shot learning performance, their potential for zero-shot learning has been underexplored. In this paper, we present a simple approach that uses both types of PLMs for fully zero-shot learning of NLU tasks without requiring any task-specific data: A unidirectional PLM generates class-conditioned texts guided by prompts, which are used as the training data for fine-tuning a bidirectional PLM. With quality training data selected based on the generation probability and regularization techniques (label smoothing and temporal ensembling) applied to the fine-tuning stage for better generalization and stability, our approach demonstrates strong performance across seven classification tasks of the GLUE benchmark (e. g. , 72. 3/73. 8 on MNLI-m/mm and 92. 8 on SST-2), significantly outperforming zero-shot prompting methods and achieving even comparable results to strong few-shot approaches using 32 training samples per class.

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NeurIPS Conference 2021 Conference Paper

COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining

Yu Meng
Chenyan Xiong
Payal Bajaj
saurabh tiwary
Paul Bennett
Jiawei Han
XIA SONG

We present a self-supervised learning framework, COCO-LM, that pretrains Language Models by COrrecting and COntrasting corrupted text sequences. Following ELECTRA-style pretraining, COCO-LM employs an auxiliary language model to corrupt text sequences, upon which it constructs two new tasks for pretraining the main model. The first token-level task, Corrective Language Modeling, is to detect and correct tokens replaced by the auxiliary model, in order to better capture token-level semantics. The second sequence-level task, Sequence Contrastive Learning, is to align text sequences originated from the same source input while ensuring uniformity in the representation space. Experiments on GLUE and SQuAD demonstrate that COCO-LM not only outperforms recent state-of-the-art pretrained models in accuracy, but also improves pretraining efficiency. It achieves the MNLI accuracy of ELECTRA with 50% of its pretraining GPU hours. With the same pretraining steps of standard base/large-sized models, COCO-LM outperforms the previous best models by 1+ GLUE average points.

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YNIMG Journal 2019 Journal Article

Computational neuroanatomy of baby brains: A review

Gang Li
Li Wang
Pew-Thian Yap
Fan Wang
Zhengwang Wu
Yu Meng
Pei Dong
Jaeil Kim

The first postnatal years are an exceptionally dynamic and critical period of structural, functional and connectivity development of the human brain. The increasing availability of non-invasive infant brain MR images provides unprecedented opportunities for accurate and reliable charting of dynamic early brain developmental trajectories in understanding normative and aberrant growth. However, infant brain MR images typically exhibit reduced tissue contrast (especially around 6 months of age), large within-tissue intensity variations, and regionally-heterogeneous, dynamic changes, in comparison with adult brain MR images. Consequently, the existing computational tools developed typically for adult brains are not suitable for infant brain MR image processing. To address these challenges, many infant-tailored computational methods have been proposed for computational neuroanatomy of infant brains. In this review paper, we provide a comprehensive review of the state-of-the-art computational methods for infant brain MRI processing and analysis, which have advanced our understanding of early postnatal brain development. We also summarize publically available infant-dedicated resources, including MRI datasets, computational tools, grand challenges, and brain atlases. Finally, we discuss the limitations in current research and suggest potential future research directions.

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YNIMG Journal 2019 Journal Article

Exploring folding patterns of infant cerebral cortex based on multi-view curvature features: Methods and applications

Dingna Duan
Shunren Xia
Islem Rekik
Yu Meng
Zhengwang Wu
Li Wang
Weili Lin
John H. Gilmore

The highly convoluted cortical folding of the human brain is intriguingly complex and variable across individuals. Exploring the underlying representative patterns of cortical folding is of great importance for many neuroimaging studies. At term birth, all major cortical folds are established and are minimally affected by the complicated postnatal environments; hence, neonates are the ideal candidates for exploring early postnatal cortical folding patterns, which yet remain largely unexplored. In this paper, we propose a novel method for exploring the representative regional folding patterns of infant brains. Specifically, first, multi-view curvature features are constructed to comprehensively characterize the complex characteristics of cortical folding. Second, for each view of curvature features, a similarity matrix is computed to measure the similarity of cortical folding in a specific region between any pair of subjects. Next, a similarity network fusion method is adopted to nonlinearly and adaptively fuse all the similarity matrices into a single one for retaining both shared and complementary similarity information of the multiple characteristics of cortical folding. Finally, based on the fused similarity matrix and a hierarchical affinity propagation clustering approach, all subjects are automatically grouped into several clusters to obtain the representative folding patterns. To show the applications, we have applied the proposed method to a large-scale dataset with 595 normal neonates and discovered representative folding patterns in several cortical regions, i. e. , the superior temporal gyrus (STG), inferior frontal gyrus (IFG), precuneus, and cingulate cortex. Meanwhile, we have revealed sex difference in STG, IFG, and cingulate cortex, as well as hemispheric asymmetries in STG and cingulate cortex in terms of cortical folding patterns. Moreover, we have also validated the proposed method on a public adult dataset, i. e. , the Human Connectome Project (HCP), and revealed that certain major cortical folding patterns of adults are largely established at term birth.

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YNIMG Journal 2019 Journal Article

Multi-task prediction of infant cognitive scores from longitudinal incomplete neuroimaging data

Ehsan Adeli
Yu Meng
Gang Li
Weili Lin
Dinggang Shen

Early postnatal brain undergoes a stunning period of development. Over the past few years, research on dynamic infant brain development has received increased attention, exhibiting how important the early stages of a child's life are in terms of brain development. To precisely chart the early brain developmental trajectories, longitudinal studies with data acquired over a long-enough period of infants' early life is essential. However, in practice, missing data from different time point(s) during the data gathering procedure is often inevitable. This leads to incomplete set of longitudinal data, which poses a major challenge for such studies. In this paper, prediction of multiple future cognitive scores with incomplete longitudinal imaging data is modeled into a multi-task machine learning framework. To efficiently learn this model, we account for selection of informative features (i. e. , neuroimaging morphometric measurements for different time points), while preserving the structural information and the interrelation between these multiple cognitive scores. Several experiments are conducted on a carefully acquired in-house dataset, and the results affirm that we can predict the cognitive scores measured at the age of four years old, using the imaging data of earlier time points, as early as 24 months of age, with a reasonable performance (i. e. , root mean square error of 0. 18).

