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Chun Wang

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7 papers

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YNIMG Journal 2026 Journal Article

State-Level Brain Dynamics Reveal Neural Correlates of Negative-Mode Rigidity in Non-suicidal Self-injury

Jian Li
Jingying Lai
Fengmin Ni
Ya Xie
Enze Tang
Yibing Tang
Chun Wang

Non-suicidal self-injury (NSSI) is marked by persistent bias toward negatively valenced or salient internal experiences and difficulty disengaging from them once activated, yet the neural dynamics that support this clinical rigidity remain poorly understood. Intrinsic brain activity normally cycles among recurrent large-scale states, and alterations in how these states are occupied or transitioned may provide a neural analogue of negatively biased internal modes. Using resting-state fMRI from 160 patients with NSSI and 50 psychiatric controls, we applied Hidden Markov Modeling to characterize latent brain states and their temporal properties. NSSI was associated with disproportionate engagement of a recurrent ventral attention-related state, reduced differentiation between states, and greater variability within states. Greater dominance of this state was linked to more severe emotion-regulation difficulties at baseline and showed prognostic relevance for subsequent improvement in NSSI behaviors over three months. These findings indicate that NSSI involves a biased tendency to settle into salience- and attention-related brain states, highlighting attentional deficits as a clinically relevant feature of this condition. When considered alongside prior evidence showing heightened variability in connectivity strength and network topology, the results point to convergent disruptions in neural flexibility across multiple organizational levels in NSSI and underscore large-scale neural dynamics as a potentially informative target for future mechanistic and translational research.

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NeurIPS Conference 2025 Conference Paper

GRE Suite: Geo-localization Inference via Fine-Tuned Vision-Language Models and Enhanced Reasoning Chains

Chun Wang
Xiaojun Ye
Xiaoran Pan
Zihao Pan
Haofan Wang
Yiren Song

Recent advances in Visual Language Models (VLMs) have demonstrated exceptional performance in visual reasoning tasks. However, geo-localization presents unique challenges, requiring the extraction of multigranular visual cues from images and their integration with external world knowledge for systematic reasoning. Current approaches to geo-localization tasks often lack robust reasoning mechanisms and explainability, limiting their effectiveness. To address these limitations, we propose the Geo Reason Enhancement (GRE) Suite, a novel framework that augments VLMs with structured reasoning chains for accurate and interpretable location inference. The GRE Suite is systematically developed across three key dimensions: dataset, model, and benchmark. First, we introduce GRE30K, a high-quality geo-localization reasoning dataset designed to facilitate fine-grained visual and contextual analysis. Next, we present the GRE model, which employs a multi-stage reasoning strategy to progressively infer scene attributes, local details, and semantic features, thereby narrowing down potential geographic regions with enhanced precision. Finally, we construct the Geo Reason Evaluation Benchmark (GREval-Bench), a comprehensive evaluation framework that assesses VLMs across diverse urban, natural, and landmark scenes to measure both coarse-grained (e. g. , country, continent) and fine-grained (e. g. , city, street) localization performance. Experimental results demonstrate that GRE significantly outperforms existing methods across all granularities of geo-localization tasks, underscoring the efficacy of reasoning-augmented VLMs in complex geographic inference. Code and data will be released at https: //anonymous. 4open. science/r/GRE-74C0.

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IJCAI Conference 2025 Conference Paper

M4Bench: A Benchmark of Multi-domain Multi-granularity Multi-image Understanding for Multi-modal Large Language Models

Xiaojun Ye
Guanbao Liang
Chun Wang
Liangcheng Li
Pengfei Ke
Rui Wang
Bingxin Jia
Gang Huang

The increasing demands in analyzing complex associated scenes pose necessities to researching multi-image understanding abilities. Compared with understanding individual images, both the alignments and differences between images are essential aspects of understanding the intricate relationships for multi-image inference tasks. However, existing benchmarks face difficulties in addressing both of these aspects simultaneously, resulting in obstacles to modeling relationships under various granularities and domains of images. In this paper, we introduce M4Bench to enhance the capability of aligning and distinguishing multi-images with multi-domain multi-granularity comparison. We carefully design five comparison tasks related to coarse and fine-grained granularities in single and multiple domains of images and evaluate them on 13 state-of-the-art multi-modal large language models with various sizes. Besides, we analyze the evaluation results and provide several observations and viewpoints for the multi-image understanding research. The data and evaluation code are available at https: //github. com/eaglelab-zju/M4Bench.

