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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Jinbiao Chen Zizhen Zhang Zhiguang Cao Yaoxin Wu Yining Ma Te Ye Jiahai Wang School of Computer Science and Engineering Sun Yat-sen University P.R. China School of Computing and Information Systems Singapore Management University Singapore Department of Industrial Engineering & Innovation Sciences Eindhoven University of Technology Netherlands Department of Industrial Systems Engineering & Management National University of Singapore Singapore School of Computer Science and Engineering Sun Yat-sen University P.R. China and Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University P.R. China and Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou P.R. China

Most of existing neural methods for multi-objective combinatorial optimization (MOCO) problems solely rely on decomposition, which often leads to repetitive solutions for the respective subproblems, thus a limited Pareto set. Beyond decomposition, we propose a novel neural heuristic with diversity enhancement (NHDE) to produce more Pareto solutions from two perspectives. On the one hand, to hinder duplicated solutions for different subproblems, we propose an indicator-enhanced deep reinforcement learning method to guide the model, and design a heterogeneous graph attention mechanism to capture the relations between the instance graph and the Pareto front graph. On the other hand, to excavate more solutions in the neighborhood of each subproblem, we present a multiple Pareto optima strategy to sample and preserve desirable solutions. Experimental results on classic MOCO problems show that our NHDE is able to generate a Pareto front with higher diversity, thereby achieving superior overall performance. Moreover, our NHDE is generic and can be applied to different neural methods for MOCO.

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UAV Virtual Antenna Array Deployment for Uplink Interference Mitigation in data Collection Networks

arXiv

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arXiv 2024年

作者： Li, Hongjuan Kang, Hui Sun, Geng Li, Jiahui Wang, Jiacheng Wang, Xue Niyato, Dusit Leung, Victor C.M. 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 College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore College of Computing and Data Science Nanyang Technological University Singapore Department of Communication Engineering Jilin University Changchun130012 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Unmanned aerial vehicles (UAVs) have gained considerable attention as a platform for establishing aerial wireless networks and communications. However, the line-of-sight dominance in air-to-ground communications often leads to significant interference with terrestrial networks, reducing communication efficiency among terrestrial terminals. This paper explores a novel uplink interference mitigation approach based on the collaborative beamforming (CB) method in multi-UAV network systems. Specifically, the UAV swarm forms a UAV-enabled virtual antenna array (VAA) to achieve the transmissions of gathered data to multiple base stations (BSs) for data backup and distributed processing. However, there is a trade-off between the effectiveness of CB-based interference mitigation and the energy conservation of UAVs. Thus, by jointly optimizing the excitation current weights and hover position of UAVs as well as the sequence of data transmission to various BSs, we formulate an uplink interference mitigation multi-objective optimization problem (MOOP) to decrease interference affection, enhance transmission efficiency, and improve energy efficiency, simultaneously. In response to the computational demands of the formulated problem, we introduce an evolutionary computation method, namely chaotic non-dominated sorting genetic algorithm II (CNSGA-II) with multiple improved operators. The proposed CNSGA-II efficiently addresses the formulated MOOP, outperforming several other comparative algorithms, as evidenced by the outcomes of the simulations. Moreover, the proposed CB-based uplink interference mitigation approach can significantly reduce the interference caused by UAVs to non-receiving BSs. Copyright © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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A Vision-based Lane Departure Warning Framework

A Vision-based Lane Departure Warning Framework

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IEEE International Conference on e-Business engineering (ICEBE)

作者： Jiaju Wu Pengshuai Yin Xin Shu Huichou Huang Fei Liu Qingyao Wu School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot Ministry of Education City University of Hong Kong Hong Kong China Pazhou Lab Guangzhou China

ISBN: (纸本)9781665444194

Lane departure warning is an essential function of driver assistance systems. In this paper, we propose a vision-based lane departure warning framework, which can determine whether the vehicle deviates from the lane only with images as input. Our framework combines the deep learning method and traditional line detection algorithm. Specifically, the framework consists of a Lane Boundaries Localization module, a Lane Boundaries Generator module, and a Departure Warning module. The Lane Boundaries Localization module is a deep neural network for finding the area containing lane Boundaries. Then the Lane Boundaries Generator module performs backbone detection and line detection to generate lane boundaries on the located area. After identifying the lane boundaries, the Departure Warning module can quickly determine whether the vehicle deviates from the lane. Additionally, we implemented and deployed our framework to an Android platform and tested its running speed.

