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National Science Review 2025年第4期 400-418页

作者： Fei Guo Renchu Guan Yaohang Li Qi Liu Xiaowo Wang Can Yang Jianxin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Xiangjiang Laboratory Key Laboratory for Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Department of Computer Science Old Dominion University School of Life Sciences and Technology Tongji University Department of Automation Tsinghua University Department of Mathematics State Key Laboratory of Molecular Neuroscience and Big Data Bio-Intelligence Lab The Hong Kong University of Science and Technology

With the adoption of foundation models(FMs),artificial intelligence(AI) has become increasingly significant in bioinformatics and has successfully addressed many historical challenges,such as pre-training frameworks,model evaluation and *** demonstrate notable proficiency in managing large-scale,unlabeled datasets,because experimental procedures are costly and labor *** various downstream tasks,FMs have consistently achieved noteworthy results,demonstrating high levels of accuracy in representing biological entities.A new era in computational biology has been ushered in by the application of FMs,focusing on both general and specific biological *** this review,we introduce recent advancements in bioinformatics FMs employed in a variety of downstream tasks,including genomics,transcriptomics,proteomics,drug discovery and single-cell *** aim is to assist scientists in selecting appropriate FMs in bioinformatics,according to four model types:language FMs,vision FMs,graph FMs and multimodal *** addition to understanding molecular landscapes,AI technology can establish the theoretical and practical foundation for continued innovation in molecular biology.

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Learning transformer-based heterogeneously salient graph representation for multimodal remote sensing image classification

arXiv

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arXiv 2023年

作者： Yang, Jiaqi Du, Bo Zhang, Liangpei State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China National Engineering Research Center for Multimedia Software Wuhan University Wuhan China Institute of Artificial Intelligence Wuhan University Wuhan China School of Computer Science Wuhan University Wuhan China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China

Data collected by different modalities can provide a wealth of complementary information, such as hyperspectral image (HSI) to offer rich spectral-spatial properties, synthetic aperture radar (SAR) to provide structural information about the Earth's surface, and light detection and ranging (LiDAR) to cover altitude information about ground elevation. Therefore, a natural idea is to combine multimodal images for refined and accurate land-cover interpretation. Although many efforts have been attempted to achieve multi-source remote sensing image classification, there are still three issues as follows: 1) indiscriminate feature representation without sufficiently considering modal heterogeneity, 2) abundant features and complex computations associated with modeling long-range dependencies, and 3) overfitting phenomenon caused by sparsely labeled samples. To overcome the above barriers, a transformer-based heterogeneously salient graph representation (THSGR) approach is proposed in this paper. First, a multimodal heterogeneous graph encoder is presented to encode distinctively non-Euclidean structural features from heterogeneous data. Then, a self-attention-free multi-convolutional modulator is designed for effective and efficient long-term dependency modeling. Finally, a mean forward is put forward in order to avoid overfitting. Based on the above structures, the proposed model is able to break through modal gaps to obtain differentiated graph representation with competitive time cost, even for a small fraction of training samples. Experiments and analyses on three benchmark datasets with various state-of-the-art (SOTA) methods show the performance of the proposed approach. Copyright © 2023, The Authors. All rights reserved.

关键词： Synthetic aperture radar

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Near Optimal Hybrid Digital-Analog Beamforming for mmWave Point-to-Point MIMO Transmissions Using OTFS Waveforms

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IEEE Transactions on Communications 2025年

作者： Liu, Mengmeng Li, Shuangyang Wei, Zhiqiang Bai, Baoming Caire, Giuseppe Ng, Derrick Wing Kwan Xidian University State Key Lab. of ISN Xi'an710071 China Technische Universität Berlin Electrical Engineering and Computer Science Department Berlin10587 Germany Xi'an Jiaotong University School of Mathematics and Statistics Xi'an710049 China Peng Cheng Laboratory Guangdong Shenzhen518055 China Guangdong Guangzhou510555 China University of New South Wales School of Electrical Engineering and Telecommunications SydneyNSW2052 Australia

In this paper, a point-to-point (P2P) orthogonal time frequency space (OTFS)-based multiple-input multiple-output (MIMO-OTFS) transmission scheme is devised for millimeter wave (mmWave) channels. The proposed transmission scheme relies on a low-complexity hybrid digital-analog beamforming (HBF) scheme that exploits the delay-Doppler (DD) domain channel properties, where detailed design criteria for different channel conditions are presented, including the case where paths are indistinguishable by angles. Thanks to the proposed HBF scheme, approximate path-wise interference-free transmission of multiple data streams is achieved, and consequently, only little pre-equalization is required for combating the residual channel impairments. The achievable rate of the proposed scheme is studied and compared with the orthogonal frequency-division multiplexing (OFDM) counterpart. In particular, we unveil that the condition number of the effective angular domain matrix for OTFS is smaller than that for OFDM, due to the enhanced path separability in the DD domain. As a result, the proposed MIMO-OTFS transmission scheme demonstrates superior performance over the MIMO-OFDM transmission scheme. Our numerical results corroborate our theoretical analysis and show a near-optimal rate performance with significantly reduced complexity compared to the optimal singular value decomposition (SVD) precoding method. © 1972-2012 IEEE.

