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Qiang Zhou

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21 papers

2 author rows

EAAI Journal 2026 Journal Article

A vision mamba-enhanced network with frequency-directional feature fusion for pavement crack segmentation

Xi Li
Yuqi Wang
Qiang Zhou
Jianhui Zhan
Deng Zuo
Weichao Chen

Driven by the rapid advancement of intelligent transportation and infrastructure digitalization, pavement crack detection has emerged as a research hotspot bridging civil engineering and computer vision. It plays a vital role in improving road safety and optimizing maintenance operations. However, the irregular morphology and susceptibility to background interference pose significant challenges to achieving accurate and robust automatic detection. To effectively address these issues, a Vision Mamba-Enhanced Network with Frequency-Directional Feature Fusion, VMFDF-Net, was proposed in this work for attaining efficient crack segmentation. The network integrates global modelling and local feature extraction capabilities, combining a Vision Mamba-inspired mechanism with convolutional neural networks (CNNs). Multi-frequency, multi-directional feature modelling, and an adaptive gated fusion module were introduced to enhance crack representation. The proposed Vision Mamba Convolutional (VMambaConv) module integrates local texture enhancement with long-range dependency modelling to improve crack feature extraction. In the bottleneck layer, a Wavelet and Directional-aware feature Cascade (WDcascade) module adaptively fuses multi-source features through a gated mechanism, enhancing the representation of diverse crack patterns in complex backgrounds. The effectiveness of the developed model was evaluated on four publicly available crack datasets: CRACK500, DeepCrack, CFD, and EdmCrack600. Six evaluation metrics were used for fair and objective assessment. The experimental results demonstrated that the model can generally achieve superior performance. Our code is open source on GitHub: https: //github. com/Acruelsummer/VMFDF-Net/tree/main.

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AAAI Conference 2026 Conference Paper

Contribution-aware Token Compression for Efficient Video Understanding via Reinforcement Learning

Yinchao Ma
Qiang Zhou
Zhibin Wang
Xianing Chen
Hanqing Yang
Jun Song
Bo Zheng

Video large language models have demonstrated remarkable capabilities in video understanding tasks. However, the redundancy of video tokens introduces significant computational overhead during inference, limiting their practical deployment. Many compression algorithms are proposed to prioritize retaining features with the highest attention scores to minimize perturbations in attention computations. However, the correlation between attention scores and their actual contribution to correct answers remains ambiguous. To address the above limitation, we propose a novel contribution-aware token compression algorithm for video understanding (CaCoVID) that explicitly optimizes the token selection policy based on the contribution of tokens to correct predictions. First, we introduce a reinforcement learning-based framework that optimizes a policy network to select video token combinations with the greatest contribution to correct predictions. This paradigm shifts the focus from passive token preservation to active discovery of optimal compressed token combinations. Secondly, we propose a combinatorial policy optimization algorithm with online combination space sampling, which dramatically reduces the exploration space for video token combinations and accelerates the convergence speed of policy optimization. Extensive experiments on diverse video understanding benchmarks demonstrate the effectiveness of CaCoVID. Codes will be released.

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AAAI Conference 2026 Conference Paper

Exploiting Pre-trained Language Model for Cross-city Urban Flow Prediction Guided by Information-theoretic Analysis

Qiang Zhou
Xudong Tong
Yuting Liu
ChuanXing Liu
Jingjing Gu

Cross-city urban flow prediction is critical for democratizing smart application benefits in data-scarce developing cities. However, existing methods face an inherent performance ceiling, constrained by both the inevitably finite samples from the source city and the distributional gap between cities. In this paper, we present PLM-CUP, the first theoretically-grounded framework that breaks this bottleneck by leveraging a pre-trained language model (PLM) as an additional source domain. Through an information-theoretic analysis of the generalization error bound, we reveal that the key challenge lies in constructing a semantic bridge encoder and a task-specific adapter to enable cross-domain alignment when incorporating a PLM. Accordingly, PLM-CUP adopts a three-stage architecture, including a semantic bridge encoder that transforms spatiotemporal flow patterns into languagealigned representations via trend-periodicity decomposition, a PLM fine-tuned for knowledge transfer, and a task adapter with spatiotemporal self-attention to conduct multi-step prediction. We further introduce GDAConv, a graph convolution module with dual activation functions that enhances spatial modeling throughout the framework. Experiments on real-world datasets demonstrate that PLM-CUP significantly outperforms state-of-the-art baselines, validating the effectiveness of the proposed PLM enhanced cross-city transfer paradigm for urban flow prediction.

