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International Conference on computer Supported Cooperative Work in Design

作者： Yiqi Wu Jiale He Xiantao Cai Dejun Zhang Changliang Li Yilin Chen School of Computer Science China University of Geosciences Wuhan China School of Computer Science Wuhan University Wuhan China The Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Wuhan China Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

In the field of autonomous driving and intelligent transportation systems, 3D object detection plays a critical role in ensuring safe and efficient driving. Achieving reliable object detection relies on robust sensing technologies, where LiDAR provides accurate spatial perception, and radar offers extended detection range and speed information because of its longer wave-lengths. To capitalize on the strengths of both sensors, a novel RCS-based LiDAR-Radar fusion network, named RLRFusion, is proposed for 3D object detection in this paper. The network takes LiDAR and radar point clouds as inputs and processes them through dual bird's-eye view (BEV) feature extraction streams, followed by the BEV fusion and detection module to produce the detection results. In input-level fusion, a cross-modal pillar encoder is introduced to address the sparse radar data and its lack of height information. In feature-level fusion, an RCS-aware fusion encoder leverages the Radar Cross Section (RCS) distribution by mapping pillar features to their surroundings, enhancing object size estimation and mitigating the challenges faced by LiDAR in adverse weather conditions. Experimental results show that RLRFusion achieves competitive performance on the nuScenes dataset, with strong detection results even in rainy conditions. The source code of our method is available at: https://***/djZzgroupIRLRFusion.

关键词： Radar cross-sections Three-dimensional displays Laser radar Accuracy Radar detection Object detection Robustness Sensors Streams Meteorology

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Multispectral and Hyperspectral Image Fusion Via Joint Low-Rank and Smooth Tensor Prior

Multispectral and Hyperspectral Image Fusion Via Joint Low-R...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Fan Li Yong Deng Shaoquan Zhang Yuyun Liang Hong Wang Chengzhi Deng Jun Li School of Computer Science Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Wuhan China School of Information Engineering Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang Institute of Technology Nanchang China Changjiang Institute of Survey Planning Design and Research Wuhan China TidBit Global Propell AI Norway

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Multispectral and hyperspectral image fusion has emerged as a highly effective technique for obtaining images with both high spatial and spectral resolution. This is an ill-posed problem that poses significant challenges to the optimization solution, which is often mitigated by incorporating low-rank and smooth priors to restrict the solution space. Traditionally, these priors are combined additively using nuclear norm and total variation (TV) regularization. However, the intricate interactions between these priors make it difficult to accurately characterize the prior structure using an additive approach. Moreover, their influence is heavily dependent on the trade-off parameter between the regularization terms. To address this issue, we propose a novel fusion method leveraging a joint low-rank and smooth tensor prior (LRST). The LRST method introduces a tensor nuclear norm on the gradient maps of various dimensions of the target image capitalizing on the analogous manifold structures shared between the original image and its gradient map, which seamlessly integrates the two priors into a unified regularization framework. This facilitates the precise and convenient exploitation of spatial and spectral correlations inherent in the desired hyperspectral image. Experimental findings demonstrate that compared to state-of-the-art fusion methods, the LRST approach yields finely fused images.

关键词： Manifolds Tensors TV Correlation Additives Geoscience and remote sensing Spatial resolution

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TRAIL: Trust-Aware Client Scheduling for Semi-Decentralized Federated Learning

arXiv

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arXiv 2024年

作者： Hu, Gangqiang Lu, Jianfeng Han, Jianmin Cao, Shuqin Liu, Jing Fu, Hao School of Computer Science and Technology Zhejiang Normal University China School of Computer Science and Technology Wuhan University of Science and Technology China Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology Ministry of Education China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan Universityof Science and Technology China

