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Angewandte Chemie 2023年第32期135卷

作者： Pengfei Hou Yumiao Tian Prof. Yu Xie Prof. Fei Du Prof. Gang Chen Dr. Aleksandra Vojvodic Prof. Jianzhong Wu Dr. Xing Meng Key Laboratory of Physics and Technology for Advanced Batteries (Ministry of Education) College of Physics Jilin University 130012 Changchun P. R. China Department of Chemical and Biomolecular Engineering University of Pennsylvania PA 19104 Philadelphia USA Department of Chemical and Environmental Engineering University of California CA 92521 Riverside USA Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University 130012 Changchun P. R. China

MXenes are 2D materials with great potential in various applications. However, the degradation of MXenes in humid environments has become a main obstacle in their practical use. Here we combine deep neural networks and an active learning scheme to develop a neural network potential (NNP) for aqueous MXene systems with ab initio precision but low cost. The oxidation behaviors of super large aqueous MXene systems are investigated systematically at nanosecond timescales for the first time. The oxidation process of MXenes is clearly displayed at the atomic level. Free protons and oxides greatly inhibit subsequent oxidation reactions, leading to the degree of oxidation of MXenes to exponentially decay with time, which is consistent with the oxidation rate of MXenes measured experimentally. Importantly, this computational study represents the first exploration of the kinetic process of oxidation of super-sized aqueous MXene systems. It opens a promising avenue for the future development of effective protection strategies aimed at controlling the stability of MXenes.

关键词： MXenes Degradation Neural NetworksActive Learning Oxidation Protection

Applications of Game Theory in Vehicular Networks: A Survey

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arXiv 2021年

作者： Sun, Zemin Liu, Yanheng Wang, Jian Li, Guofa Anil, Carie Li, Keqiang Guo, Xinyu Sun, Geng Tian, Daxin Cao, Dongpu College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Institute of Human Factors and Ergonomics College of Mechatronics and Control Engineering Shenzhen University Guangdong Shenzhen518060 China School of Computer Science and Engineering VIT-AP Andhra Pradesh Amaravati522237 India College of Engineering Nanjing Agricultural University Nanjing210031 China State Key Laboratory of Automotive Safety and Energy Department of Automotive Engineering Tsinghua University Beijing100084 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing100191 China Waterloo Cognitive Autonomous Driving Lab University of Waterloo N2L 3G1 Canada

In the Internet of things (IoT) era, vehicles and other intelligent components in an intelligent transportation system (ITS) are connected, forming vehicular networks (VNs) that provide efficient and safe traffic and ubiquitous access to various applications. However, as the number of nodes in an ITS increases, it is challenging to satisfy a varied and large number of service requests with different quality of service (QoS) and security requirements in highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for limited network resources to achieve the objective for either an individual or a group. Game theory (GT), a theoretical framework designed for strategic interactions among rational decision makers sharing scarce resources, can be used to model and analyze individual or group behaviors of communicating entities in VNs. This paper primarily surveys the recent developments of GT in solving various challenges of VNs. This survey starts with an introduction to the background of VNs. A review of GT models studied in the VNs is then introduced, including the basic concepts, classifications, and applicable vehicular issues. After discussing the requirements of VNs and the motivation of using GT, a comprehensive literature review on GT applications in dealing with the challenges of current VNs is provided. Furthermore, recent contributions of GT to VNs that are integrated with diverse emerging 5G technologies are surveyed. Finally, the lessons learned are given, and several key research challenges and possible solutions of applying GT in VNs are outlined. Copyright © 2021, The Authors. All rights reserved.

关键词： Quality of service

Copy-Move Forgery Detection Based on Interest Point and Local Search Algorithm

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Copy-Move Forgery Detection Based on Interest Point and Loca...

