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arXiv 2025年

作者： Adhya, Suman Lahiri, Avishek Sanyal, Debarshi Kumar Das, Partha Pratim School of Mathematical and Computational Sciences Indian Association for the Cultivation of Science 2A & 2B Raja S C Mullick Road Kolkata700032 India Department of Computer Science Ashoka University Haryana Sonipat131029 India Department of Computer Science and Engineering Indian Institute of Technology West Bengal Kharagpur721302 India

Negative sampling has emerged as an effective technique that enables deep learning models to learn better representations by introducing the paradigm of "learn-to-compare". The goal of this approach is to add robustness to deep learning models to learn better representation by comparing the positive samples against the negative ones. Despite its numerous demonstrations in various areas of computer vision and natural language processing, a comprehensive study of the effect of negative sampling in an unsupervised domain like topic modeling has not been well explored. In this paper, we present a comprehensive analysis of the impact of different negative sampling strategies on neural topic models. We compare the performance of several popular neural topic models by incorporating a negative sampling technique in the decoder of variational autoencoder based neural topic models. Experiments on four publicly available datasets demonstrate that integrating negative sampling into topic models results in significant enhancements across multiple aspects, including improved topic coherence, richer topic diversity, and more accurate document classification. Manual evaluations also indicate that the inclusion of negative sampling into neural topic models enhances the quality of the generated topics. These findings highlight the potential of negative sampling as a valuable tool for advancing the effectiveness of neural topic models. © 2025, CC BY.

关键词： Contrastive Learning

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Harnessing Rydberg Atomic Receivers: From Quantum Physics to Wireless Communications

arXiv

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arXiv 2025年

作者： Chen, Yuanbin Guo, Xufeng Yuen, Chau Zhao, Yufei Guan, Yong Liang See, Chong Meng Samson Debbah, Mérouane Hanzo, Lajos School of Electrical and Electronics Engineering Nanyang Technological University 639798 Singapore DSO National Laboratories 639798 Singapore Center for 6G Technology Khalifa University of Science and Technology Abu Dhabi United Arab Emirates School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

The intrinsic integration of Rydberg atomic receivers into wireless communication systems is proposed, by harnessing the principles of quantum physics in wireless communications. More particularly, we conceive a pair of Rydberg atomic receivers, one incorporates a local oscillator (LO), referred to as an LO-dressed receiver, while the other operates without an LO and is termed an LO-free receiver. The appropriate wireless model is developed for each configuration, elaborating on the receiver’s responses to the radio frequency (RF) signal, on the potential noise sources, and on the system performance. Next, we investigate the association distortion effects that might occur, specifically demonstrating the boundaries of linear dynamic regions, which provides critical insights into its practical implementations in wireless systems. Extensive simulation results are provided for characterizing the performance of wireless systems, harnessing this pair of Rydberg atomic receivers. Our results demonstrate that they deliver complementary benefits: LO-free systems excel in proximity operations, while LO-dressed systems are eminently suitable for long-distance sensing at extremely low power levels. More specifically, LO-dressed systems achieve a significant signal-to-noise ratio (SNR) gain of approximately 44 dB over conventional RF receivers, exhibiting an effective coverage range extension over conventional RF receivers by a factor of 150. Furthermore, LO-dressed systems support higher-order quadrature amplitude modulation (QAM) at reduced symbol error rates (SER) compared to conventional RF receivers, hence significantly enhancing wireless communication performance. © 2025, CC BY-NC-ND.

关键词： Signal to noise ratio

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Mixture of Gradient: A Unified Enhancing Approach for Deep Learning-Based Wireless Network Optimization

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IEEE Internet of Things Journal 2025年

作者： Cheng, Nan Ma, Longfei Dai, Yanpeng Wang, Xiucheng Li, Qihao Quan, Wei Liang, Hui Dongguan University of Technology School of Electrical Engineering and Intelligentization Dongguan523808 China Xidian University State Key Laboratory of ISN and School of Telecommunications Engineering Xi’an710071 China Dalian Maritime University School of Information Science and Technology Dalian116026 China Jilin University College of Communication Engineering Jilin Changchun130021 China Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China Peng Cheng Laboratory Shenzhen518000 China University of Waterloo Department of Electrical and Computer Engineering WaterlooN2L 3G1 Canada

Deep learning plays increasingly important role in future wireless network management and optimization. Existing training methods such as label-based supervised learning and label-free learning have inherent limitations. The performance of supervised learning is limited by labels, while label-free training methods require extensive exploration. To address these limitations, this paper proposes a novel mixture of gradients (MoG) method, which integrates gradients from different sources within the training process in order to improve the convergence performance of neural networks (NNs). Particularly, MoG is a modular, plug-and-play solution requiring no structural modifications to existing NNs. Its implementation necessitates only minor modifications to the loss function, where the label-based supervised loss is combined with a label-free loss through weighted summation. The label-free loss can be either unsupervised loss or reinforcement learning loss. This flexibility allows seamless integration into nearly all NN-based methods, making it applicable to a wide range of wireless optimization problems with minimal implementation cost. Extensive simulations across multiple classic wireless scenarios demonstrate that MoG can significantly enhance the performance of NN decision-making, leading to higher transmission rates. © 2014 IEEE.

