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arXiv 2021年

作者： Cao, Congqi Lu, Yue Zhang, Yifan Jiang, Dongmei Zhang, Yanning Natl. Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Lab on Speech and Image Information Processing School of Computer Science Northwestern Poly-technical University Xi'an710129 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing100190 China

Contextual information plays an important role in action recognition. Local operations have difficulty to model the relation between two elements with a long-distance interval. However, directly modeling the contextual information between any two points brings huge cost in computation and memory, especially for action recognition, where there is an additional temporal dimension. Inspired from 2D criss-cross attention used in segmentation task, we propose a recurrent 3D criss-cross attention (RCCA-3D) module to model the dense long-range spatiotemporal contextual information in video for action recognition. The global context is factorized into sparse relation maps. We model the relationship between points in the same line along the direction of horizon, vertical and depth at each time, which forms a 3D criss-cross structure, and duplicate the same operation with recurrent mechanism to transmit the relation between points in a line to a plane finally to the whole spatiotemporal space. Compared with the non-local method, the proposed RCCA-3D module reduces the number of parameters and FLOPs by 25% and 30% for video context modeling. We evaluate the performance of RCCA-3D with two latest action recognition networks on three datasets and make a thorough analysis of the architecture, obtaining the optimal way to factorize and fuse the relation maps. Comparisons with other state-of-the-art methods demonstrate the effectiveness and efficiency of our model. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Inductive Subgraph Embedding for Link Prediction

arXiv

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arXiv 2021年

作者： Miao, Chunyu Xie, Chenxuan Zhou, Jiajun Yu, Shanqing Chen, Lina Xuan, Qi The College of Mathematics Physics and Information Engineering Zhejiang Normal University Zhejiang Jinhua310023 China The Key Laboratory of Peace-building Big Data of Zhejiang Province Hangzhou310051 China The Institute of Cyberspace Security Zhejiang University of Technology Hangzhou310023 China The Binjiang Cyberspace Security Institute of ZJUT Hangzhou310023 China

Link prediction, which aims to infer missing edges or predict future edges based on currently observed graph connections, has emerged as a powerful technique for diverse applications such as recommendation, relation completion, etc. While there is rich literature on link prediction based on node representation learning, direct link embedding is relatively less studied and less understood. One common practice in previous work characterizes a link by manipulate the embeddings of its incident node pairs, which is not capable of capturing effective link features. Moreover, common link prediction methods such as random walks and graph auto-encoder usually rely on full-graph training, suffering from poor scalability and high resource consumption on large-scale graphs. In this paper, we propose Inductive Subgraph Embedding for Link Prediciton (SE4LP) - an end-to-end scalable representation learning framework for link prediction, which utilizes the strong correlation between central links and their neighborhood subgraphs to characterize links. We sample the "link-centric induced subgraphs" as input, with a subgraph-level contrastive discrimination as pretext task, to learn the intrinsic and structural link features via subgraph classification. Extensive experiments on five datasets demonstrate that SE4LP has significant superiority in link prediction in terms of performance and scalability, when compared with state-of-the-art methods. Moreover, further analysis demonstrate that introducing self-supervision in link prediction can significantly reduce the dependence on training data and improve the generalization and scalability of model. The source code will be available online. Copyright © 2021, The Authors. All rights reserved.

关键词： Graph neural networks

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An optimized capsule-LSTM model for facial expression recognition with video sequences

arXiv

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arXiv 2021年

作者： Liu, Siwei Long, Yuanpeng Xu, Gao Yang, Lijia Xu, Shimei Yao, Xiaoming Shu, Kunxian School of Computer Science and Technology Chongqing Key Laboratory on Big Data for Bio Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China School of Economic Information Engineering Southwestern University of Finance and Economics Chengdu611130 China *** Hetie International Square Building 2 Room404 Sichuan Chengdu China

To overcome the limitations of convolutional neural network in the process of facial expression recognition, a facial expression recognition model Capsule-LSTM based on video frame sequence is proposed. This model is composed of three networks includingcapsule encoders, capsule decoders and LSTM network. The capsule encoder extracts the spatial information of facial expressions in video frames. Capsule decoder reconstructs the images to optimize the network. LSTM extracts the temporal information between video frames and analyzes the differences in expression changes between frames. The experimental results from the MMI dataset show that the Capsule-LSTM model proposed in this paper can effectively improve the accuracy of video expression recognition. © 2021, CC BY.

