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arXiv 2021年

作者： Li, Yong Sun, Yufei Cui, Zhen Shan, Shiguang Yang, Jian Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology CAS Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China CAS Center for Excellence in Brain Science and Intelligence Technology

Face recognition (FR) has made extraordinary progress owing to the advancement of deep convolutional neural networks. However, demographic bias among different racial cohorts still challenges the practical face recognition system. The race factor has been proven to be a dilemma for fair FR (FFR) as the subject-related specific attributes induce the classification bias whilst carrying some useful cues for FR. To mitigate racial bias and meantime preserve robust FR, we abstract face identityrelated representation as a signal denoising problem and propose a progressive cross transformer (PCT) method for fair face recognition. Originating from the signal decomposition theory, we attempt to decouple face representation into i) identity-related components and ii) noisy/identity-unrelated components induced by race. As an extension of signal subspace decomposition, we formulate face decoupling as a generalized functional expression model to cross-predict face identity and race information. The face expression model is further concretized by designing dual cross-transformers to distill identity-related components and suppress racial noises. In order to refine face representation, we take a progressive face decoupling way to learn identity/racespecific transformations, so that identity-unrelated components induced by race could be better disentangled. We evaluate the proposed PCT on the public fair face recognition benchmarks (BFW, RFW) and verify that PCT is capable of mitigating bias in face recognition while achieving state-of-the-art FR performance. Besides, visualization results also show that the attention maps in PCT can well reveal the race-related/biased facial regions. © 2021, CC BY-NC-SA.

关键词： Face recognition

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A Novel Solution of Underdetermined Equations for DGTD based on Prior knowledge

A Novel Solution of Underdetermined Equations for DGTD based...

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International Conference on Microwave and Millimeter Wave Technology Proceedings

作者： Qi Qi Xinyuan Cao Mingsheng Chen Zhixiang Huang Yi Liu Xianliang Wu Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei Anhui Province Key Laboratory of Simulation and Design for Electronic Information System Hefei Normal University Hefei China School of Electronic Engineering Huainan Normal University Huainan China

ISBN: (数字)9781728157337

ISBN: (纸本)9781728157344

To accelerate solving transient electromagnetic problems by discontinuous Galerkin time domain (DGTD) method, a solution of underdetermined equations is established based on prior knowledge. In the proposed method, nodal basis function and a leapfrog scheme are used to Maxwell curl equations for spatial and time discretization. Electromagnetic fields in each element of computation domain are updated individually by applying conventional DGTD in initial time. When the electromagnetic wave covers the domain, the fields of all elements are updated as a whole. The underdetermined equations are established by randomly extracting rows from global mass matrix. Taking the results of a few previous time steps as the prior knowledge, a sparse transform is constructed. Then the underdetermined system of equations can be solved by recovery algorithms. Meanwhile,in order to maintain the advantage of low computational complexity, a restart mechanism is presented. The validity of the proposed method is verified by numerical experiments.

关键词： Microwave technology Millimeter wave technology Transforms Sparse matrices Electromagnetic fields Transient analysis Time-domain analysis

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Invariant-preserving schemes with arbitrarily high-order accuracy for the two-component Camassa–Holm system via Hamiltonian boundary value methods

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Communications in Nonlinear Science and Numerical Simulation 2025年 150卷

作者： Qian Luo Aiguo Xiao Min Li Xiaoqiang Yan School of Mathematics and Computational Science & Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Hunan 411105 China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Hunan 411105 China School of Mathematics and Physics China University of Geosciences Wuhan 430074 China National Center for Applied Mathematics in Hunan Xiangtan University Hunan 411105 China

In this paper, we present an arbitrarily high-order numerical scheme for the two-component Camassa–Holm system, ensuring the preservation of three invariants: energy and two Casimir functions. The spatial discretization is achieved using Fourier–Galerkin methods, resulting in a semi-discrete system which retains a Hamiltonian structure and approximates the invariants of the original continuous system. Subsequently, an energy-preserving Runge–Kutta method, such as Hamiltonian boundary value methods, is applied to the semi-discrete system, yielding a fully discrete scheme of arbitrarily high order. The proposed scheme is validated through numerical simulations, demonstrating its accuracy and capability in capturing different types of solutions of the two-component Camassa–Holm equation.

