Author name cluster

Feng Xue

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17 papers

2 author rows

EAAI Journal 2026 Journal Article

A look-ahead dispatch method via evolution strategies embedded with domain knowledge

Yuchen Dai
Weiran Jiao
Yi Tang
Minghui Yan
Feng Xue
Jianfeng Zhao

Modern power systems require proactive look-ahead dispatch strategies to address various uncertainties. Traditional decision-making methods based on physical models often suffer from slow processing speeds and struggle to handle multiple uncertain scenarios. Meanwhile, reinforcement learning methods face challenges such as hyperparameter sensitivity and a tendency to converge to local optima. To overcome these limitations, a look-ahead dispatch method via evolution strategies embedded with domain knowledge is proposed. First, a knowledge-embedded Markov decision process model of look-ahead dispatch is developed. This model encodes critical physical knowledge into the action space without computational burden. Second, a decision-making approach based on evolution strategies and physical models is introduced. This method enhances parallel exploration efficiency and reduce communication burden by leveraging synchronous random seeds and mirror perturbation techniques. Then, physical models are used to fine-tune agents in new scenarios with limited data. Finally, case studies based on the IEEE 118 system show that the proposed method significantly improves decision-making efficiency without sacrificing accuracy. Compared to deep reinforcement learning, the evolution strategies algorithm offers superior training efficiency and performance, effectively addressing the high-dimensional uncertainties and complexities of modern power systems. This establishes the proposed method as an effective solution for complex decision-making tasks in power system operations.

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AAAI Conference 2026 Conference Paper

Bidirectional Counterfactual Distillation for Review-Based Recommendation

Sheng Sang
Shujie Li
Shuaiyang Li
Kang Liu
Teng Li
Wei Jia
Dan Guo
Feng Xue

Review-based recommendation methods typically integrate multiple behaviors, including interactions, reviews, and ratings, to model user preferences. To effectively extract preference signals from diverse behaviors, some studies train multiple student models to capture distinct behavioral patterns, and leverage online distillation to facilitate collaborative learning among them. However, we argue that these techniques suffer from bias contamination from rating distributions and feature homogenization during cross-behavior knowledge transfer: (1) Rating distribution bias, arising from non-uniform historical ratings, propagates across behaviors through distillation, contaminating the true preference representations of other behaviors. (2) Static distillation strategies often lead to homogenized behavioral features, hindering the learning of behavior-specific preferences. To address these issues, we propose a novel Bidirectional Counterfactual Distillation (BiCoD) framework for review-based recommendation. In BiCoD, we first design an adversarial counterfactual distillation module to suppress the impact of non-uniform rating distributions on distillation, thereby preventing it from contaminating the user's true preference representations across behaviors. Subsequently, we introduce a stage-aware bidirectional distillation strategy to enhance the distinctiveness of behavioral features, facilitating the effective learning of behavior-specific preferences. Extensive experiments on five real-world datasets validate the effectiveness and superiority of the proposed framework.

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EAAI Journal 2026 Journal Article

Direction-aware deep policy learning for efficient capacitated arc routing

Feng Xue
Runze Guo
Anlong Ming
Nicu Sebe

Line coverage of urban-scale routes by multiple vehicles can be formulated as the Capacitated Arc Routing Problem (CARP). Early solutions, including constructive heuristics, meta-heuristics, and exact algorithms, struggle to achieve efficient runtimes. Recently, neural network (NN)-based methods have emerged as promising alternatives for efficiently solving CARP; however, their solution quality significantly lags behind non-NN approaches. This significant gap mainly results from the inappropriate modeling and learning of non-Euclidean graphs, traversal direction and capacity constraints. In this paper, we introduce an NN-based solver tailored for these complexities, which significantly narrows the gap with advanced meta-heuristics while achieving much shorter runtimes. First, we propose the direction-aware attention model (DaAM) to incorporate directionality into the embedding process, facilitating more effective one-stage decision-making. Second, we design a supervised reinforcement learning scheme that involves supervised pre-training to establish a robust initial policy for subsequent reinforcement fine-tuning. It proves particularly valuable for solving CARP that has a higher complexity than the node routing problems (NRPs). Finally, a path optimization method is introduced to adjust the depot return positions within the path generated by DaAM. Experiments show that DaAM surpasses constructive heuristics and achieves decision quality comparable to state-of-the-art meta-heuristics for the first time while maintaining superior efficiency, even in large-scale CARP instances. The code and datasets are provided in the link and will be released on GitHub.

