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SSRN 2025年

作者： Zhang, Zhuan Wang, Lihui Chen, Qijian Wang, Li Jian, Caiqing Zhu, Yue-Min Wei, Hongjiang Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China University Lyon INSA Lyon CNRS Inserm IRP Metislab CREATIS UMR5220 U1206 Lyon69621 France

Due to the heterogeneous distributions of multi-center rs-fMRI data, currently it is still challenging to identify accurately autism spectrum disorder (ASD) patients from these heterogenous data. To deal with the influence of heterogeneous distributions on multi-center ASD diagnosis, a novel causal adversarial graph neural network (CAG) for multi-center ASD prediction is proposed in this work. CAG mainly consists of a graph filtering (GF) module and a learnable causal graph predictor (CGP). Specifically, the brain connection graph is first constructed from rs-fMRI data, after passing a GF module, the filtered graph is further divided by a causal subgraph and causally irrelevant subgraph. The ground-truth environmental labels and ASD labels are predicted from causal subgraph by a clustering method and a classification block, respectively. In addition, from the filtered graph and causally irrelevant subgraph, the environmental labels are predicted, while from the causally irrelevant subgraph the ASD labels are predicted. Assuming that the predicted environmental labels are independent to causal subgraph, while the predicted ASD labels are independent to causally irrelevant subgraph, accordingly, several adversarial losses are implemented by reversing the gradient. Through this adversarial learning strategy, the derived causal graph can predict multi-center ASD. The comparison experiments on both ABIDE-I and ABIDE-II datasets demonstrate that, the ACC and AUC of CAG has been improved by 3.78\% and 7.38\% at least comparing against the existing methods. Moreover, some abnormal functional connectivities in ASD group are identified from the causal subgraph which may provide useful imaging biomarkers for the early diagnosis of ASD. © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

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STGAN: Spatial-temporal Graph Autoregression Network for Pavement Distress Deterioration Prediction

arXiv

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arXiv 2025年

作者： Tong, Shilin Wu, Difei Liu, Xiaona Zheng, Le Du, Yuchuan Zou, Difan Department of Computer Science The University of Hong Kong Hong Kong Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji University China Institute of IoT Shenzhen Polytechnic University China School of Information and Electronics Beijing Institute of Technology China Department of Computer Science Insitute of Data Science The University of Hong Kong Hong Kong

Pavement distress, manifested as cracks, potholes, and rutting, significantly compromises road integrity and poses risks to drivers. Accurate prediction of pavement distress deterioration is essential for effective road management, cost reduction in maintenance, and improvement of traffic safety. However, real-world data on pavement distress is usually collected irregularly, resulting in uneven, asynchronous, and sparse spatial-temporal datasets. This hinders the application of existing spatial-temporal models, such as DCRNN [1], since they are only applicable to regularly and synchronously collected data. To overcome these challenges, we propose the Spatial-Temporal Graph Autoregression Network (STGAN), a novel graph neural network (GNN) model designed for accurately predicting irregular pavement distress deterioration using complex spatial-temporal data. Specifically, STGAN integrates the temporal domain into the spatial domain, creating a larger graph where nodes are represented by spatial-temporal tuples and edges are formed based on a similarity-based connection mechanism. Furthermore, based on the constructed spatiotemporal graph, we formulate pavement distress deterioration prediction as a graph autoregression task, i.e., the graph size increases incrementally and the prediction is performed sequentially. This is accomplished by a novel spatial-temporal attention mechanism deployed by the proposed STGAN model. Utilizing the ConTrack dataset [2], which contains pavement distress records collected from different locations in Shanghai, we demonstrate the superior performance of STGAN in capturing spatial-temporal correlations and addressing the aforementioned challenges. Experimental results further show that STGAN outperforms baseline models, and ablation studies confirm the effectiveness of its novel modules. Our findings contribute to promoting proactive road maintenance decision-making and ultimately enhancing road safety and resilience. © 2025, CC BY.

关键词： Graph neural networks

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Deep-Learning-Based Integration of Sequence and Structure Information for Efficiently Predicting miRNA-Drug Associations

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Journal of Chemical Information and Modeling 2025年 2025 May 17页

作者： Sheng, Nan Liu, Yunzhi Gao, Ling Wang, Lei Si, Chenxu Huang, Lan Wang, Yan Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education College of Computer Science and Technology Jilin University ChangChun 130012 China