IEEE International Conference on Cloud computing and Intelligence Systems (CCIS)

作者： Fang Mei Jing Lyu Yingda Lyu Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

ISBN: (数字)9781728138633

ISBN: (纸本)9781728138640

Copy-move forgery detection mainly includes block-based and interest point-based methods, but both of them have shortcomings, such as weak resistance to affine transformations, weak ability to locate the forged region. To solve these problems, a novel algorithm is proposed in this paper, which involves two-stage search algorithm. The first stage takes image blocks as the search unit, and in the second stage, the forger region is searched at the pixel level. Experimental results demonstrate that the proposed method performants better compared to recent state-of-the-art methods.

关键词： Rail to rail outputs

Disentangled Cascaded Graph Convolution Networks for Multi-Behavior Recommendation

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arXiv 2024年

作者： Cheng, Zhiyong Dong, Jianhua Liu, Fan Zhu, Lei Yang, Xun Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology No. 485 Danxia Road Anhui Hefei230009 China Shandong Artificial Intelligence Institute Qilu University of Technology Shandong Academy of Sciences No. 19 Keyuan Road Shandong Jinan250014 China School of Computing National University of Singapore 21 Lower Kent Ridge Road Singapore119077 Singapore School of Electronic and Information Engineering University of Tongji No. 4800 Caoan Road Shanghai201804 China School of Information Science and Technology University of Science and Technology of China No. 443 Huangshan Road Anhui Hefei230027 China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Institute of Artificial Intelligence Hefei Comprehensive National Science Centery No. 485 Danxia Road Anhui Hefei230009 China

Multi-behavioral recommender systems have emerged as a solution to address data sparsity and cold-start issues by incorporating auxiliary behaviors alongside target behaviors. However, existing models struggle to accurately capture varying user preferences across different behaviors and fail to account for diverse item preferences within behaviors. Various user preference factors (such as price or quality) entangled in the behavior may lead to sub-optimization problems. Furthermore, these models overlook the personalized nature of user behavioral preferences by employing uniform transformation networks for all users and items. To tackle these challenges, we propose the Disentangled Cascaded Graph Convolutional Network (Disen-CGCN), a novel multi-behavior recommendation model. Disen-CGCN employs disentangled representation techniques to effectively separate factors within user and item representations, ensuring their independence. In addition, it incorporates a multi-behavioral meta-network, enabling personalized feature transformation across user and item behaviors. Furthermore, an attention mechanism captures user preferences for different item factors within each behavior. By leveraging attention weights, we aggregate user and item embeddings separately for each behavior, computing preference scores that predict overall user preferences for items. Our evaluation on benchmark datasets demonstrates the superiority of Disen-CGCN over state-of-the-art models, showcasing an average performance improvement of 7.07% and 9.00% on respective datasets. These results highlight Disen-CGCN’s ability to effectively leverage multi-behavioral data, leading to more accurate recommendations. Copyright © 2024, The Authors. All rights reserved.

关键词： Recommender systems

GEOM-GCN: Geometric graph convolutional networks

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arXiv 2020年

作者： Pei, Hongbin Wei, Bingzhe Chang, Kevin Chen-Chuan Lei, Yu Yang, Bo College of Computer Science and Technology Jilin University China Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign United States Department of Computer Science University of Illinois at Urbana-Champaign United States Department of Computing Hong Kong Polytechnic University Hong Kong Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education China

Message-passing neural networks (MPNNs) have been successfully applied to representation learning on graphs in a variety of real-world applications. However, two fundamental weaknesses of MPNNs' aggregators limit their ability to represent graph-structured data: losing the structural information of nodes in neighborhoods and lacking the ability to capture long-range dependencies in disassortative graphs. Few studies have noticed the weaknesses from different perspectives. From the observations on classical neural network and network geometry, we propose a novel geometric aggregation scheme for graph neural networks to overcome the two weaknesses. The behind basic idea is the aggregation on a graph can benefit from a continuous space underlying the graph. The proposed aggregation scheme is permutation-invariant and consists of three modules, node embedding, structural neighborhood, and bi-level aggregation. We also present an implementation of the scheme in graph convolutional networks, termed Geom-GCN, to perform transductive learning on graphs. Experimental results show the proposed Geom-GCN achieved state-of-the-art performance on a wide range of open datasets of graphs. Copyright © 2020, The Authors. All rights reserved.

