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Kernel Adaptive Filtering with Weibull M-transform Maximum Versoria Criterion

Kernel Adaptive Filtering with Weibull M-transform Maximum V...

引用

IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Chenchong Bi Yingsong Li Liping Li Nikola Zlatanov Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Faculty of Computer and Engineering Sciences Innopolis University Innopolis Russia

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

A kernel Weibull M-transform maximum-versoria-criterion (KWMMVC) algorithm is constructed, which is to use for nonlinear system identification. The KWMMVC algorithm ingeniously integrates the Weibull M-transform scheme into the maximum-versoria-criterion to formulate a novel cost function. The ability of KWMMVC algorithm to identify nonlinear channels under various impulse noise environments is tested in the kernel adaptive filters, and the results proved its superior performance.

关键词： Adaptive systems Noise Adaptive filters Filtering algorithms Information filters Cost function Information and communication technology

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Foundation Model Driven Appearance Extraction for Robust Multiple Object Tracking 39

Foundation Model Driven Appearance Extraction for Robust Mul...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Fu, Teng Yu, Haiyang Niu, Ke Li, Bin Xue, Xiangyang Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University China

ISBN: (纸本)157735897X

Multiple Object Tracking (MOT) is a fundamental task in computer vision. Existing methods utilize motion information or appearance information to perform object tracking. However, these algorithms still struggle with special circumstances, such as occlusion and blurring in complex scenes. Inspired by the fact that people can pinpoint objects through verbal descriptions, we explore performing long-term robust tracking using semantic features of objects. Motivated by the success of the multimodal foundation model in text-image alignment, we reconsider the appearance feature extraction module in MOT and propose a Foundation model Driven multi-object tracker (FDTracker). Specifically, we propose a two-stage trained appearance feature extractor. In the first stage, using a single image of the object as input, the model could capture the attributes of objects with the assistance of natural language instructions. In the second stage, using a sequence of images of objects as input, the model learns how to use these attributes to distinguish between different objects and connect the same object at different times. Finally, for coordinating appearance and motion information, we propose a reasonable combined strategy, which better facilitates trajectory assignment and reconnection. Extensive experiments on benchmarks demonstrate the robustness of FDTracker. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Object detection

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Efficient Online Training for Zero-Shot Time-Lapse Microscopy Denoising and Super-Resolution 39

Efficient Online Training for Zero-Shot Time-Lapse Microscop...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： He, Ruian Cheng, Ri Lyu, Xinkai Tan, Weimin Yan, Bo School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University China

ISBN: (纸本)157735897X

In time-lapse microscopy, inherent noise significantly limits imaging sensitivity and increases measurement uncertainty. Due to the scarcity of clean data, zero-shot approaches have emerged as highly data-efficient solutions for microscopy denoising. However, existing methods typically process video frames independently, resulting in long training times and issues such as temporal noise and over-smoothing. In this paper, we introduce MDSR-Zero, a zero-shot online learning method designed for plug-and-play noise suppression and super-resolution of microscopy videos. Our approach leverages an efficient online training strategy that reuses denoising models from previous frames. By treating the video as a continuous stream, our model significantly reduces training time and ensures temporally consistent denoising. Additionally, we propose a novel loss function tailored for denoising in the context of super-resolution, which enhances the detail in the denoised results. Extensive experiments on both synthetic and real-world noise demonstrate that our method achieves state-of-the-art performance among zero-shot denoising approaches and is competitive with self-supervised methods. Notably, our method can reduce training time by up to 10x compared to the previous SOTA method. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Zero-shot learning

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Accelerating Unsupervised Federated Graph Neural Networks via Semi-asynchronous Communication 18th

Accelerating Unsupervised Federated Graph Neural Networks vi...

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18th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2023

作者： Liao, Yuanming Wu, Duanji Lin, Pengyu Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819996360

Graph neural networks have shown excellent performance in many fields owing to their powerful processing ability of graph data. In recent years, federated graph neural network has become a reasonable solution due to the enactment of privacy-related regulations. However, frequent communication between the coordinator and participants in federated graph neural network results in longer model training time and consumes many communication resources. To address this challenge, in this paper, we propose a novel semi-asynchronous federated graph learning communication protocol that simultaneously alleviates the negative impact of stragglers(slow participants) and accelerate the training process in the unsupervised federated graph neural network scenario. First, the weighted enforced synchronization strategy is intended to preserve the information carried by stragglers while preventing their stale models from harming the global model update. Second, the adaptive local update strategy is developed to make the local model of the participant with poor computing performance as close as possible to the global model. Experiments combine federated learning with graph contrastive learning. The results demonstrate that our proposed protocol outperforms the existing protocols in real-world networks. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet protocols

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AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation 38

AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmenta...

