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arXiv 2024年

作者： Yang, Yang Chen, Mingzhe Blankenship, Yufei Lee, Jemin Ghassemlooy, Zabih Cheng, Julian Mao, Shiwen The School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL United States The Ericsson SchaumburgIL United States School of Electrical and Electronic Engineering Yonsei University Seoul Korea Republic of The Optical Communications Research Group Faculty of Engineering and Environment Northumbria University Newcastle upon TyneNE1 8ST United Kingdom The School of Engineering The University of British Columbia KelownaBC Canada The Wireless Engineering Research and Education Center Auburn University AuburnAL United States

Positioning has recently received considerable attention as a key enabler in emerging applications such as extended reality, unmanned aerial vehicles and smart environments. These applications require both data communication and high-precision positioning, and thus they are particularly well-suited to be offered in wireless networks (WNs). The purpose of this paper is to provide a comprehensive overview of existing works and new trends in the field of positioning techniques from both the academic and industrial perspectives. The paper provides a comprehensive overview of positioning in WNs, covering the background, applications, measurements, state-of-the-art technologies and future challenges. The paper outlines the applications of positioning from the perspectives of public facilities, enterprises and individual users. We investigate the key performance indicators and measurements of positioning systems, followed by the review of the key enabler techniques such as artificial intelligence/large models and adaptive systems. Next, we discuss a number of typical wireless positioning technologies. We extend our overview beyond the academic progress, to include the standardization efforts, and finally, we provide insight into the challenges that remain. The comprehensive overview of exisitng efforts and new trends in the field of positioning from both the academic and industrial communities would be a useful reference to researchers in the field. Copyright © 2024, The Authors. All rights reserved.

关键词： Wireless networks

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RecycleNet: Latent Feature Recycling Leads to Iterative Decision Refinement

arXiv

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arXiv 2023年

作者： Koehler, Gregor Wald, Tassilo Ulrich, Constantin Zimmerer, David Jaeger, Paul F. Franke, Jörg K.H. Kohl, Simon Isensee, Fabian Maier-Hein, Klaus H. Heidelberg Division of Medical Image Computing Germany Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Helmholtz Imaging DKFZ Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Interactive Machine Learning Group DKFZ Applied Computer Vision Lab DKFZ Machine Learning Lab University of Freiburg Freiburg Germany London United Kingdom Pattern Analysis and Learning Group Heidelberg University Hospital Heidelberg Germany

Despite the remarkable success of deep learning systems over the last decade, a key difference still remains between neural network and human decision-making: As humans, we can not only form a decision on the spot, but also ponder, revisiting an initial guess from different angles, distilling relevant information, arriving at a better decision. Here, we propose RecycleNet, a latent feature recycling method, instilling the pondering capability for neural networks to refine initial decisions over a number of recycling steps, where outputs are fed back into earlier network layers in an iterative fashion. This approach makes minimal assumptions about the neural network architecture and thus can be implemented in a wide variety of contexts. Using medical image segmentation as the evaluation environment, we show that latent feature recycling enables the network to iteratively refine initial predictions even beyond the iterations seen during training, converging towards an improved decision. We evaluate this across a variety of segmentation benchmarks and show consistent improvements even compared with top-performing segmentation methods. This allows trading increased computation time for improved performance, which can be beneficial, especially for safety-critical applications. © 2023, CC BY.

关键词： Network layers

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Investigating effective transfer of deep learning models from adults to children for lung ultrasound data analysis

Investigating effective transfer of deep learning models fro...

引用

IEEE Symposium (IUS) Ultrasonics

作者： Russell Thompson Umair Khan Jason Li Lauren. P. Etter Ingrid Camello Rachel. C. Pieciak Ilse. Castro-Aragon Bindu Setty Christopher. C. Gill Libertario Demi Margrit Betke University of Massachusetts Dartmouth MA USA Department of Information Engineering & Computer Science University of Trento TN Italy The Faculty of Computing & Data Sciences Boston University Boston MA USA University of Wisconsin Madison Madison WI USA Pediatric Infectious Disease Augusta University Augusta GA USA Department of Global Health Boston University School of Public Health Boston MA USA Department of Radiology Boston Medical Center Boston MA USA