关键词： Graphic methods

Hypergraph-driven soft semantics flexible learning for visible–infrared person re-identification

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engineering Applications of Artificial Intelligence 2025年 158卷

作者： Zhu, Jiacheng Ge, Hongwei Liu, Yuxuan Wu, Chunguo Fan, Jiulin School of Computer Science and Technology Dalian University of Technology No. 2 Linggong Road Dalian Liaoning 116024 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University No. 2699 Qianjin Street Changchun Jilin 130012 China Huadian Coal Industry Group Co Ltd. No. 2 Xuanwumennei Street Beijing 100031 China

Visible–infrared person re-identification (VI-ReID) aims to match the images of the person across different modalities. The main challenge in the engineering application of VI-ReID lies in the considerable modality gap between visible and infrared images. Existing methods primarily focus on learning low-order hard semantics to reduce the modality gap, such as specific body parts (e.g., arms, legs, and hands), which are modality-specific and sensitive to cross-modality variations, leading to difficult modality alignment. However, high-order soft semantics contain more modality-invariant features that can be flexibly learned by the model to better align the cross-modality features and reduce the modality gap. To better learn soft semantics, we propose a novel, hypergraph-driven soft semantics flexible learning network (HSFLNet), which extracts hierarchical semantics and flexibly explores the relationships among the extracted soft semantics by using hypergraph neural networks (HGNNs). Specifically, first, we propose a soft semantics mining (SSM) module to capture and flexibly fuse hierarchical features, which integrates low- and high-level semantics across channel and spatial dimensions to align modalities. Second, a hypergraph-driven soft semantics flexible learning (HSFL) module is designed, which employs HGNNs to explore the relationships among soft semantics. These semantics are adaptively learned through gating mechanisms to capture modality-invariant features, thereby reducing the modality gap. We evaluate the performance of HSFLNet on SYSU-MM01, RegDB, and low-light cross-modality (LLCM) datasets, achieving a Rank-1/mean average precision of 75.09%/72.11%, 95.82%/92.42%, and 65.42%/68.55%, respectively. Experimental results demonstrate that HSFLNet outperforms state-of-the-art VI-ReID methods. Our code is available at https://***/Jiacheng813/HSFLNet. © 2025 Elsevier Ltd

关键词： Cross-modality retrieval Hypergraph neural networks Soft semantics Visible–infrared person re-identification

Attention-based multi-level feature fusion for named entity recognition 29

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Attention-based multi-level feature fusion for named entity ...

29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Yang, Zhiwei Chen, Hechang Zhang, Jiawei Ma, Jing Chang, Yi College of Computer Science and Technology Jilin University Changchun China School of Artificial Intelligence Jilin University Changchun China IFM Lab Department of Computer Science Florida State University TallahasseeFL United States Department of Computer Science Hong Kong Baptist University Hong Kong Hong Kong Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education China International Center of Future Science Jilin University Changchun China

ISBN: (纸本)9780999241165

Named entity recognition (NER) is a fundamental task in the natural language processing (NLP) area. Recently, representation learning methods (e.g., character embedding and word embedding) have achieved promising recognition results. However, existing models only consider partial features derived from words or characters while failing to integrate semantic and syntactic information (e.g., capitalization, inter-word relations, keywords, lexical phrases, etc.) from multi-level perspectives. Intuitively, multi-level features can be helpful when recognizing named entities from complex sentences. In this study, we propose a novel framework called attention-based multi-level feature fusion (AMFF), which is used to capture the multi-level features from different perspectives to improve NER. Our model consists of four components to respectively capture the local character-level, global character-level, local word-level, and global word-level features, which are then fed into a BiLSTM-CRF network for the final sequence labeling. Extensive experimental results on four benchmark datasets show that our proposed model outperforms a set of state-of-the-art baselines. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Embeddings