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AAAI Conference 2026 Conference Paper

GeoBayes: Probabilistic Image Geo-Localization Inference via Sequential Bayesian Updating

Weimin Shi
Xiang Li
Kaige Li
Junhao Fang
Qiang Zhou
Qichuan Geng
Zhong Zhou

Image geo-localization aims to determine the geographic location of a query image. While Multimodal Large Language Models (MLLMs) show potential for this task due to their rich world knowledge and explainable abilities, they often struggle with confirmation bias, i.e., committing to early, potentially incorrect guesses driven by visual clues with varied geographic likelihoods. In this paper, we propose GeoBayes, a novel training-free framework that formulates geolocalization as a Maximum a Posteriori (MAP) estimation task over multiple geographic hypotheses and performs probabilistic thought via sequential Bayesian reasoning. GeoBayes treats each visual object and its associated geographic clues as probabilistic evidence, integrating them iteratively through a Hypothesize–Verify–Update loop. At each step, it evaluates how new evidence supports existing hypotheses and updates their posterior probabilities, gradually converging on the most probable location. This allows GeoBayes to explicitly quantify and fuse the varied geographic probabilities implied by various visual elements, reducing the risk of overcommitting to misleading clues. Furthermore, considering the natural hierarchy of geographic labels (e.g., country, city), GeoBayes introduces a state memory mechanism that stores hypotheses, inference context, and evidence scores across levels. This design enables the framework to propagate prior knowledge across levels of the geographic hierarchy and incorporate geographic structural constraints into the Bayesian update process, achieving a coarse-to-fine geo-localization. Experiments on IM2GPS3k and YFCC4K show that GeoBayes improves MLLM-based geo-localization accuracy without extra training. This demonstrates the effectiveness of probabilistic reasoning for robust and interpretable geo-localization.

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AAAI Conference 2026 Conference Paper

MMG-Vid: Maximizing Marginal Gains at Segment-level and Token-level for Efficient Video LLMs

Junpeng Ma
Qizhe Zhang
Ming Lu
Zhibin Wang
Qiang Zhou
Jun Song
Shanghang Zhang

Video Large Language Models (VLLMs) excel in video understanding, but their excessive visual tokens pose a significant computational challenge for real-world applications. Current methods aim to enhance inference efficiency by visual token pruning. However, they do not consider the dynamic characteristics and temporal dependencies of video frames, as they perceive video understanding as a multi-frame task. To address these challenges, we propose MMG-Vid, a novel training-free visual token pruning framework that removes redundancy by Maximizing Marginal Gains at both segment-level and token-level. Specifically, we first divide the video into segments based on frame similarity, and then dynamically allocate the token budget for each segment to maximize the marginal gain of each segment. Subsequently, we propose a temporal-guided DPC algorithm that jointly models inter-frame uniqueness and intra-frame diversity, thereby maximizing the marginal gain of each token. By combining both stages, MMG-Vid can maximize the utilization of the limited token budget, significantly improving efficiency while maintaining strong performance. Extensive experiments demonstrate that MMG-Vid can maintain over 99.5% of the original performance, while effectively reducing 75% visual tokens and accelerating the prefilling stage by 3.9x on LLaVA-OneVision-7B.

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EAAI Journal 2025 Journal Article

Bearing fault diagnosis for variable working conditions via lightweight transformer and homogeneous generalized contrastive learning with inter-class repulsive discriminant