关键词： Blended iteration Fourier–Galerkin methods Hamiltonian boundary value methods Invariant-preserving scheme Two-component Camassa–Holm system

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3R: Word and Phoneme Edition based Data Augmentation for Lexical Punctuation Prediction

3R: Word and Phoneme Edition based Data Augmentation for Lex...

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International Conference on Computational Intelligence and Security

作者： Aihua Zheng Naipeng Ye Xiao Wang Xiao Song Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Hefei China Peking University Shenzhen Institute Shenzhen China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China

Existing Lexical Punctuation Prediction methods are mainly trained on the standard clean data while losing the generalization in practical automatic speech recognition (ASR) system with ubiquitous transcription errors. To bridge the gap between clean training data and noisy testing data, we propose three random (3R) data augmentation strategies: random word deletion (RWD), random word substitution (RWS), and random phoneme edition (RPE) in both word and phoneme levels on the training dataset. Specifically, we contribute an acoustically similar vocabulary with phoneme level editions for acoustically similar word substitution. In addition, we first introduce the RoBERTa-large model into a punctuation prediction task to capture the semantics and the long-distance dependencies in language. Extensive experiments on the English dataset IWSLT2011 yield to a new state-of-the-art comparing to the prevalent punctuation prediction methods.

关键词： Training Vocabulary Semantics Training data Predictive models Noise measurement Security

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A Pilot Allocation Method of Jointing Cell Grouping and Alliance Game in Massive MIMO System

A Pilot Allocation Method of Jointing Cell Grouping and Alli...

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第二届材料科学应用与能源材料国际研讨会

作者： Feiyue Wang Hui Zhi Ziju Huang Key Lab of Computing Intelligent and Signal Processing(Ministry of Education) Anhui University

In order to improve the uplink channel estimation performance and reduce pilot contamination(PC),this paper proposes a pilot allocation method of jointing cell grouping and alliance game(JCG-AG) for massive multiple-input multiple-output(MIMO) cellular *** can be divided into two ***,by properly dividing all cells into different cell-groups,the users in different cell-groups are allocated different orthogonal pilot ***,the users in the same cell-group are further divided into several mutually disjoint *** belonging to different sub-alliances are allocated different pilots,and users belonging to the same sub-alliance reuse the same *** the second step,the alliance formation algorithm is used to suppress *** JCG-AG method takes practical application as the starting point,focuses on the random distribution of users,and uses the idea of alliance *** these make it more flexible and *** demonstrated in the simulation results,the JCG-AG method can greatly mitigate the PC and reduce the average mean square error(MSE) for all channel estimations in the uplink.

关键词： MSE red A Pilot Allocation Method of Jointing Cell Grouping and Alliance Game in Massive MIMO System

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The Downlink Performance of Massive MIMO Systems under Different Uplink Pilot Allocation Schemes

The Downlink Performance of Massive MIMO Systems under Diffe...

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第二届材料科学应用与能源材料国际研讨会

作者： Ziju Huang Hui Zhi Feiyue Wang Key Lab of Computing Intelligent and Signal Processing(Ministry of Education) Anhui University

In the scenario of time division duplexing(TDD) massive multiple-input multiple-output(MIMO) system,when there is pilot contamination(PC) in the uplink,different pilot allocation schemes will affect the results of the uplink channel *** the channel estimation results are used for downlink transmission precoding,which will further affects the downlink *** paper analyses the impact of different pilot allocation(PA) schemes on downlink *** theoretical analysis,the system downlink signal to interference plus noise ratio(SINR) and spectral efficiency expressions of different pilot allocation schemes are obtained *** simulation results show that the pilot allocation schemes with cell grouping is better than the schemes without cell *** SINR increases as the number of cell grouping increases,and the spectrum efficiency increases first and then decreases as the number of cell grouping increases.

