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arXiv 2022年

作者： Si, Chongjie Jia, Yuheng Wang, Ran Zhang, Min-Ling Feng, Yanghe Qu, Chongxiao The Chien-Shiung Wu College Southeast University Nanjing210096 China The MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China The School of Computer Science and Engineering Southeast University Nanjing210096 China Ministry of Education China School of Computing & Information Sciences Caritas Institute of Higher Education Hong Kong The Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China The School of Mathematical Science Shenzhen University Shenzhen518060 China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China The College of Systems Engineering National University of Defense Technology China The 52nd Research Institute of China Electronics Technology Group China

Exploiting label correlations is important to multi-label classification. Previous methods capture the high-order label correlations mainly by transforming the label matrix to a latent label space with low-rank matrix factorization. However, the label matrix is generally a full-rank or approximate full-rank matrix, making the low-rank factorization inappropriate. Besides, in the latent space, the label correlations will become implicit. To this end, we propose a simple yet effective method to depict the high-order label correlations explicitly, and at the same time maintain the high-rank of the label matrix. Moreover, we estimate the label correlations and infer model parameters simultaneously via the local geometric structure of the input to achieve mutual enhancement. Comparative studies over twelve benchmark data sets validate the effectiveness of the proposed algorithm in multi-label classification. The exploited high-order label correlations are consistent with common sense empirically. Our code is publicly available at https://***/Chongjie-Si/HOMI. Copyright © 2022, The Authors. All rights reserved.

关键词： Matrix algebra

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FedDAG: Federated DAG Structure learning

arXiv

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arXiv 2021年

作者： Gao, Erdun Chen, Junjia Shen, Li Liu, Tongliang Gong, Mingming Bondell, Howard School of Mathematics and Statistics The University of Melbourne Australia Faculty of Electronic and Information Engineering Xi'an Jiaotong University China JD Explore Academy China TML Lab Sydney AI Centre The University of Sydney Australia Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates

To date, most directed acyclic graphs (DAGs) structure learning approaches require data to be stored in a central server. However, due to the consideration of privacy protection, data owners gradually refuse to share their personalized raw data to avoid private information leakage, making this task more troublesome by cutting off the first step. Thus, a puzzle arises: how do we discover the underlying DAG structure from decentralized data? In this paper, focusing on the additive noise models (ANMs) assumption of data generation, we take the first step in developing a gradient-based learning framework named FedDAG, which can learn the DAG structure without directly touching the local data and also can naturally handle the data heterogeneity. Our method benefits from a two-level structure of each local model. The first level structure learns the edges and directions of the graph and communicates with the server to get the model information from other clients during the learning procedure, while the second level structure approximates the mechanisms among variables and personally updates on its own data to accommodate the data heterogeneity. Moreover, FedDAG formulates the overall learning task as a continuous optimization problem by taking advantage of an equality acyclicity constraint, which can be solved by gradient descent methods to boost the searching efficiency. Extensive experiments on both synthetic and real-world datasets verify the efficacy of the proposed method. Copyright © 2021, The Authors. All rights reserved.

关键词： Directed graphs

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Correction: Multivariate clustering for maximizing the small cell users’ performance based on the dynamic interference alignment

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Wireless Networks 2023年第1期30卷 593-593页

作者： Dakshinamoorthy, Prabakar Vaitilingam, Saminadan Sundar, Ramesh Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Department of Electronics and Communication Engineering Puducherry Technological University Puducherry India Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

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Dispatching and Control Information Freshness-Aware Federated learning for Simplified Power IoT

Dispatching and Control Information Freshness-Aware Federate...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Zehan Jia Ziqi Yu Haijun Liao Zhao Wang Zhenyu Zhou Xiaoyan Wang Guoqing He Shahid Mumtaz Mohsen Guizani Hebei Key Laboratory of Power Internet of Things Technology North China Electric Power University Beijing Baoding Hebei China Graduate School of Science and Engineering Ibaraki University Ibaraki-ken Japan State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute Beijing China Instituto de Telecomunicações Aveiro Portugal Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) UAE