Due to the sensitivity of data, Federated Learning (FL) is employed to enable distributed machine learning while safeguarding data privacy and accommodating the requirements of various devices. However, in the context of semidecentralized FL, clients' communication and training states are dynamic. This variability arises from local training fluctuations, heterogeneous data distributions, and intermittent client participation. Most existing studies primarily focus on stable client states, neglecting the dynamic challenges inherent in real-world scenarios. To tackle this issue, we propose a TRust-Aware clIent scheduLing mechanism called TRAIL, which assesses client states and contributions, enhancing model training efficiency through selective client participation. We focus on a semi-decentralized FL framework where edge servers and clients train a shared global model using unreliable intra-cluster model aggregation and inter-cluster model consensus. First, we propose an adaptive hidden semi- Markov model to estimate clients' communication states and contributions. Next, we address a client-server association optimization problem to minimize global training loss. Using convergence analysis, we propose a greedy client scheduling algorithm. Finally, our experiments conducted on real-world datasets demonstrate that TRAIL outperforms state-of-the-art baselines, achieving an improvement of 8.7% in test accuracy and a reduction of 15.3% in training loss. © 2024, CC BY.

关键词： Federated learning

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Computational modeling of binding site for RNA-ligand complex by learning Multi-modal features

TechRxiv

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TechRxiv 2023年

作者： Wang, Junkai Quan, Lijun Jin, Zhi Wu, Hongjie Ma, Xuhao Wang, Xuejiao Xie, Jingxin Pan, Deng Chen, Taoning Wu, Tingfang Lyu, Qiang School of Computer Science and Technology of Soochow University Jiangsu215006 China Province Key Lab for Information Processing Technologies Soochow University Jiangsu215006 China Collaborative Innovation Center of Novel Software Technology and Industrialization Jiangsu210000 China School of Electronic and Information Engineering Suzhou University of Science and Technology Jiangsu215009 China

This study aims to address the complexity of predicting RNA-small molecule binding sites to explore the potential value in the field of RNA drug targets. To address this challenge, we propose the MultiModRLBP method, which integrates multi-modal features using deep learning algorithms. These features include 3D structural properties at the nucleotide base level, relational graphs based on overall RNA structure, and semantically rich RNA sequence information. Different deep modules are employed to learn high-dimensional different modility of semantic features. RNAbert is fine-tuned to learn sequence information, relational graph convolutional network focuses on capturing various relationships between different nodes in the RNA graph, and CNN directly and indirectly captures local and global 3D structural features. Consequently, MultiModRLBP can better reflect the inherent complex relationships within RNA. Furthermore, we expanded the dataset to include RNA complexes with the same RNA sequences but varying structures, enabling the model to better grasp subtle changes at the structural level. The research results demonstrate that the performance of MultiModRLBP outperforms state of the art methods across multiple classic test sets, especially in predicting binding sites located in pockets or those that are distantly distributed along the sequences. This advancement holds promise in reducing the costs of RNA-targeted drugs development. © 2023, CC BY.

关键词： Binding sites

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Robust and Flexible Multimaterial Aerogel Fabric Toward Outdoor Passive Heating

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Advanced Fiber Materials 2022年第6期4卷 1545-1555页

作者： Jiawei Wu Manni Zhang Minyu Su Yuqi Zhang Jun Liang Shaoning Zeng Baishun Chen Li Cui Chong Hou Guangming Tao Wuhan National Laboratory for Optoelectronics and State Key Laboratory of Material Processing and Die&Mould Technology School of Materials Science and EngineeringHuazhong University of Science and TechnologyWuhan 430074HubeiPeople’s Republic of China School of Optics and Electronic Information Huazhong University of Science and TechnologyWuhan 430074HubeiPeople’s Republic of China Department of Textile Garment and DesignChangshu Institute of TechnologySuzhou 215500JiangsuPeople’s Republic of China College of Materials and Textile Engineering Jiaxing UniversityJiaxing 314001ZhejiangPeople’s Republic of China