Qiang Zhou
Wengang Ma
Yadong Zhang
Jin Guo

As indispensable components of rolling axle boxes, the condition of the bearings affects the safety of the traveling train. Therefore, bearing fault diagnosis is an imperative prerequisite for train safety. However, the diagnosis performance under variable working conditions is degraded owing to the large difference in the sample distribution and fewer samples. Although unsupervised domain adaptation models can solve these problems, environmental noise causes the fault features extracted from the two domains to overlap. Ultimately, the discriminative properties of the different samples remain insufficient. Therefore, we propose a rolling fault diagnosis approach for variable working conditions via lightweight Transformer and homogeneous generalized contrastive learning with inter-class repulsive discriminant (HGCL-ICRD). First, a deformable Transformer with lightweight manner is constructed to extract fault features from historical working conditions. Then, the source domain clustering cluster points are used to construct the positive and negative samples of the target domain to achieve the redistribution of the number. On this basis, the homogeneous generalized contrastive learning approach is built to make the samples to be tested have better classifiability. Finally, an inter-class repulsive discriminant term is constructed to minimize the sample distributional difference between the two domains. Furthermore, we construct an improved gray wolf algorithm to optimize the HGCL-ICRD. Extensive experiments on three datasets demonstrate that our model can perform high-precision and high-efficiency diagnosis under variable working conditions.

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AAAI Conference 2025 Conference Paper

DLF: Disentangled-Language-Focused Multimodal Sentiment Analysis

Pan Wang
Qiang Zhou
Yawen Wu
Tianlong Chen
Jingtong Hu

Multimodal Sentiment Analysis (MSA) leverages heterogeneous modalities, such as language, vision, and audio, to enhance the understanding of human sentiment. While existing models often focus on extracting shared information across modalities or directly fusing heterogeneous modalities, such approaches can introduce redundancy and conflicts due to equal treatment of all modalities and the mutual transfer of information between modality pairs. To address these issues, we propose a Disentangled-Language-Focused (DLF) multimodal representation learning framework, which incorporates a feature disentanglement module to separate modality-shared and modality-specific information. To further reduce redundancy and enhance language-targeted features, four geometric measures are introduced to refine the disentanglement process. A Language-Focused Attractor (LFA) is further developed to strengthen language representation by leveraging complementary modality-specific information through a language-guided cross-attention mechanism. The framework also employs hierarchical predictions to improve overall accuracy. Extensive experiments on two popular MSA datasets, CMU-MOSI and CMU-MOSEI, demonstrate the significant performance gains achieved by the proposed DLF framework. Comprehensive ablation studies further validate the effectiveness of the feature disentanglement module, language-focused attractor, and hierarchical predictions.

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NeurIPS Conference 2025 Conference Paper

SEEA-R1: Tree-Structured Reinforcement Fine-Tuning for Self-Evolving Embodied Agents

Wanxin Tian
Shijie Zhang
Kevin Zhang
Xiaowei Chi
Chun-Kai Fan
Junyu Lu
Yulin Luo
Qiang Zhou

Self-evolution, the ability of agents to autonomously improve their reasoning and behavior, is essential for the embodied domain with long-horizon, real-world tasks. Despite current advancements in reinforcement fine-tuning (RFT) showing strong performance in enhancing reasoning in LLMs, its potential to enable self-evolving embodied intelligence with multi-modal interactions remains largely unexplored. Specifically, reinforcement fine-tuning faces two fundamental obstacles in embodied settings: (i) the lack of accessible intermediate rewards in multi-step reasoning tasks limits effective learning signals, and (ii) reliance on hand-crafted reward functions restricts generalization to novel tasks and environments. To address these challenges, we present Self-Evolving Embodied Agents-R1, SEEA-R1, the first RFT framework designed for enabling the self-evolving capabilities of embodied agents. Specifically, to convert sparse delayed rewards into denser intermediate signals that improve multi-step reasoning, we propose Tree-based group relative policy optimization ( Tree-GRPO ) integrates Monte Carlo Tree Search into GRPO. To generalize reward estimation across tasks and scenes, supporting autonomous adaptation and reward-driven self-evolution, we further introduce Multi-modal Generative Reward Model ( MGRM ). To holistically evaluate the effectiveness of SEEA-R1, we evaluate on the ALFWorld benchmark, surpassing state-of-the-art methods with scores of 85. 07\% (textual) and 46. 27\% (multi-modal), outperforming prior models including GPT-4o. SEEA-R1 also achieves scores of 80. 3\% (textual) and 44. 03\% (multi-modal) without ground truth reward, surpassing all open-source baselines and highlighting its scalability as a self-evolving embodied agent. Additional experiments and qualitative analysis further support the potential of SEEA-R1 for future research in scalable embodied intelligence. Project page is at https: //seea-r1. github. io/.

