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Qiang Xu

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30 papers

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AIIM Journal 2026 Journal Article

A Character-level Convolutional Recurrent Interaction Network for joint traditional Chinese medicine clinical named entity recognition and relation extraction

Qiang Xu
Zhi-hui Zhao
Wei-wei Liu
Yu Fang
Wen-jun Tang
Yi Zhou
ke zhu
Hai Xiang

The electronic medical record (EMR) of traditional Chinese medicine (TCM) is a crucial document for recording patients’ clinical data, structured around four main dimensions: inspection, listening and smelling, inquiry, and palpation. Analyzing these records using natural language processing holds promise for further structuring and modeling TCM medical data. Currently, deep learning-based named entity recognition is considered the prevailing method for processing TCM EMRs. However, these state-of-the-art models fail to consider the four diagnostic dimensions of TCM clinical data and their impact on entity type extraction, as well as to fully understand the semantic features of ancient Chinese representations in TCM. To address these issues, we introduce a joint clinical named recognition and relation extraction method designed to recognize and classify clinical entities – such as location and symptom attributes – along with their associative relationships (four diagnostic dimensions). In this study, we propose a Character-level Convolutional Recurrent Interaction Network (CCRIN), which treats the four diagnostic dimensions as relationships, locations as head entities, and symptom attributes as tail entities. The CCRIN integrates Chinese character embeddings and Chinese inter-character contextual convolutional feature vectors to capture the semantic information of the ancient Chinese language, while combining entity and relation extraction with a self-attention mechanism to generate rich feature representations through multi-task dynamic interaction. This approach enables the efficient extraction of TCM entities and relations related to the four diagnostic dimensions. Empirical studies on the NYT and the TCM-cases datasets demonstrate the superiority of the proposed model. The model novelly employs a multi-task joint extraction method for entities and relations. The method is performed based on the four diagnostic methods in traditional Chinese medicine. Chinese character embeddings and inter-character contextual feature vectors are integrated. The effectiveness is validated on publicly available and self-constructed datasets.

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EAAI Journal 2026 Journal Article

A communication-efficient federated learning method for traffic flow prediction

Kaiju Li
Qiang Xu
Dong Wang
Xiang Nie
Hao Wang

Federated learning is increasingly adopted for traffic flow prediction (TFP) to enable privacy preserving collaboration across distributed sensors. However, real-world deployments are highly heterogeneous in computational capability, causing stragglers that dominate per-round latency and severely slow down model updates. Most existing approaches mitigate stragglers by suppressing or discarding slow clients, which reduce data representativeness and introduce training bias. It is a harmful trade-off for TFP where broad spatial coverage is crucial for accuracy. We propose a communication-efficient logical clustering federated learning framework (LCFed) that mitigates stragglers by logically balancing effective training time while preserving full client participation. LCFed combines a coarse-grained logical dynamic clustering algorithm ( LoDynClust ) to balance computational resources across clusters and reduce synchronization delays, with a fine-grained intra-cluster adaptive collaborative training mechanism ( ICACT ) to regulate aggregation intervals and mitigate training bias. We further provide a convergence analysis. Extensive experiments on three real-world traffic datasets show that LCFed significantly reduces training latency caused by stragglers while maintaining competitive prediction accuracy compared with state-of-the-art baselines.

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AAAI Conference 2026 Conference Paper

Activations as Features: Probing LLMs for Generalizable Essay Scoring Representations

Jinwei Chi
Ke Wang
Yu Chen
Xuanye Lin
Qiang Xu

Automated essay scoring (AES) is a challenging task in cross-prompt settings due to the diversity of scoring criteria. While previous studies have focused on the output of large language models (LLMs) to improve scoring accuracy, we believe activations from intermediate layers may also provide valuable information. To explore this possibility, we evaluated the discriminative power of LLMs’ activations in cross-prompt essay scoring task. Specifically, we used activation to fit probes and further analyzed the effects of different models and input content of LLMs on this discriminative power. By computing the directions of essays across various trait dimensions under different prompts, we analyzed the variation in evaluation perspectives of large language models concerning essay types and traits. Results show that the activations possess strong discriminative power in evaluating essay quality and that LLMs can adapt their evaluation perspectives to different traits and essay types, effectively handling the diversity of scoring criteria in cross-prompt settings.

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AAAI Conference 2026 Conference Paper

DynamicRTL: RTL Representation Learning for Dynamic Circuit Behavior

Ruiyang Ma
Yunhao Zhou
Yipeng Wang
Yi Liu
Zhengyuan Shi
Ziyang Zheng
Kexin Chen
Zhiqiang He

There is a growing body of work on using Graph Neural Networks (GNNs) to learn representations of circuits, focusing primarily on their static characteristics. However, these models fail to capture circuit runtime behavior, which is crucial for tasks like circuit verification and optimization. To address this limitation, we introduce DR-GNN (DynamicRTL-GNN), a novel approach that learns RTL circuit representations by incorporating both static structures and multi-cycle execution behaviors. DR-GNN leverages an operator-level Control Data Flow Graph (CDFG) to represent Register Transfer Level (RTL) circuits, enabling the model to capture dynamic dependencies and runtime execution. To train and evaluate DR-GNN, we build the first comprehensive dynamic circuit dataset, comprising over 6,300 Verilog designs and 63,000 simulation traces. Our results demonstrate that DR-GNN outperforms existing models in branch hit prediction and toggle rate prediction. Furthermore, its learned representations transfer effectively to related dynamic circuit tasks, achieving strong performance in power estimation and assertion prediction.

