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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Hao Kong Di Liu Xiangzhong Luo Shuo Huai Ravi Subramaniam Christian Makaya Qian Lin Weichen Liu School of Computer Science and Engineering Nanyang Technological University Singapore and HP-NTU Digital Manufacturing Corporate Lab Nanyang Technological University Singapore Department of Computer Science Norwegian University of Science and Technology Trondheim Norway. School of Computer Science and Engineering Nanyang Technological University Singapore HP Inc. Palo Alto California USA

ISBN: (纸本)9798350323481

In this paper, we propose TECO, a multi-dimensional pruning framework to collaboratively prune the three dimensions (depth, width, and resolution) of convolutional neural networks (CNNs) for better execution efficiency on embedded hardware. In TECO, we first introduce a two-stage importance evaluation framework, which efficiently and comprehensively evaluates each pruning unit according to both the local importance inside each dimension and the global importance across different dimensions. Based on the evaluation framework, we present a heuristic pruning algorithm to progressively prune the three dimensions of CNNs towards the optimal trade-off between accuracy and efficiency. Experiments on multiple benchmarks validate the advantages of TECO over existing state-of-the-art (SOTA) approaches. The code and pre-trained models are available anonymously at https://***/ntuliuteam/Teco.

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FM-FCN:A Neural Network with Filtering Modules for Accurate Vital Signs Extraction

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Research 2025年第1期2024卷 92-106页

作者： Fangfang Zhu Qichao Niu Xiang Li Qi Zhao Honghong Su Jianwei Shuai Department of Physics and Fujian Provincial Key Laboratory for Soft Functional Materials ResearchXiamen UniversityXiamen 361005China National Institute for Data Science in Health and Medicine and State Key Laboratory of Cellular Stress BiologyInnovation Center for Cell Signaling NetworkXiamen UniversityXiamen 361005China Vitalsilicon Technology Co.Ltd. JiaxingZhejiang 314006China School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan 114051China Yangtze Delta Region Institute of Tsinghua University ZhejiangJiaxing 314006China Wenzhou Institute University of Chinese Academy of SciencesWenzhou 325001China Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine Vision and Brain Health)Wenzhou 325001China

Neural networks excel at capturing local spatial patterns through convolutional modules,but they may struggle to identify and effectively utilize the morphological and amplitude periodic nature of physiological *** this work,we propose a novel network named filtering module fully convolutional network(FM-FCN),which fuses traditional filtering techniques with neural networks to amplify physiological signals and suppress ***,instead of using a fully connected layer,we use an FCN to preserve the time-dimensional correlation information of physiological signals,enabling multiple cycles of signals in the network and providing a basis for signal ***,we introduce the FM as a network module that adapts to eliminate unwanted interference,leveraging the structure of the *** approach builds a bridge between deep learning and signal processing ***,we evaluate the performance of FM-FCN using remote *** results demonstrate that FM-FCN outperforms the second-ranked method in terms of both blood volume pulse(BVP)signal and heart rate(HR)*** substantially improves the quality of BVP waveform reconstruction,with a decrease of 20.23%in mean absolute error(MAE)and an increase of 79.95%in signal-to-noise ratio(SNR).Regarding HR estimation accuracy,FM-FCN achieves a decrease of 35.85%in MAE,29.65%in error standard deviation,and 32.88%decrease in 95%limits of agreement width,meeting clinical standards for HR accuracy *** results highlight its potential in improving the accuracy and reliability of vital sign measurement through high-quality BVP signal *** codes and datasets are available online at https://***/zhaoqi106/FM-FCN.