关键词： Self-supervised learning

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A Comprehensive Survey on Magnetic Resonance Image Reconstruction

arXiv

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arXiv 2025年

作者： Kui, Xiaoyan Fan, Zijie Ji, Zexin Li, Qinsong Liu, Chengtao Si, Weixin Zou, Beiji School of Computer Science and Engineering Central South University Changsha410083 China Big Data Institute Central South University Hunan Changsha410083 China Shenzhen Institute of Advanced Technology Chinese Academy of Science Guangdong Shenzheng518055 China

Magnetic resonance imaging (MRI) reconstruction is a fundamental task aimed at recovering high-quality images from undersampled or low-quality MRI data. This process enhances diagnostic accuracy and optimizes clinical applications. In recent years, deep learning-based MRI reconstruction has made significant progress. Advancements include single-modality feature extraction using different network architectures, the integration of multimodal information, and the adoption of unsupervised or semi-supervised learning strategies. However, despite extensive research, MRI reconstruction remains a challenging problem that has yet to be fully resolved. This survey provides a systematic review of MRI reconstruction methods, covering key aspects such as data acquisition and preprocessing, publicly available datasets, single and multi-modal reconstruction models, training strategies, and evaluation metrics based on image reconstruction and downstream tasks. Additionally, we analyze the major challenges in this field and explore potential future directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Double-RIS Enabled Physical Layer Security for Wireless-Powered Communication Systems Over Rayleigh Fading Channels

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IEEE Transactions on Communications 2025年

作者： Chen, Jingyu Cao, Kunrui Ding, Haiyang Lv, Lu Ye, Yinghui Chi, Haolian Wang, Tao Yang, Liang National University of Defense Technology School of Information and Communications Wuhan430035 China Xidian University School of Telecommunications Engineering Xi'an710071 China Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Xi'an710121 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

This paper investigates the physical layer security for a double-reconfigurable intelligent surface (DRIS) aided wireless-powered communication (WPC) system in the presence of an eavesdropper, where one RIS (termed as RIS-1) is deployed between power station (PS) and information user (U) while the other RIS (termed as RIS-2) is deployed between U and access point (AP). Moreover, an idle user acts as a cooperative jamming user (J) to emit the artificial noise to improve the transmission security of U. According to different types in energy harvesting (EH) and information transmission (IT)/noise transmission (NT) of U/J, we propose four DRIS enabled wireless-powered cooperative jamming transmission schemes based on jointly enhancing EH and IT of U (namely DRIS-1), jointly enhancing EH of U and NT of J (namely DRIS-2), jointly enhancing EH of J and IT of U (namely DRIS-3), and jointly enhancing EH and NT of J (namely DRIS-4), respectively. We analyze connection outage probability (COP), secrecy outage probability (SOP), and effective secrecy throughput (EST) of the proposed four schemes, respectively. The results show that DRIS-1 scheme has the highest diversity gain among the four schemes. Besides, the gain of the number of reflecting elements at RIS-1 has the same order as that at RIS-2 in DRIS-1 scheme and DRIS-4 scheme. In DRIS-2 scheme, the gains of the number of reflecting elements at RIS-1 and RIS-2 are two powers and one power, respectively, while the opposite is true in DRIS-3 scheme. In addition, DRIS-1 scheme achieves the best COP, while DRIS-4 scheme achieves the best SOP. DRIS-1 scheme, DRIS-3 scheme, and DRIS-4 scheme respectively achieve the best EST in the low, middle, and high region of transmission power or number of RIS elements. © 1972-2012 IEEE.

关键词： Inductive power transmission

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DATA-VSR: Dynamic Trajectory Attention and Texture Adaptive Rooter for Video Super-Resolution

DATA-VSR: Dynamic Trajectory Attention and Texture Adaptive ...