Automated assessment of LUS data from adult population is an ongoing research over the last few years. To this extent, various deep learning DL-based methods have been proposed to assess lung alterations caused by pneumonia. However, no work has been done in this regard to evaluate the LUS data from children for the presence/absence of consolidations due to pneumonia. Pediatric healthcare data acquisition in a resource constraint environment is a challenging task. As a result of which limited data is acquired. This prevents the effective training of DL models for the development of reliable computer aided assessment systems. To address this challenge, we employed pre-trained DL models, previously trained on LUS data from adults, and evaluated them over LUS data from children in a transfer learning (TL) setting. Two TL strategies i.e. linear probing (LP) and fine-tuning (FT) are utilized. The models are trained and tested in a leave-one-out cross validation manner with results showing that all the pre-trained models achieved higher classification performance for FT in comparison to LP. Among the models it is found that with the addition of spatial attention and custom loss to the base CNN, the model outperformed with the highest mean F1-Score of 81.9%. While evaluating the model’s performance w.r.t age, it is found that the more accurate the model, the less impact of age has on the underlying classification performance. These findings lay down the foundation for accurate consolidation classification in LUS data from children in a resource constraint environment

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Free-form tumor synthesis in computed tomography images via richer generative adversarial network

arXiv

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arXiv 2021年

作者： Jin, Qiangguo Cui, Hui Sun, Changming Meng, Zhaopeng Su, Ran School of Computer Software College of Intelligence and Computing Tianjin University Tianjin China Department of Computer Science and Information Technology La Trobe University Melbourne Australia Csiro Data61 Sydney Australia Tianjin University of Traditional Chinese Medicine Tianjin China

The insufficiency of annotatedmedical imaging scans for cancer makes it challenging to train and validate data-hungry deep learning models in precision oncology. We propose a new richer generative adversarial network for free-form3D tumor/lesion synthesis in computed tomography (CT) images. The network is composed of a new richer convolutional feature enhanced dilated-gated generator (RicherDG) and a hybrid loss function. The RicherDG has dilated-gated convolution layers to enable tumor-painting and to enlarge perceptive fields;and it has a novel richer convolutional feature association branch to recover multi-scale convolutional features especially from uncertain boundaries between tumor and surrounding healthy tissues. The hybrid loss function, which consists of a diverse range of losses, is designed to aggregate complementary information to improve optimization. We perform a comprehensive evaluation of the synthesis results on a wide range of public CT image datasets covering the liver, kidney tumors, and lung nodules. The qualitative and quantitative evaluations and ablation study demonstrated improved synthesizing results over advanced tumor synthesis methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolution

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G3SR: Global Graph Guided Session-based Recommendation

arXiv

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arXiv 2022年

作者： Deng, Zhi-Hong Wang, Chang-Dong Huang, Ling Lai, Jian-Huang Yu, Philip S. School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Information Security Technology Guangzhou China College of Mathematics and Informatics South China Agricultural University Guangzhou China Guangdong Provincial Key Laboratory of Public Finance and Taxation with Big Data Application China University of Illinois at Chicago ChicagoIL United States

Session-based recommendation tries to make use of anonymous session data to deliver high-quality recommendation under the condition that user-profiles and the complete historical behavioral data of a target user are unavailable. Previous works consider each session individually and try to capture user interests within a session. Despite their encouraging results, these models can only perceive intra-session items and cannot draw upon the massive historical relational information. To solve this problem, we propose a novel method named G3SR (Global Graph Guided Session-based Recommendation). G3SR decomposes the session-based recommendation workflow into two steps. First, a global graph is built upon all session data, from which the global item representations are learned in an unsupervised manner. Then, these representations are refined on session graphs under the graph networks, and a readout function is used to generate session representations for each session. Extensive experiments on two real-world benchmark datasets show remarkable and consistent improvements of the G3SR method over the state-of-the-art methods, especially for cold items. Copyright © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

