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Degree of Spiking Neural P Systems Without Delay

Chinese Journal of Electronics 2023年第3期21卷 419-424页

作者： Keqin JIANG Xiaolong SHI Department of Control Science and Engineering Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China School of Computer and Information Anqing Teachers College Anqing China

Spiking neural P systems are a class of distributed parallel computing models inspired from the way neurons communicate with each other by means of electrical impulses (called “spikes”). In this paper, we continue the research of normal forms for spiking neural P systems. Specifically, we prove that the degree of spiking neural P systems without delay can be decreased to two without losing the computational completeness (both in the generating and accepting modes).

关键词： Computational modeling Neurons Parallel processing Delays

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KCE-Unet: A novel music denoising method with KANConv ECA Unet*

KCE-Unet: A novel music denoising method with KANConv ECA Un...

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

作者： Shijie Zhang Yulun Wu Ganghui Ru Yi Yu Wei Li School of Computer Science and Technology Fudan University Shanghai China Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Japan School of Computer Science and Technology Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

During concerts, people often spontaneously record memorable moments with their phones. However, these recordings are frequently accompanied by noise, such as cheering and applause, which diminishes the playback experience. In this paper, we introduce a novel task specifically designed for denoising music in concert environments, a challenge that has been largely overlooked in previous research. To support this task, we created a new concert denoising dataset that includes songs performed in various major languages at concerts, with noise segments like cheering and applause. Building on this, we propose KANConv ECA Unet (KCE-Unet), a method that combines the U-Net network, efficient channel attention (ECA), and the recently proposed KAN network to flexibly remove noise in the mid-to-high frequency range of spectrograms. Extensive experiments demonstrate that our method outperforms previous models in denoising performance and effectively restore disrupted musical structures.

关键词： Noise reduction Noise Buildings Acoustics Recording Multiple signal classification Speech processing Spectrogram

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Suppress content shift: better diffusion features via off-the-shelf generation techniques 24

Suppress content shift: better diffusion features via off-th...

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Proceedings of the 38th International Conference on Neural information processing Systems

作者： Benyuan Meng Qianqian Xu Zitai Wang Zhiyong Yang Xiaochun Cao Qingming Huang Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Peng Cheng Laboratory Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS School of Computer Science and Tech. University of Chinese Academy of Sciences School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Key Laboratory of Big Data Mining and Knowledge Management CAS

ISBN: (纸本)9798331314385

Diffusion models are powerful generative models, and this capability can also be applied to discrimination. The inner activations of a pre-trained diffusion model can serve as features for discriminative tasks, namely, diffusion feature. We discover that diffusion feature has been hindered by a hidden yet universal phenomenon that we call content shift. To be specific, there are content differences between features and the input image, such as the exact shape of a certain object. We locate the cause of content shift as one inherent characteristic of diffusion models, which suggests the broad existence of this phenomenon in diffusion feature. Further empirical study also indicates that its negative impact is not negligible even when content shift is not visually perceivable. Hence, we propose to suppress content shift to enhance the overall quality of diffusion features. Specifically, content shift is related to the information drift during the process of recovering an image from the noisy input, pointing out the possibility of turning off-the-shelf generation techniques into tools for content shift suppression. We further propose a practical guideline named GATE to efficiently evaluate the potential benefit of a technique and provide an implementation of our methodology. Despite the simplicity, the proposed approach has achieved superior results on various tasks and datasets, validating its potential as a generic booster for diffusion features. Our code is available at https://***/Darkbblue/diffusion-content-shift.

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Domain Adversarial Interaction Network for Cross-Domain Fault Diagnosis 4th

Domain Adversarial Interaction Network for Cross-Domain Fau...

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Lu, Weikai Chen, Jian Zheng, Hao Fan, Haoyi Wei, Eng Yee Cao, Xinrong Zhang, Deyang School of Electronic Electrical Engineering and Physics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China School of Computer Sciences Universiti Sains Malaysia Penang11800 Malaysia Henan Provincial Institute of Scientific and Technical Information Zhengzhou450003 China

ISBN: (纸本)9783031200984

Intelligent fault diagnosis has been widely used in the industry and plays a crucial role in the health management of machinery. In recent years, unsupervised domain adaptation (UDA) has been applied to fault diagnosis, showing excellent performance under variable working conditions. However, most existing UDA-based methods do not consider the temporal relations in the fault signal, resulting in sub-optimal performance. In this paper, we proposed a domain adversarial interaction network (DAIN) to solve this problem. By downsampling sub-sequences of fault signals and interacting with their features, DAIN can obtain feature representations containing the temporal relations. In addition, domain adversarial learning and maximum mean discrepancy (MMD) are applied to DAIN to align the domain discrepancy and distribution discrepancy of source and target domains. We conducted extensive experiments on the public Paderborn University (PU) dataset, and the results demonstrate that the proposed method can achieve higher cross-domain fault diagnosis accuracy than the existing methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Failure analysis