关键词： Location awareness Deep learning Conferences Neural networks Transforms Generators Convolutional neural networks

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Gaining water bodies by climate change benefits water crisis mitigation in central Asia

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Science Bulletin 2025年

作者： Su, Yanan Chen, Shengqian Sui, Yijie Li, Xin Xu, Jinhao Che, Xianghong Xie, Tingting Chen, Jianhui Sheng, Yongwei Feng, Min Chen, Fahu State Key Laboratory of Tibetan Plateau Earth System Environment and Resources Institute of Tibetan Plateau Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing100049 China Research Center of Geospatial Big Data Application Chinese Academy of Surveying and Mapping Beijing100830 China College of Water Resources and Architectural Engineering Tarim University Alar843300 China Key Laboratory of Western China's Environmental Systems Ministry of Education College of Earth and Environmental Sciences Lanzhou University Lanzhou730000 China Department of Geography University of California Los AngelesCA90095 United States Academy of Plateau Science and Sustainability Qinghai Normal University Xining810016 China

Central Asia (CA) faces a severe water crisis exemplified by the shrinking Aral Sea. However, little is known about the entire region, particularly the numerous small water bodies that are vulnerable to climate change yet vital for regional sustainability. We examined water bodies as small as 0.0045 km2 across CA from 1992 to 2020, identifying 66,215 water bodies in 2020, 82.2% of which were previously unstudied. In contrast to the well-documented decline of the Aral Sea, other water bodies have expanded by 10.7% (8714.3 km2), with a net gain of 15,831 lakes since 1992. These findings challenge the perception of a drying CA and provide evidence of a warm-wet climate trend, which is redistributing water resources and creating opportunities for transforming water management to address the long-standing water crisis in CA. © 2025 Science China Press

关键词： Central Asia Climate Change Small Water Bodies Water Crisis Water Resource Management

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Topological relations between spherical spatial regions with holes

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International Journal of Digital Earth 2020年第4期13卷 429-456页

作者： Jingwei Shen Lan Zhang Min Chen Chongqing Engineering Research Center for Remote Sensing Big Data Application School of Geographical SciencesSouthwest UniversityChongqingPeople’s Republic of China Key Laboratory of Virtual Geographic Environment(Ministry of Education) Nanjing Normal UniversityNanjingPeople’s Republic of China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application NanjingJiangsuPeople’s Republic of China

There is growing interest in globally modelling the entire *** topological relations between spherical simple regions and topological relations between regions with holes in the plane have been investigated,few studies have focused on the topological relations between spherical spatial regions with *** 16-intersection model(16IM)is proposed to describe the topological relations between spatial regions with holes.A total of 25 negative conditions are proposed to eliminate the impossible topological relations between spherical spatial regions with *** results show that(1)3 disjoint relations,3 meet relations,66 overlap relations,7 cover relations,3 contain relations,1 equal relation,7 coveredBy relations,3 inside relations,1 attach relation,52 entwined relations,and 28 embrace relations can be distinguished by the 16IM and that(2)the formalisms of attach,entwined,and embrace relations between the spherical spatial regions without holes based on the 9IM and that between the spherical spatial regions with holes based on the simplified 16IM are different,whereas the formalisms of other types of relations between spherical spatial regions without holes based on the 9IM and that between the spherical spatial regions with holes based on a simplified 16IM are the same.