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A maturity model for catalogues of semantic artefacts

arXiv

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arXiv 2023年

作者： Corcho, Oscar Ekaputra, Fajar J. Heibi, Ivan Jonquet, Clement Micsik, Andras Peroni, Silvio Storti, Emanuele Computer Science School Universidad Politécnica de Madrid Madrid Spain Vienna Austria Data Science Research Unit TU Wien Vienna Austria Department of Classical Philology and Italian Studies University of Bologna Bologna Italy Research Centre for Open Scholarly Metadata Department of Classical Philology and Italian Studies University of Bologna Bologna Italy MISTEA University of Montpellier INRAE Institut Agro France LIRMM University of Montpellier CNRS France Budapest Hungary Department of Information Engineering Polytechnic University of Marche Ancona Italy Brussels Belgium

This work presents a maturity model for assessing catalogues of semantic artefacts, one of the keystones that permit semantic interoperability of systems. We defined the dimensions and related features to include in the maturity model by analysing the current literature and existing catalogues of semantic artefacts provided by experts. In addition, we assessed 26 different catalogues to demonstrate the effectiveness of the maturity model, which includes 12 different dimensions (Metadata, Openness, Quality, Availability, Statistics, PID, Governance, Community, Sustainability, Technology, Transparency, and Assessment) and 43 related features (or sub-criteria) associated with these dimensions. Such a maturity model is one of the first attempts to provide recommendations for governance and processes for preserving and maintaining semantic artefacts and helps assess/address interoperability challenges. © 2023, CC BY.

关键词： Semantics

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Homogeneous and Heterogeneous Graph Learning for Hybrid Beamforming in mmWave Systems

引用

IEEE Transactions on Wireless Communications 2025年

作者： Li, Yuhang Lu, Yang Zhang, Guangyang Ai, Bo Niyato, Dusit Cui, Shuguang State Key Laboratory of Advanced Rail Autonomous Operation China Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China Beijing Jiaotong University School of Electronics and Information Engineering Beijing100044 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Shenzhen518066 China

Hybrid analog and digital beamforming (HBF) is a cost-efficient technique to achieve high data rates in millimeterwave (mmWave) communication systems. This paper applies the emerging graph neural networks (GNNs) to HBF by leveraging the topological information in wireless networks for better adaptation to dynamic environments. To address the issue of limited feature extraction capability of the existing single-type GNNs, such as node-GNN or edge-GNN, we model the mmWave communication systems as both homogeneous and heterogeneous graphs, and separate the HBF design into node- and edge-level subtasks. Then, the two graphs are presented by two novel models based on homogeneous graph attention network (GAT) and heterogeneous GAT (HGAT), respectively, and mapped to the desired power allocation, radio frequency precoder and baseband precoder. Both the proposed GAT and HGAT are generalizable in the user variation scenarios, while the HGAT is also generalizable in antenna variation scenarios through the incorporation of a complex embedding layer. Furthermore, we introduce a constraint adaptive layer in the GAT and HGAT to ensure feasible outputs. Extensive numerical results based on the public dataset DeepMIMO are provided to evaluate the GAT and HGAT. The proposed approaches generally outperform existing baselines in terms of adaptability to system settings and generalizability to (unseen) problem parameters/sizes, while the HGAT can even achieve faster and better inference than traditional optimization approaches. © 2002-2012 IEEE.

关键词： Network theory (graphs)

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Focus on Local: Finding Reliable Discriminative Regions for Visual Place Recognition

arXiv

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arXiv 2025年

作者： Wang, Changwei Chen, Shunpeng Song, Yukun Xu, Rongtao Zhang, Zherui Zhang, Jiguang Yang, Haoran Zhang, Yu Fu, Kexue Du, Shide Xu, Zhiwei Gao, Longxiang Guo, Li Xu, Shibiao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences China School of Artificial Intelligence Beijing University of Posts and Telecommunications China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science China MAIS Institute of Automation Chinese Academy of Sciences China Tongji University China College of Computer and Data Science Fuzhou University China Shandong University China