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NeurIPS Conference 2019 Conference Paper

Spherical Text Embedding

Yu Meng
Jiaxin Huang
Guangyuan Wang
Chao Zhang
Honglei Zhuang
Lance Kaplan
Jiawei Han

Unsupervised text embedding has shown great power in a wide range of NLP tasks. While text embeddings are typically learned in the Euclidean space, directional similarity is often more effective in tasks such as word similarity and document clustering, which creates a gap between the training stage and usage stage of text embedding. To close this gap, we propose a spherical generative model based on which unsupervised word and paragraph embeddings are jointly learned. To learn text embeddings in the spherical space, we develop an efficient optimization algorithm with convergence guarantee based on Riemannian optimization. Our model enjoys high efficiency and achieves state-of-the-art performances on various text embedding tasks including word similarity and document clustering.

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AAAI Conference 2019 Conference Paper

Weakly-Supervised Hierarchical Text Classification

Yu Meng
Jiaming Shen
Chao Zhang
Jiawei Han

Hierarchical text classification, which aims to classify text documents into a given hierarchy, is an important task in many real-world applications. Recently, deep neural models are gaining increasing popularity for text classification due to their expressive power and minimum requirement for feature engineering. However, applying deep neural networks for hierarchical text classification remains challenging, because they heavily rely on a large amount of training data and meanwhile cannot easily determine appropriate levels of documents in the hierarchical setting. In this paper, we propose a weakly-supervised neural method for hierarchical text classification. Our method does not require a large amount of training data but requires only easy-to-provide weak supervision signals such as a few class-related documents or keywords. Our method effectively leverages such weak supervision signals to generate pseudo documents for model pre-training, and then performs self-training on real unlabeled data to iteratively refine the model. During the training process, our model features a hierarchical neural structure, which mimics the given hierarchy and is capable of determining the proper levels for documents with a blocking mechanism. Experiments on three datasets from different domains demonstrate the efficacy of our method compared with a comprehensive set of baselines.

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ICRA Conference 2018 Conference Paper

A Synchronization Scheme for Position Control of Multiple Rope-Climbing Robots

Guangli Sun
Xiang Li 0009
Peng Li 0019
Yu Meng
Yang Zhou
Enzhi Xu
Yun-Hui Liu 0001

The ability of rope-climbing robots in aloft operation is limited by its self-supporting and locomotion ability. In many applications, a given task is also too complex to be achieved by a single rope-climbing robot acting alone. The solution of multiple rope-climbing robots can overcome the limitations. However, existing control methods for rope-climbing robots are limited to single robot, and the open issue of coordination between multiple rope-climbing robots has not been systematically addressed. This paper presents a new synchronization scheme for position control of multiple rope-climbing robots, such that each robot moves to the corresponding desired position while synchronizing the heights between each other. Maintaining the same height is very important to guarantee the stability of the task-oriented manipulator installed among multiple robots, when it is performing the manipulation task. The development of the proposed controller is based on the singular perturbation approach, by treating the fast actuator dynamics as a perturbation of the slow robot dynamics, such that the lowest control complexity is achieved. The exponential stability of the overall system that consists of the fast and slow subsystems is proved by using Tikhonov’ s theorem. Experimental results are presented to illustrate the performance of the proposed controller.

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YNIMG Journal 2014 Journal Article

Spatial distribution and longitudinal development of deep cortical sulcal landmarks in infants

Yu Meng
Gang Li
Weili Lin
John H. Gilmore
Dinggang Shen

Sulcal pits, the locally deepest points in sulci of the highly convoluted and variable cerebral cortex, are found to be spatially consistent across human adult individuals. It is suggested that sulcal pits are genetically controlled and have close relationships with functional areas. To date, the existing imaging studies of sulcal pits are mainly focused on adult brains, yet little is known about the spatial distribution and temporal development of sulcal pits in the first 2years of life, which is the most dynamic and critical period of postnatal brain development. Studying sulcal pits during this period would greatly enrich our limited understandings of the origins and developmental trajectories of sulcal pits, and would also provide important insights into many neurodevelopmental disorders associated with abnormal cortical foldings. In this paper, by using surface-based morphometry, for the first time, we systemically investigated the spatial distribution and temporal development of sulcal pits in major cortical sulci from 73 healthy infants, each with three longitudinal 3T MR scans at term birth, 1year, and 2years of age. Our results suggest that the spatially consistent distributions of sulcal pits in major sulci across individuals have already existed at term birth and this spatial distribution pattern keeps relatively stable in the first 2years of life, despite that the cerebral cortex expands dramatically and the sulcal depth increases considerably during this period. Specially, the depth of sulcal pits increases regionally heterogeneously, with more rapid growth in the high-order association cortex, including the prefrontal and temporal cortices, than the sensorimotor cortex in the first 2years of life. Meanwhile, our results also suggest that there exist hemispheric asymmetries of the spatial distributions of sulcal pits in several cortical regions, such as the central, superior temporal and postcentral sulci, consistently from birth to 2years of age, which likely has close relationships with the lateralization of brain functions of these regions. This study provides detailed insights into the spatial distribution and temporal development of deep sulcal landmarks in infants.

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