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AAAI Conference 2026 Conference Paper

Event-Guided Scene Text Image Super-Resolution

Zihan Qi
Zeyu Xiao
Haoyi Zhao
Yang Zhao
Feng Xue
Wei Jia

Scene text image super-resolution aims to enhance text legibility by recovering high-resolution text images from low-resolution inputs. However, maintaining fine details such as text strokes, edges, and textual accuracy remains challenging, particularly in low-light environments and high-speed motion scenarios, where degradation is more severe. Event cameras, with their high temporal resolution and ability to capture intensity changes, offer a promising solution for restoring lost fine details and mitigating degradation in these challenging conditions. In this paper, we propose EvTSR, the first framework that integrates Event data for scene Text image Super-Resolution. The core of EvTSR is the dual-stream frequency boost (DSFB) mechanism, which separates image features into high- and low-frequency components. High-frequency details like edges and strokes are enhanced using event data via the event-guided high-frequency (EGH) mechanism, while low-frequency components, responsible for global structure, are refined using the Text-Guided Low-frequency (TGL) mechanism with a pre-trained text recognizer, ensuring textual coherence. To further improve cross-modal integration, we introduce the cross-modal fusion (CMF) mechanism, which effectively aligns event and image features, enabling robust information fusion. Extensive experiments demonstrate that EvTSR achieves superior performance over existing methods.

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AAAI Conference 2026 Conference Paper

LinProVSR: Linguistics-Knowledge Guided Progressive Disambiguation Network for Visual Speech Recognition

Feng Xue
Baochao Zhu
Wei Jia
Shujie Li
Yu Li
Jinrui Zhang
Shengeng Tang
Dan Guo

Visual Speech Recognition (VSR), commonly known as lipreading, enables the recognition of spoken text by analyzing lip visual features. Due to the subtlety of lip movements, its recognition is much harder than other motion recognition tasks. Existing VSR models face the challenge of viseme ambiguity when processing phonemes with similar pronunciations—multiple phonemes share similar viseme features, leading to a notable drop in lipreading accuracy. To address this issue, this study proposes a Linguistics-Knowledge Guided Progressive Disambiguation Network for Visual Speech Recognition(LinProVSR) framework. First, an ambiguous sample set is constructed based on linguistic knowledge to provide supervisory signals for the model's training. Then, a Progressive Contrastive Disambiguation Network (PCDN) is designed, which progressively enhances the model's ability to capture the subtle viseme differences corresponding to similar phonemes through viseme-phoneme contrastive disambiguation in the encoding stage and text contrastive disambiguation in the decoding stage. Furthermore, we pioneer the Ambiguous Word Error Rate (AWER) metric specifically for evaluating recognition of phonetically ambiguous text, and verify the effectiveness of the proposed method on multiple public datasets, achieving a significant breakthrough especially in distinguishing visually similar phonemes.

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EAAI Journal 2025 Journal Article

Deep reinforcement learning explanation-assisted integer variable reduction method for security-constrained unit commitment

Yuchen Dai
Wei Xu
Minghui Yan
Feng Xue
Jianfeng Zhao

The large-scale security-constrained unit commitment (SCUC) is pivotal for ensuring the secure and economical operation of modern power systems. Formulated as a mixed-integer nonlinear programming problem, mathematical model-based methods struggle to balance computation efficiency and solution accuracy. While artificial intelligence methods offer promising potential, they face several obstacles, including limited interpretability and generalizability constraints. In light of these challenges, this paper proposes an interpretation method for deep reinforcement learning models that is used to reduce integer variables for large-scale SCUC problem. This method employs a Gaussian Mixture Model to cluster the decision outcomes of the agents and utilizes an improved decision tree to interpret the clustering results. We analyze the physical implications behind the phenomenon of unit output distributions exhibiting multiple independent Gaussian distributions. Then, these interpretations are applied to identify active integer variables, thereby simplifying the complexity of the SCUC problem and enhancing solution efficiency. Furthermore, an improved Markov decision process model with domain knowledge pertinent of power systems is constructed to enhance the interpretability and reliability of the agents. A distinctive feature of this model is the incorporation of a bidirectional mapping of unsafe and safe actions. The case studies on the SG-126 system demonstrate that the proposed method achieves a significant increase in solution speed without loss of accuracy. The identified active integer variables are proven to be accurate and effective, contributing to improve computation efficiency of unit commitment. The proposed method also provides a novel explainable artificial intelligence-assisted method for complex decision-making problems in other fields.