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38th Conference on Neural Information processing Systems, NeurIPS 2024

作者： Han, Boyu Xu, Qianqian Yang, Zhiyong Bao, Shilong Wen, Peisong Jiang, Yangbangyan Huang, Qingming Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China School of Computer Science and Tech. University of Chinese Academy of Sciences China Peng Cheng Laboratory China Key Laboratory of Big Data Mining and Knowledge Management CAS China

The Area Under the ROC Curve (AUC) is a well-known metric for evaluating instance-level long-tail learning problems. In the past two decades, many AUC optimization methods have been proposed to improve model performance under long-tail distributions. In this paper, we explore AUC optimization methods in the context of pixel-level long-tail semantic segmentation, a much more complicated scenario. This task introduces two major challenges for AUC optimization techniques. On one hand, AUC optimization in a pixel-level task involves complex coupling across loss terms, with structured inner-image and pairwise inter-image dependencies, complicating theoretical analysis. On the other hand, we find that mini-batch estimation of AUC loss in this case requires a larger batch size, resulting in an unaffordable space complexity. To address these issues, we develop a pixel-level AUC loss function and conduct a dependency-graph-based theoretical analysis of the algorithm's generalization ability. Additionally, we design a Tail-Classes Memory Bank (T-Memory Bank) to manage the significant memory demand. Finally, comprehensive experiments across various benchmarks confirm the effectiveness of our proposed AUCSeg method. The code is available at https://***/boyuh/AUCSeg. © 2024 Neural information processing systems foundation. All rights reserved.

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Wind Vector Inversion of ADS-B Data Based on Improved PSO-PF Algorithm

Wind Vector Inversion of ADS-B Data Based on Improved PSO-PF...

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International Conference on Guidance, Navigation and Control, ICGNC 2022

作者： Chen, Wantong Wu, Duo Ren, Shiyu Diao, Tianru Tianjin Key Laboratory of Intelligent Signal and Image Processing Civil Aviation University of China Tianjin300300 China School of Electronic Information and Automation Civil Aviation University of China Tianjin300300 China

ISBN: (纸本)9789811966125

At present, inversing wind vector from aircraft trajectory captured by automatic dependent surveillance-broadcast (ADS-B) is one of the main solutions to obtain wind field in aviation meteorology. However, some algorithms in this scheme still have some shortcomings, especially the poor performance when the aircraft turns at a small angle. In this paper, the geometric structure of ADS-B data is used to improve particle swarm optimized particle filter algorithm (PSO-PF) to inverse wind vectors in small area of single aircraft data and large area of multi-aircraft data respectively. The mean absolute error of wind estimation under different turning angles is evaluated. Experimental results show that the algorithm is feasible, and it still has a good inversion effect when the aircraft turns at a small angle. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Aircraft

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Rethinking Distribution Alignment for Inter-class Fairness 1

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1st International Conference on Artificial Intelligence Security and Privacy, AIS and P 2023

作者： Ye, Jinhuang Wu, Jiawei Li, Zuoyong Zheng, Xianghan College of Computer and Big Data Fuzhou University Fuzhou350108 China School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen518107 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China

ISBN: (数字)9789819997886

ISBN: (纸本)9789819997879

Semi-supervised learning (SSL) is a successful paradigm that can use unlabelled data to alleviate the labelling cost problem in supervised learning. However, the excellent performance brought by SSL does not transfer well to the task of class imbalance. The reason is that the class bias of pseudo-labelling further misleads the decision boundary. To solve this problem, we propose a new plug-and-play approach to handle the class imbalance problem based on a theoretical extension and analysis of distribution alignment. The method, called Basis Transformation Based Distribution Alignment (BTDA), efficiently aligns class distributions while taking into account inter-class *** implements the basis transformation through a learnable transfer matrix, thereby reducing the performance loss caused by pseudo-labelling biases. Extensive experiments show that our proposed BTDA approach can significantly improve performance in class imbalance tasks in terms of both accuracy and recall metrics when integrated with advanced SSL algorithms. Although the idea of BTDA is not complex, it can show advanced performance on datasets such as CIFAR and SVHN. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image classification

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Deep Representation Learning for Electron Ionization Mass Spectra Retrieval

