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

作者： Haihong Guo Fengxin Li Jiao Li Hongyan Liu School of Information Renmin University of China China Institute of Medical Information / Medical Library Chinese Academy of Medical Sciences / Peking Union Medical College China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China School of Economics and Management Tsinghua University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Kolmogorov-Arnold Networks (KAN) is an emerging neural network architecture in machine learning. It has greatly interested the research community about whether KAN can be a promising alternative to the commonly used Multi-Layer Perceptions (MLP). Experiments in various fields demonstrated that KAN-based machine learning can achieve comparable if not better performance than MLP-based methods, but with much smaller parameter scales and are more explainable. In this paper, we explore the incorporation of KAN into the actor and critic networks for offline reinforcement learning (RL). We evaluated the performance, parameter scales, and training efficiency of various KAN and MLP-based conservative Q-learning (CQL) on the classical D4RL benchmark for offline RL. Our study demonstrates that KAN can achieve performance close to the commonly used MLP with significantly fewer parameters. This allows us to choose the base networks according to the offline RL task requirements.

关键词： Training Performance evaluation Hands Q-learning Neural networks Memory management Signal processing Benchmark testing Splines (mathematics) Speech processing

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Causal-Inspired Multitask Learning for Video-Based Human Pose Estimation

arXiv

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arXiv 2025年

作者： Chen, Haipeng Wu, Sifan Wang, Zhigang Yin, Yifang Jiao, Yingying Lyu, Yingda Liu, Zhenguang College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Computer Science and Technology Zhejiang Gongshang University China A*STAR Singapore Public Computer Education and Research Center Jilin University China The State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China

Video-based human pose estimation has long been a fundamental yet challenging problem in computer vision. Previous studies focus on spatio-temporal modeling through the enhancement of architecture design and optimization strategies. However, they overlook the causal relationships in the joints, leading to models that may be overly tailored and thus estimate poorly to challenging scenes. Therefore, adequate causal reasoning capability, coupled with good interpretability of model, are both indispensable and prerequisite for achieving reliable results. In this paper, we pioneer a causal perspective on pose estimation and introduce a causal-inspired multitask learning framework, consisting of two stages. In the first stage, we try to endow the model with causal spatio-temporal modeling ability by introducing two self-supervision auxiliary tasks. Specifically, these auxiliary tasks enable the network to infer challenging keypoints based on observed keypoint information, thereby imbuing causal reasoning capabilities into the model and making it robust to challenging scenes. In the second stage, we argue that not all feature tokens contribute equally to pose estimation. Prioritizing causal (keypoint-relevant) tokens is crucial to achieve reliable results, which could improve the interpretability of the model. To this end, we propose a Token Causal Importance Selection module to identify the causal tokens and non-causal tokens (e.g., background and objects). Additionally, non-causal tokens could provide potentially beneficial cues but may be redundant. We further introduce a non-causal tokens clustering module to merge the similar non-causal tokens. Extensive experiments show that our method outperforms state-of-the-art methods on three large-scale benchmark datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

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Delving Globally into Texture and Structure for Image Inpainting

arXiv

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arXiv 2022年

作者： Liu, Haipeng Wang, Yang Wang, Meng Rui, Yong School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei China Lenovo Research Beijing China

Image inpainting has achieved remarkable progress and inspired abundant methods, where the critical bottleneck is identified as how to fulfill the high-frequency structure and low-frequency texture information on the masked regions with semantics. To this end, deep models exhibit powerful superiority to capture them, yet constrained on the local spatial regions. In this paper, we delve globally into texture and structure information to well capture the semantics for image inpainting. As opposed to the existing arts trapped on the independent local patches, the texture information of each patch is reconstructed from all other patches across the whole image, to match the coarsely filled information, especially the structure information over the masked regions. Unlike the current decoder-only transformer within the pixel level for image inpainting, our model adopts the transformer pipeline paired with both encoder and decoder. On one hand, the encoder captures the texture semantic correlations of all patches across image via self-attention module. On the other hand, an adaptive patch vocabulary is dynamically established in the decoder for the filled patches over the masked regions. Building on this, a structure-texture matching attention module anchored on the known regions comes up to marry the best of these two worlds for progressive inpainting via a probabilistic diffusion process. Our model is orthogonal to the fashionable arts, such as Convolutional Neural Networks (CNNs), Attention and Transformer model, from the perspective of texture and structure information for image inpainting. The extensive experiments over the benchmarks validate its superiority. Our code is available here. Copyright © 2022, The Authors. All rights reserved.