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AAAI Conference 2024 Conference Paper

Explainable Origin-Destination Crowd Flow Interpolation via Variational Multi-Modal Recurrent Graph Auto-Encoder

Qiang Zhou
Xinjiang Lu
Jingjing Gu
Zhe Zheng
Bo Jin
Jingbo Zhou

Origin-destination (OD) crowd flow, if more accurately inferred at a fine-grained level, has the potential to enhance the efficacy of various urban applications. While in practice for mining OD crowd flow with effect, the problem of spatially interpolating OD crowd flow occurs since the ineluctable missing values. This problem is further complicated by the inherent scarcity and noise nature of OD crowd flow data. In this paper, we propose an uncertainty-aware interpolative and explainable framework, namely UApex, for realizing reliable and trustworthy OD crowd flow interpolation. Specifically, we first design a Variational Multi-modal Recurrent Graph Auto-Encoder (VMR-GAE) for uncertainty-aware OD crowd flow interpolation. A key idea here is to formulate the problem as semi-supervised learning on directed graphs. Next, to mitigate the data scarcity, we incorporate a distribution alignment mechanism that can introduce supplementary modals into variational inference. Then, a dedicated decoder with a Poisson prior is proposed for OD crowd flow interpolation. Moreover, to make VMR-GAE more trustworthy, we develop an efficient and uncertainty-aware explainer that can provide explanations from the spatiotemporal topology perspective via the Shapley value. Extensive experiments on two real-world datasets validate that VMR-GAE outperforms the state-of-the-art baselines. Also, an exploratory empirical study shows that the proposed explainer can generate meaningful spatiotemporal explanations.

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NeurIPS Conference 2024 Conference Paper

I2EBench: A Comprehensive Benchmark for Instruction-based Image Editing

Yiwei Ma
Jiayi Ji
Ke Ye
Weihuang Lin
Zhibin Wang
Yonghan Zheng
Qiang Zhou
Xiaoshuai Sun

Significant progress has been made in the field of Instruction-based Image Editing (IIE). However, evaluating these models poses a significant challenge. A crucial requirement in this field is the establishment of a comprehensive evaluation benchmark for accurately assessing editing results and providing valuable insights for its further development. In response to this need, we propose I2EBench, a comprehensive benchmark designed to automatically evaluate the quality of edited images produced by IIE models from multiple dimensions. I2EBench consists of 2, 000+ images for editing, along with 4, 000+ corresponding original and diverse instructions. It offers three distinctive characteristics: 1) Comprehensive Evaluation Dimensions: I2EBench comprises 16 evaluation dimensions that cover both high-level and low-level aspects, providing a comprehensive assessment of each IIE model. 2) Human Perception Alignment: To ensure the alignment of our benchmark with human perception, we conducted an extensive user study for each evaluation dimension. 3) Valuable Research Insights: By analyzing the advantages and disadvantages of existing IIE models across the 16 dimensions, we offer valuable research insights to guide future development in the field. We will open-source I2EBench, including all instructions, input images, human annotations, edited images from all evaluated methods, and a simple script for evaluating the results from new IIE models. The code, dataset, and generated images from all IIE models are provided in GitHub: https: //github. com/cocoshe/I2EBench.

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NeurIPS Conference 2024 Conference Paper

LeDex: Training LLMs to Better Self-Debug and Explain Code

Nan Jiang
Xiaopeng Li
Shiqi Wang
Qiang Zhou
Soneya B. Hossain
Baishakhi Ray
Varun Kumar
Xiaofei Ma

In the domain of code generation, self-debugging is crucial. It allows LLMs to refine their generated code based on execution feedback. This is particularly important because generating correct solutions in one attempt proves challenging for complex tasks. Prior works on self-debugging mostly focus on prompting methods by providing LLMs with few-shot examples, which work poorly on small open-sourced LLMs. In this work, we propose LeDex, a training framework that significantly improves the self-debugging capability of LLMs. Intuitively, we observe that a chain of explanations on the wrong code followed by code refinement helps LLMs better analyze the wrong code and do refinement. We thus propose an automated pipeline to collect a high-quality dataset for code explanation and refinement by generating a number of explanations and refinement trajectories from the LLM itself or a larger teacher model and filtering via execution verification. We perform supervised fine-tuning (SFT) and further reinforcement learning (RL) on both success and failure trajectories with a novel reward design considering code explanation and refinement quality. SFT improves the pass@1 by up to 15. 92\% and pass@10 by 9. 30\% over four benchmarks. RL training brings additional up to 3. 54\% improvement on pass@1 and 2. 55\% improvement on pass@10. The trained LLMs show iterative refinement ability and can keep refining code continuously. Lastly, our human evaluation shows that the LLMs trained with our framework generate more useful code explanations and help developers better understand bugs in source code.