关键词： Long short-term memory

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Adaptive scale model reconstruction for radio synthesis imaging

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Research in Astronomy and Astrophysics 2021年第3期21卷 119-129页

作者：张利g Li-Gong Mi Long Xu Ming Zhang Dan-Yang Li Xiang Liu Feng Wang Yi-Fan Xiao Zhong-Zu Wu College of Big Data and Information Engineering Guizhou University Key Laboratory of Solar Activity National Astronomical Observatories Chinese Academy of Sciences Xinjiang Astronomical Observatory Chinese Academy of Sciences Key Laboratory of Radio Astronomy Chinese Academy of Sciences Center for Astrophysics Guangzhou University College of Physics Guizhou University

A sky model from CLEAN deconvolution is a particularly effective high dynamic range reconstruction in radio astronomy,which can effectively model the sky and remove the sidelobes of the point spread function(PSF) caused by incomplete sampling in the spatial frequency *** to scale-free and multi-scale sky models,adaptive-scale sky modeling,which can model both compact and diffuse features,has been proven to have better sky modeling capabilities in narrowband simulated data,especially for large-scale features in high-sensitivity observations which are exactly one of the challenges of data processing for the Square Kilometre Array(SKA).However,adaptive scale CLEAN algorithms have not been verified by real observation data and allow negative components in the *** this paper,we propose an adaptive scale model algorithm with non-negative constraint and wideband imaging capacities,and it is applied to simulated SKA data and real observation data from the Karl *** Very Large Array(JVLA),an SKA *** show that the new algorithm can reconstruct more physical models with rich *** work is a step forward for future SKA image reconstruction and developing SKA imaging pipelines.

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Interpretable Multimodal Learning for Tumor Protein-Metal Binding: Progress, Challenges, and Perspectives

arXiv

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arXiv 2025年

作者： Liu, Xiaokun Rastegari, Sayedmohammadreza Huang, Yijun Cheong, Sxe Chang Liu, Weikang Zhao, Wenjie Tian, Qihao Wang, Hongming Zhou, Shuo Guo, Yingjie Tabakhi, Sina Liu, Xianyuan Zhu, Zheqing Sang, Wei Lu, Haiping Institute of Big Data Science and Industry Shanxi University Taiyuan China School of Computer and Information Technology Shanxi University Taiyuan China Key Laboratory of Evolutionary Science Intelligence of Shanxi Province Shanxi Taiyuan China Faculty of Computer Engineering University of Isfahan Isfahan Iran The First Clinical Medical School Shanxi Medical University Taiyuan China School of Medicine & Population Health University of Sheffield Sheffield United Kingdom School of Computer Science University of Sheffield Sheffield United Kingdom Centre for Machine Intelligence University of Sheffield Sheffield United Kingdom Department of Biochemistry and Molecular Biology School of Basic Medical Sciences Shanxi Medical University Taiyuan China Institute of Medical Technology Shanxi Medical University Taiyuan China

In cancer therapeutics, protein-metal binding mechanisms critically govern drug pharmacokinetics and targeting efficacy, thereby fundamentally shaping the rational design of anticancer metallodrugs. While conventional laboratory methods used to study such mechanisms are often costly, low throughput, and limited in capturing dynamic biological processes, machine learning (ML) has emerged as a promising alternative. Despite increasing efforts to develop protein–metal binding datasets and ML algorithms, the application of ML in tumor protein-metal binding remains limited. key challenges include a shortage of high-quality, tumor-specific datasets, insufficient consideration of multiple data modalities, and the complexity of interpreting results due to the "black box" nature of complex ML models. This paper summarizes recent progress and ongoing challenges in using ML to predict tumor protein-metal binding, focusing on data, modeling, and interpretability. We present multimodal protein-metal binding datasets and outline strategies for acquiring, curating, and preprocessing them for training ML models. Moreover, we explore the complementary value provided by different data modalities and examine methods for their integration. We also review approaches for improving model interpretability to support more trustworthy decisions in cancer research. Finally, we offer our perspective on research opportunities and propose strategies to address the scarcity of tumor protein data and the limited number of predictive models for tumor protein–metal binding. We also highlight two promising directions for effective metal-based drug design: integrating protein–protein interaction data to provide structural insights into metal-binding events and predicting structural changes in tumor proteins after metal binding. Copyright © 2025, The Authors. All rights reserved.