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AAAI Conference 2026 Conference Paper

FIXME: Towards End-to-End Benchmarking of LLM-Aided Design Verification

Gwok-Waa Wan
SamZaak Wong
Shengchu Su
Chenxu Niu
Ning Wang
Xinlai Wan
Qixiang Chen
Mengnv Xing

We introduce FIXME, the first end-to-end and large-scale benchmark for evaluating Large Language Models (LLMs) in hardware design functional verification (FV). Comprising 747 tasks derived from real-world hardware designs, FIXME spans five core FV sub-sets: specification comprehension, reference model generation, testbench generation, assertion design, and RTL debugging. To ensure high data quality, we developed an AI-human collaborative framework for agile data curation and annotation. This process resulted in 25,000 lines of verified RTL, 35,000 lines of enhanced testbenches, and over 1,200 SystemVerilog Assertions. Furthermore, through expert-guided optimization within the multi-agent aided flow, we achieved a remarkable 45.57% improvement in average functional coverage, underscoring the benchmark's robustness. Through evaluation of state-of-the-art LLMs like GPT-4.1, FIXME identifies key limitations and provides actionable insights, advancing the potential of LLM-driven automation in hardware design functional verification.

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AAAI Conference 2026 Conference Paper

Multi-Faceted Attack: Exposing Cross-Model Vulnerabilities in Defense-Equipped Vision-Language Models

Yijun Yang
Lichao Wang
Jianping Zhang
Chi Harold Liu
Lanqing Hong
Qiang Xu

The growing misuse of Vision-Language Models (VLMs) has led providers to deploy multiple safeguards—alignment tuning, system prompt, and content moderation. Yet the real-world robustness of these defenses against adversarial attack remains underexplored. We introduce Multi-Faceted Attack (MFA), a framework that systematically uncovers general safety vulnerabilities in leading defense-equipped VLMs, including GPT-4o, Gemini-Pro, and LLaMA 4, etc. Central to MFA is the Attention-Transfer Attack (ATA), which conceals harmful instructions inside a meta task with competing objectives. We offer a theoretical perspective grounded in reward-hacking to explain why such an attack can succeed. To maximize cross-model transfer, we introduce a lightweight transfer-enhancement algorithm combined with a simple repetition strategy that jointly evades both input- and output-level filters—without any model-specific fine-tuning. We empirically show that adversarial images optimized for one vision encoder transfer broadly to unseen VLMs, indicating that shared visual representations create a cross-model safety vulnerability. Combined, MFA reaches a 58.5% overall attack success rate, consistently outperforming existing methods. Notably, on state-of-the-art commercial models, MFA achieves a 52.8% success rate, outperforming the second-best attack by 34%. These findings challenge the perceived robustness of current defensive mechanisms, systematically expose general safety loopholes within defense-equipped VLMs, and offer a practical probe for diagnosing and evaluating the safety of VLMs.

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EAAI Journal 2025 Journal Article

A robust deep learning model selection with data augmentation for automatic detection of tessellated fundus images and explainable artificial intelligence based interpretation

Kachi Anvesh
Shanmugasundaram Hariharan
Bharati M. Reshmi
Qiang Xu
Joan Lu
Vinay Kukreja
Murugaperumal Krishnamoorthy

A robust deep learning system for automatically classifying retinal fundus images into two classes—normal and tessellated—is presented in this study. Visual Geometry Group – 16 is used as the base model, taking advantage of transfer learning to develop an efficient framework for fundus image classification. The approach uses nine different model architectures and makes use of a dataset of 352 fundus images that was increased to 4865 samples using sophisticated data augmentation techniques. Of these, Model_8 performed the best, achieving a loss of 0. 129 % and an impressive accuracy of 99. 39 %. The suggested approach ensures higher performance and dependability by combining rigorous data augmentation, efficient preprocessing, and model fine-tuning techniques. Furthermore, Explainable Artificial Intelligence was used to improve the interpretability of the model and visualize important aspects such as features or imposed pathologies in fundus images more clearly. The study offers promising support to ophthalmologists by offering precise automated diagnoses for the early identification and treatment of retinal disorders.

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NeurIPS Conference 2025 Conference Paper

Dependency Matters: Enhancing LLM Reasoning with Explicit Knowledge Grounding

Xiangyu Wen
Min Li
Junhua Huang
Jianyuan Zhong
Zhijian Xu
Zeju Li
Yongxiang Huang
Mingxuan Yuan

Large language models (LLMs) often produce reasoning steps that are superficially coherent yet internally inconsistent, leading to unreliable outputs. Since such failures typically arise from implicit or poorly-grounded knowledge, we introduce \emph{Grounded Reasoning in Dependency (GRiD)}, a novel dependency-aware reasoning framework that explicitly grounds reasoning steps in structured knowledge. GRiD represents reasoning as a graph consisting of interconnected knowledge extraction nodes and reasoning nodes, enforcing logical consistency through explicit dependencies. Each reasoning step is validated via a lightweight, step-wise verifier that ensures logical correctness relative to its premises. Extensive experiments across diverse reasoning benchmarks—including StrategyQA, CommonsenseQA, GPQA, and TruthfulQA—demonstrate that GRiD substantially improves reasoning accuracy, consistency, and faithfulness compared to recent state-of-the-art structured reasoning methods. Notably, GRiD enhances performance even when applied purely as a lightweight verification module at inference time, underscoring its generalizability and practical utility. Code is available at: https: //github. com/cure-lab/GRiD.