关键词： Topological relations spatial region with holes 16-intersection model(16IM) spatial analysis

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Stacked convolutional sparse auto-encoders for representation learning

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ACM Transactions on knowledge Discovery from data 2021年第2期15卷 31-31页

作者： Zhu, Yi Li, Lei Wu, Xindong School of Information Engineering Yangzhou University China School of Computer Science and Information Engineering Hefei University of Technology China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China Mininglamp Academy of Sciences Minininglamp Beijing 100084 China

Deep learning seeks to achieve excellent performance for representation learning in image datasets. However, supervised deep learning models such as convolutional neural networks require a large number of labeled image data, which is intractable in applications, while unsupervised deep learning models like stacked denoising auto-encoder cannot employ label information. Meanwhile, the redundancy of image data incurs performance degradation on representation learning for aforementioned models. To address these problems, we propose a semi-supervised deep learning framework called stacked convolutional sparse auto-encoder, which can learn robust and sparse representations from image data with fewer labeled data records. More specifically, the framework is constructed by stacking layers. In each layer, higher layer feature representations are generated by features of lower layers in a convolutional way with kernels learned by a sparse auto-encoder. Meanwhile, to solve the data redundance problem, the algorithm of Reconstruction Independent Component Analysis is designed to train on patches for sphering the input data. The label information is encoded using a Softmax Regression model for semi-supervised learning. With this framework, higher level representations are learned by layers mapping from image data. It can boost the performance of the base subsequent classifiers such as support vector machines. Extensive experiments demonstrate the superior classification performance of our framework compared to several state-of-the-art representation learning methods. © 2021 ACM.

关键词： Representation learning Sparse auto-encoder

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Weipeng Cao Xuyang Yao Zhiwu Xu Yinghui Pan Yixuan Sun Dachuan Li Bohua Qiu Muheng Wei Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model's capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods.

关键词： Visualization Adaptation models Semantics Zero shot learning Neural networks Termination of employment Transformers Market research Sun Optimization

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Balanced Tree Partitioning with Succinct Logic

Balanced Tree Partitioning with Succinct Logic

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IEEE International Conference on big knowledge (ICBK)

作者： Xindong Wu Shaojing Sheng Peng Zhou Key Laboratory of Knowledge Engineering with Big Data (Heifei Unversity of Technology) Ministry of Education Heifei China School of Compuster Science and Technology Anhui University Heifei China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

As a widely used data structure, graphs are good at characterizing data with internal associations, such as social and biological data. Tree structured data are special and are widely used in many real-world applications, such as organizational structure analysis and genealogical knowledge graph reasoning. For example, in kinship knowledge graph analysis, when a genealogical tree is particularly large (more than 25 levels and 45,000 nodes), it is a great challenge to partition this large tree into a specified number of subtrees with succinct logic and a balanced number of nodes. Therefore, in this paper, we propose the TPA (tree partitioning algorithm) algorithm to achieve a balanced and succinct logic partition of large-scale tree structured data. TPA first extracts all related nodes from a massive graph database and then constructs the convergent subgraph into a complete tree with a specified root node. Specifically, several virtual nodes are supplemented for generation-skipping connected nodes to achieve correct node numbering and partitioning. Finally, a graph partitioning algorithm is executed on the complete tree to obtain a specified number of subtrees with succinct logic and balanced node scales. Experiments conducted on four real-world datasets verify the effectiveness of our TPA algorithm.

关键词： Partitioning algorithms data visualization Convergence data analysis Distributed databases big data

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CT-based radiomics to predict development of macrovascular invasion in hepatocellular carcinoma:A multicenter study

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Hepatobiliary & Pancreatic Diseases International 2022年第4期21卷 325-333页

作者： Jing-Wei Wei Si-Rui Fu Jie Zhang Dong-Sheng Gu Xiao-Qun Li Xu-Dong Cheng Shuai-Tong Zhang Xiao-Fei He Jian-Feng Yan Li-Gong Lu Jie Tian Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijing 100190China Beijing Key Laboratory of Molecular Imaging Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China Zhuhai Interventional Medical Center Zhuhai Precision Medical CenterZhuhai People’s HospitalZhuhai Hospital of Jinan UniversityZhuhai 519000China Department of Radiology Zhuhai Precision Medical CenterZhuhai People’s HospitalZhuhai Hospital of Jinan UniversityZhuhai 519000China Department of Interventional Treatment Zhongshan City People’s HospitalZhongshan 528400China Department of Radiology Shenzhen People’s HospitalShenzhen 518000China Interventional Diagnosis and Treatment Department Nanfang HospitalSouthern Medical UniversityGuangzhou510000China Department of Radiology Yangjiang People’s HospitalYangjiang 529500China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine and EngineeringBeihang UniversityBeijing 100191China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi’an 710126China