关键词： Orthogonal frequency division multiplexing

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Challenges and countermeasures of interaction in autonomous vehicles

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Science China(Information Sciences) 2019年第5期62卷 5-7页

作者： Deyi LI Meng LIU Fei ZHAO Yu LIU State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang University Chinese Academy of Engineering School of Transportation Science and Engineering Beihang University

Autonomous vehicles can bring changes into ways of traveling, traffic, and even production modes [1].At present, the majority of researches [2] on autonomous vehicles are limited in perception, planning, decision-making and other calculation front,while interactive cognitive perspective is barely looked into. The interactive sector involved in self-driving is rather sophisticated [3]. However,

关键词： Challenges and countermeasures of interaction in autonomous vehicles

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Cluster-guided Contrastive Class-imbalanced Graph Classification

arXiv

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arXiv 2024年

作者： Ju, Wei Mao, Zhengyang Yi, Siyu Qin, Yifang Gu, Yiyang Xiao, Zhiping Shen, Jianhao Qiao, Ziyue Zhang, Ming College of Computer Science Sichuan University Chengdu China School of Computer Science State Key Laboratory for Multimedia Information Processing PKU-Anker LLM Lab Peking University Beijing China College of Mathematics Sichuan University Chengdu China Paul G. Allen School of Computer Science and Engineering University of Washington SeattleWA United States Huawei Hisilicon Shanghai China School of Computing and Information Technology Great Bay University Dongguan China

This paper studies the problem of class-imbalanced graph classification, which aims at effectively classifying the categories of graphs in scenarios with imbalanced class distribution. Despite the tremendous success of graph neural networks (GNNs), their modeling ability for imbalanced graph-structured data is inadequate, which typically leads to predictions biased towards the majority classes. Besides, existing class-imbalanced learning methods in visions may overlook the rich graph semantic substructures of the majority classes and excessively emphasize learning from the minority classes. To tackle this issue, this paper proposes a simple yet powerful approach called C3GNN that incorporates the idea of clustering into contrastive learning to enhance class-imbalanced graph classification. Technically, C3GNN clusters graphs from each majority class into multiple subclasses, ensuring they have similar sizes to the minority class, thus alleviating class imbalance. Additionally, it utilizes the Mixup technique to synthesize new samples and enrich the semantic information of each subclass, and leverages supervised contrastive learning to hierarchically learn effective graph representations. In this way, we can not only sufficiently explore the semantic substructures within the majority class but also effectively alleviate excessive focus on the minority class. Extensive experiments on real-world graph benchmark datasets verify the superior performance of our proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Federated Learning for Recognizing Private Handwritten Numbers

Federated Learning for Recognizing Private Handwritten Numbe...

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2021 IEEE International Conference on Data Science and computer Application, ICDSCA 2021

作者： Wang, Di Jiang, Yuchen Zhao, Yibo Chen, Panyang Li, Ang Pan, Kun Institution of Information Engineering Chinese Academy of Sciences State Key Laboratory of Information Security Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Computer Science and Technology College of Computer Science Chongqing University Chongqing China Zibo Shiyan High School International Division Zibo Shandong China Computer Science Department of Computer Science and Software Engineering Auburn University Auburn United States Shanghai Shangde Experimental School Shanghai China School of Computing University of Leeds Leeds United Kingdom

ISBN: (纸本)9781665440530

Federated learning (FL) is a new technology in the field of machine learning, which trains an algorithm across multiple decentralized edge devices or servers holding data samples, without exchanging their data samples. Horizontal Federated Learning (HFL) is a FL method focusing on using the same feature space across all users, which is suitable for situations where users' data feature dimensions overlap more. Because FL has the advantage of privacy protection in the field of manufacturing, we propose an HFL-based deep learning method named FedCNN, which realizes the digits handwritten recognition without violating the privacy or data sharing. Three different methods (local model, centralized model, and HFL-based FedCNN) are used on a public data set (MINIST) to compare the pros and cons of the solutions. The experimental results demonstrate that FedCNN scheme can simultaneously achieve practical privacy preservation and good model generalization performance with Homomorphic Encryption. © 2021 IEEE.

关键词： Data privacy

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A genetic programming method for classifier construction and cost learning in high-dimensional unbalanced classification 20

A genetic programming method for classifier construction and...

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2020 Genetic and Evolutionary Computation Conference, GECCO 2020

作者： Pei, Wenbin Xue, Bing Shang, Lin Zhang, Mengjie School of Engineering and Computer Science Victoria University of Wellington New Zealand State Key Laboratory for Novel Software Technology Nanjing University China

ISBN: (纸本)9781450371278

Cost-sensitive learning has been widely used to address the problem of class imbalance. However, cost matrices are often manually designed. In many real-world applications, cost values are often unknown because of the limited domain knowledge. This paper proposes a new genetic programming method to construct cost-sensitive classifiers, which do not require the manually designed cost values. The experimental results show that the proposed method often outperforms existing GP methods. © 2020 Owner/Author.