关键词： Textures

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Salient Patch Based NAS for Grading of Colorectal Cancer Histology Images 13

Salient Patch Based NAS for Grading of Colorectal Cancer His...

引用

13th International Multi-Conference on Complexity, Informatics and Cybernetics, IMCIC 2022

作者： Meng, Hang Li, Fei Yang, Zhen Zhu, Qiang Fan, Chaogang Zhan, Shu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Anhui Province Hefei230009 China The Second Affiliated Hospital of Anhui Medical University Anhui Province Hefei230601 China Tongcheng Teachers College Anhui Province Tongcheng231400 China Department of General Surgery The Affiliated BenQ Hospital of Nanjing Medical University Jiangsu Province Nanjing210021 China

ISBN: (纸本)9781950492619

Digital histopathological tissue images are gold material for cancer diagnosis and grades. Convolutional Neural Networks (CNNs) are state-of-the-art models in many image classification tasks. However, considering the limited computing resources, it is not practical to directly train CNNs on gigapixel resolution histopathological tissue images. Therefore, the entire histopathological image is commonly divided into a grid of patches and then fed each patch to CNNs, but not all patches are necessary for specific tasks. Besides, although CNNs are excellent image classifiers, many pre-trained models are based on natural images. Neural Architecture Search (NAS) can realize automatic search, construction, and training specific problemoriented networks. We present salient patch based NAS for grading colorectal cancer (CRC) histology images with these motivations. The proposed framework first applies the classic classification network to extract significant patches from the whole image, then use the network obtained by NAS to grade the patches, and finally aggregates the patch-level predictions to generate the entire CRC histological image label. Our experiments on microscopic colorectal tissue images demonstrate that the proposed method is sufficient to obtain more accurate classification results compared to its counterparts. We also conducted a comprehensive analysis of the different scenarios of the proposed method. © by the International Institute of Informatics and Systemics.

关键词： Grading

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Network Embedding with Topology-Aware Textual Representations 18th

Network Embedding with Topology-Aware Textual Representation...

引用

18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Chen, Jiaxing Xu, Zenan Su, Qinliang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China

ISBN: (纸本)9783030891879

Textual network embedding aims to learn meaningful low-dimensional representations for vertices with the consideration of the associated texts. When learning the representations for texts in network embedding, existing methods mostly only exploit information from neighboring texts (i.e., contexts), while rarely taking advantages of the valuable network topological (structural) information. To bridge the gap, in this paper, a model based on adaptive-filter convolutional neural networks (CNN) is developed, in which the filters are adapted to local network topologies, rather than clamped to fixed values as in traditional CNNs. The dependency enables the learned text representations to be aware of local network topologies. It is shown that the proposed topology-aware representations can be viewed as a complement to existing context-aware ones. When the two are used together, experimental results on three real-world benchmarks demonstrate that significant performance improvements on the tasks of link prediction and vertex classification. © 2021, Springer Nature Switzerland AG.

关键词： Network topology

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Ggt-Snn: Graph Learning and Gaussian Prior Integrated Spiking Graph Neural Network for Event-Driven Tactile Object Recognition

SSRN

引用

SSRN 2024年

作者： Yu, Zukun Yang, Jing Li, Shaobo Cao, Yang Hu, JianJun Xu, Ji Key Laboratory of Advanced Manufacturing Technology Ministry of Education Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data Ministry of Education Guizhou University Guiyang550025 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai201100 China School of Mechanical Engineering Guizhou University Guiyang550025 China Department of Computer Science and Engineering University of South Carolina Columbia29208 United States