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A review of progress in coupled ocean-atmosphere model developments for ENSO studies in China

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Journal of Oceanology and Limnology 2020年第4期38卷 930-961页

作者： ZHANG Rong-Hua YU Yongqiang SONG Zhenya REN Hong-Li TANG Youmin QIAO Fangli WU Tongwen GAO Chuan HU Junya TIAN Feng ZHU Yuchao CHEN Lin LIU Hailong LIN Pengfei WU Fanghua WANG Lin Key Laboratory of Ocean Circulation and Waves Institute of Oceanologyand Center for Ocean Mega-ScienceChinese Academy of SciencesQingdao 266071China Laboratory for Ocean and Climate Dynamics Pilot National Laboratory for Marine Science and TechnologyQingdao 266237China Center for Excellence in Quaternary Science and Global Change Chinese Academy of SciencesXi’an 710061China University of Chinese Academy of Sciences Beijing 100049China LASG Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100049China First Institute of Oceanography Ministry of Natural ResourcesQingdao 266061China Laboratory for Regional Oceanography and Numerical Modeling Pilot National Laboratory for Marine Science and TechnologyQingdao 266237China Key Laboratory of Marine Science and Numerical Modeling(MASNUM) Ministry of Natural ResourcesQingdao 266061China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Qingdao 266061China State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesBeijing 100081China Laboratory for Climate Studies and CMA-NJU Joint Laboratory for Climate Prediction Studies National Climate CenterChina Meteorological AdministrationBeijing 100081China Environmental Science and Engineering University of Northern British ColumbiaBritish Columbia V2N 4Z9Canada State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of OceanographyMinistry of Natural ResourcesHangzhou 310012China Key Laboratory of Meteorological Disaster Ministry of Education(KLME)and Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD)Nanjing University of Information Science and TechnologyNanjing 210044China Beijing Climate Center China Meteorological AdministrationBeijing 100081China

El Nino-Southern Oscillation(ENSO) is the strongest interannual signal that is producedby basinscale processes in the tropical Pacific,with significant effects on weather and climate *** the past,extensive and intensive international efforts have been devoted to coupled model developments for ENSO studies.A hierarchy of coupled ocean-atmo sphere models has been formulated;in terms of their complexity,they can be categorized into intermediate coupled models(ICMs),hybrid coupled models(HCMs),and fully coupled general circulation models(CGCMs).ENSO modeling has made significant progress over the past decades,reaching a stage where coupled models can now be used to successfully predict ENSO events 6 months to one year in ***,ENSO exhibits great diversity and complexity as observed in nature,which still cannot be adequately captured by current state-of-the-art coupled models,presenting a challenge to ENSO *** primarily reviewed the long-term efforts in ENSO modeling continually and steadily made at different institutions in China;some selected representative examples are presented here to review the current status of ENSO model developments and applications,which have been actively pursued with noticeable progress being made *** ENSO simulations are very sensitive to model formulations and process representations etc.,dedicated efforts have been devoted to ENSO model developments and ***,different ocean-atmosphere coupled models have been available in China,which exhibit good model performances and have already had a variety of applications to climate modeling,including the Coupled Model Intercomparison Project Phase 6(CMIP6).Nevertheless,large biases and uncertainties still exist in ENSO simulations and predictions,and there are clear rooms for their improvements,which are still an active area of researches and ***,model performances of ENSO simulations are assessed in terms of advantages and disadvantage

关键词： El Niño-Southern Oscillation(ENSO) coupled ocean-atmosphere models simulations and predictions model biases and uncertainties

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Application of convolutional neural networks to large-scale naphtha pyrolysis kinetic modeling

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Chinese Journal of Chemical engineering 2018年第12期26卷 2562-2572页

作者： Feng Hua Zhou Fang Tong Qiu Department of Chemical Engineering Tsinghua University Beijing 100084China Beijing Key Laboratory of Industrial Big Data System and Application Beijing 100084China

System design and optimization problems require large-scale chemical kinetic models. Pure kinetic models of naphtha pyrolysis need to solve a complete set of stiff ODEs and is therefore too computational expensive. On the other hand, artificial neural networks that completely neglect the topology of the reaction networks often have poor generalization. In this paper, a framework is proposed for learning local representations from largescale chemical reaction networks. At first, the features of naphtha pyrolysis reactions are extracted by applying complex network characterization methods. The selected features are then used as inputs in convolutional architectures. Different CNN models are established and compared to optimize the neural network *** the pre-training and fine-tuning step, the ultimate CNN model reduces the computational cost of the previous kinetic model by over 300 times and predicts the yields of main products with the average error of less than 3%. The obtained results demonstrate the high efficiency of the proposed framework.