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JBHI Journal 2025 Journal Article

Identifying Acute Thoracolumbar Vertebral Compression Fractures From Low-Quality Small-Sample X-Ray Images: A Transfer Learning-Based Approach

Yilin Wang
Weijun Li
Siyu Chen
Yang Yang
Aidi Fan
Chenhao Lei
Yuhui Kou
Na Han

Timely and accurate diagnosis of acute thoracolumbar vertebral compression fractures in X-ray images is critical for initiating prompt and effective treatment, preventing potential neurological damage and long-term disability. Recent advancements in artificial intelligence (AI) have significantly improved medical imaging analysis, providing sophisticated tools to assist clinicians in diagnosing acute thoracolumbar vertebral compression fractures. Nonetheless, detecting these fractures through imaging remains challenging due to the complex overlapping of bony structures in the thoracolumbar region, variability in fracture patterns, and often subtle nature of these injuries. Additionally, the limited availability and sometimes poor quality of medical images further complicate accurate AI-based detection. Addressing these challenges, this study introduces a transfer learning model optimized for recognizing acute thoracolumbar vertebral compression fractures from a small set of low-quality X-ray images. The model starts with a feature extraction model that analyzes multiple texture features of X-ray images. It then employs a Vision Transformer Detector (ViTDet) combined with a faster region-based convolutional neural network (Faster R-CNN) to recognize fractures efficiently. To enhance its performance on small datasets, the model employs a transfer learning approach for training. Extensive experiments with a large dataset of real-world images have shown that this model can effectively recognize acute thoracolumbar vertebral compression fractures from low-quality images, outperforming professionals with specialized knowledge in some cases.

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NeurIPS Conference 2023 Conference Paper

CAPP-130: A Corpus of Chinese Application Privacy Policy Summarization and Interpretation

pengyun zhu
Long Wen
Jinfei Liu
Feng Xue
Jian Lou
Zhibo Wang
Kui Ren

A privacy policy serves as an online internet protocol crafted by service providers, which details how service providers collect, process, store, manage, and use personal information when users engage with applications. However, these privacy policies are often filled with technobabble and legalese, making them "incomprehensible''. As a result, users often agree to all terms unknowingly, even some terms may conflict with the law, thereby posing a considerable risk to personal privacy information. One potential solution to alleviate this challenge is to automatically summarize privacy policies using NLP techniques. However, existing techniques primarily focus on extracting key sentences, resulting in comparatively shorter agreements, but failing to address the poor readability caused by the "incomprehensible'' of technobabble and legalese. Moreover, research on Chinese application privacy policy summarization is currently almost nonexistent, and there is a lack of a high-quality corpus suitable for addressing readability issues. To tackle these challenges, we introduce a fine-grained CAPP-130 corpus and a TCSI-pp framework. CAPP-130 contains 130 Chinese privacy policies from popular applications that have been carefully annotated and interpreted by legal experts, resulting in 52, 489 annotations and 20, 555 rewritten sentences. TCSI-pp first extracts sentences related to the topic specified by users and then uses a generative model to rewrite the sentences into comprehensible summarization. Built upon TSCI-pp, we construct a summarization tool TSCI-pp-zh by selecting RoBERTa from six classification models for sentence extraction and selecting mT5 from five generative models for sentence rewriting. Experimental results show that TCSI-pp-zh outperforms GPT-4 and other baselines in Chinese application privacy policy summarization, demonstrating exceptional readability and reliability. Our data, annotation guidelines, benchmark models, and source code are publicly available at https: //github. com/EnlightenedAI/CAPP-130.