关键词： TDD The Downlink Performance of Massive MIMO Systems under Different Uplink Pilot Allocation Schemes

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Pneumatic soft bionic actuators inspired by elephant trunk for multidisciplinary robotics applications

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Physica Scripta 2025年第7期100卷 075013-075013页

作者： Xiaohui Guo Deli Kong Zihan Lin Xianghui Li Yupeng Shao Zhaobin Li Yinuo Chen Xinyu Wu Weiqiang Hong Tianxu Zhang Xiaowen Zhu Congguang He Junjie Qiu Xiangchen Zhao Qi Hong Yunong Zhao Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Integrated Circuits Anhui University Hefei 230601 People’s Republic of China State Key Laboratory of High-Performance Precision Manufacturing Dalian University of Technology Dalian 116024 People’s Republic of China Key Laboratory for Micro/Nano Technology and System of Liaoning Province Dalian University of Technology Dalian 116024 People’s Republic of China School of Physics University of Science and Technology of China Hefei 230000 People’s Republic of China

Soft robots in technology makes rapid development in the field of robotics, so that robots are widely used in all aspects of life. This paper proposed a bionic elephant trunk structure of pneumatic actuator (SPA). To solve the insufficient tip force and bending angle of SPA, mathematical model and Finite Element Analysis (FEA) are used to determine the optimal parameters of the SPA. Based on optimized SPAs, robots for multidisciplinary applications are developed. These applications include soft gripper with high gripping force of up to 480 N, a quadruped soft robot capable of adapting complex amphibious environments, and a robot designed to assist with sign language communication. These demonstrate the versatility of soft robots in addressing challenges across various domains, such as healthcare, human-machine interaction, and environmental exploration.

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Correction: Self-orthogonal codes over a non-unital ring and combinatorial matrices

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Designs, Codes and Cryptography 2023年第2期91卷 691-691页

作者： Shi, Minjia Wang, Shukai Kim, Jon-Lark Solé, Patrick Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical Sciences Anhui University Hefei China School of Mathematical Sciences Anhui University Hefei China Department of Mathematics Sogang University Seoul South Korea Aix Marseille Univ CNRS Centrale Marseille I2M Marseilles France

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Human-multimodal deep learning collaboration in ‘precise’ diagnosis of lupus erythematosus subtypes and similar skin diseases

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Journal of the European Academy of Dermatology and Venereology 2024年第12期38卷 2268-2279页

作者： Li, Qianwen Yang, Zhi Chen, Kaili Zhao, Ming Long, Hai Deng, Yueming Hu, Haoran Jia, Chen Wu, Meiyu Zhao, Zhidan Zhu, Huan Zhou, Suqing Zhao, Mingming Cao, Pengpeng Zhou, Shengnan Song, Yang Tang, Guishao Liu, Juan Jiang, Jiao Liao, Wei Zhou, Wenhui Yang, Bingyi Xiong, Feng Zhang, Suhan Gao, Xiaofei Jiang, Yiqun Zhang, Wei Zhang, Bo He, Yan-ling Ran, Liwei Zhang, Chunlei Wu, Wenting Suolang, Quzong Luo, Hanhuan Kang, Xiaojing Wu, Caoying Jin, Hongzhong Chen, Lei Guo, Qing Gui, Guangji Li, Shanshan Si, Henan Guo, Shuping Liu, Hong-Ye Liu, Xiguang Ma, Guo-Zhang Deng, Danqi Yuan, Limei Lu, Jianyun Zeng, Jinrong Jiang, Xian Lyu, Xiaoyan Chen, Liuqing Hu, Bin Tao, Juan Liu, Yuhao Wang, Gang Zhu, Guannan Yao, Zhirong Xu, Qianyue Yang, Bin Wang, Yu Ding, Yan Yang, Xianxu Kai, Hu Wu, Haijing Lu, Qianjin Department of Dermatology Hunan Key Laboratory of Medical Epigenomics The Second Xiangya Hospital of Central South University Changsha China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan China Department of Dermatology Hunan Children's Hospital Changsha China Institute of Dermatology Chinese Academy of Medical Sciences and Peking Union Medical College Nanjing China Department of Dermatology Beijing Chao-Yang Hospital Capital Medical University Beijing China Department of Dermatology Peking University Third Hospital Beijing China Department of Dermatology People's Hospital of Tibet Autonomous Region Lhasa China Department of Dermatology People's Hospital of Xinjiang Uygur Autonomous Region Urumqi China Department of Dermatology State Key Laboratory of Complex Severe and Rare Diseases Peking Union Medical College Hospital Chinese Academy of Medical Science and Peking Union Medical College Beijing China Department of Dermatology Sun Yat-Sen Memorial Hospital Sun Yat-Sen University Guangzhou China Department of Dermatology The First Bethune Hospital of Jilin University Changchun China Department of Dermatology The First Hospital of Shanxi Medical University Taiyuan China Department of Dermatology The Hei Long Jiang Provincial Hospital Harbin China Department of Dermatology The Second Affiliated Hospital of Kunming Medical University Kunming China Department of Dermatology The Third Xiangya Hospital Central South University Changsha China Department of Dermatology West China Hospital Sichuan University Chengdu China Department of Dermatology Wuhan No. 1 Hospital Wuhan China Department of Dermatology Wuhan Union Hospital of China Wuhan China Department of Dermatology Xijing Hospital Xi'an China Department of Dermatology Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Dermatology Hospital of Southern Medical University Guangzhou Chin