Spiking neural networks possess asynchronous, discrete, and sparse characteristics that enable direct processing of event-based tactile sensor data and more efficient transmission of information. These networks have been widely applied in the tactile perception field. However, due to the sparsity of tactile data, these models are prone to generalization issues. Additionally, existing methods for tactile graph construction are constrained when spatially connecting high-dimensional tactile data, making it challenging for the model to fuse and process such information effectively. To address these challenges, we propose the GGT-SNN, which introduces a parameter estimation method with Gaussian priors to alleviate generalization problems caused by sparse tactile data. Furthermore, we design M-tree and Z-tree tactile graph construction methods to compensate for deficiencies of other approaches when handling high-dimensional tactile spatial connections. This approach enhances the model's ability to efficiently process high-dimensional event-driven tactile information. We introduce an approximate LIF neuron activation function to enable backpropagation within the model and comprehensively compare and evaluate its performance against different approximate functions. The experimental results demonstrate that our proposed method performs significantly better than SOTA methods, exhibiting a 10.83% improvement over TactileSGNet on the EvTouch-Containers dataset and a 2.92% improvement over TactileSGNet on the EvTouch-Objects dataset. © 2024, The Authors. All rights reserved.

关键词： Object recognition

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Trim21 depletion alleviates bone loss in osteoporosis via activation of YAP1/β-catenin signaling

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Bone Research 2023年第4期11卷 819-833页

作者： Ri-Xu Liu Rong-He Gu Zhi-Peng Li Zhi-Quan Hao Qin-Xiao Hu Zhen-Yan Li Xiao-Gang Wang Wang Tang Xiao-He Wang Yu-Kai Zeng Zhen-Wei Li Qiu Dong Xiao-Feng Zhu Di Chen Ke-Wei Zhao Rong-Hua Zhang Zhen-Gang Zha Huan-Tian Zhang Department of Bone and Joint Surgery the First Affiliated Hospital of Jinan UniversityKey Laboratory of Regenerative Medicine of Ministry of EducationJinan UniversityGuangzhou 510630 GuangdongChina Department of Orthopedic and Spine Surgery The First Affiliated Hospital of Guangzhou Medical UniversityGuangzhou 510120 GuangdongChina School of Basic Medical Sciences of Guangxi Medical University the Fifth Affiliated Hospital of Guangxi Medical UniversityNanning 530022 GuangxiChina Key Laboratory of Big Data-Based Precision Medicine School of Engineering MedicineBeihang University100191 BeijingChina Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization College of PharmacyJinan UniversityGuangzhou 510630 GuangdongChina Research Center for Computer-aided Drug Discovery Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences518005 ShenzhenChina Guangzhou Key Laboratory of Chinese Medicine Research on Prevention and Treatment of Osteoporosis the Third Affiliated Hospital of Guangzhou University of Chinese Medicine510375 GuangzhouChina

Despite the diverse roles of tripartite motif(Trim)-containing proteins in the regulation of autophagy,the innate immune response,and cell differentiation,their roles in skeletal diseases are largely *** recently demonstrated that Trim21 plays a crucial role in regulating osteoblast(OB)differentiation in ***,how Trim21 contributes to skeletal degenerative disorders,including osteoporosis,remains ***,human and mouse bone specimens were evaluated,and the results showed that Trim21 expression was significantly elevated in bone tissues obtained from osteoporosis ***,we found that global knockout of the Trim21 gene(KO,Trim2^(1-/-))resulted in higher bone mass compared to that of the control *** further demonstrated that loss of Trim21 promoted bone formation by enhancing the osteogenic differentiation of bone marrow mesenchymal stem cells(BMSCs)and elevating the activity of OBs;moreover,Trim21 depletion suppressed osteoclast(OC)formation of RAW264.7 *** addition,the differentiation of OCs from bone marrow-derived macrophages(BMMs)isolated from Trim21^(-/-)and Ctsk-cre;Trim21^(f/f)mice was largely compromised compared to that of the littermate control ***,YAP1/β-catenin signaling was identified and demonstrated to be required for the Trim21-mediated osteogenic differentiation of *** importantly,the loss of Trim21 prevented ovariectomy(OVX)-and lipopolysaccharide(LPS)-induced bone loss in vivo by orchestrating the coupling of OBs and OCs through YAP1 *** current study demonstrated that Trim21 is crucial for regulating OB-mediated bone formation and OC-mediated bone resorption,thereby providing a basis for exploring Trim21 as a novel dual-targeting approach for treating osteoporosis and pathological bone loss.