ISBN: (纸本)9781665435413

Dispatching and control information freshness conducts an important impact on the training accuracy of distributed energy dispatching and control model. Poor information freshness will increase the loss function of the training model, and reduce the reliability and economy of dispatching and control. Simplified power internet of things can provide plug-and-play and multi- mode fusion communication support, but it still faces challenges of the coupling of model training and data transmission as well as the difficulty in guaranteeing dispatching and control information freshness. In this paper, a semi-distributed federated learning- based framework for dispatching and control model training decision-making is proposed, and a dispatChing and control informAtion fReshness-aware batch size Optimization aLgorithm (CAROL) is presented. CAROL leverages deep Q network and dispatching and control information freshness awareness to learn the batch size optimization strategy. CAROL can minimize model loss function while guaranteeing long-term dispatching and control information freshness constraints. Compared with existing feder- ated learning algorithms, CAROL achieves superior performance in global loss function and information freshness.

关键词： Training Computational modeling Simulation Decision making Minimization Dispatching Data models

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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

arXiv

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

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An End-to-End Multi-task learning Network with Scope Controller for Emotion-Cause Pair Extraction 9th

An End-to-End Multi-task Learning Network with Scope Control...

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9th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2020

作者： Fan, Rui Wang, Yufan He, Tingting Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning China Central Normal University Wuhan430079 China School of Computer Central China Normal University Wuhan430079 China National Engineering Research Center for E-Learning China Central Normal University Wuhan430079 China National Language Resources Monitor & Research Center for Network Media Wuhan430079 China

ISBN: (纸本)9783030604493

Emotion-cause pair extraction (ECPE) aims to extract all potential pairs of emotions and corresponding causes in a document. It has an advantage over traditional emotion cause extraction (ECE) that it does not require annotating emotions manually. Existing methods for ECPE task are based on two-step framework. However, they ignore the fact that the emotion-cause pair is regarded as a whole unit and there are cascading errors in two-step framework. In this paper, we propose an end-to-end hierarchical neural network model, which directly extracts emotion-cause pairs and enhances mutual interaction between emotions and causes via multi-task learning. In addition, we introduce a scope controller to constrain the emotion-cause pair predictions in a high probability area, according to the position correlation between emotions and causes. The experimental results demonstrate that our method achieves the state-of-the-art performance and improves F-measure by 2.24%. © 2020, Springer Nature Switzerland AG.

关键词： Extraction

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Cybertwin driven resource allocation using optimized proximal policy based federated learning in 6G enabled edge environment

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Digital Communications and Networks 2025年

作者： Sowmya Madhavan M.G. Aruna G.P. Ramesh Abdul Lateef Haroon Phulara Shaik Dhulipalla Ramya Krishna Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Yelahanka Bangalore India Department of Artificial Intelligence and Machine Learning Dayanand Sagar College of Engineering Bangalore India Department of Electronics and Communication Engineering St Peter's Institute of Higher Education and Research Chennai India Department of Electronics and Communication Engineering Ballari Institute of Technology and Management Ballari India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Hyderabad India

Sixth-generation (6G) communication system promises unprecedented data density and transformative applications over different industries. However, managing heterogeneous data with different distributions in 6G-enabled multi-access edge cloud networks presents challenges for efficient machine learning (ML) training and aggregation, often leading to increased energy consumption and reduced model generalization. To solve this problem, this research proposes a Weighted Proximal Policy-based Federated learning approach integrated with ResNet50 and Scaled Exponential Linear Unit activation function (WPPFL-RS). The proposed method optimizes resource allocation such as CPU and memory, through enhancing the Cyber-twin technology to estimate the computing capacities of edge clouds. The proposed WPPFL-RS approach significantly minimizes the latency and energy consumption, solving complex challenges in 6G-enabled edge computing. This make sures that efficient resource utilization and enhanced performance in heterogeneous edge networks. The proposed WPPFL-RS achieves a minimum latency of 8.20 s on 100 tasks, a significant improvement over the baseline Deep Reinforcement learning (DRL), which recorded 11.39 s. This approach highlights its potential to enhance resource utilization and performance in 6G edge networks.

关键词： Cybertwin Federated learning ResNet50 Resource allocation Scaled exponential linear Unit Weighted proximal policy

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QuantYOLO: A High-Throughput and Power-Efficient Object Detection Network for Resource and Power Constrained UAVs

QuantYOLO: A High-Throughput and Power-Efficient Object Dete...

