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SSRN 2025年

作者： Zhang, Wenyong Xia, Dawen Chang, Guoyan Zheng, Zhiquan Hu, Yang Huo, Yujia Wang, Ziqiang Feng, Fujian Li, Yantao Li, Huaqing College of Data Science and Information Engineering Guizhou Minzu University Guiyang550025 China School of Computer Science and Engineering South China University of Technology Guangzhou510006 China Engineering Research Center of Micro-nano and Intelligent Manufacturing Ministry of Education Kaili University Kaili556011 China Affiliated Hospital of Guizhou Medical University Guiyang550001 China State Key Laboratory of Public Big Data Guizhou University Guiyang550008 China College of Computer Science Chongqing University Chongqing400044 China College of Electronic and Information Engineering Southwest University Chongqing400715 China

Accurate and timely air quality prediction is important for supporting daily activities and safeguarding respiratory health. However, the complex aerodynamic properties of air pollutants pose significant challenges to accurate air quality prediction. Most existing spatiotemporal prediction models extract spatial features in a coarse-grained manner, which cannot fully utilize the spatial feature data, resulting in poor prediction accuracy. To this end, we propose a novel air quality prediction method based on a Spatiotemporal Transformer with a Kolmogorov-Arnold Network (ST-KAN-Former). Specifically, in the temporal dimension, we use the wavelet transform, KAN, and ProbAttention network to construct the temporal feature extraction model. Based on this, the temporal dimension data are decomposed into high-frequency and low-frequency components, and then feature extraction is performed for fine-grained acquisition of temporal dimension features. Then, in the spatial dimension, we divide the spatial region into four subregions according to the distance parameter between the master site and the slave site and build a spatial feature extraction model for each subregion using KAN and ProbAttention networks. Next, we simulate the attenuation of pollutants during their dispersion to introduce the concept of diffusion coefficients. This approach allows us to apply diffusion processing to data from each subregion, enabling a finer granularity in capturing spatial characteristics. Finally, we employ the KAN and ProbAttention networks to construct a spatiotemporal feature fusion model that integrates temporal and spatial characteristics. Extensive experiments on the Beijing dataset demonstrate that ST-KAN-Former achieves better results compared with the other 11 benchmarks. © 2025, The Authors. All rights reserved.

关键词： Prediction models

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Self-supervised 3D Skeleton Action Representation Learning with Motion Consistency and Continuity

Self-supervised 3D Skeleton Action Representation Learning w...

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International Conference on Computer Vision (ICCV)

作者： Yukun Su Guosheng Lin Qingyao Wu School of Software and Engineering South China University of Technology Key Laboratory of Big Data and Intelligent Robot Ministry of Education School of Computer Science and Engineering Nanyang Technological University

ISBN: (纸本)9781665428132

Recently, self-supervised learning (SSL) has been proved very effective and it can help boost the performance in learning representations from unlabeled data in the image domain. Yet, very little is explored about its usefulness in 3D skeleton-based action recognition understanding. Directly applying existing SSL techniques for 3D skeleton learning, however, suffers from trivial solutions and imprecise representations. To tackle these drawbacks, we consider perceiving the consistency and continuity of motion at different playback speeds are two critical issues. To this end, we propose a novel SSL method to learn the 3D skeleton representation in an efficacious way. Specifically, by constructing a positive clip (speed-changed) and a negative clip (motion-broken) of the sampled action sequence, we encourage the positive pairs closer while pushing the negative pairs to force the network to learn the intrinsic dynamic motion consistency information. Moreover, to enhance the learning features, skeleton interpolation is further exploited to model the continuity of human skeleton data. To validate the effectiveness of the proposed method, extensive experiments are conducted on Kinetics, NTU60, NTU120, and PKUMMD datasets with several alternative network architectures. Experimental evaluations demonstrate the superiority of our approach and through which, we can gain significant performance improvement without using extra labeled data.