Visual Place Recognition (VPR) is aimed at predicting the location of a query image by referencing a database of geotagged images. For VPR task, often fewer discriminative local regions in an image produce important effects while mundane background regions do not contribute or even cause perceptual aliasing because of easy overlap. However, existing methods lack precisely modeling and full exploitation of these discriminative regions. In this paper, we propose the Focus on Local (FoL) approach to stimulate the performance of image retrieval and re-ranking in VPR simultaneously by mining and exploiting reliable discriminative local regions in images and introducing pseudo-correlation supervision. First, we design two losses, Extraction-Aggregation Spatial Alignment Loss (SAL) and Foreground-Background Contrast Enhancement Loss (CEL), to explicitly model reliable discriminative local regions and use them to guide the generation of global representations and efficient re-ranking. Second, we introduce a weakly-supervised local feature training strategy based on pseudo-correspondences obtained from aggregating global features to alleviate the lack of local correspondences ground truth for the VPR task. Third, we suggest an efficient re-ranking pipeline that is efficiently and precisely based on discriminative region guidance. Finally, experimental results show that our FoL achieves the state-of-the-art on multiple VPR benchmarks in both image retrieval and re-ranking stages and also significantly outperforms existing two-stage VPR methods in terms of computational efficiency. Copyright © 2025, The Authors. All rights reserved.

关键词： Photointerpretation

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Motif Graph Neural Network

arXiv

引用

arXiv 2021年

作者： Chen, Xuexin Cai, Ruichu Fang, Yuan Wu, Min Li, Zijian Hao, Zhifeng The School of Computer Science Guangdong University of Technology Guangzhou510006 China The School of Computer Science Guangdong University of Technology Guangdong Provincial Key Laboratory of Public Finance and Taxation with Big Data Application Guangzhou China Peng Cheng Laboratory Shenzhen518066 China The School of Computing and Information Systems Singapore Management University 178902 Singapore A*STAR 138632 Singapore The College of Science Shantou University Shantou515063 China

Graphs can model complicated interactions between entities, which naturally emerge in many important applications. These applications can often be cast into standard graph learning tasks, in which a crucial step is to learn low-dimensional graph representations. Graph neural networks (GNNs) are currently the most popular model in graph embedding approaches. However, standard GNNs in the neighborhood aggregation paradigm suffer from limited discriminative power in distinguishing high-order graph structures as opposed to low-order structures. To capture high-order structures, researchers have resorted to motifs and developed motif-based GNNs. However, existing motif-based GNNs still often suffer from less discriminative power on high-order structures. To overcome the above limitations, we propose Motif Graph Neural Network (MGNN), a novel framework to better capture high-order structures, hinging on our proposed motif redundancy minimization operator and injective motif combination. First, MGNN produces a set of node representations w.r.t. each motif. The next phase is our proposed redundancy minimization among motifs which compares the motifs with each other and distills the features unique to each motif. Finally, MGNN performs the updating of node representations by combining multiple representations from different motifs. In particular, to enhance the discriminative power, MGNN utilizes an injective function to combine the representations w.r.t. different motifs. We further show that our proposed architecture increases the expressive power of GNNs with a theoretical analysis. We demonstrate that MGNN outperforms state-of-the-art methods on seven public benchmarks on both node classification and graph classification tasks. Copyright © 2021, The Authors. All rights reserved.

关键词： Graph neural networks

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Enhancing utility in the watchdog privacy mechanism

arXiv

引用

arXiv 2021年

作者： Zarrabian, Mohammad Amin Ding, Ni Sadeghi, Parastoo Rakotoarivelo, Thierry College of Engineering and Computer Science Australian National University Canberra Australia School of Computing and Information Systems University of Melbourne Melbourne Australia School of Engineering and Information Technology University of New South Wales Canberra Australia Data61 Commonwealth Scientific and Industrial Research Organisation Eveleigh Australia

This paper is concerned with enhancing data utility in the privacy watchdog method for attaining information-theoretic privacy. For a specific privacy constraint, the watchdog method filters out the high-risk data symbols through applying a uniform data regulation scheme, e.g., merging all high-risk symbols together. While this method entirely trades the symbols resolution off for privacy, we show that the data utility can be greatly improved by partitioning the high-risk symbols set and individually privatizing each subset. We further propose an agglomerative merging algorithm that finds a suitable partition of high-risk symbols: it starts with a singleton high-risk symbol, which is iteratively fused with others until the resulting subsets are private. Numerical simulations demonstrate the efficacy of this algorithm in privately achieving higher utilities in the watchdog *** Codes 68P27, © 2021, CC BY.

关键词： information theory

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