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NeurIPS Conference 2023 Conference Paper

PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detection

Qiang Zhou
Weize Li
Lihan Jiang
Guoliang Wang
Guyue Zhou
Shanghang Zhang
Hao Zhao

Object anomaly detection is an important problem in the field of machine vision and has seen remarkable progress recently. However, two significant challenges hinder its research and application. First, existing datasets lack comprehensive visual information from various pose angles. They usually have an unrealistic assumption that the anomaly-free training dataset is pose-aligned, and the testing samples have the same pose as the training data. However, in practice, anomaly may exist in any regions on a object, the training and query samples may have different poses, calling for the study on pose-agnostic anomaly detection. Second, the absence of a consensus on experimental protocols for pose-agnostic anomaly detection leads to unfair comparisons of different methods, hindering the research on pose-agnostic anomaly detection. To address these issues, we develop Multi-pose Anomaly Detection (MAD) dataset and Pose-agnostic Anomaly Detection (PAD) benchmark, which takes the first step to address the pose-agnostic anomaly detection problem. Specifically, we build MAD using 20 complex-shaped LEGO toys including 4K views with various poses, and high-quality and diverse 3D anomalies in both simulated and real environments. Additionally, we propose a novel method OmniposeAD, trained using MAD, specifically designed for pose-agnostic anomaly detection. Through comprehensive evaluations, we demonstrate the relevance of our dataset and method. Furthermore, we provide an open-source benchmark library, including dataset and baseline methods that cover 8 anomaly detection paradigms, to facilitate future research and application in this domain. Code, data, and models are publicly available at https: //github. com/EricLee0224/PAD.

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AAAI Conference 2023 Conference Paper

Point-Teaching: Weakly Semi-supervised Object Detection with Point Annotations

Yongtao Ge
Qiang Zhou
Xinlong Wang
Chunhua Shen
Zhibin Wang
Hao Li

Point annotations are considerably more time-efficient than bounding box annotations. However, how to use cheap point annotations to boost the performance of semi-supervised object detection is still an open question. In this work, we present Point-Teaching, a weakly- and semi-supervised object detection framework to fully utilize the point annotations. Specifically, we propose a Hungarian-based point-matching method to generate pseudo labels for point-annotated images. We further propose multiple instance learning (MIL) approaches at the level of images and points to supervise the object detector with point annotations. Finally, we propose a simple data augmentation, named Point-Guided Copy-Paste, to reduce the impact of those unmatched points. Experiments demonstrate the effectiveness of our method on a few datasets and various data regimes. In particular, Point-Teaching outperforms the previous best method Group R-CNN by 3.1 AP with 5% fully labeled data and 2.3 AP with 30% fully labeled data on the MS COCO dataset. We believe that our proposed framework can largely lower the bar of learning accurate object detectors and pave the way for its broader applications. The code is available at https://github.com/YongtaoGe/Point-Teaching.

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UAI Conference 2022 Conference Paper

Convergence Analysis of Linear Coupling with Inexact Proximal Operator

Qiang Zhou
Sinno Jialin Pan

Linear coupling is recently proposed to accelerate first-order algorithms by linking gradient descent and mirror descent together, which is able to achieve the accelerated convergence rate for first-order algorithms. This work focuses on the convergence analysis of linear coupling for convex composite minimization when the proximal operator cannot be exactly computed. It is of particular interest to study the convergence of linear coupling because it not only achieves the accelerated convergence rate for first-order algorithm but also works for generic norms. We present convergence analysis of linear coupling by allowing the proximal operator to be computed up to a certain precision. Our analysis illustrates that the accelerated convergence rate of linear coupling with inexact proximal operator can be preserved if the error sequence of inexact proximal operator decreases in a sufficiently fast rate. More importantly, our analysis leads to better bounds than existing works on inexact proximal operator. Experiment results on several real-world datasets verify our theoretical results.