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Inferring DTIs Based on Similarity Clustering and CaGCN-DTI Model from Heterogeneous Network

Inferring DTIs Based on Similarity Clustering and CaGCN-DTI ...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Li, Aoxing Lin, Xiaoli Yu, Haiping Wuhan University of Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Real-time Industrial System School of Computer Science and Technology Wuhan China Huanggang Normal University Hubei Key Laboratory of Intelligent Information Processing and Real-time Industrial System School of Computer Science Wuhan China

ISBN: (纸本)9781665401265

Although much progress has been made in new drug development, it is still a costly, complicated and less efficient process. Therefore, drug repositioning studies are highly desirable. So far, many methods based on known drug-target interactions (DTIs) have been designed to detect potential DTIs, but there are many challenges for improving the performance of prediction DTIs. This paper proposes a new method (CaGCN-DTI) for DTIs prediction, which uses a heterogeneous network that incorporates a wide variety of biological data (drug, target, disease, and side effect) to discover potential interactions between drugs and targets. First, the drug-protein similarity network is preprocessed by Spectral clustering, then the graph convolutional network (GCN) combined with attention mechanism and random walk with restart (RWR) is used to aggregate message transmission to the network. Finally, the embedding vectors are used to discover potential DTIs by matrix decomposition. Experiments are performed based on ten-fold cross-validation, and the results show that CaGCN-DTI outperforms other previous methods in terms of AUC and AUPR. © 2021 IEEE.

关键词： Convolution

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HCU-Net: An Efficient Fully Convolutional Neural Network for Thyroid Nodules Ultrasound Image Segmentation

HCU-Net: An Efficient Fully Convolutional Neural Network for...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Mengxi Ren Jun Liu School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Thyroid nodules, which can manifest as solitary or multiple growths, are among the most prevalent diseases with a high incidence rate. When examining thyroid nodules, ultrasound is typically the first step. Ultrasound image segmentation of thyroid nodules can assist physicians in diagnosis. However, the following three factors limit the development of thyroid nodule segmentation: (1) most of the existing studies optimize the segmentation of single nodules, and the incidence of multiple nodules is higher, so the segmentation of multiple nodules is also very important; (2) the ultrasound image of thyroid nodules is difficult to segment because of its low resolution and fuzzy boundary; (3) Due to the small data set of thyroid nodule ultrasound, in such a small dataset, attention mechanisms may be prone to overfitting, resulting in poorer generalization performance. Therefore, the attention-based model may not be suitable for the segmentation of thyroid nodules. Hence, we propose Hybrid Convolutional U-Net (HCU-Net), a simple and strong fully convolutional neural network for the segmentation of single or multiple thyroid nodules. HCU-Net incorporates a large receptive field to ensure accurate and stable segmentation. Specifically, we design Hybrid Branch Block to enhance the network's feature extraction capability for single or multiple nodules in ultrasound images by integrating multi-scale features. Furthermore, we add Enhanced ConvNeXt Blocks to the U-Net's final layer, enabling the extraction of global contextual information for segmentation performance. The Experimental results show that our proposed method can effectively improve these issues on two publicly available datasets. In addition, compared to attention-based models, our network is simpler and more suitable for small datasets like ultrasound images.

关键词： Image segmentation Ultrasonic imaging Image resolution Medical services Feature extraction Convolutional neural networks Medical diagnostic imaging Thyroid Overfitting Diseases

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Balance Multi-Head Attention based on Software and Hardware Co-design 9

Balance Multi-Head Attention based on Software and Hardware ...

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9th IEEE International Conference on Cyber Security and Cloud Computing and 8th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2022

作者： Xu, Dian Hu, Wei Liu, Fang Fan, Zimeng Shi, Qingsong Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System College of Computer Science Wuhan China Wuhan University Department of Information Engineering Wuhan Institute of City School of Computer Science Wuhan China Zhejiang University College of Computer Science Hangzhou China

ISBN: (数字)9781665480666

ISBN: (纸本)9781665480666

Recently, the Transformer-based models have achieved leading results in many research areas such as natural language processing and computer vision. However, since Transformer-based models have a huge computational complexities, optimization for Transformer is the focus of current research. the core of Transformer models is the attention module, and in this paper we propose a software and hardware co-designed attention module, which reduces the computational units of the multi-head attention module by balancing the The inference time of the attention module is reduced by balancing the computational units of multiple attention modules. At the same time, we design the corresponding structure on FPGA, which accelerate 14.6x inference time in comparison with the original attention module implementation on GPU. We applied this attention module to multiple visual Transformer models and tested the accuracy difference between the balanced multi-head attention module-based model and the original model on CIFAR-10, and obtained a result of 80.46%, which lower from the original version by 0.18%. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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HPC-Atlas:Computationally Constructing A Comprehensive Atlas of Human Protein Complexes

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Genomics, Proteomics & Bioinformatics 2023年第5期21卷 976-990页