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AIIM Journal 2022 Journal Article

ADHD classification using auto-encoding neural network and binary hypothesis testing

Yibin Tang
Jia Sun
Chun Wang
Yuan Zhong
Aimin Jiang
Gang Liu
Xiaofeng Liu

Attention Deficit Hyperactivity Disorder (ADHD) is a highly prevalent neurodevelopmental disease of school-age children. Early diagnosis is crucial for ADHD treatment, wherein its neurobiological diagnosis (or classification) is helpful and provides the objective evidence to clinicians. The existing ADHD classification methods suffer two problems, i. e. , insufficient data and feature noise disturbance from other associated disorders. As an attempt to overcome these difficulties, a novel deep-learning classification architecture based on a binary hypothesis testing framework and a modified auto-encoding (AE) network is proposed in this paper. The binary hypothesis testing framework is introduced to cope with insufficient data of ADHD database. Brain functional connectivities (FCs) of test data (without seeing their labels) are incorporated during feature selection along with those of training data and affect the sequential deep learning procedure under binary hypotheses. On the other hand, the modified AE network is developed to capture more effective features from training data, such that the difference of inter- and intra-class variability scores between binary hypotheses can be enlarged and effectively alleviate the disturbance of feature noise. On the test of ADHD-200 database, our method significantly outperforms the existing classification methods. The average accuracy reaches 99. 6% with the leave-one-out cross validation. Our method is also more robust and practically convenient for ADHD classification due to its uniform parameter setting across various datasets.

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AIIM Journal 2020 Journal Article

ADHD classification by dual subspace learning using resting-state functional connectivity

Ying Chen
Yibin Tang
Chun Wang
Xiaofeng Liu
Li Zhao
Zhishun Wang

As one of the most common neurobehavioral diseases in school-age children, Attention Deficit Hyperactivity Disorder (ADHD) has been increasingly studied in recent years. But it is still a challenge problem to accurately identify ADHD patients from healthy persons. To address this issue, we propose a dual subspace classification algorithm by using individual resting-state Functional Connectivity (FC). In detail, two subspaces respectively containing ADHD and healthy control features, called as dual subspaces, are learned with several subspace measures, wherein a modified graph embedding measure is employed to enhance the intra-class relationship of these features. Therefore, given a subject (used as test data) with its FCs, the basic classification principle is to compare its projected component energy of FCs on each subspace and then predict the ADHD or control label according to the subspace with larger energy. However, this principle in practice works with low efficiency, since the dual subspaces are unstably obtained from ADHD databases of small size. Thereby, we present an ADHD classification framework by a binary hypothesis testing of test data. Here, the FCs of test data with its ADHD or control label hypothesis are employed in the discriminative FC selection of training data to promote the stability of dual subspaces. For each hypothesis, the dual subspaces are learned from the selected FCs of training data. The total projected energy of these FCs is also calculated on the subspaces. Sequentially, the energy comparison is carried out under the binary hypotheses. The ADHD or control label is finally predicted for test data with the hypothesis of larger total energy. In the experiments on ADHD-200 dataset, our method achieves a significant classification performance compared with several state-of-the-art machine learning and deep learning methods, where our accuracy is about 90 % for most of ADHD databases in the leave-one-out cross-validation test.

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IJCAI Conference 2019 Conference Paper

Attributed Graph Clustering: A Deep Attentional Embedding Approach

Chun Wang
Shirui Pan
Ruiqi Hu
Guodong Long
Jing Jiang
Chengqi Zhang

Graph clustering is a fundamental task which discovers communities or groups in networks. Recent studies have mostly focused on developing deep learning approaches to learn a compact graph embedding, upon which classic clustering methods like k-means or spectral clustering algorithms are applied. These two-step frameworks are difficult to manipulate and usually lead to suboptimal performance, mainly because the graph embedding is not goal-directed, i. e. , designed for the specific clustering task. In this paper, we propose a goal-directed deep learning approach, Deep Attentional Embedded Graph Clustering (DAEGC for short). Our method focuses on attributed graphs to sufficiently explore the two sides of information in graphs. By employing an attention network to capture the importance of the neighboring nodes to a target node, our DAEGC algorithm encodes the topological structure and node content in a graph to a compact representation, on which an inner product decoder is trained to reconstruct the graph structure. Furthermore, soft labels from the graph embedding itself are generated to supervise a self-training graph clustering process, which iteratively refines the clustering results. The self-training process is jointly learned and optimized with the graph embedding in a unified framework, to mutually benefit both components. Experimental results compared with state-of-the-art algorithms demonstrate the superiority of our method.

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AAMAS Conference 2007 Conference Paper

A Winner Determination Algorithm for Auction-Based Decentralized Scheduling

Chun Wang
Hamada H. Ghenniwa
Weiming Shen

This paper presents a formulation and an algorithm for the winner determination problem in auction-based decentralized scheduling. Without imposing a time line discretization, the proposed approach allows bidders to bid for the processing of a set of tasks under release time and due date constraints using an expressive bidding language designed for decentralized scheduling. The proposed winner determination algorithm uses a depth first branch and bound search. The search branches on bids and a constraint directed scheduling procedure is used at each node to verify the feasibility of the allocation. Experiments against a commercial optimization package, CPLEX 10. 0, show that the proposed algorithm is more than an order of magnitude faster on average over a set of winner determination problems of decentralized scheduling generated based on a suite of job shop constraint satisfaction benchmark problems previously developed in the literature.

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