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YNIMG Journal 2025 Journal Article

Flexible ability-willingness trade-offs in cooperative partner choice: Evidence from a drift-diffusion model and ERP data

Qiang Xu
Jing Wang
Peng Li

Partner choice plays a crucial role in human social behavior, with previous research highlighting the importance of both partner ability and willingness to cooperate in decision-making processes. However, the dynamic interplay between these factors across different contexts and how they are influenced by individual characteristics remain poorly understood. This study investigated how task demands and individual traits modulate the tradeoff between partner ability and willingness to make social decisions. We employed a novel experimental paradigm combining three tasks (prisoner's dilemma, time estimation, and a combined task) with computational modeling and EEG recording. Fifty-three participants completed partner-selection trials across these tasks, while their behavioral and neural responses were recorded. Drift diffusion model (DDM) analysis revealed that decision weights for partner ability and willingness were systematically modulated by task demands, with willingness prioritized in cooperative contexts, but ability prioritized in skill-based tasks. Using these model-estimated weights, we found that participants with high self-perceived ability had higher ability weights, whereas those with high self-perceived willingness had lower ability weights in their decision making. EEG analysis, utilizing trial-level weights computed by the DDM, showed that the processing of willingness and ability information began approximately 300 ms post-stimulus, with distinct spatiotemporal patterns across tasks. In the combined task, EEG data indicated earlier and more extensive processing of willingness than of ability information. These findings reveal that individuals flexibly adjust their weighting of partner willingness and ability based on task demands, challenging the notion of a fixed "willingness priority" and demonstrating the dynamic nature of social decision-making across contexts.

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NeurIPS Conference 2025 Conference Paper

Functional Matching of Logic Subgraphs: Beyond Structural Isomorphism

Ziyang Zheng
Kezhi Li
Zhengyuan Shi
Qiang Xu

Subgraph matching in logic circuits is foundational for numerous Electronic Design Automation (EDA) applications, including datapath optimization, arithmetic verification, and hardware trojan detection. However, existing techniques rely primarily on structural graph isomorphism and thus fail to identify function-related subgraphs when synthesis transformations substantially alter circuit topology. To overcome this critical limitation, we introduce the concept of functional subgraph matching, a novel approach that identifies whether a given logic function is implicitly present within a larger circuit, irrespective of structural variations induced by synthesis or technology mapping. Specifically, we propose a two-stage multi-modal framework: (1) learning robust functional embeddings across AIG and post-mapping netlists for functional subgraph detection, and (2) identifying fuzzy boundaries using a graph segmentation approach. Evaluations on standard benchmarks (ITC99, OpenABCD, ForgeEDA) demonstrate significant performance improvements over existing structural methods, with average 93. 8% accuracy in functional subgraph detection and a dice score of 91. 3% in fuzzy boundary identification.

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AAAI Conference 2025 Conference Paper

MotionCraft: Crafting Whole-Body Motion with Plug-and-Play Multimodal Controls

Yuxuan Bian
Ailing Zeng
Xuan Ju
Xian Liu
Zhaoyang Zhang
Wei Liu
Qiang Xu

Whole-body multimodal motion generation, controlled by text, speech, or music, has numerous applications including video generation and character animation. However, employing a unified model to process different condition modalities presents two main challenges: motion distribution drifts across different tasks (e.g., co-speech gestures and text-driven daily actions) and the complex optimization of mixed conditions with varying granularities (e.g., text and audio). In this paper, we propose MotionCraft, a unified diffusion transformer that crafts whole-body motion with plug-and-play multimodal control. Our framework employs a coarse-to-fine training strategy, starting with the text-to-motion semantic pre-training, followed by the multimodal low-level control adaptation. To effectively learn and transfer motion knowledge across different distributions, we design MC-Attn for parallel modeling of static and dynamic human topology graphs. To overcome the motion format inconsistency of existing benchmarks, we introduce MC-Bench, the first available multimodal whole-body motion generation benchmark based on the unified SMPL-X format. Extensive experiments show that MotionCraft achieves state-of-the-art performance on various standard motion generation tasks.

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NeurIPS Conference 2024 Conference Paper

GuardT2I: Defending Text-to-Image Models from Adversarial Prompts

Yijun Yang
Ruiyuan Gao
Xiao Yang
Jianyuan Zhong
Qiang Xu

Recent advancements in Text-to-Image models have raised significant safety concerns about their potential misuse for generating inappropriate or Not-Safe-For-Work contents, despite existing countermeasures such as Not-Safe-For-Work classifiers or model fine-tuning for inappropriate concept removal. Addressing this challenge, our study unveils GuardT2I a novel moderation framework that adopts a generative approach to enhance Text-to-Image models’ robustness against adversarial prompts. Instead of making a binary classification, GuardT2I utilizes a large language model to conditionally transform text guidance embeddings within the Text-to-Image models into natural language for effective adversarial prompt detection, without compromising the models’ inherent performance. Our extensive experiments reveal that GuardT2I outperforms leading commercial solutions like OpenAI-Moderation and Microsoft Azure Moderator by a significant margin across diverse adversarial scenarios. Our framework is available at https: //github. com/cure-lab/GuardT2I.