Deep Representation Learning for Electron Ionization Mass Sp...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Shibo Zhang, Xuan Jiao, Anlei Sun, Shiwei Dian, Longyang Cui, Xuefeng Shandong University School of Computer Science and Technology Qingdao China Chinese Academy of Sciences Key Lab of Intelligent Information Processing Institute of Computing Technology Beijing China Shandong University State Key Laboratory of Microbial Technology Qingdao China

ISBN: (纸本)9798350386226

The task of retrieving and analyzing mass spectra is indispensable for the identification of compounds in mass spectrometry (MS). This methodology is of critical importance as it enables researchers to correlate observed spectra with established databases, thereby precisely determining the chemical composition of samples. The primary challenges to its efficacy lie in optimizing the balance between retrieval accuracy and processing speed. Empirical studies have demonstrated that by converting mass spectra into embeddings via deep learning, it is possible to achieve both high accuracy and speed in retrieval. Nevertheless, there are complex challenges associated with employing deep learning for spectral embedding, particularly within the domain of electron ionization mass spectrometry (EI-MS). In this paper, we introduce a novel representation learning technique termed EI-MS2VEC for EI-MS retrieval. Our spectrum retrieval methodology surpasses current state-of-the-art techniques such as FastEI. For the in-silico library, we attain hit rate@1 and hit rate@10 of 43.6% and 84.5%, respectively, compared to FastEI's 36.7% and 80.4%. Moreover, our retrieval approach operates with an order of magnitude greater speed than FastEI. The source code is available on Github (https://***/xfcui/EI-MS2VEC). © 2024 IEEE.

关键词： Embeddings

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Edge-Preserving Multi-Exposure Fusion with Guided Filtering for Structural Patch Decomposition 6

Edge-Preserving Multi-Exposure Fusion with Guided Filtering ...

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6th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2023

作者： Zhen, Yang Qi, Junwei Li, Yingsong College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Ministry of Education Anhui University Key Laboratory of Intelligent Computing and Signal Processing Hefei China College of Computer Science and Technology Harbin Engineering University Harbin150001 China

ISBN: (纸本)9798350399059

Multi-exposure image fusion (MEF) is one of the effective ways to obtain high dynamic range (HDR) images. Multi-scale structural patch decomposition based MEF(MSPD) and edge-preserving structural patch decomposition (MESPD) have achieved state-of-the-art fusion quality. MESPD incorporates edge-preserving factors into MSPD to preserve details in a single-scale setting. However, we find that the edge-preserving factors put too much emphasis on the edge, resulting in artifacts on some edges and loss of details. To tackle this issue, guided filter is introduced into MSPD, which is directly used as an edge-preserving filter instead of mean filter to implicitly decompose images. In addition, we have designed a flexible adaptive weight, which can further maintain details of the light and dark areas. Likewise, we inherit MESPD and MSPD to extend our method to multi-scale framework. Extensive experimental results demonstrate our method can produce satisfactory results in both static and dynamic scenes with fewer artifacts and low computational cost. © 2023 IEEE.

关键词： Image fusion

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Short-Term Smart Grid Load Forecasting Based on CNN-BiLSTM with Attention Mechanism 6

Short-Term Smart Grid Load Forecasting Based on CNN-BiLSTM w...

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6th International Conference on Energy, Power and Grid, ICEPG 2024

作者： Sun, Feihu Zhong, Yanru Guilin University of Electronic Technology School of Computer Science and Information Security Guangxi Guilin China Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guangxi Guilin China

ISBN: (纸本)9798350377798

The existing power load data exhibits complex multidimensionality, while existing techniques often focus on learning and forecasting from single load data. To fully exploit the complex multidimensionality of the existing power load data and enhance the accuracy of short-term load forecasting, a smart grid short-term load forecasting model based on attention mechanism and CNN-BiLSTM is proposed. Firstly, the one-dimensional convolutional network (1D-CNN) slides along the temporal dimension to extract multidimensional features such as temperature and humidity from the raw power load data. Secondly, the Bidirectional Long Short-Term Memory network (BiLSTM) captures both historical and future information of the data through gate mechanisms and the bidirectional network structure. Lastly, the attention mechanism allocates weights to focus on important parts of the load sequence data, enhancing the model's ability to identify key information and further improving the accuracy of short-term power load forecasting. The method is experimented on a real-world multivariate load dataset and compared with existing learning methods. The proposed model achieved lower RMSE in 24-hour load forecasting compared to CNN-LSTM, CNN-BiLSTM, LSTM-Attention, BiLSTM-Attention and CNN-LSTM-Attention models by 13.2%, 10.6%, 6.7%, 5.7% and 5.9% respectively. © 2024 IEEE.

关键词： Long short-term memory

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