Extensive research has shown that microRNAs (miRNAs) play a crucial role in cancer progression, treatment, and drug resistance. They have been recognized as promising potential therapeutic targets for overcoming drug resistance in cancer treatment. However, limited attention has been paid to predicting the association between miRNAs and drugs by computational methods. Existing approaches typically focus on constructing miRNA-drug interaction graphs, which may result in their performance being limited by interaction density. In this work, we propose a novel deep learning method that integrates sequence and structural information to infer miRNA-drug associations (MDAs), called DLST-MDA. This approach innovates by utilizing attribute information on miRNAs and drugs instead of relying on the commonly used interaction graph information. Specifically, considering the sequence lengths of miRNAs and drugs, DLST-MDA employs multiscale convolutional neural network (CNN) to learn sequence embeddings at different granularity levels from miRNA and drug sequences. Additionally, it leverages the power of graph neural networks to capture structural information from drug molecular graphs, providing a more representational analysis of the drug features. To evaluate DLST-MDA’s effectiveness, we manually constructed a benchmark data set for various experiments based on the latest databases. Results indicate that DLST-MDA performs better than other state-of-the-art methods. Furthermore, case studies of three common anticancer drugs can evidence their usefulness in discovering novel MDAs. The data and source code are released at https://***/sheng-n/DLST-MDA. © 2025 American Chemical Society.

关键词： Drug interactions

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Improving Conversion Kinetics of Sodium Polysulfides through Electron Spillover Effect with V/Co Dual-Atomic Site Anchoring on N-Doped MXene

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Advanced Materials 2025年第0期 e2501371页

作者： Liu, Ronghui Feng, Chao Wu, Pengfei Sun, Yuzhuo Chu, Zhaoqin Hu, Jingtian Chen, Wenxing Guo, Lingling Huang, Qing Wang, Degao Zhejiang Key Laboratory of Data-Driven High-Safety Energy Materials and Applications Advanced Interdisciplinary Science Research Center Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Zhejiang Ningbo315201 China Key Laboratory of Bio-Inspired Smart Interfacial Science and Technology of Ministry of Education School of Chemistry Beihang University Beijing100191 China College of Chemical and Biological Engineering Shandong University of Science and Technology Shandong Qingdao266590 China Energy & Catalysis Center School of Materials Science and Engineering Beijing Institute of Technology Beijing100081 China Shanghai Synchrotron Radiation Facility Shanghai Advanced Research Institute Chinese Academy of Science Shanghai201204 China

Room-temperature sodium─sulfur (RT/Na─S) batteries, with a theoretical capacity of 1672 mAh g⁻1, face challenges such as the insulating nature of sulfur and slow redox kinetics, particularly during complex liquid–solid (Na2S4→Na2S2) and solid–solid (Na2S2→Na2S) conversions. Herein, vanadium-cobalt (VCo) diatomic sites implanted in vacancy-rich N-doped MXene (VCo DACs/N-MXene) are introduced to address these issues. The N-bridged VCo diatomic pairs are demonstrated and their strong electronic interactions are also validated through experimental and theoretical analyses. The RT/Na─S battery with optimized VCo DACs/N-MXene delivers an average capacity of 1255.3 mAh g⁻1 at 0.1 C and remarkable cycling stability, with only ≈0.001% capacity decay per cycle over 1500 cycles at 1 C. DFT calculations reveal that VCo diatomic sites enhance reaction kinetics by reducing the Gibbs free energy for polysulfide conversions, notably reducing the solid–solid conversion energy barriers from 1.17/0.96 eV for V/Co SACs/N-MXene to 0.53 eV for VCo DACs/N-MXene. XANES and DFT analyses attribute this improvement to a unique electron spillover effect, facilitating efficient electron transport during charge and discharge. This work highlights the potential of optimizing electronic configurations and coordinating environments to activate bidirectional kinetics with improved capacity and longevity of RT/Na─S batteries. © 2025 Wiley-VCH GmbH.

关键词： Gibbs free energy

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JGC-IAGCL: Fusing joint graph convolution and intent-aware graph contrastive learning for explainable recommendation

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Information Fusion 2025年 123卷

作者： Yang, Zhi Lin, Chuan Qin, Yongbin Huang, Ruizhang Chen, Yanping Qin, Jiwei State Key Laboratory of Public Big Data Guizhou University Guiyang Guizhou550025 China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and Technology Guizhou University Guiyang Guizhou550025 China College of Information Science and Engineering Xinjiang University Xinjiang Uygur Autonomous Region Urumqi830046 China

Graph contrastive learning (GCL) enhances recommendation accuracy by leveraging self-supervised features to refine node representations from large-scale unlabeled data. Traditional GCL-based recommendation models typically construct contrastive views via graph augmentation (e.g., stochastic node/edge dropout) or embedding-space perturbation, aiming to maximize representation consistency. However, these methods struggle to effectively model and interpret user preferences and consumption intents, limiting explainability and recommendation performance. To address these challenges, we propose JGC-IAGCL (Joint Graph Convolution and Intent-Aware Graph Contrastive Learning), an explainable recommendation framework. JGC-IAGCL integrates joint graph convolution to capture implicit user preferences and employs intent-aware graph contrastive learning to extract explicit user intents from user–item interactions. By fusing these features, our method generates evenly distributed, intent-propensity-aware user/item representations. Theoretical analysis shows that JGC-IAGCL mitigates popularity bias while enhancing the exposure of long-tail items. Extensive experiments on four highly sparse public datasets validate its effectiveness, demonstrating superior recommendation accuracy and improved interpretability. © 2025