关键词： Representation learning Interpolation Three-dimensional displays Dynamics Transfer learning Force Network architecture

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Aerial Secure Collaborative Communications under Eavesdropper Collusion in Low-altitude Economy: A Generative Swarm Intelligent Approach

arXiv

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arXiv 2025年

作者： Li, Jiahui Sun, Geng Wu, Qingqing Liang, Shuang Wang, Jiacheng Niyato, Dusit Kim, Dong In College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China School of Information Science and Technology Northeast Normal University Changchun130117 China Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

The rapid development of the low-altitude economy (LAE) has significantly increased the utilization of autonomous aerial vehicles (AAVs) in various applications, necessitating efficient and secure communication methods among AAV swarms. In this work, we aim to introduce distributed collaborative beamforming (DCB) into AAV swarms and handle the eavesdropper collusion by controlling the corresponding signal distributions. Specifically, we consider a two-way DCB-enabled aerial communication between two AAV swarms and construct these swarms as two AAV virtual antenna arrays. Then, we minimize the two-way known secrecy capacity and maximum sidelobe level to avoid information leakage from the known and unknown eavesdroppers, respectively. Simultaneously, we also minimize the energy consumption of AAVs when constructing virtual antenna arrays. Due to the conflicting relationships between secure performance and energy efficiency, we consider these objectives by formulating a multi-objective optimization problem, which is NP-hard and with a large number of decision variables. Accordingly, we design a novel generative swarm intelligence (GenSI) framework to solve the problem with less overhead, which contains a conditional variational autoencoder (CVAE)-based generative method and a proposed powerful swarm intelligence algorithm. In this framework, CVAE can collect expert solutions obtained by the swarm intelligence algorithm in other environment states to explore characteristics and patterns, thereby directly generating high-quality initial solutions in new environment factors for the swarm intelligence algorithm to search solution space efficiently. Simulation results show that the proposed swarm intelligence algorithm outperforms other state-of-the-art baseline algorithms, and the GenSI can achieve similar optimization results by using far fewer iterations than the ordinary swarm intelligence algorithm. Experimental tests demonstrate that introducing the CVAE mechanism ach

关键词： Multiobjective optimization

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Predicting drug transcriptional response similarity using Signed Graph Convolutional Network

Predicting drug transcriptional response similarity using Si...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ziyan Wang Chengzhi Hong Xuan Liu Zhankun Xiong Feng Liu Wen Zhang School of Computer Science Wuhan University Wuhan China College of Informatics Huazhong Agricultural University Wuhan China Hubei Engineering Technology Research Center of Agricultural Big Data Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Agricultural Bioinformatics Key Laboratory of Hubei Province Key Laboratory of Smart Farming for Agricultural Animals Ministry of Agriculture Huazhong Agricultural University Wuhan China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Exploring the transcriptional response after employing chemical compounds assists in treating gene-related diseases and understanding biological activity of compounds. Calculating the similarity of drug transcriptional response can help to discover novel compounds that have the similar biological activity to known drugs for treating the same disease. Considering the transcriptional profiles of compounds are limited and harder to get than the structure of compounds, it is worth modeling the structure-transcriptional response similarity relationship. In this paper, we propose a signed graph convolutional network (SGCN)-based method, namely SGCN-DTRS, to predict drug transcriptional response similarity, which is quantitatively measured by Connectivity Map (CMap) scores, from their structures. SGCNDTRS constructs a CMap signed network from compounds and their CMap scores in the training data, which takes compounds as nodes, molecular structural representations of compounds as the attributes of nodes, and similarity relations between compounds as edges. Then SGCN-DTRS learns the CMap compound embeddings to predict CMap scores of pairwise compounds. Extensive experiments verify the superiority of the proposed method against the compared state-of-the-art methods and reveal that the relational information of the CMap data, which is learned from CMap signed network, is important for the CMap score prediction. SGCN-DTRS can not only work for the compounds in the training set but also is applicable to unseen compounds.