关键词： physiological signalsin filtering module fully convolutional network fm fcn which vital signs extraction amplify physiological signals convolutional modulesbut neural networks filtering module capturing local spatial patterns

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Unmanned aerial vehicle based multi-person detection via deep neural network models

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Frontiers in Neurorobotics 2025年 19卷 1582995页

作者： Alshehri, Mohammed Zahoor, Laiba AlQahtani, Yahya Alshahrani, Abdulmonem AlHammadi, Dina Abdulaziz Jalal, Ahmad Liu, Hui Department of Computer Science King Khalid University Abha Saudi Arabia Faculty of Computer Science Air University Islamabad Pakistan Department of Informatics and Computer Systems King Khalid University Abha Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia Department of Computer Science and Engineering College of Informatics Korea University Seoul Korea Republic of Cognitive Systems Lab University of Bremen Bremen Germany Guodian Nanjing Automation Company Ltd. Nanjing China Jiangsu Key Laboratory of Intelligent Medical Image Computing School of Future Technology Nanjing University of Information Science and Technology Nanjing China

Introduction: Understanding human actions in complex environments is crucial for advancing applications in areas such as surveillance, robotics, and autonomous systems. Identifying actions from UAV-recorded videos becomes more challenging as the task presents unique challenges, including motion blur, dynamic background, lighting variations, and varying viewpoints. The presented work develops a deep learning system that recognizes multi-person behaviors from data gathered by UAVs. The proposed system provides higher recognition accuracy while maintaining robustness along with dynamic environmental adaptability through the integration of different features and neural network models. The study supports the wider development of neural network systems utilized in complicated contexts while creating intelligent UAV applications utilizing neural networks. Method: The proposed study uses deep learning and feature extraction approaches to create a novel method to recognize various actions in UAV-recorded video. The proposed model improves identification capacities and system robustness by addressing motion dynamic problems and intricate environmental constraints, encouraging advancements in UAV-based neural network systems. Results: We proposed a deep learning-based framework with feature extraction approaches that may effectively increase the accuracy and robustness of multi-person action recognition in the challenging scenarios. Compared to the existing approaches, our system achieved 91.50% on MOD20 dataset and 89.71% on Okutama-Action. These results do, in fact, show how useful neural network-based methods are for managing the limitations of UAV-based application. Discussion: Results how that the proposed framework is indeed effective at multi-person action recognition under difficult UAV conditions. Copyright © 2025 Alshehri, Zahoor, AlQahtani, Alshahrani, AlHammadi, Jalal and Liu.

关键词： Drones

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Federated and transfer learning for cancer detection based on image analysis

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Neural Computing and Applications 2025年第4期37卷 2239-2284页

作者： Bechar, Amine Medjoudj, Rafik Elmir, Youssef Himeur, Yassine Amira, Abbes Laboratoire LITAN École supérieure en Sciences et Technologies de l’Informatique et du Numérique RN 75 Amizour Bejaia06300 Algeria SGRE-Lab University Tahri Mohammed of Bechar Bechar08000 Algeria College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates Department of Computer Science University of Sharjah sharjah United Arab Emirates Institute of Artificial Intelligence De Montfort University Leicester United Kingdom

This review highlights the efficacy of combining federated learning (FL) and transfer learning (TL) for cancer detection via image analysis. By integrating these techniques, research has shown improvements in diagnostic accuracy and efficiency. Specifically, the use of FL and TL has led to a measurable improvement in the precision of cancer diagnoses, with some studies reporting up to a 20% increase in accuracy compared to traditional methods. This synthesis of FL and TL optimizes distributed data usage while leveraging existing models to expedite learning and application in cancer detection tasks. A concrete assessment of the two methods, including their strengths and weaknesses, is presented. Moving on, their applications in cancer detection are discussed, including potential directions for the future. Finally, this article offers a thorough description of the functions of TL and FL in image-based cancer detection. The authors also make insightful suggestions for additional study in this rapidly developing area. The findings underscore the potential of these combined approaches to significantly advance medical imaging and cancer diagnosis, setting a promising direction for future research. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Federated learning

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VNLU-Net: Visual Network with Lightweight Union-net for Acute Myeloid Leukemia Detection on Heterogeneous Dataset

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Biomedical Signal Processing and Control 2025年 107卷