Background: Lupus erythematosus (LE) is a spectrum of autoimmune diseases. Due to the complexity of cutaneous LE (CLE), clinical skin image-based artificial intelligence is still experiencing difficulties in distinguishing subtypes of LE. Objectives: We aim to develop a multimodal deep learning system (MMDLS) for human-AI collaboration in diagnosis of LE subtypes. Methods: This is a multi-centre study based on 25 institutions across China to assist in diagnosis of LE subtypes, other eight similar skin diseases and healthy subjects. In total, 446 cases with 800 clinical skin images, 3786 multicolor-immunohistochemistry (multi-IHC) images and clinical data were collected, and EfficientNet-B3 and ResNet-18 were utilized in this study. Results: In the multi-classification task, the overall performance of MMDLS on 13 skin conditions is much higher than single or dual modals (Sen = 0.8288, Spe = 0.9852, Pre = 0.8518, AUC = 0.9844). Further, the MMDLS-based diagnostic-support help improves the accuracy of dermatologists from 66.88% ± 6.94% to 81.25% ± 4.23% (p = 0.0004). Conclusions: These results highlight the benefit of human-MMDLS collaborated framework in telemedicine by assisting dermatologists and rheumatologists in the differential diagnosis of LE subtypes and similar skin diseases. © 2024 European Academy of Dermatology and Venereology.

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Multi-scale convolutional neural network for road extraction in remote sensing imagery 2nd

Multi-scale convolutional neural network for road extraction...

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2nd CCF International Conference on Artificial Intelligence, CCF-ICAI 2019

作者： Li, Jun Gong, Xue Zhang, Yiming Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Xiangtan University Xiangtan411105 China College of Civil Engineering and Mechanics Xiangtan University Xiangtan411105 China

ISBN: (纸本)9789813292970

Road is an important semantic region in remote sensing imagery and plays an important role in many applications. Deep learning has obtained a great success in image classification, since it can directly learn from labeled training samples and extract different level image features to encode the input image. In this paper, we propose a multi-scale convolutional neural network (MSCNN) for extracting road from high-resolution remote sensing image, in which road detection can be seen as a regional classification. This core trainable detection engine consists of an encoder-decoder network and a fusion model. Firstly, image was encoded as a feature representation with several stacked convolutional layers. And then guided by the different scale road training data, the pre-trained decoder networks output a series of classification maps. Finally, we investigate the fusion model utilizing different scale classification maps and obtain a final road decision map. To validate the performance of the proposed method, we test our MSCNN based method and other state-of-the-art approaches on two challenging datasets of high-resolution images. Experiments show our method gets the best results both in quantitative and qualitative evaluation. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Semantics

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