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NeurIPS Conference 2024 Conference Paper

MiraData: A Large-Scale Video Dataset with Long Durations and Structured Captions

Xuan Ju
Yiming Gao
Zhaoyang Zhang
Ziyang Yuan
Xintao Wang
Ailing Zeng
Yu Xiong
Qiang Xu

Sora's high-motion intensity and long consistent videos have significantly impacted the field of video generation, attracting unprecedented attention. However, existing publicly available datasets are inadequate for generating Sora-like videos, as they mainly contain short videos with low motion intensity and brief captions. To address these issues, we propose MiraData, a high-quality video dataset that surpasses previous ones in video duration, caption detail, motion strength, and visual quality. We curate MiraData from diverse, manually selected sources and meticulously process the data to obtain semantically consistent clips. GPT-4V is employed to annotate structured captions, providing detailed descriptions from four different perspectives along with a summarized dense caption. To better assess temporal consistency and motion intensity in video generation, we introduce MiraBench, which enhances existing benchmarks by adding 3D consistency and tracking-based motion strength metrics. MiraBench includes 150 evaluation prompts and 17 metrics covering temporal consistency, motion strength, 3D consistency, visual quality, text-video alignment, and distribution similarity. To demonstrate the utility and effectiveness of MiraData, we conduct experiments using our DiT-based video generation model, MiraDiT. The experimental results on MiraBench demonstrate the superiority of MiraData, especially in motion strength.

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AAAI Conference 2024 Conference Paper

Text Image Inpainting via Global Structure-Guided Diffusion Models

Shipeng Zhu
Pengfei Fang
Chenjie Zhu
Zuoyan Zhao
Qiang Xu
Hui Xue

Real-world text can be damaged by corrosion issues caused by environmental or human factors, which hinder the preservation of the complete styles of texts, e.g., texture and structure. These corrosion issues, such as graffiti signs and incomplete signatures, bring difficulties in understanding the texts, thereby posing significant challenges to downstream applications, e.g., scene text recognition and signature identification. Notably, current inpainting techniques often fail to adequately address this problem and have difficulties restoring accurate text images along with reasonable and consistent styles. Formulating this as an open problem of text image inpainting, this paper aims to build a benchmark to facilitate its study. In doing so, we establish two specific text inpainting datasets which contain scene text images and handwritten text images, respectively. Each of them includes images revamped by real-life and synthetic datasets, featuring pairs of original images, corrupted images, and other assistant information. On top of the datasets, we further develop a novel neural framework, Global Structure-guided Diffusion Model (GSDM), as a potential solution. Leveraging the global structure of the text as a prior, the proposed GSDM develops an efficient diffusion model to recover clean texts. The efficacy of our approach is demonstrated by thorough empirical study, including a substantial boost in both recognition accuracy and image quality. These findings not only highlight the effectiveness of our method but also underscore its potential to enhance the broader field of text image understanding and processing. Code and datasets are available at: https://github.com/blackprotoss/GSDM.

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NeurIPS Conference 2024 Conference Paper

Vector Quantization Prompting for Continual Learning

Li Jiao
Qiuxia Lai
Yu Li
Qiang Xu

Continual learning requires to overcome catastrophic forgetting when training a single model on a sequence of tasks. Recent top-performing approaches are prompt-based methods that utilize a set of learnable parameters (i. e. , prompts) to encode task knowledge, from which appropriate ones are selected to guide the fixed pre-trained model in generating features tailored to a certain task. However, existing methods rely on predicting prompt identities for prompt selection, where the identity prediction process cannot be optimized with task loss. This limitation leads to sub-optimal prompt selection and inadequate adaptation of pre-trained features for a specific task. Previous efforts have tried to address this by directly generating prompts from input queries instead of selecting from a set of candidates. However, these prompts are continuous, which lack sufficient abstraction for task knowledge representation, making them less effective for continual learning. To address these challenges, we propose VQ-Prompt, a prompt-based continual learning method that incorporates Vector Quantization (VQ) into end-to-end training of a set of discrete prompts. In this way, VQ-Prompt can optimize the prompt selection process with task loss and meanwhile achieve effective abstraction of task knowledge for continual learning. Extensive experiments show that VQ-Prompt outperforms state-of-the-art continual learning methods across a variety of benchmarks under the challenging class-incremental setting.

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AAAI Conference 2023 Conference Paper

Are Transformers Effective for Time Series Forecasting?