作者： Saikia, Rabul Deka, Roopam Sarma, Anupam Singh, Ngangbam Herojit Khan, Muhammad Attique Devi, Salam Shuleenda Department of Electronics & Communication Engineering National Institute of Technology Meghalaya Shillong India Department of Pathology & Lab Medicine All India Institute of Medical Sciences Guwahati Guwahati India Department of Onco-Pathology Dr. Bhubaneswar Borooah Cancer Institute Guwahati India Department of Computer Science & Engineering National Institute of Technology Meghalaya Shillong India Department of AI College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al Khobar Saudi Arabia

Recent advancements in Artificial Intelligence (AI) and Deep Learning (DL) have shown promising results in Acute Myeloid Leukemia (AML) detection. However, challenges remain due to limited, annotated datasets and the need for precise architectures. This paper proposes VNLU-Net, a novel DL framework, by integrating the frozen VGG16 with a lightweight Union-net module. The Union-net module substitutes the last three convolutional layers along with fully connected layers of VGG16. In the framework, the frozen layers of VGG16 provide robust feature extraction, leveraging the pretrained weights. Subsequently, the Union-net refines these features with minimal parameters, enhancing model robustness and generalization. The proposed method achieves better performance, with 99.37% accuracy on the BBCI_AML_2024 dataset and 99.71% on a heterogeneous dataset. Additionally, the qualitative analysis using Gradient-weighted Class Activation Mapping (Grad-CAM) establishes the efficacy of the model. Moreover, the comparative analysis signifies its superiority over the standard existing approaches in terms of accuracy, precision, recall, F1-score, and specificity. © 2025 Elsevier Ltd

关键词： Convolutional neural networks

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Applicability of the Minimal Dominating Set for Influence Maximisation in Multilayer Networks

arXiv

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arXiv 2025年

作者： Czuba, Michal Jia, Mingshan Bródka, Piotr Musial, Katarzyna Department of Artificial Intelligence Wroclaw University of Science and Technology 27 wybrzeze Wyspiańskiego st Wroclaw50-370 Poland Complex Adaptive Systems Lab Data Science Institute School of Computer Science University of Technology Sydney UltimoNSW2007 Australia

The minimal dominating set (MDS) is a well-established concept in network controllability and has been successfully applied in various domains, including sensor placement, network resilience, and epidemic containment. In this study, we adapt the local-improvement MDS routine and explore its potential for enhancing seed selection for influence maximisation in multilayer networks (MLN). We employ the Linear Threshold Model (LTM), which offers an intuitive representation of influence spread or opinion dynamics by accounting for peer influence accumulation. To ensure interpretability, we utilise rank-refining seed selection methods, with the results further filtered with MDS. Our findings reveal that incorporating MDS into the seed selection process improves spread only within a specific range of situations. Notably, the improvement is observed for larger seed set budgets, lower activation thresholds, and when an"AND" strategy is used to aggregate influence across network layers. This scenario reflects situations where an individual does not require the majority of their acquaintances to hold a target opinion, but must be influenced across all social circles. Copyright © 2025, The Authors. All rights reserved.

关键词： Multilayers

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Vision-Based Multimodal Interfaces: A Survey and Taxonomy for Enhanced Context-Aware System Design 25

Vision-Based Multimodal Interfaces: A Survey and Taxonomy fo...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

作者： Yongquan ‘Owen’ Hu Jingyu Tang Xinya Gong Zhongyi Zhou Shuning Zhang Don Samitha Elvitigala Florian ‘Floyd’ Mueller Wen Hu Aaron J. Quigley School of Computer Science and Engineering University of New South Wales Sydney Australia School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China The University of Tokyo Tokyo Japan Tsinghua University Beijing China Exertion Games Lab Department of Human-Centred Computing Monash University Melbourne Australia School of Computer Science and Engineering University of New South Wales Syndey Australia CSIRO's Data61 Sydney Australia and University of New South Wales Sydney Australia