关键词： Convolutional neural network Network motif Naphtha pyrolysis Kinetic modeling

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PatentNet: A large-scale incomplete multiview, multimodal, multilabel industrial goods image database

arXiv

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arXiv 2021年

作者： Lei, Fangyuan Huang, Da Jiang, Jianjian Ma, Ruijun Wang, Senhong Cao, Jiangzhong Lin, Yusen Dai, Qingyun Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou510665 China School of Electronic and Information Guangdong Polytechnic Normal University Guangzhou510665 China Guangdong Industrial Training Center Guangdong Polytechnic Normal University Guangzhou510665 China School of Information Engineering Guangdong University of Technology Guangzhou510006 China

In deep learning area, large-scale image datasets bring a breakthrough in the success of object recognition and retrieval. Nowadays, as the embodiment of innovation, the diversity of the industrial goods is significantly larger, in which the incomplete multiview, multimodal and multilabel are different from the traditional dataset. In this paper, we introduce an industrial goods dataset, namely PatentNet, with numerous highly diverse, accurate and detailed annotations of industrial goods images, and corresponding texts. In PatentNet, the images and texts are sourced from design patent. Within over 6M images and corresponding texts of industrial goods labeled manually checked by professionals, PatentNet is the first ongoing industrial goods image database whose varieties are wider than industrial goods datasets used previously for benchmarking. PatentNet organizes millions of images into 32 classes and 219 subclasses based on the Locarno Classification Agreement. Through extensive experiments on image classification, image retrieval and incomplete multiview clustering, we demonstrate that our PatentNet is much more diverse, complex, and challenging, enjoying higher potentials than existing industrial image datasets. Furthermore, the characteristics of incomplete multiview, multimodal and multilabel in PatentNet are able to offer unparalleled opportunities in the artificial intelligence community and beyond. © 2021, CC BY.

关键词： Object recognition

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Correction: Copper particle-free ink with enhanced performance for inkjet-printed flexible UWB antennas

引用

Journal of Materials Chemistry C 2023年第44期11卷 15774-15774页

作者： Wendong Yang Zhichao Dong Zihao Guo Haoqiang Sun School of Electronic and Information Engineering Liaoning Technical University No. 188 Longwan South Street Huludao City 125105 Liaoning China Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Liaoning Technical University Huludao City 125105 Liaoning China

Correction for ‘Copper particle-free ink with enhanced performance for inkjet-printed flexible UWB antennas’ by Wendong Yang et al., J. Mater. Chem. C, 2023, 11, 14429–14438, https://***/10.1039/D3TC02515B.

Correction for ‘Copper particle-free ink with enhanced performance for inkjet-printed flexible UWB antennas’ by Wendong Yang et al., J. Mater. Chem. C, 2023, 11, 14429–14438, https://***/10.1039/D3TC02515B.

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An Asynchronous Clustering and Mobile data Gathering Schema Based on Timer Mechanism in Wireless Sensor Networks

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Computers, Materials & Continua 2019年第3期58卷 711-725页

作者： Jin Wang Yu Gao Wei Liu Wenbing Wu Se-Jung Lim Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer&Communication EngineeringChangsha University of Science&TechnologyChangsha410114China School of Information Science and Engineering Fujian University of TechnologyFuzhou350000China College of Information Engineering Yangzhou UniversityYangzhou225000China Department of Computer Engineering Chonnam National UniversityYeosu596597Korea.

Recently,Wireless sensor networks(WSNs)have become very popular research topics which are applied to many *** provide pervasive computing services and techniques in various potential applications for the Internet of Things(IoT).An Asynchronous Clustering and Mobile data Gathering based on Timer Mechanism(ACMDGTM)algorithm is proposed which would mitigate the problem of“hot spots”among sensors to enhance the lifetime of *** clustering process takes sensors’location and residual energy into consideration to elect suitable cluster ***,one mobile sink node is employed to access cluster heads in accordance with the data overflow time and moving time from cluster heads to *** experimental results display that the presented method can avoid long distance communicate between sensor ***,this algorithm reduces energy consumption effectively and improves package delivery rate.

关键词： Internet of things wireless sensor networks clustering mobile data collection timer.

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