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Muhammad Gohar Javed Minahil Raza Muhammad Mohsin Ghaffar Christian Weis Norbert Wehn Muhammad Shahzad Faisal Shafait School of Electrical Engineering and Computer Science National University of Sciences and Technology Pakistan Microelectronic Systems Design Research Group Technische Universität Kaiserslautern Germany Deep Learning Laboratory National Center of Artificial Intelligence (NCAI) Pakistan Data Science in Earth Observation TU Munich Germany

Convolutional Neural Networks (CNNs) are producing state-of-the-art results in the object detection field. However, deep topologies of CNN are computationally intensive and typically require excessive resources (i.e. high-end GPUs), which hinder their deployment on resource and power constrained UAVs. In this work, we present a high-throughput and power efficient quantized object detection network, QuantYOLO, which is based on the Tiny-YOLOv2 topology. We conduct a detailed exploration of precision and filter pruning vs. accuracy, throughput and power consumption trade-off for the object detection task. As a result of these explorations, we select a network with binarized weights and 4-bit activations (except the output layer), which is 21.8× smaller than the Tiny-YOLOv2 achieving a mean Average Precision (mAP) of 51.5% on the PASCAL-VOC dataset. Finally, we present an FPGA based accelerator, which achieves 1.6× higher throughput (FPS) and is 3.1× more power efficient as compared to prior FPGA architectures.

关键词： Quantization (signal) Power demand Network topology Object detection computer architecture Throughput Topology

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UnModNet: learning to unwrap a modulo image for high dynamic range imaging 20

UnModNet: learning to unwrap a modulo image for high dynamic...

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Proceedings of the 34th International Conference on Neural Information Processing Systems

作者： Chu Zhou Hang Zhao Jin Han Chang Xu Chao Xu Tiejun Huang Boxin Shi Key Lab of Machine Perception (MOE) Dept. of Machine Intelligence Peking University Massachusetts Institute of Technology School of Computer Science Faculty of Engineering University of Sydney NELVT Dept. of Computer Science Peking University and Institute for Artificial Intelligence Peking University

ISBN: (纸本)9781713829546

A conventional camera often suffers from over- or under-exposure when recording a real-world scene with a very high dynamic range (HDR). In contrast, a modulo camera with a Markov random field (MRF) based unwrapping algorithm can theoretically accomplish unbounded dynamic range but shows degenerate performances when there are modulus-intensity ambiguity, strong local contrast, and color misalignment. In this paper, we reformulate the modulo image unwrapping problem into a series of binary labeling problems and propose a modulo edge-aware model, named as UnModNet, to iteratively estimate the binary rollover masks of the modulo image for unwrapping. Experimental results show that our approach can generate 12-bit HDR images from 8-bit modulo images reliably, and runs much faster than the previous MRF-based algorithm thanks to the GPU acceleration.

关键词：

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Refining BERT embeddings for document hashing via mutual information maximization

arXiv

引用

arXiv 2021年

作者： Ou, Zijing Su, Qinliang Yu, Jianxing Zhao, Ruihui Zheng, Yefeng Liu, Bang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Artificial Intelligence Sun Yat-sen University Guangdong China Tencent Jarvis Lab Rali & Mila Université de Montréal Guangdong Key Lab. of Big Data Analysis and Processing Guangzhou China Key Lab. of Machine Intelligence and Advanced Computing Ministry of Education China

Existing unsupervised document hashing methods are mostly established on generative models. Due to the difficulties of capturing long dependency structures, these methods rarely model the raw documents directly, but instead to model the features extracted from them (e.g. bag-of-words (BOW), TFIDF). In this paper, we propose to learn hash codes from BERT embeddings after observing their tremendous successes on downstream tasks. As a first try, we modify existing generative hashing models to accommodate the BERT embeddings. However, little improvement is observed over the codes learned from the old BOW or TFIDF features. We attribute this to the reconstruction requirement in the generative hashing, which will enforce irrelevant information that is abundant in the BERT embeddings also compressed into the codes. To remedy this issue, a new unsupervised hashing paradigm is further proposed based on the mutual information (MI) maximization principle. Specifically, the method first constructs appropriate global and local codes from the documents and then seeks to maximize their mutual information. Experimental results on three benchmark datasets demonstrate that the proposed method is able to generate hash codes that outperform existing ones learned from BOW features by a substantial margin. Copyright © 2021, The Authors. All rights reserved.

关键词： Embeddings

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