作者： Yuliang Pan Ruiyi Li Wengen Li Liuzhenghao Lv Jihong Guan Shuigeng Zhou Department of Computer Science and Technology College of Electronic and Information EngineeringTongji UniversityShanghai 201804China Translational Medical Center for Stem Cell Therapy Shanghai East HospitalSchool of MedicineTongji UniversityShanghai 200120China Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China

A fundamental principle of biology is that proteins tend to form complexes to play important roles in the core functions of *** a complete understanding of human cellular functions,it is crucial to have a comprehensive atlas of human protein ***,we still lack such a comprehensive atlas of experimentally validated protein complexes,which prevents us from gaining a complete understanding of the compositions and functions of human protein complexes,as well as the underlying biological *** fill this gap,we built Human Protein Complexes Atlas(HPC-Atlas),as far as we know,the most accurate and comprehensive atlas of human protein complexes available to *** integrated two latest protein interaction networks,and developed a novel computational method to identify nearly 9000 protein complexes,including many previously uncharacterized *** with the existing methods,our method achieved outstanding performance on both testing and independent ***,with HPC-Atlas we identified 751 severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)-affected human protein complexes,and 456 multifunctional proteins that contain many potential moonlighting *** results suggest that HPC-Atlas can serve as not only a computing framework to effectively identify biologically meaningful protein complexes by integrating multiple protein data sources,but also a valuable resource for exploring new biological *** HPCAtlas webserver is freely available at http://***/HPC-Atlas.

关键词： Human protein complex Protein interaction network SARS-CoV-2-affected complex Multifunctional protein Complex identification method

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NB-GTR: Narrow-Band Guided Turbulence Removal

NB-GTR: Narrow-Band Guided Turbulence Removal

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yifei Xia Chu Zhou Chengxuan Zhu Minggui Teng Chao Xu Boxin Shi National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University National Key Lab of General AI School of Intelligence Science and Technology Peking University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

The removal of atmospheric turbulence is crucial for long-distance imaging. Leveraging the stochastic nature of atmospheric turbulence, numerous algorithms have been developed that employ multi-frame input to mitigate the tur-bulence. However, when limited to a single frame, existing algorithms face substantial performance drops, partic-ularly in diverse real-world scenes. In this paper, we propose a robust solution to turbulence removal from an RGB image under the guidance of an additional narrow-band image, broadening the applicability of turbulence mitigation techniques in real-world imaging scenarios. Our approach exhibits a substantial suppression in the magnitude of tur-bulence artifacts by using only a pair of images, thereby enhancing the clarity and fidelity of the captured scene.

关键词： computer vision Prevention and mitigation Face recognition computer network reliability Imaging Reliability theory

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Radiologist-in-the-Loop Self-Training for Generalizable CT Metal Artifact Reduction

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IEEE Transactions on Medical Imaging 2025年第6期44卷 2504-2514页

作者： Ma, Chenglong Li, Zilong Li, Yuanlin Han, Jing Zhang, Junping Zhang, Yi Liu, Jiannan Shan, Hongming Institute of Science and Technology for Brain-Inspired Intelligence MOE Frontiers Center for Brain Science Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Shanghai200433 China Fudan University Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Shanghai200433 China Shanghai Jiao Tong University School of Medicine Department of Oral Maxillofacial Head and Neck Oncology Shanghai Ninth People’s Hospital Shanghai200025 China Shanghai Jiao Tong University College of Stomatology Shanghai200240 China National Center for Stomatology Shanghai200011 China Shanghai Research Institute of Stomatology National Clinical Research Center for Oral Diseases Shanghai Key Laboratory of Stomatology Shanghai200011 China Sichuan University School of Cyber Science and Engineering Sichuan Chengdu610065 China

Metal artifacts in computed tomography (CT) images can significantly degrade image quality and impede accurate diagnosis. Supervised metal artifact reduction (MAR) methods, trained using simulated datasets, often struggle to perform well on real clinical CT images due to a substantial domain gap. Although state-of-the-art semi-supervised methods use pseudo ground-truths generated by a prior network to mitigate this issue, their reliance on a fixed prior limits both the quality and quantity of these pseudo ground-truths, introducing confirmation bias and reducing clinical applicability. To address these limitations, we propose a novel radiologist-in-the-loop self-training framework for MAR, termed RISE-MAR, which can integrate radiologists’ feedback into the semi-supervised learning process, progressively improving the quality and quantity of pseudo ground-truths for enhanced generalization on real clinical CT images. For quality assurance, we introduce a clinical quality assessor model that emulates radiologist evaluations, effectively selecting high-quality pseudo ground-truths for semi-supervised training. For quantity assurance, our self-training framework iteratively generates additional high-quality pseudo ground-truths, expanding the clinical dataset and further improving model generalization. Extensive experimental results on multiple clinical datasets demonstrate the superior generalization performance of our RISE-MAR over state-of-the-art methods, advancing the development of MAR models for practical application. © 1982-2012 IEEE.

关键词： computerized tomography

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