Background:Macrovascular invasion(MaVI)occurs in nearly half of hepatocellular carcinoma(HCC)patients at diagnosis or during follow-up,which causes severe disease deterioration,and limits the possibility of surgical *** study aimed to investigate whether computed tomography(CT)-based radiomics analysis could help predict development of MaVI in ***:A cohort of 226 patients diagnosed with HCC was enrolled from 5 hospitals with complete MaVI and prognosis ***-based radiomics signature was built via multi-strategy machine learning ***,MaVI-related clinical factors and radiomics signature were integrated to construct the final prediction model(CRIM,clinical-radiomics integrated model)via random forest ***-regression analysis was used to select independent risk factors to predict the time of MaVI ***-Meier analysis was conducted to stratify patients according to the time of MaVI development,progression-free survival(PFS),and overall survival(OS)based on the selected risk ***:The radiomics signature showed significant improvement for MaVI prediction compared with conventional clinical/radiological predictors(P<0.001).CRIM could predict MaVI with satisfactory areas under the curve(AUC)of 0.986 and 0.979 in the training(n=154)and external validation(n=72)datasets,*** presented with excellent generalization with AUC of 0.956,1.000,and 1.000 in each external cohort that accepted disparate CT scanning protocol/***9_fos_InterquartileRange[hazard ratio(HR)=1.98;P<0.001]was selected as the independent risk *** cox-regression model successfully stratified patients into the high-risk and low-risk groups regarding the time of MaVI development(P<0.001),PFS(P<0.001)and OS(P=0.002).Conclusions:The CT-based quantitative radiomics analysis could enable high accuracy prediction of subsequent MaVI development in HCC with prognostic implications.

关键词： Hepatocellular carcinoma Macrovascular invasion Radiomics Computed tomography Prognosis

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The dynamics of three-dimensional chromatin organization and phase separation in cell fate transitions and diseases

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Cell Regeneration 2022年第1期11卷 427-448页

作者： Xiaoru Ling Xinyi Liu Shaoshuai Jiang Lili Fan Junjun Ding Advanced Medical Technology Center The First Affiliated HospitalZhongshan School of MedicineSun Yat-Sen UniversityGuangzhouGuangdongChina RNA Biomedical Institute Sun Yat-Sen Memorial HospitalZhongshan School of MedicineSun Yat-Sen UniversityGuangzhouGuangdongChina Center for Stem Cell Biology and Tissue Engineering Key Laboratory for Stem Cells and Tissue EngineeringMinistry of EducationZhongshan School of MedicineSun Yat-Sen UniversityGuangzhouGuangdongChina Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine School of Traditional Chinese MedicineJinan UniversityGuang-zhouGuangdongChina Department of Histology and Embryology School of Basic Medical SciencesGuangzhou Medical UniversityGuangzhou 511436China West China Biomedical Big Data Center West China HospitalSichuan UniversityChengdu 610041China

Cell fate transition is a fascinating process involving complex dynamics of three-dimensional(3D)chromatin organization and phase separation,which play an essential role in cell fate decision by regulating gene *** separation is increasingly being considered a driving force of chromatin *** this review,we have summarized the dynamic features of 3D chromatin and phase separation during physiological and pathological cell fate transitions and systematically analyzed recent evidence of phase separation facilitating the chromatin *** addition,we discuss current advances in understanding how phase separation contributes to physical and functional enhancerpromoter *** highlight the functional roles of 3D chromatin organization and phase separation in cell fate transitions,and more explorations are required to study the regulatory relationship between 3D chromatin organization and phase separation.

关键词： 3D chromatin organization Phase separation Cell fate transitions Disease

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