Curvature regularization to prevent distortion in graph embedding

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arXiv 2020年

作者： Pei, Hongbin Wei, Bingzhe Zhang, Chunxu Chang, Kevin Chen-Chuan Yang, Bo College of Computer Science and Technology Jilin University China Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign United States Department of Computer Science University of Illinois at Urbana-Champaign United States Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education China

Recent research on graph embedding has achieved success in various applications. Most graph embedding methods preserve the proximity in a graph into a manifold in an embedding space. We argue an important but neglected problem about this proximity-preserving strategy: Graph topology patterns, while preserved well into an embedding manifold by preserving proximity, may distort in the ambient embedding Euclidean space, and hence to detect them becomes difficult for machine learning models. To address the problem, we propose curvature regularization, to enforce flatness for embedding manifolds, thereby preventing the distortion. We present a novel angle-based sectional curvature, termed ABS curvature, and accordingly three kinds of curvature regularization to induce flat embedding manifolds during graph embedding. We integrate curvature regularization into five popular proximity-preserving embedding methods, and empirical results in two applications show significant improvements on a wide range of open graph datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Topology

Sure: Screening Unlabeled Samples for Reliable Negative Samples Based on Reinforcement Learning

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SSRN

SSRN 2022年

作者： Li, Ying Sun, Hang Fang, Wensi Ma, Qin Han, Siyu Wang-Sattler, Rui Du, Wei Yu, Qiong Key Laboratory of Symbolic Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Qianjin Street Jilin Changchun130012 China Department of Biomedical Informatics Ohio State University Lane Ave ColumbusOH43210 United States TUM School of Medicine Technical University of Munich Ismaninger Straße 22 Bavaria Munich81675 Germany Helmholtz Center Munich German Research Center for Environmental Health Ingolstadter Landstraße 1 Bavaria Neuherberg85764 Germany Department of Epidemiology and Biostatistics School of public health Jilin University Qianjin Street Jilin Changchun130012 China

For many classification tasks, only experimentally validated positive samples are available, and experimentally validated negative samples are not recorded. The lack of negative samples poses a great challenge for using supervised machine learning. To address this problem, we propose a novel deep reinforcement learning based model to screen reliable negative samples from unlabeled samples, named SURE. SURE has two modules: sample selector and sample inspector. The sample selector screens reliable negative samples from unlabeled samples by two reinforcement strategies. The sample inspector classifies samples and provides rewards to the sample selector. In this paper, we focus on one popular issue in the field of bioinformatics: the ncRNA-protein interaction (NPI) prediction task, which lacks reliable negative samples. Thirty datasets for NPI prediction are used to test the screening effect of SURE. The Experimental results show that our model has a robust negative sample screening capability and is superior to all outstanding sample screening methods used in the NPI prediction task. In addition, we refine 5 NPI datasets containing reliable negative samples screened by SURE, and a webserver (***/sure) is available offering the NPI prediction refined by SURE. © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

Affine-periodic solutions by averaging methods

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Science China Mathematics 2018年第3期61卷 439-452页

作者： Jiamin Xing Xue Yang Yong Li College of Mathematics Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University School of Mathematics and Statistics Northeast Normal University Center for Mathematics and Interdisciplinary Sciences Northeast Normal University

This paper concerns the existence of affine-periodic solutions for perturbed affine-periodic *** kind of affine-periodic solutions has the form of x(t+T)≡Qx(t) with some nonsingular matrix Q,which may be quasi-periodic when Q is an orthogonal matrix. It can be even unbounded but x(t)/|x(t)| is quasi-periodic,like a helical line. for example x(t)=e^(at)(cos ωt, sin ωt), when Q is not an orthogonal matrix. The averaging method of higher order for finding affine-periodic solutions is given by topological degree.

关键词： affine-periodic solutions averaging method topological degree