关键词： Drugs Training databases Training data Predictive models Biology Graph neural networks

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Spiking-Detr: A Spike-Driven End-to-End Object Detection Framework on Spike-Form data Streams Using Spiking-Transformer and Spiking Residual Learning

SSRN

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SSRN 2024年

作者： Ouyang, Hanyu Jiang, Jie School of Instrumentation and Optoelectronic Engineering Beihang University 37 Xueyuan Road Haidian District Beijing100191 China The Precision Opto-Mechatronics Technology Key Laboratory of Education Ministry Beihang University 37 Xueyuan Road Haidian District Beijing100191 China The Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University 37 Xueyuan Road Haidian District Beijing100191 China

In the domain of object detection, deep Spiking Neural Networks (SNNs) present a potential alternative to its CNNs counterpart due to its energy-efficient property. However, performance of state-of-the-art deep SNNs-based object detection approaches suffer from information loss during the event-to-tensor projection and redundant computations introduced by the artificial post-processing modules. To improve the information richness during data transformation and reduce computation cost, we propose a novel spike-driven object detection framework named Spiking-DETR on spike-form data streams by introducing Spiking-Transformer and DETR architecture. It consists of four modules, namely Mixed Time Bin Cube (MTB Cube) module for event-tensor transformation, Spatio-temporal Feature Extraction module for spatio-temporal features extraction, Spiking-Transformer-based Encoder-Decoder module for object pattern and location learning and Post-Processing module for loss computation. We evaluate the proposed model on two public event-based object detection datasets: GEN1 and 1Mpx. Comprehensive experiments show that Spiking-DETR achieves state-of-the-art performance while maintaining relatively low number of parameters and low computation cost. Additionally, it is the first model fully composed of SNNs and achieving end-to-end object detection. The code for this work can be found on the project page here: https://***/JosephBH0622/Spiking-DETR. © 2024, The Authors. All rights reserved.

关键词： Object detection

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Decoupled Prototype Learning for Reliable Test-Time Adaptation

arXiv

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arXiv 2024年

作者： Wang, Guowei Ding, Changxing Tan, Wentao Tan, Mingkui the School of Electronic and Information Engineering South China University of Technology 381 Wushan Road Tianhe District Guangzhou510000 China the Pazhou Lab Guangzhou510330 China the School of Future Technology South China University of Technology 381 Wushan Road Tianhe District Guangzhou510000 China the School of Software Engineering the Key Laboratory of Big Data and Intelligent Robot Ministry of Education South China University of Technology Guangzhou510006 China

Test-time adaptation (TTA) is a task that continually adapts a pre-trained source model to the target domain during inference. One popular approach involves fine-tuning model with cross-entropy loss according to estimated pseudo-labels. However, its performance is significantly affected by noisy pseudo-labels. This study reveals that minimizing the classification error of each sample causes the cross-entropy loss’s vulnerability to label noise. To address this issue, we propose a novel Decoupled Prototype Learning (DPL) method that features prototype-centric loss computation. First, we decouple the optimization of class prototypes. For each class prototype, we reduce its distance with positive samples and enlarge its distance with negative samples in a contrastive manner. This strategy prevents the model from overfitting to noisy pseudo-labels. Second, we propose a memory-based strategy to enhance DPL’s robustness for the small batch sizes often encountered in TTA. We update each class’s pseudo-feature from a memory in a momentum manner and insert an additional DPL loss. Finally, we introduce a consistency regularization-based approach to leverage samples with unconfident pseudo-labels. This approach transfers feature styles of samples with unconfident pseudo-labels to those with confident pseudo-labels. Thus, more reliable samples for TTA are created. The experimental results demonstrate that our methods achieve state-of-the-art performance on domain generalization benchmarks, and reliably improve the performance of self-training-based methods on image corruption benchmarks. The code will be released. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Versatile hydrogel based on polyvinyl alcohol/chitosan/regenerated silk fibroin 5

Versatile hydrogel based on polyvinyl alcohol/chitosan/regen...