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JMLR Journal 2022 Journal Article

On Acceleration for Convex Composite Minimization with Noise-Corrupted Gradients and Approximate Proximal Mapping

Qiang Zhou
Sinno Jialin Pan

The accelerated proximal methods (APM) have become one of the most important optimization tools for large-scale convex composite minimization problems, due to their wide range of applications and the optimal convergence rate in first-order algorithms. However, most existing theoretical results of APM are obtained by assuming that the gradient oracle is exact and the proximal mapping must be exactly solved, which may not hold in practice. This work presents a theoretical study of APM by allowing to use inexact gradient oracle and approximate proximal mapping. Specifically, we analyze inexact APM by improving the approximate duality gap technique (ADGT) which was originally designed for convergence analysis for first-order methods with both exact gradient oracle and proximal mapping. Our approach has several advantages: 1) we provide a unified convergence analysis that allows both inexact gradient oracle and approximate proximal mapping; 2) our proof is generic that naturally recovers the convergence rates of both accelerated and non-accelerated proximal methods, on top of which the advantages and the disadvantages of acceleration can be easily derived; 3) we derive the same convergence bound as previous methods in terms of inexact gradient oracle, but a tighter convergence bound in terms of approximate proximal mapping. [abs] [ pdf ][ bib ] &copy JMLR 2022. ( edit, beta )

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ICRA Conference 2022 Conference Paper

Prototype-Voxel Contrastive Learning for LiDAR Point Cloud Panoptic Segmentation

Minzhe Liu
Qiang Zhou
Hengshuang Zhao
Jianing Li 0001
Yuan Du
Kurt Keutzer
Li Du
Shanghang Zhang

LiDAR point cloud panoptic segmentation, including both semantic and instance segmentation, plays a critical role in meticulous scene understanding for autonomous driving. Existing 3D voxelized approaches either utilize 3D sparse convolution that only focuses on local scene understanding, or add extra and time-consuming PointNet branch to capture global feature structures. To address these limitations, we propose an end-to-end Prototype-Voxel Contrastive Learning (PVCL) framework for learning stable and discriminative semantic representations, which includes voxel-level and prototype-level contrastive learning (CL). The voxel-level CL decreases intra-class distance and increases inter-class distance among sample representations, while the prototype-level CL further reduces the dependence of CL on negative sampling and avoids the influence of outliers from the same class, enabling PVCL to be more effective for outdoor point cloud panoptic segmentation. Extensive experiments are conducted on the public point cloud panoptic segmentation datasets, Semantic-KITTI and nuScenes, where evaluations and ablation studies demonstrate PVCL achieves superior performance compared with the state-of-the-art. Our approach ranks the top on the public leaderboard of Semantic-KITTI at the time of submission, and surpasses the published 2nd rank, EfficientLPS, by 1. 7% in PQ.

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AAAI Conference 2021 Conference Paper

Modeling Heterogeneous Relations across Multiple Modes for Potential Crowd Flow Prediction

Qiang Zhou
Jingjing Gu
Xinjiang Lu
Fuzhen Zhuang
Yanchao Zhao
Qiuhong Wang
Xiao Zhang

Potential crowd flow prediction for new planned transportationsites is a fundamental task for urban planners and administrators. Intuitively, the potential crowd flow of the new comingsite can be implied by exploring the nearby sites. However, the transportation modes of nearby sites (e. g. bus stations, bicycle stations) might be different from the target site (e. g. subway station), which results in severe data scarcity issues. To this end, we propose a data-driven approach, named MOHER, to predict the potential crowd flow in a certain mode for a new planned site. Specifically, we first identify the neighbor regions of the target site by examining the geographical proximity as well as the urban function similarity. Then, to aggregate these heterogeneous relations, we devise a cross-mode relational GCN, a novel relation-specific transformation model, which can learn not only the correlations but also the differences between different transportation modes. Afterward, we design an aggregator for inductive potential flow representation. Finally, an LTSM module is used for sequential flow prediction. Extensive experiments on realworld data sets demonstrate the superiority of the MOHER framework comparedwith the state-of-the-art algorithms.