Ailing Zeng
Muxi Chen
Lei Zhang
Qiang Xu

Recently, there has been a surge of Transformer-based solutions for the long-term time series forecasting (LTSF) task. Despite the growing performance over the past few years, we question the validity of this line of research in this work. Specifically, Transformers is arguably the most successful solution to extract the semantic correlations among the elements in a long sequence. However, in time series modeling, we are to extract the temporal relations in an ordered set of continuous points. While employing positional encoding and using tokens to embed sub-series in Transformers facilitate preserving some ordering information, the nature of the permutation-invariant self-attention mechanism inevitably results in temporal information loss. To validate our claim, we introduce a set of embarrassingly simple one-layer linear models named LTSF-Linear for comparison. Experimental results on nine real-life datasets show that LTSF-Linear surprisingly outperforms existing sophisticated Transformer-based LTSF models in all cases, and often by a large margin. Moreover, we conduct comprehensive empirical studies to explore the impacts of various design elements of LTSF models on their temporal relation extraction capability. We hope this surprising finding opens up new research directions for the LTSF task. We also advocate revisiting the validity of Transformer-based solutions for other time series analysis tasks (e.g., anomaly detection) in the future.

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NeurIPS Conference 2023 Conference Paper

HiBug: On Human-Interpretable Model Debug

Muxi Chen
Yu Li
Qiang Xu

Machine learning models can frequently produce systematic errors on critical subsets (or slices) of data that share common attributes. Discovering and explaining such model bugs is crucial for reliable model deployment. However, existing bug discovery and interpretation methods usually involve heavy human intervention and annotation, which can be cumbersome and have low bug coverage. In this paper, we propose HiBug, an automated framework for interpretable model debugging. Our approach utilizes large pre-trained models, such as chatGPT, to suggest human-understandable attributes that are related to the targeted computer vision tasks. By leveraging pre-trained vision-language models, we can efficiently identify common visual attributes of underperforming data slices using human-understandable terms. This enables us to uncover rare cases in the training data, identify spurious correlations in the model, and use the interpretable debug results to select or generate new training data for model improvement. Experimental results demonstrate the efficacy of the HiBug framework.

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NeurIPS Conference 2022 Conference Paper

SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction

Minhao Liu
Ailing Zeng
Muxi Chen
Zhijian Xu
Qiuxia Lai
Lingna Ma
Qiang Xu

One unique property of time series is that the temporal relations are largely preserved after downsampling into two sub-sequences. By taking advantage of this property, we propose a novel neural network architecture that conducts sample convolution and interaction for temporal modeling and forecasting, named SCINet. Specifically, SCINet is a recursive downsample-convolve-interact architecture. In each layer, we use multiple convolutional filters to extract distinct yet valuable temporal features from the downsampled sub-sequences or features. By combining these rich features aggregated from multiple resolutions, SCINet effectively models time series with complex temporal dynamics. Experimental results show that SCINet achieves significant forecasting accuracy improvements over both existing convolutional models and Transformer-based solutions across various real-world time series forecasting datasets. Our codes and data are available at https: //github. com/cure-lab/SCINet.

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YNIMG Journal 2021 Journal Article

Genes associated with gray matter volume alterations in schizophrenia

Yuan Ji
Xue Zhang
Zirui Wang
Wen Qin
Huaigui Liu
Kaizhong Xue
Jie Tang
Qiang Xu

Although both schizophrenia and gray matter volume (GMV) show high heritability, however, genes accounting for GMV alterations in schizophrenia remain largely unknown. Based on risk genes identified in schizophrenia by the genome-wide association study of the Schizophrenia Working Group of the Psychiatric Genomics Consortium, we used transcription-neuroimaging association analysis to test that which of these genes are associated with GMV changes in schizophrenia. For each brain tissue sample, the expression profiles of 196 schizophrenia risk genes were extracted from six donated normal brains of the Allen Human Brain Atlas, and GMV differences between patients with schizophrenia and healthy controls were calculated based on five independent case-control structural MRI datasets (276 patients and 284 controls). Genes associated with GMV changes in schizophrenia were identified by performing cross-sample spatial correlations between expression levels of each gene and case-control GMV difference derived from the five MRI datasets integrated by harmonization and meta-analysis. We found that expression levels of 98 genes consistently showed significant cross-sample spatial correlations with GMV changes in schizophrenia. These genes were functionally enriched for chemical synaptic transmission, central nervous system development, and cell projection. Overall, this study provides a set of genes possibly associated with GMV changes in schizophrenia, which could be used as candidate genes to explore biological mechanisms underlying the structural impairments in schizophrenia.

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IJCAI Conference 2021 Conference Paper

Information Bottleneck Approach to Spatial Attention Learning

Qiuxia Lai
Yu Li
Ailing Zeng
Minhao Liu
Hanqiu Sun
Qiang Xu

The selective visual attention mechanism in the human visual system (HVS) restricts the amount of information to reach visual awareness for perceiving natural scenes, allowing near real-time information processing with limited computational capacity. This kind of selectivity acts as an ‘Information Bottleneck (IB)’, which seeks a trade-off between information compression and predictive accuracy. However, such information constraints are rarely explored in the attention mechanism for deep neural networks (DNNs). In this paper, we propose an IB-inspired spatial attention module for DNN structures built for visual recognition. The module takes as input an intermediate representation of the input image, and outputs a variational 2D attention map that minimizes the mutual information (MI) between the attention-modulated representation and the input, while maximizing the MI between the attention-modulated representation and the task label. To further restrict the information bypassed by the attention map, we quantize the continuous attention scores to a set of learnable anchor values during training. Extensive experiments show that the proposed IB-inspired spatial attention mechanism can yield attention maps that neatly highlight the regions of interest while suppressing backgrounds, and bootstrap standard DNN structures for visual recognition tasks (e. g. , image classification, fine-grained recognition, cross-domain classification). The attention maps are interpretable for the decision making of the DNNs as verified in the experiments. Our code is available at this https URL.