Outdoor passive heating to maintain a constant human body temperature is critical for human ***,most traditional energy-exhausted heating systems and inefficient passive heating technologies are incapable of dealing with the cold outdoor *** fabrics with low thermal radiation and conduction to passively heat the human body is a viable way to overcome the constraints of existing passive heating ***,a multimaterial aerogel fabric was developed to realize passive personal heating without any energy *** multimaterial aerogel fabric was fabricated by coating an Ag layer on an aerogel composite *** lightweight aerogel composite fabric,woven from aerogel composite fibers with multi-scale porous structure,exhibits excellent thermal insulation,self-cleaning,mechanical and thermal ***,by coating with an Ag layer,the multimaterial aerogel fabric exhibits both low thermal conductivity and low infrared emissivity at 7–14μm,demonstrating superior thermal insulating *** a result,the proposed multimaterial aerogel fabric with a thickness of only 1.29 mm is capable of improving the human body temperarure of 5.7℃ in a cold environment without energy *** strategy offers a potential energy-saving alternative for future outdoor passive heating.

关键词： Outdoor passive heating Aerogel fabric Thermal insulation Porous structure

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Deep Learning Applications for COVID-19 Analysis:A State-of-the-Art Survey

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computer Modeling in Engineering & Sciences 2021年第10期129卷 65-98页

作者： Wenqian Li Xing Deng Haijian Shao Xia Wang School of Computer Jiangsu University of Science and TechnologyZhenjiang212003China School of Automation Key Laboratory of Measurement and Control for CSEMinistry of EducationSoutheast UniversityNanjing210096China School of Information Science and Technology Nantong UniversityNantong226019China

The COVID-19 has resulted in catastrophic situation and the deaths of millions of people all over the *** this paper,the predictions of epidemiological propagation models,such as SIR and SEIR,are introduced to analyze the earlier COVID-19 *** deep learning methods combined with transfer learning are familiar with classification-detection approaches based on chest X-ray and CT images are presented in ***,deep learning approaches have also been applied to lung ultrasound(LUS),which has been shown to be more sensitive than chest X-ray and CT images in detecting *** the absence of a vaccine,the machine learning-related approaches are applied to analyze vaccine candidates in the realm of biology and *** telehealth system played a major role in combating the pandemic from all aspects and reducing contact with patients during this *** language processing-related methods are utilized to analyze tweets related to the COVID-19 epidemic on social media,and further analyze public sentiment and subject modeling,so as to arrange corresponding measures to appease public *** particular,this survey is to summarize and analyze the contributions made in various fields during the COVID-19 pandemic by considering both the contribution of deep learning in chest X-ray and CT images,as well as the application of the latest LUS during the COVID-19 *** and the importance of public sentiment analysis during a pandemic were also described in detail.

关键词： COVID-19 epidemiological propagation models deep learning transfer learning classification-detection lung ultrasound telehealth system

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Lightweight image super-resolution via an adaptive information fusion attention network

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Soft Computing 2025年第8期29卷 4075-4089页

作者： He, Yi Huan, Hai Zou, Nan Zhang, Yi Xie, Yaqin Wang, Chao School of Artificial Intelligence Nanjing University of Information Science & Technology Nanjing210044 China School of Electronic & Information Engineering Nanjing University of Information Science & Technology Nanjing210044 China Jiangsu Key Laboratory of Meteorological Observation and Information Processing Nanjing University of Information Science and Technology Nanjing210044 China Nanjing Nanji Agricultural Machinery Science and Technology Research Institute Nanjing210019 China

The Convolutional Neural Networks (CNNs) have achieved outstanding performance in the field of Image Super-Resolution (SR). However, many existing methods focus excessively on increasing network depth, which leads to an excessive number of parameters and computational complexity. Some methods, aimed at reducing model capacity, significantly reduce the receptive field of the network, and employ a single upsampling method, resulting in suboptimal model performance. To better balance model size and performance, this paper proposes a lightweight Adaptive information Fusion Attention Network (AIFAN). Specifically, a Dual Path Block (DPB) is designed to adaptively aggregate multi-scale features from different paths through the Adaptive Path Fusion Block (APFB). To take full advantage of the hierarchical features, all DPBs are connected using a dense connection mode. Meanwhile, a receptive field attention (RFA) module is proposed to increase the receptive field of the network and force the network to pay attention to the region with more information in the feature. In addition, a hybrid up-sampling module is designed. The Adaptive Multi-scale Up-sampling (AMSU) module and Feature Update Up-sampling Block (FUUB) are combined in the module to obtain better reconstruction results. The experimental results show that the proposed AIFAN achieves an effective balance between performance and model complexity. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： information fusion

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MonTransformer: Self-Supervised Phonetic to Glyph Conversion Leveraging Positional Context for Traditional Mongolian Texts

MonTransformer: Self-Supervised Phonetic to Glyph Conversion...