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IJCAI Conference 2021 Conference Paper

Noise Doesn't Lie: Towards Universal Detection of Deep Inpainting

Ang Li
Qiuhong Ke
Xingjun Ma
Haiqin Weng
Zhiyuan Zong
Feng Xue
Rui Zhang

Deep image inpainting aims to restore damaged or missing regions in an image with realistic contents. While having a wide range of applications such as object removal and image recovery, deep inpainting techniques also have the risk of being manipulated for image forgery. A promising countermeasure against such forgeries is deep inpainting detection, which aims to locate the inpainted regions in an image. In this paper, we make the first attempt towards universal detection of deep inpainting, where the detection network can generalize well when detecting different deep inpainting methods. To this end, we first propose a novel data generation approach to generate a universal training dataset, which imitates the noise discrepancies exist in real versus inpainted image contents to train universal detectors. We then design a Noise-Image Cross-fusion Network (NIX-Net) to effectively exploit the discriminative information contained in both the images and their noise patterns. We empirically show, on multiple benchmark datasets, that our approach outperforms existing detection methods by a large margin and generalize well to unseen deep inpainting techniques. Our universal training dataset can also significantly boost the generalizability of existing detection methods.

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IROS Conference 2020 Conference Paper

Toward Hierarchical Self-Supervised Monocular Absolute Depth Estimation for Autonomous Driving Applications

Feng Xue
Guirong Zhuo
Ziyuan Huang 0003
Wufei Fu
Zhuoyue Wu
Marcelo H. Ang

In recent years, self-supervised methods for monocular depth estimation has rapidly become an significant branch of depth estimation task, especially for autonomous driving applications. Despite the high overall precision achieved, current methods still suffer from a) imprecise object-level depth inference and b) uncertain scale factor. The former problem would cause texture copy or provide inaccurate object boundary, and the latter would require current methods to have an additional sensor like LiDAR to provide depth ground-truth or stereo camera as additional training inputs, which makes them difficult to implement. In this work, we propose to address these two problems together by introducing DNet. Our contributions are twofold: a) a novel dense connected prediction (DCP) layer is proposed to provide better object-level depth estimation and b) specifically for autonomous driving scenarios, dense geometrical constrains (DGC) is introduced so that precise scale factor can be recovered without additional cost for autonomous vehicles. Extensive experiments have been conducted and, both DCP layer and DGC module are proved to be effectively solving the aforementioned problems respectively. Thanks to DCP layer, object boundary can now be better distinguished in the depth map and the depth is more continues on object level. It is also demonstrated that the performance of using DGC to perform scale recovery is comparable to that using ground-truth information, when the camera height is given and the ground point takes up more than 1. 03% of the pixels. Code is available at https://github.com/TJ-IPLab/DNet.

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YNIMG Journal 2019 Journal Article

Image processing and analysis methods for the Adolescent Brain Cognitive Development Study

Donald J. Hagler
SeanN. Hatton
M. Daniela Cornejo
Carolina Makowski
Damien A. Fair
Anthony Steven Dick
Matthew T. Sutherland
B.J. Casey

The Adolescent Brain Cognitive Development (ABCD) Study is an ongoing, nationwide study of the effects of environmental influences on behavioral and brain development in adolescents. The main objective of the study is to recruit and assess over eleven thousand 9-10-year-olds and follow them over the course of 10 years to characterize normative brain and cognitive development, the many factors that influence brain development, and the effects of those factors on mental health and other outcomes. The study employs state-of-the-art multimodal brain imaging, cognitive and clinical assessments, bioassays, and careful assessment of substance use, environment, psychopathological symptoms, and social functioning. The data is a resource of unprecedented scale and depth for studying typical and atypical development. The aim of this manuscript is to describe the baseline neuroimaging processing and subject-level analysis methods used by ABCD. Processing and analyses include modality-specific corrections for distortions and motion, brain segmentation and cortical surface reconstruction derived from structural magnetic resonance imaging (sMRI), analysis of brain microstructure using diffusion MRI (dMRI), task-related analysis of functional MRI (fMRI), and functional connectivity analysis of resting-state fMRI. This manuscript serves as a methodological reference for users of publicly shared neuroimaging data from the ABCD Study.