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YNIMG Journal 2021 Journal Article

Multivariate pattern analysis of electroencephalography data reveals information predictive of charitable giving

Qiuyan Huang
Danyang Li
Can Zhou
Qiang Xu
Peng Li
Christopher M. Warren

Charitable donations are an altruistic behavior whereby individuals donate money or other resources to benefit others while the recipient is normally absent from the context. Several psychological factors have been shown to influence charitable donations, including a cost-benefit analysis, the motivation to engage in altruistic behavior, and the perceived psychological benefits of donation. Recent work has identified the ventral medial prefrontal cortex (MPFC) for assigning value to options in social decision making tasks, with other regions involved in empathy and emotion contributing input to the value computation (e.g. Hare et al., 2010; Hutcherson et al., 2015; Tusche et al., 2016). Most impressively, multivariate pattern analysis (MVPA) has been applied to fMRI data to predict donation behavior on a trial-by-trial basis from ventral MPFC activity (Hare et al., 2010) while identifying the contribution of emotional processing in other regions to the value computation (e.g. Tusche et al., 2016). MVPA of EEG data may be able to provide further insight into the timing and scalp topography of neural activity related to both value computation and emotional effects on donation behavior. We examined the effect of incidental emotional states and the perceived urgency of the charitable cause on donation behavior using support vector regression on EEG data to predict donation amount on a trial by trial basis. We used positive, negative, and neutral pictures to induce incidental emotional states in participants before they made donation decisions concerning two types of charities. One category of charity was oriented toward saving people from current suffering, and the other was to prevent future suffering. Behaviorally, subjects donated more money in a negative emotional state relative to other emotional states, and more money to alleviate current over future suffering. The data-driven multivariate pattern analysis revealed that the electrophysiological activity elicited by both emotion-priming pictures and charity cues could predict the variation in donation magnitude on a trial-by-trial basis.

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AIIM Journal 2021 Journal Article

Network differentiation: A computational method of pathogenesis diagnosis in traditional Chinese medicine based on systems science

Qiang Xu
Qiang Guo
Chun-Xia Wang
Song Zhang
Chuan-Biao Wen
Tao Sun
Wei Peng
Jun Chen

Resembling the role of disease diagnosis in Western medicine, pathogenesis (also called Bing Ji) diagnosis is one of the utmost important tasks in traditional Chinese medicine (TCM). In TCM theory, pathogenesis is a complex system composed of a group of interrelated factors, which is highly consistent with the character of systems science (SS). In this paper, we introduce a heuristic definition called pathogenesis network (PN) to represent pathogenesis in the form of the directed graph. Accordingly, a computational method of pathogenesis diagnosis, called network differentiation (ND), is proposed by integrating the holism principle in SS. ND consists of three stages. The first stage is to generate all possible diagnoses by Cartesian Product operated on specified prior knowledge corresponding to the input symptoms. The second stage is to screen the validated diagnoses by holism principle. The third stage is to pick out the clinical diagnosis by physician-computer interaction. Some theorems are stated and proved for the further optimization of ND in this paper. We conducted simulation experiments on 100 clinical cases. The experimental results show that our proposed method has an excellent capability to fit the holistic thinking in the process of physician inference.

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YNIMG Journal 2021 Journal Article

Recycling diagnostic MRI for empowering brain morphometric research – Critical & practical assessment on learning-based image super-resolution

Gaoping Liu
Zehong Cao
Qiang Xu
Qirui Zhang
Fang Yang
Xinyu Xie
Jingru Hao
Yinghuan Shi

Preliminary studies have shown the feasibility of deep learning (DL)-based super-resolution (SR) technique for reconstructing thick-slice/gap diagnostic MR images into high-resolution isotropic data, which would be of great significance for brain research field if the vast amount of diagnostic MRI data could be successively put into brain morphometric study. However, less evidence has addressed the practicability of the strategy, because lack of a large-sample available real data for constructing DL model. In this work, we employed a large cohort (n = 2052) of peculiar data with both low through-plane resolution diagnostic and high-resolution isotropic brain MR images from identical subjects. By leveraging a series of SR approaches, including a proposed novel DL algorithm of Structure Constrained Super Resolution Network (SCSRN), the diagnostic images were transformed to high-resolution isotropic data to meet the criteria of brain research in voxel-based and surface-based morphometric analyses. We comprehensively assessed image quality and the practicability of the reconstructed data in a variety of morphometric analysis scenarios. We further compared the performance of SR approaches to the ground truth high-resolution isotropic data. The results showed (i) DL-based SR algorithms generally improve the quality of diagnostic images and render morphometric analysis more accurate, especially, with the most superior performance of the novel approach of SCSRN. (ii) Accuracies vary across brain structures and methods, and (iii) performance increases were higher for voxel than for surface based approaches. This study supports that DL-based image super-resolution potentially recycle huge amount of routine diagnostic brain MRI deposited in sleeping state, and turning them into useful data for neurometric research.