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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Zhou, Chenyang Monghjaya Wu, Licheng School of Chinese Ethnic Minority Languages and Literatures Minzu University of China Beijing100081 China College of Computer Science Inner Mongolia University Hohhot010021 China School of Information Engineering Minzu University of China Beijing100081 China Key Laboratory of Ethnic Language Intelligent Analysis and Security Management of MOE Minzu University of China Beijing100081 China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot010021 China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Hohhot010021 China

ISBN: (纸本)9798350368741

The traditional Mongolian script poses significant challenges for text rendering due to its vertical orientation, complex glyph variations, and context-dependent shapes, compounded by limited linguistic resources. This paper introduces the first method specifically designed to address the out-of-vocabulary (OOV) word problem in traditional Mongolian script conversion. We present a novel dataset and propose MonTransformer, the first Transformer-based model tailored for converting Unicode phonetic sequences into glyph sequences. MonTransformer utilizes dynamic optimization and contrastive learning to effectively manage OOV cases and improve sequence generation accuracy, transforming Unicode inputs into visually accurate glyph representations. Our approach achieves state-of-the-art performance, maintaining the script's visual fidelity and advancing efforts in digital preservation for complex scripts. © 2025 IEEE.

关键词： Contrastive learning Dynamic optimization Out-of-vocabulary (OOV) Sequence generation Traditional Mongolian script

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Dual Encoders for Diffusion-based Image Inpainting

Dual Encoders for Diffusion-based Image Inpainting

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Dezhi Zheng Kaijun Deng Jinbao Wang Linlin Shen Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current diffusion-based inpainting models struggle to preserve unmasked regions or generate highly coherent content. Additionally, it is hard for them to generate meaningful content for 3D inpainting. To tackle these challenges, we design a plug-and-play branch that runs through the entire generation process to enhance existing models. Specifically, we utilize dual encoders - a Convolutional Neural Network (CNN) encoder and the pre-trained Variational AutoEncoder (VAE) encoder, to encode masked images. The latent code and the feature map from the dual encoders are fed to diffusion models simultaneously. In addition, we apply Zero-padded initialization to solve the problem of mode collapse caused by this branch. Experiments on BrushBench and EditBench demonstrate that models with our plug-and-play branch can improve the coherence of inpainting, and our model achieves new state-of-the-art results.

关键词： Visualization Three-dimensional displays Codes Autoencoders Coherence Signal processing Diffusion models Decoding Convolutional neural networks Speech processing

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Feature pyramid graph convolutional networks for temporal action detection 3

Feature pyramid graph convolutional networks for temporal ac...

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3rd International Conference on computer Science and Communication technology, ICCSCT 2022

作者： Du, Shanshan Zhang, Xiaomeng Zhu, Shanbang Zhao, Ruiwei Xu, Weidong Feng, Rui School of Information Science and Technology Fudan University Shanghai China School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China Department of Orthopedics Changhai Hospital Naval Medical University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China

ISBN: (纸本)9781510661240

In some long and untrimmed videos, locating the important and key segments can be a very challenging task for temporal action detection. Current methods make remarkable progress when it comes to RGB images. The aim of this paper is to develop a method with the assistance of dynamic model of human body skeletons to address this problem. To this end, we propose a Feature Pyramid Graph Convolutional Network (FP-GCN). The introduced model contains a Feature Encoding Module to encode skeleton data with graph convolutional networks, a Feature Pyramid Module to exploit the inherent pyramidal hierarchy and an Action Detection Module to generate the final prediction results of the detection. We experiment our approach on NTU RGB+D and THUMOS14 datasets and obtain a satisfactory result. © 2022 SPIE.

关键词： Convolution

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