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ICRA Conference 2019 Conference Paper

Cell Injection Microrobot Development and Evaluation in Microfluidic Chip

Lin Feng 0002
Dixiao Chen
Qiang Zhou
Bin Song 0001
Wei Zhang 0049

We propose an innovative design of microrobot, which can achieve donor cell suction, delivery and injection in a mammalian oocyte on microfluidic chip. The microrobot body contains a hollow space that produces suction and ejection forces for injection of cell nuclei using a nozzle at the tip of the robot. Specifically, a controller changes the hollow volume by balancing the magnetic and elastic forces of the membrane, and along with motion of stages in the XY plane. A glass capillary attached at the tip of the robot contains the nozzle is able to absorb and inject cell nuclei. The microrobot provides three degrees of freedom and generates micronewton forces. We demonstrate the effectiveness of the proposed microrobot through an experiment of absorption and ejection of 20 μm particles from the nozzle using magnetic control in a microfluidic chip.

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IROS Conference 2019 Conference Paper

On-Chip Three-dimension Cell Rotation Using Whirling Flows Generated by Oscillating Asymmetrical Microstructures

Bin Song 0008
Yanmin Feng
Qiang Zhou
Lin Feng 0002

The capability to precisely rotate the cells and other microscale objects is invaluable in biomedicine, bioengineering, and biophysics. We propose a novel on-chip three-dimension (3D) cell rotation method using whirling flows generated by oscillating asymmetrical microstructures. In an acoustic field excited by the vibration of a piezoelectric transducer, two different modes of microvortices are generated around our custom-designed microstructures that are utilized to precisely achieve in-plane and out-of-plane rotational manipulation of microparticles and cells. The rotation mechanism is studied and verified using numerical simulations. We also investigate the effect of various parameters on the acoustically induced flows such as the frequency, the driving voltage and the distance from the microstructure tip to the oocyte center, thus indicating the rotational speed can be effectively tuned on demand for single-cell studies. Finally, by observing the maturation stages of M2 after excluding the first polar body of operated oocytes, the proposed method is proved noninvasive. Comparing with the conventional works, our acoustofluidic cell rotation approach is simple-to-fabricate and easy-to-operate, thereby allowing rotations irrespective of the physical properties of the specimen under investigation.

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IROS Conference 2016 Conference Paper

Design of wall-climbing robot using electrically activated rotational-flow adsorption unit

Qiang Zhou
Xin Li 0085

Traditional climbing robots that use vacuum suckers have some technical problems, e. g. , inability to climb coarse walls, frictional resistance and abrasion of suckers, and poor obstacle-surmounting ability. In this study, a new negative pressure adsorption mechanism is applied to the design of a climbing robot. This mechanism generates and maintains negative pressure and adsorption force by using the air's rotational inertia effect; therefore, the structure incorporating this mechanism is called the electrically activated rotational-flow adsorption unit. The most important characteristic of the adsorption unit is that it can function without being in contact with the wall, which fundamentally solves these technical problems associated with traditional climbing robots. In this study, we designed a square-shaped rotational-flow adsorption unit to improve the robot's load ability (18% increase in the adsorption force) and designed a soft skirt structure to improve the robot's obstacle-surmounting ability. Finally, we fabricated a prototype of the climbing robot and tested it on a actual wall (extremely coarse wall, wall containing a large groove). The test results show that our prototype robot can move stably on extremely coarse walls and can pass over large grooves easily.

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ICML Conference 2015 Conference Paper

Safe Subspace Screening for Nuclear Norm Regularized Least Squares Problems

Qiang Zhou
Qi Zhao

Nuclear norm regularization has been shown very promising for pursing a low rank matrix solution in various machine learning problems. Many efforts have been devoted to develop efficient algorithms for solving the optimization problem in nuclear norm regularization. Solving it for large-scale matrix variables, however, is still a challenging task since the complexity grows fast with the size of matrix variable. In this work, we propose a novel method called safe subspace screening (SSS), to improve the efficiency of the solver for nuclear norm regularized least squares problems. Motivated by the fact that the low rank solution can be represented by a few subspaces, the proposed method accurately discards a predominant percentage of inactive subspaces prior to solving the problem to reduce problem size. Consequently, a much smaller problem is required to solve, making it more efficient than optimizing the original problem. The proposed SSS is safe, in that its solution is identical to the solution from the solver. In addition, the proposed SSS can be used together with any existing nuclear norm solver since it is independent of the solver. Extensive results on several synthetic and real data sets show that the proposed SSS is very effective in inactive subspace screening.

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