关键词： YAP1 osteoporosis depletion

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RGB-D Based Visual Navigation Using Direction Estimation Module 18th

RGB-D Based Visual Navigation Using Direction Estimation Mod...

引用

18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Luo, Chao Bi, Sheng Dong, Min Nie, Hongxu School of Computer Science and Engineering South China University of Technology Guangdong Guangzhou510006 China Key Laboratory of Big Data and Intelligent Robot Ministry of Education Guangdong Guangzhou510006 China

ISBN: (纸本)9783030893699

Target-driven visual navigation without mapping works to solve navigation problems that given a target object, mobile robots can navigate to the target object. Recently, visual navigation has been researched and improved largely by learning-based methods. However, their methods lack depth information and spatial perception, using only single RGB images. To overcome these problems, two methods are presented in this paper. Firstly, we encode visual features of objects by dynamic graph convolutional network and extract 3D spatial features for objects by 3D geometry, a high level visual feature for agent to easily understand object relationship. Secondly, as human beings, they solve this problem in two steps, first exploring a new environment to find the target object and second planning a path to arrive. Inspired by the way of humans navigation, we propose direction estimation module (DEM) based on RGB-D images. DEM provides direction estimation of the target object to our learning model by a wheel odometry. Given a target object, first stage, our agent explores an unseen scene to detect the target object. Second stage, when detected the target object, we can estimate current location of the target object by 3D geometry, after that, each step of the agent, DEM will estimate new location of target object, and give direction information of the target object from a first-view image. It can guide our agent to navigate to the target object. Our experiment results outperforms the result of state of the art method in the artificial environment AI2-Thor. © 2021, Springer Nature Switzerland AG.

关键词： Reinforcement learning

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A Secure and Efficient Privacy data Aggregation Mechanism 18th

A Secure and Efficient Privacy Data Aggregation Mechanism

引用

18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Ma, Wenshuo Liu, Xinru Yu, Kan Luo, Chuanwen Wang, Guopeng Liu, Xiaowu School of Artificial Intelligence and Big Data Zibo Vocational Institute Zibo China Weifang Engineering Vocational College Weifang China School of Computer Science and Engineering Macau University of Science and Technology Taipa China The Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing China School of Information Science and Technology Beijing Forestry University Beijing China School of Computer Science Qufu Normal University Rizhao China Zhejiang Ocean University Zhoushan China The Open University of China Beijing China

ISBN: (纸本)9783031714665

data aggregation (DA) plays an important role in conserving the limited resources and extending the network lifetime of Wireless Sensor Networks (WSNs). Privacy preservation has garnered considerable attention and is considered one of the most viable schemes to address issues such as communication eavesdropping, information leakage, and unauthorized access in WSNs employing DA. In this paper, we propose an improved Secure Privacy-preserving data Aggregation (iSECPDA) mechanism. It dynamically selects cluster heads through an enhanced Stable Election Protocol (SEP), which considers the residual energy of sensor nodes to enhance network lifetime. In our proposed SEPPDA, we integrate interference information into the sensing data and ensure data transmission privacy through a proposed slicing mechanism. Theoretical analysis and simulation experiments demonstrate that iSECPDA exhibits significant performance improvements in terms of communication overhead and privacy preservation levels. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： data aggregation

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Monopulse transmitarray antenna fed by aperture-coupled microstrip structure

引用

Frontiers of Information Technology & Electronic engineering 2022年第3期23卷 502-510页

作者： Na KOU Shixing YU Zhao DING Zhengping ZHANG College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Key Laboratory of Micro-Nano-Electronics and Software Technology ofGuizhou Province Guiyang 550025China Engineering Research Center of Power Semiconductor Device Reliability of the Ministry of Education Guiyang 550025China

An X-band monopulse antenna based on transmitarray with light-weight and low-cost features is designed,fabricated,and *** proposed antenna is composed of a transmitarray lens,a feed source which combines aperture-coupled microstrip antennas,and a sum-difference(SUM-DIFF)compar-atoi\According to simulation and measurement results of the transmitarray based monopulse antenna,gain of sum beam reaches 21.5 dBi.

关键词： antenna pulse transmit

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