关键词： Self-supervised learning

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Complementary knowledge Compilation Using the Hyper Extension Rule

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Chinese Journal of Electronics 2019年第2期28卷 259-265页

作者： NIU Dangdang LIU Lei LYU Shuai XU Yue College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University

We introduce the concept of Complementary formula(COMF), which is a new and non-equivalent way for knowledge compilation(KC). Based on the Hyper extension rule(HER) which is an expansion of Extension rule(ER), we design a compilation algorithm which can formula compile each Conjunctive normal form(CNF)formula to complementary Fully complementary connected diagram(c-FCCD), named as C2C(CNF formula to cFCCD). Theoretically, c-FCCD is a kind of complementary formulae of the input formulae and can support all queries and partial transformations in KC map. Experimentally,C2C is competitive with the EPCCL compilers KCER,C2E, UKCHER, DKCHER and IKCHER.

关键词： knowledge compilation Extension rule Hyper extension rule Compilation language Each pair contains complementary literals(EPCCL) theory

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Copy-Paste Detection Based on a SIFT Marked Graph Feature Vector

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Chinese Journal of Electronics 2017年第2期26卷 345-350页

作者： LYU Yingda SHEN Xuanjing CHEN Haipeng College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University

To detect copy-paste tampering,an improved SIFT(Scale invariant feature transform)-based algorithm was *** angle is defined and a maximum angle-based marked graph is *** marked graph feature vector is provided to each SIFT key point via discrete polar coordinate *** points are matched to detect the copy-paste tampering *** experimental results show that the proposed algorithm can effectively identify and detect the rotated or scaled copy-paste regions,and in comparison with the methods reported previously,it is resistant to postprocessing,such as blurring,Gaussian white noise and JPEG *** proposed algorithm performs better than the existing algorithm to dealing with scaling transformation.

关键词： Copy-paste tampering Image forensics Marked graph Maximum angle

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Model-based diagnosis of incomplete discrete-event system with rough set theory

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Science China(Information Sciences) 2017年第1期60卷 190-200页

作者： Xuena GENG Dantong OUYANG Yonggang ZHANG Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of EducationJilin University College of Computer Science and Technology Jilin University

Fault diagnosis of discrete-event system(DES) is important in the preventing of harmful events in the system. In an ideal situation, the system to be diagnosed is assumed to be complete; however, this assumption is rather restrictive. In this paper, a novel approach, which uses rough set theory as a knowledge extraction tool to deal with diagnosis problems of an incomplete model, is investigated. DESs are presented as information tables and decision tables. Based on the incomplete model and observations, an algorithm called Optimizing Incomplete Model is proposed in this paper in order to obtain the repaired model. Furthermore, a necessary and sufficient condition for a system to be diagnosable is given. In ensuring the diagnosability of a system, we also propose an algorithm to minimize the observable events and reduce the cost of sensor selection.

关键词： model-based diagnosis diagnosability discrete-event system finite state machine rough set theory

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Autonomous Domain Correlation-Based Cross-domain Network View Caching Method for SDN Distributed Controller

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Chinese Journal of Electronics 2016年第6期25卷 1127-1133页

作者： FU Tao HU Liang CHAI Sheng CHE Xilong College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computing and Knowledge Engineering of Ministry of Education Jilin University

Software-defined networks(SDN) maintain a global view of the network, thus improving the intelligence of forwarding decisions. With the expansion of the network scale, distributed controllers are used in a variety of large-scale networks in which subnetworks managed by controller instance are called autonomous domains. We analyze statistic frequency of communication across the autonomous domain. We calculate the autonomous domain correlations for controller instances using acquired statistical information. We cache network views to highly correlated controller instances. Distributed controllers are capable of considering both the average response time and overall storage. An experiment shows that our method can fully take advantage of these two performance indicators.

关键词： Software-defined networks(SDN) Distributed controller Network analyze Network view cache

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Prediction-based event-triggered identification of quantized input FIR systems with quantized output observations

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Science China(Information Sciences) 2020年第1期63卷 140-151页

作者： Jin GUO Jing-Dong DIAO School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education

This paper addresses the identification of finite impulse response(FIR) systems with both quantized and event-triggered observations. An event-triggered communication scheme for the binary-valued output quantization is introduced to save communication resources. Combining the empirical-measure-based identification technique and the weighted least-squares optimization, an algorithm is proposed to estimate the unknown parameter by full use of the received data and the not-triggered condition. Under quantized inputs, it is shown that the estimate can strongly converge to the real values and the estimator is asymptotically efficient in terms of the Cram′er-Rao lower bound. Further, the limit of the average communication rate is derived and the tradeoff between this limit and the estimation performance is discussed. Moreover, the case of multi-threshold quantized observations is considered. Numerical examples are included to illustrate the obtained main results.

关键词： identification FIR systems prediction-based event-triggered communication quantized observations convergence performance

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