关键词： Genetic programming

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Representation Learning for Compressed Video Action Recognition via Attentive Cross-modal Interaction with Motion Enhancement

arXiv

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arXiv 2022年

作者： Li, Bing Chen, Jiaxin Zhang, Dongming Bao, Xiuguo Huang, Di State Key Laboratory of Software Development Environment Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China

Compressed video action recognition has recently drawn growing attention, since it remarkably reduces the storage and computational cost via replacing raw videos by sparsely sampled RGB frames and compressed motion cues (e.g., motion vectors and residuals). However, this task severely suffers from the coarse and noisy dynamics and the insufficient fusion of the heterogeneous RGB and motion modalities. To address the two issues above, this paper proposes a novel framework, namely Attentive Cross-modal Interaction Network with Motion Enhancement (MEACI-Net). It follows the two-stream architecture, i.e. one for the RGB modality and the other for the motion modality. Particularly, the motion stream employs a multi-scale block embedded with a denoising module to enhance representation learning. The interaction between the two streams is then strengthened by introducing the Selective Motion Complement (SMC) and Cross-Modality Augment (CMA) modules, where SMC complements the RGB modality with spatio-temporally attentive local motion features and CMA further combines the two modalities with selective feature augmentation. Extensive experiments on the UCF-101, HMDB-51 and Kinetics-400 benchmarks demonstrate the effectiveness and efficiency of MEACI-Net. © 2022, CC BY.

关键词： Image compression

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Integral Inequalities for the Analysis of Distributed Parameter Systems: A Complete Characterization via the Least-Squares Principle

Integral Inequalities for the Analysis of Distributed Parame...

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American Control Conference (ACC)

作者： Qian Feng Alexandre Seuret Sing Kiong Nguang Feng Xiao School of Control and Computer Engineering North China Electric Power University Beijing China Dpto de Ingeniería de Sistemas y Automática Universidad de Sevilla Camino de los Descubrimientos Sevilla Spain Department of Electrical Computer and Software Engineering The University of Auckland New Zealand State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

A wide variety of integral inequalities (IIs) have been developed and studied for the stability analysis of distributed parameter systems using the Lyapunov functional approach. However, no unified mathematical framework has been proposed that could characterize the similarity and connection between these IIs, as most of them was introduced in a dispersed manner for the analysis of specific types of systems. Additionally, the extent to which the generality of these IIs can be expanded and the optimality of their lower bounds (LBs) remains open questions. In this study, we introduce two general classes of IIs that can generalize nearly all IIs in the literature. The integral kernels of the LBs of our IIs can contain an unlimited number of weighted £2 functions that are linearly independent in a Lebesgue sense. Moreover, we not only estalish the equivalence relations between the LBs of our IIs, but also demonstrate that these LBs are guaranteed by the least squares principle, implying asymptotic convergence to the upper bound when the kernels functions constitutes a Schauder basis of the underlying Hilbert space. Owing to their general structures, our IIs are applicable in a variety of contexts, such as the stability analysis of coupled PDE-ODE systems or cybernetic systems with delay structures.

关键词： Upper bound Integral equations Stability analysis Hilbert space Linear matrix inequalities Delays Kernel

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The neutron transport equation in exact differential form

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Science China(Physics,Mechanics & Astronomy) 2025年第7期Mechanics & Astronomy) .卷 87-107页

作者： Dong Liu Yang Liu Haoning Dang Kai Wang Bin Zhang Fei Wang Zhouyu Liu Yong Jiang Nuclear Power Institute of China State Key Laboratory of Advanced Nuclear Energy Technology College of Computer Science (School of Software and School of Artificial Intelligence) Sichuan University Zhejiang Institute of Modern Physics School of PhysicsZhejiang University School of Mathematics and Statistics Xi'an Jiaotong University School of Energy and Power Engineering Xi'an Jiaotong University

Derived from the Boltzmann equation,the neutron transport equation describes the motions and interactions of neutrons with nuclei in nuclear devices such as nuclear *** collision or fission effect are described as integral terms which arrive in an integro-differential neutron transport equation(IDNT).Only for mono-material or simple geometries conditions,elegant approximation can simplify the transport equation to provide analytic *** solve this integro-differential equation becomes a practical engineering *** development of deep-learning techniques provides a new approach to solve them but for some complicated conditions,it is also time *** optimize solving the integro-differential equation particularly under the deep-learning method,we propose to convert the integral terms in the integro-differential neutron transport equation into their corresponding antiderivatives,providing a set of fixed solution constraint conditions for these antiderivatives,thus yielding an exact differential neutron transport equation(EDNT).The paper elucidates the physical meaning of the antiderivatives and analyzes the continuity and computational complexity of the new transport equation *** illustrate the significant advantage of ENDT,numerical validations have been conducted using various numerical methods on typical benchmark *** numerical experiments demonstrate that the EDNT is compatible with various numerical methods,including the finite difference method(FDM),finite volume method(FVM),and *** to the IDNT,the EDNT offers significant efficiency advantages,with reductions in computational time ranging from several times to several orders of *** EDNT approach may also be applicable for other integro-differential transport theories such as radiative energy transport and has potential application in astrophysics or other fields.

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