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YNIMG Journal 2021 Journal Article

Systematically disrupted functional gradient of the cortical connectome in generalized epilepsy: Initial discovery and independent sample replication

Yao Meng
Siqi Yang
Huafu Chen
Jiao Li
Qiang Xu
Qirui Zhang
Guangming Lu
Zhiqiang Zhang

Genetic generalized epilepsy is a network disorder typically involving distributed areas identified by classical neuroanatomy. However, the finer topological relationships in terms of continuous spatial arrangement between these systems are still ambiguous. Connectome gradients provide the topological representations of human macroscale hierarchy in an abstract low-dimensional space by embedding the functional connectome into a set of axes. Leveraging connectome gradients, we systematically scrutinized abnormalities of functional connectome gradient in patients with genetic generalized epilepsy with tonic-clonic seizure (GGE-GTCS, n = 78) compared to healthy controls (HC, n = 85), and further examined the reproducibility across multiple processing configurations and in an independent validation sample (patients with GGE-GTCS, n = 28; HC, n = 31). Our findings demonstrated an extended principal gradient at different spatial scales, network-level and vertex-level, in patients with GGE-GTCS. We found consistent results across processing parameters and in validation sample. The extended principal gradient revealed the excessive functional segregation between unimodal and transmodal systems associated with duration of epilepsy and age at seizure onset in patients. Furthermore, the connectivity profile of regions with abnormal principal gradients verified the disrupted functional hierarchy revealed by gradients. Together, our findings provided a novel view of functional system hierarchy alterations, which facilitated a continuous spatial arrangement of macroscale networks, to increase our understanding of the functional connectome hierarchy in generalized epilepsy.

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NeurIPS Conference 2021 Conference Paper

TestRank: Bringing Order into Unlabeled Test Instances for Deep Learning Tasks

Yu Li
Min Li
Qiuxia Lai
Yannan Liu
Qiang Xu

Deep learning (DL) systems are notoriously difficult to test and debug due to the lack of correctness proof and the huge test input space to cover. Given the ubiquitous unlabeled test data and high labeling cost, in this paper, we propose a novel test prioritization technique, namely TestRank, which aims at revealing more model failures with less labeling effort. TestRank brings order into the unlabeled test data according to their likelihood of being a failure, i. e. , their failure-revealing capabilities. Different from existing solutions, TestRank leverages both intrinsic and contextual attributes of the unlabeled test data when prioritizing them. To be specific, we first build a similarity graph on both unlabeled test samples and labeled samples (e. g. , training or previously labeled test samples). Then, we conduct graph-based semi-supervised learning to extract contextual features from the correctness of similar labeled samples. For a particular test instance, the contextual features extracted with the graph neural network and the intrinsic features obtained with the DL model itself are combined to predict its failure-revealing capability. Finally, TestRank prioritizes unlabeled test inputs in descending order of the above probability value. We evaluate TestRank on three popular image classification datasets, and results show that TestRank significantly outperforms existing test prioritization techniques.

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JBHI Journal 2020 Journal Article

Multi-Task Joint Learning Model for Segmenting and Classifying Tongue Images Using a Deep Neural Network

Qiang Xu
Yu Zeng
Wenjun Tang
Wei Peng
Tingwei Xia
Zongrun Li
Fei Teng
Weihong Li

Automatic tongue image segmentation and tongue image classification are two crucial tongue characterization tasks in traditional Chinese medicine (TCM). Due to the complexity of tongue segmentation and fine-grained traits of tongue image classification, both tasks are challenging. Fortunately, from the perspective of computer vision, these two tasks are highly interrelated, making them compatible with the idea of Multi-Task Joint learning (MTL). By sharing the underlying parameters and adding two different task loss functions, an MTL method for segmenting and classifying tongue images is proposed in this paper. Moreover, two state-of-the-art deep neural network variants (UNET and Discriminative Filter Learning (DFL)) are fused into the MTL to perform these two tasks. To the best of our knowledge, our method is the first attempt to manage both tasks simultaneously with MTL. We conducted extensive experiments with the proposed method. The experimental results show that our joint method outperforms the existing tongue characterization methods. Besides, visualizations and ablation studies are provided to aid in understanding our approach, which suggest that our method is highly consistent with human perception.

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YNIMG Journal 2020 Journal Article

Neural mechanisms of AVPR1A RS3-RS1 haplotypes that impact verbal learning and memory