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YNICL Journal 2019 Journal Article

Neurocognitive decision-making processes of casual methamphetamine users

Vita Droutman
Feng Xue
Emily Barkley-Levenson
Hei Yeung Lam
Antoine Bechara
Benjamin Smith
Zhong-Lin Lu
Gue Xue

Neuroadaptations caused by chronic methamphetamine (MA) use are likely major contributors to high relapse rate following treatment. Thus, focusing intervention efforts at pre-empting addiction in vulnerable populations, thereby preventing MA-use-induced neurological changes that make recovery so challenging, may prove more effective than targeting chronic users. One approach is studying casual/recreational users, not diagnosed with substance use disorder. This group may be at high risk for addiction due to their experience with MA. On the other hand, they may be resilient against addiction since they were able to maintain casual use over the years and not become addicted. Understanding their neuro-cognitive characteristics during decision-making and risk-taking would help solve this dilemma and, may help identify intervention strategies. Unfortunately, research on neuro-cognitive characteristics of casual MA users is currently lacking. In this work we begin to address this deficit. This study was part of a larger investigation of neural correlates of risky sexual decision-making in men who have sex with men. While undergoing functional magnetic resonance imaging, 31 casual MA users and 66 non-users performed the CUPS task, in which they decided to accept or refuse a series of mixed gambles. Convergent results from whole brain, region of interest and psychophysiological interaction (PPI) analyses are presented. Whole brain analysis identified an amygdala-striatal cluster with weaker activation in casual MA users compared to non-users during decision-making. Activity in that cluster inversely correlated with decisions to gamble: lower activation corresponded to higher risk taking. Using this cluster as a seed in PPI analyses, we identified a wide range of neural network differences between casual MA users and non-users. Parametric whole brain analyses identified clusters in the ventral striatum, posterior insula and precuneus where activations modulated by risk and reward were significantly weaker in casual MA users than in non-users. The striatal cluster identified in these analyses overlapped with the amygdala-striatal cluster. This work identified neural differences in casual MA users' reward processing and outcome learning systems which may underlie their increased real-world risk-taking. It suggests that while making decisions casual MA users focus primarily on potential gain unlike non-users who also take the riskiness of the choice into consideration.

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YNIMG Journal 2014 Journal Article

Fiber connectivity between the striatum and cortical and subcortical regions is associated with temperaments in Chinese males

Xuemei Lei
Chuansheng Chen
Feng Xue
Qinghua He
Chunhui Chen
Qi Liu
Robert K. Moyzis
Gui Xue

The seven-factor biopsychosocial model of personality distinguished four biologically based temperaments and three psychosocially based characters. Previous studies have suggested that the four temperaments—novelty seeking (NS), reward dependence (RD), harm avoidance (HA), and persistence (P)—have their respective neurobiological correlates, especially in the striatum-connected subcortical and cortical networks. However, few studies have investigated their neurobiological basis in the form of fiber connectivity between brain regions. This study correlated temperaments with fiber connectivity between the striatum and subcortical and cortical hub regions in a sample of 50 Chinese adult males. Generally consistent with our hypotheses, results showed that: (1) NS was positively correlated with fiber connectivity from the medial and lateral orbitofrontal cortex (mOFC, lOFC) and amygdala to the striatum; (2) RD was positively correlated with fiber connectivity from the mOFC, posterior cingulate cortex/retrosplenial cortex (PCC), hippocampus, and amygdala to the striatum; (3) HA was positively linked to fiber connectivity from the dorsolateral prefrontal cortex (dlPFC) and PCC to the striatum; and (4) P was positively linked to fiber connectivity from the mOFC to the striatum. These results extended the research on the neurobiological basis of temperaments by identifying their anatomical fiber connectivity correlates within the subcortical–cortical neural networks.

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YNIMG Journal 2014 Journal Article

Resting-state functional connectivity and reading abilities in first and second languages

Mingxia Zhang
Jin Li
Chuansheng Chen
Gui Xue
Zhonglin Lu
Leilei Mei
Hongli Xue
Feng Xue

An intriguing discovery in recent years is that resting-state functional connectivity (RSFC) is associated with cognitive performance. The current study investigated whether RSFC within the reading network was correlated with Chinese adults' reading abilities in their native language (L1, Chinese) and second language (L2, English). Results showed that RSFC within the reading network was positively correlated to reading abilities in L1 and L2, and RSFC between reading areas and the default network was negatively correlated to reading abilities in L1 and L2. Further conjunction and contrast analyses revealed that L1 and L2 shared similar RSFC correlates including connectivities between the areas for visual analysis (e. g. , bilateral posterior fusiform gyrus, lateral occipital cortices, and right superior parietal lobules) and those for phonological processing (e. g. , bilateral precentral gyri and postcentral gyrus, Wernicke's area). These results indicate that RSFC is a potential neural marker for reading abilities in both L1 and L2, with important theoretical implications for reading in L1 and L2.