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2021 5th International Conference on Green Composite Materials and Nanotechnology, GCMN 2021

作者： Sun, Qinyue Fu, Jizhu Zhang, Heng Yang, Huihui Wang, Yumin Xiong, Yi Zeng, Wei Wang, Faniu Xu, Chao National Engineering Research Center for Agro-Ecological Big Data Analysis and Application School of Electronics and Information Engineering Anhui University Anhui Hefei230601 China Science and Technology Institute Key Laboratory of Textile Fiber and Products Ministry of Education Laboratory for Electron Microscopy Wuhan Textile University Hubei Wuhan430073 China School of Physics and Technology Moe Key Laboratory of Artificial Micro- and Nano-Structures and Center for Electron Microscopy Wuhan University Hubei Wuhan430072 China

Versatile sensors have broad application prospects in human motion detection, health monitoring, wearable electronic devices and flexible electronic skin and other emerging fields. In this work, the enhanced hydrogel was prepared by freezing and thawing process with calcium ion crosslinking. This composite material shows excellent flexibility and elasticity, and after being cut in half, it can automatically heal well in a short time without external force. It shows great potential in flexible and wearable devices. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Hydrogels

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Correction to: A remote sensing image encryption and compression scheme using novel hyperchaotic system and plaintext related random S-box

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Nonlinear Dynamics 2024年第3期113卷 2791-2791页

作者： Li, Zhen Yang, Siqi Tan, Weijie Wu, Xianming College of Big Data and Information Engineering Guizhou University Guiyang China Key Laboratory of Advanced Manufacturing Technology of Ministry of Education State Key Laboratory of Public Big Data Guizhou University Guiyang China School of Mechanical and Electrical Engineering Guizhou Normal University Guiyang China

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Causal structure learning of nonlinear additive noise model based on streaming feature

Causal structure learning of nonlinear additive noise model ...

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IEEE International Conference on data Mining Workshops (ICDM Workshops)

作者： Jing Yang Gaojin Fan Anbo Shen Aiguo Wang Ministry of Education Key Laboratory of Knowledge Engineering With Big Data(Hefei University of Technology) School of Computer Science and Information Engineering Hefei University of Technology Hefei P. R. China School of Electronic and Information Engineering Foshan University China

ISBN: (纸本)9781665424288

The current streaming feature structure learning needs to be improved in the processing of nonlinear continuous data and the dynamic acquisition of causal structures. In this paper, we propose a causal structure learning algorithm, CANSF, based on the streaming feature of additive noise models. We have made three contributions. First, by using the information carried by the noise of nonlinear continuous data, we propose a real correlation identification method based on logarithmic likelihood, which can identify the real correlation and redundant features of target features, and dynamically select parent and child nodes for each feature. Second, based on regression analysis, a method to determine the causal direction is proposed, which can be used for dynamic orientation. Third, a learning method of causal structure based on streaming features is proposed, which can obtain the Causal structure diagram directly and dynamically.

关键词： Additive noise Learning systems Correlation Heuristic algorithms Conferences data models Regression analysis

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AdaFuse: Adaptive Medical Image Fusion Based on Spatial-Frequential Cross Attention

arXiv

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arXiv 2023年

作者： Gu, Xianming Wang, Lihui Deng, Zeyu Cao, Ying Huang, Xingyu Zhu, Yue-Min Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China University Lyon INSA Lyon CNRS Inserm IRP Metislab CREATIS UMR5220 U1206 Lyon69621 France

Multi-modal medical image fusion is essential for the precise clinical diagnosis and surgical navigation since it can merge the complementary information in multi-modalities into a single image. The quality of the fused image depends on the extracted single modality features as well as the fusion rules for multi-modal information. Existing deep learning-based fusion methods can fully exploit the semantic features of each modality, they cannot distinguish the effective low and high frequency information of each modality and fuse them adaptively. To address this issue, we propose AdaFuse, in which multimodal image information is fused adaptively through frequency-guided attention mechanism based on Fourier transform. Specifically, we propose the cross-attention fusion (CAF) block, which adaptively fuses features of two modalities in the spatial and frequency domains by exchanging key and query values, and then calculates the cross-attention scores between the spatial and frequency features to further guide the spatial-frequential information fusion. The CAF block enhances the high-frequency features of the different modalities so that the details in the fused images can be retained. Moreover, we design a novel loss function composed of structure loss and content loss to preserve both low and high frequency information. Extensive comparison experiments on several datasets demonstrate that the proposed method outperforms state-of-the-art methods in terms of both visual quality and quantitative metrics. The ablation experiments also validate the effectiveness of the proposed loss and fusion strategy. © 2023, CC BY.

关键词： Deep learning

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