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SuperFlow++: Enhanced Spatiotemporal Consistency for Cross-Modal Data Pretraining

arXiv

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arXiv 2025年

作者： Xu, Xiang Kong, Lingdong Shuai, Hui Zhang, Wenwei Pan, Liang Chen, Kai Liu, Ziwei Liu, Qingshan The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China The School of Computing Department of Computer Science National University of Singapore CNRS@CREATE Singapore The School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Shanghai AI Laboratory Shanghai China S-Lab Nanyang Technological University Singapore

LiDAR representation learning has emerged as a promising approach to reducing reliance on costly and labor-intensive human annotations. While existing methods primarily focus on spatial alignment between LiDAR and camera sensors, they often overlook the temporal dynamics critical for capturing motion and scene continuity in driving scenarios. To address this limitation, we propose SuperFlow++, a novel framework that integrates spatiotemporal cues in both pretraining and downstream tasks using consecutive LiDAR-camera pairs. SuperFlow++ introduces four key components: (1) a view consistency alignment module to unify semantic information across camera views, (2) a dense-to-sparse consistency regularization mechanism to enhance feature robustness across varying point cloud densities, (3) a flow-based contrastive learning approach that models temporal relationships for improved scene understanding, and (4) a temporal voting strategy that propagates semantic information across LiDAR scans to improve prediction consistency. Extensive evaluations on 11 heterogeneous LiDAR datasets demonstrate that SuperFlow++ outperforms state-of-the-art methods across diverse tasks and driving conditions. Furthermore, by scaling both 2D and 3D backbones during pretraining, we uncover emergent properties that provide deeper insights into developing scalable 3D foundation models. With strong generalizability and computational efficiency, SuperFlow++ establishes a new benchmark for data-efficient LiDAR-based perception in autonomous driving. The code is publicly available at https://***/Xiangxu-0103/SuperFlow. © 2025, CC BY-SA.

关键词： Contrastive Learning

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Mediating The Marginal: A Quantitative Analysis of Curated LGBTQ+ Content on Instagram 25

Mediating The Marginal: A Quantitative Analysis of Curated L...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

作者： Garrett Souza Nina Lutz Katlyn M Turner Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts USA Department of Human Centered Design and Engineering University of Washington Seattle Washington USA Media Lab Massachusetts Institute of Technology Cambridge Massachusetts USA

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A Survey of Integrating Generative Artificial Intelligence and 6G Mobile Services: Architectures, Solutions, Technologies and Outlooks

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Liu, Yi-Jing Du, Hongyang Xu, Xinyi Zhang, Ruichen Feng, Gang Cao, Bin Niyato, Dusit Kim, Dong In Jamalipour, Abbas Letaief, Khaled B. Tafazolli, Rahim University of Electronic Science and Technology of China National Key Lab on Wireless Communications Chengdu China University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Nanyang Technological University College of Computing and Data Science Singapore Singapore Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of University of Sydney School of Electrical and Computer Engineering Sydney Australia Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong University of Surrey Institute for Communication Systems School of Computer Science and Electronic Engineering United Kingdom

Generative artificial intelligence (GenAI) is rapidly driving a new phase of artificial intelligence revolution, marked by various applications such as ChatGPT, Sora and DeepSeek. With powerful capabilities in content generation, personalization and user interaction, GenAI can drive significant enhancements in various mobile services to satisfy individual user preferences. Meanwhile, mobile services offer substantial support for accessibility, functionality and intelligence of GenAI applications. However, current research on integrating GenAI and mobile services remains nascent, lacking necessary theoretical and technical support. In this survey, we break down a new vision to guide research on integrating GenAI and mobile services, aiming to improve 6G intelligence levels and enable 6G mobile networks to support the integration of GenAI and mobile services effectively. We begin by examining the necessity of integrating GenAI with 6G mobile services and exploring potential learning architectures of GenAI models within mobile networks. Next, we analyze potential solutions from two perspectives: how 6G mobile networks empower GenAI and how GenAI enables mobile services. We subsequently explore a promising use case, along with essential features and techniques, to facilitate the integration of GenAI and mobile services. Finally, we discuss future research directions, aiming at realizing intelligent 6G networks. © 2015 IEEE.

关键词： Generative adversarial networks

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