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RLDM Conference 2013 Conference Abstract

Common and Distinct Neural Mechanisms for Associative Learning by Reward and Punishment

Gui Xue
Feng Xue
Vita Droutman
Stephen Read

It is still debated whether there are similar or distinct neural substrates for reinforcement learning via reward and punishment, which is complicated by the use of monetary gains and losses in existing stud- ies. To address this issue, the present study used monetary gain and mild shock as reward and punishment in a deterministic reinforcement learning task. Forty male subjects were asked to learn the association (with 5 to 8 repetitions) between a novel image and a left or right key press through deterministic feedback. The contingency was then reversed and subjects learned the new contingency over 5 repetitions to achieve high accuracy. Under the reward condition, subjects received one point (convertible to real money at the end of the experiment) for each correct response but otherwise nothing; under the punishment condition, subjects received a mild electric shock for each incorrect response but otherwise nothing. Behavioral results sug- gest that subjects learned equally well via both reward and punishment. Functional results suggest that for both reward and punishment learning, there was significant activation in the striatum (caudate, putamen and nucleus accumbens) and in the ventromedial prefrontal cortex for positive PE (i. e. , not fully predicted pres- ence of an appetitive outcome, not fully predicted omission of an aversive outcome), but strong activation in the anterior cingulate cortex (ACC) and adjacent preSMA and right prefrontal cortex for negative PE. In addition, whereas no difference for positive PE was found between reward and punishment learning, there was stronger response to negative PE in ACC, bilateral insula and amygdala for punishment learning. These results suggest that the striatum is commonly involved in positive prediction error by the presence of a not fully predicted appetitive outcome and the avoidance of a not fully predicted aversive outcome, whereas the ACC, insula and amygdala are particularly important for punishment learning.

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YNIMG Journal 2010 Journal Article

The “visual word form area” is involved in successful memory encoding of both words and faces

Leilei Mei
Gui Xue
Chuansheng Chen
Feng Xue
Mingxia Zhang
Qi Dong

Previous studies have identified the critical role of the left fusiform cortex in visual word form processing, learning, and memory. However, this so-called visual word form area's (VWFA) other functions are not clear. In this study, we used fMRI and the subsequent memory paradigm to examine whether the putative VWFA was involved in the processing and successful memory encoding of faces as well as words. Twenty-two native Chinese speakers were recruited to memorize the visual forms of faces and Chinese words. Episodic memory for the studied material was tested 3h after the scan with a recognition test. The fusiform face area (FFA) and the VWFA were functionally defined using separate localizer tasks. We found that, both within and across subjects, stronger activity in the VWFA was associated with better recognition memory of both words and faces. Furthermore, activation in the VWFA did not differ significantly during the encoding of faces and words. Our results revealed the important role of the so-called VWFA in face processing and memory and supported the view that the left mid-fusiform cortex plays a general role in the successful processing and memory of different types of visual objects (i. e. , not limited to visual word forms).

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YNIMG Journal 2007 Journal Article

Neural bases of asymmetric language switching in second-language learners: An ER-fMRI study

Yapeng Wang
Gui Xue
Chuansheng Chen
Feng Xue
Qi Dong

Using the ER-fMRI technique, the present study was designed to investigate the neural substrates of language switching among second-language learners. Twelve Chinese college students who were learning English were scanned when they performed language switching tasks (naming pictures in their first [L1, Chinese] and second [L2, English] languages according to response cues). Compared to non-switching conditions, language switching elicited greater activation in the right superior prefrontal cortex (BA9/10/32), left middle and superior frontal cortex (BA8/9/46), and right middle cingulum and caudate (BA11). When the direction of switching was considered, forward switching (from L1 to L2), but not backward switching (from L2 to L1), activated several brain regions related to executive functions (i. e. , bilateral frontal cortices and left ACC) relative to non-switching conditions. These results suggest that neural correlates of language switching differ depending on the direction of the switch and that there does not seem to be a specific brain area acting as a “language switch”.

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