Yan Zhang
Dan Zhu
Peng Zhang
Wei Li
Wen Qin
Feng Liu
Jiayuan Xu
Qiang Xu

Converging evidence from both human and animal studies has highlighted the pervasive role of the neuropeptide arginine vasopressin (AVP), which is mediated by arginine vasopressin receptor 1A (AVPR1A), in both social and nonsocial learning and memory. However, the effect of genetic variants in AVPR1A on verbal learning and memory is unknown. The hippocampus is a heterogeneous structure that consists of several anatomically and functionally distinct subfields, and it is the principal target structure for the memory-enhancing effect of AVP. We tested the hypothesis that genetic variants in the RS3 and RS1 repeat polymorphisms may influence verbal learning and memory performance evaluated by the California Verbal Learning Test-II (CVLT-II) by modulating the gray matter volume (GMV) and resting-state functional connectivity (rsFC) of whole hippocampus and its subfields in a large cohort of young healthy subjects (n = 1001). Using a short/long classification scheme for the repeat length of RS3 and RS1, we found that the individuals carrying more short alleles of RS3-RS1 haplotypes had poorer learning and memory performance compared to that of those carrying more long alleles. We also revealed that individuals carrying more short alleles exhibited a significantly smaller GMV in the left cornu ammonis (CA)2/3 and weaker rsFC of the left CA2/3-bilateral thalamic (primarily in medial prefrontal subfields) compared to those carrying more long alleles. Furthermore, multiple mediation analysis confirmed that these two hippocampal imaging measures jointly and fully mediated the relationship between the genetic variants in AVPR1A RS3-RS1 haplotypes and the individual differences in verbal learning and memory performance. Our results suggest that genetic variants in AVPR1A RS3-RS1 haplotypes may affect verbal learning and memory performance in part by modulating the left hippocampal CA2/3 structure and its rsFC with the thalamus.

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YNICL Journal 2019 Journal Article

Effects of high- and low-frequency repetitive transcranial magnetic stimulation on motor recovery in early stroke patients: Evidence from a randomized controlled trial with clinical, neurophysiological and functional imaging assessments

Juan Du
Fang Yang
Jianping Hu
Jingze Hu
Qiang Xu
Nathan Cong
Qirui Zhang
Ling Liu

BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) can modulate cortical excitability, and may be beneficial for motor recovery after stroke. However, the neuroplasticity effects of rTMS have not been thoroughly investigated in the early stage after stroke. OBJECTIVE: To comprehensively assess the effects of high- and low-frequency repetitive transcranial magnetic stimulations on motor recovery in early stroke patients, using a randomized controlled trial based on clinical, neurophysiological and functional imaging assessments. METHODS: Sixty hospitalized, first-ever ischemic stroke patients (within 2 weeks after stroke) with motor deficits were randomly allocated to receive, in addition to standard physical therapy, five consecutive sessions of either: (1) High-frequency (HF) rTMS at 10 Hz over the ipsilesional primary motor cortex (M1); (2) Low-frequency (LF) rTMS at 1 Hz over the contralesional M1; (3) sham rTMS. The primary outcome measure was a motor impairment score (Upper Extremity Fugl-Meyer) evaluated at baseline, after rTMS intervention, and at 3-month follow-up. Cortical excitability and functional magnetic resonance imaging (fMRI) data were obtained within 24 h before and after rTMS intervention. Analyses of variance were conducted to compare the recovery effects among the three rTMS groups, assessed using clinical, neurophysiological and fMRI tests. RESULTS: Motor improvement was significantly larger in the two rTMS groups than in the control group. The HF-rTMS group showed significantly increased cortical excitability and motor-evoked fMRI activation in ipsilesional motor areas, whereas the LF-rTMS group had significantly decreased cortical excitability and motor-evoked fMRI activation in contralesional motor areas. Activity in ipsilesional motor cortex significantly correlated with motor function, after intervention as well as at 3-month follow-up. CONCLUSION: HF- and LF-rTMS can both improve motor function by modulating motor cortical activation in the early phase of stroke.

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AAAI Conference 2018 Conference Paper

Towards Imperceptible and Robust Adversarial Example Attacks Against Neural Networks

Bo Luo
Yannan Liu
Lingxiao Wei
Qiang Xu

Machine learning systems based on deep neural networks, being able to produce state-of-the-art results on various perception tasks, have gained mainstream adoption in many applications. However, they are shown to be vulnerable to adversarial example attack, which generates malicious output by adding slight perturbations to the input. Previous adversarial example crafting methods, however, use simple metrics to evaluate the distances between the original examples and the adversarial ones, which could be easily detected by human eyes. In addition, these attacks are often not robust due to the inevitable noises and deviation in the physical world. In this work, we present a new adversarial example attack crafting method, which takes the human perceptual system into consideration and maximizes the noise tolerance of the crafted adversarial example. Experimental results demonstrate the ef- ﬁcacy of the proposed technique.

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YNIMG Journal 2011 Journal Article

Small-world directed networks in the human brain: Multivariate Granger causality analysis of resting-state fMRI

Wei Liao
Jurong Ding
Daniele Marinazzo
Qiang Xu
Zhengge Wang
Cuiping Yuan
Zhiqiang Zhang
Guangming Lu

Small-world organization is known to be a robust and consistent network architecture, and is a hallmark of the structurally and functionally connected human brain. However, it remains unknown if the same organization is present in directed influence brain networks whose connectivity is inferred by the transfer of information from one node to another. Here, we aimed to reveal the network architecture of the directed influence brain network using multivariate Granger causality analysis and graph theory on resting-state fMRI recordings. We found that some regions acted as pivotal hubs, either being influenced by or influencing other regions, and thus could be considered as information convergence regions. In addition, we observed that an exponentially truncated power law fits the topological distribution for the degree of total incoming and outgoing connectivity. Furthermore, we also found that this directed network has a modular structure. More importantly, according to our data, we suggest that the human brain directed influence network could have a prominent small-world topological property.

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