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

作者： Linfeng He Meiqin Liu Qi Tang Chao Yao Yao Zhao Institute of Information Science Beijing Jiaotong University Beijing China Visual Intelligence + X International Cooperation Joint Laboratory of MOE Beijing Jiaotong University Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Video Super-Resolution (VSR) is essential for reconstructing high-definition sequences from correlated video frames. While Transformer-based VSR methods have improved reconstruction quality, they require substantial computational resources, limiting deployment on resource-constrained devices. To tackle this issue, we propose a novel framework named Dynamic Trajectory Attention and Texture Adaptive Rooter for Video Super-Resolution (DATA-VSR). There are two key innovations: the Temporal Redundancy-aware Alignment Network (TRAN) and the Spatial Redundancy-aware Refinement Network (SRRN). Specifically, features are aligned by focusing on dynamic temporal trajectories instead of static redundancies in TRAN, and then features are adaptively refined based on the texture complexity of different regions in SRRN. Additionally, the Dual-Domain Enhancement Block (DDEB) is incorporated to effectively capture global dependencies in the frequency domain and enhance the representation of local features in the spatial domain. The experimental results on standard VSR benchmarks show that DATA-VSR achieves competitive performance with fewer parameters, lower FLOPs, and a specific reduction of 17%.

关键词： Technological innovation Limiting Superresolution Redundancy Transformers Trajectory Speech processing Spatial resolution Standards Signal resolution

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Enhancing Fintech Fraud Detection through Graph Convolutional Neural Networks

Enhancing Fintech Fraud Detection through Graph Convolutiona...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Pavan V Omprakash Omprakash Bhagya Prasad Bugge S Thangamani Anvesh Perada Sampathirao Suneetha Manipal Law School Manipal Academy of Higher Education India School of Law Jain University Bengaluru India Department of Electronics and Communication Engineering S.R.K.R Engineering College Bhimavaram India Department of Information Technology Nandha Engineering College Erode India Department of Electrical and Computer Engineering Drexel University Philadelphia Pennsylvania USA Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vijayawada India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

The swift advancement of fintech has transformed financial services, offering users seamless digital experiences. Nonetheless, this expansion has also presented intricate cybersecurity difficulties, especially in fraud detection. This research investigates novel methods for addressing fintech fraud through the utilization of GCNN and the ResNet101-C model. GCNN adeptly addresses dynamic fraud trends via graph-based feature aggregation, whereas ResNet101-C, augmented with dropout and fully connected layers, attains enhanced classification performance. Preprocessing procedures, such as imputation, noise reduction, and feature standardization, enhance data quality, whereas feature extraction emphasizes essential transaction variables like frequency and value. Assessment of synthetic datasets reveals that ResNet101-C achieves an accuracy of 93.46%, surpassing leading models. These results underscore the transformational capability of GCNN and ResNet101-C in tackling the dynamic nature of fraud, providing scalable, precise, and privacy-preserving solutions for the financial sector.

关键词： Training Industries Accuracy Federated learning Fintech Data preprocessing Feature extraction Fraud Convolutional neural networks Synthetic data

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The NeRF Signature: Codebook-Aided Watermarking for Neural Radiance Fields

arXiv

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arXiv 2025年

作者： Luo, Ziyuan Rocha, Anderson Shi, Boxin Guo, Qing Li, Haoliang Wan, Renjie Department of Computer Science Hong Kong Baptist University Hong Kong Institute of Computing University of Campinas Brazil State Key Laboratory of Multimedia Information Processing and National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing100871 China A*STAR Singapore Department of Electrical Engineering City University of Hong Kong Hong Kong

Neural Radiance Fields (NeRF) have been gaining attention as a significant form of 3D content representation. With the proliferation of NeRF-based creations, the need for copyright protection has emerged as a critical issue. Although some approaches have been proposed to embed digital watermarks into NeRF, they often neglect essential model-level considerations and incur substantial time overheads, resulting in reduced imperceptibility and robustness, along with user inconvenience. In this paper, we extend the previous criteria for image watermarking to the model level and propose NeRF Signature, a novel watermarking method for NeRF. We employ a Codebook-aided Signature Embedding (CSE) that does not alter the model structure, thereby maintaining imperceptibility and enhancing robustness at the model level. Furthermore, after optimization, any desired signatures can be embedded through the CSE, and no fine-tuning is required when NeRF owners want to use new binary signatures. Then, we introduce a joint pose-patch encryption watermarking strategy to hide signatures into patches rendered from a specific viewpoint for higher robustness. In addition, we explore a Complexity-Aware Key Selection (CAKS) scheme to embed signatures in high visual complexity patches to enhance imperceptibility. The experimental results demonstrate that our method outperforms other baseline methods in terms of imperceptibility and robustness. The source code is available at: https://***/luo-ziyuan/NeRF_Signature. Copyright © 2025, The Authors. All rights reserved.

关键词： Image watermarking

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Comparison of SVM and Random Forest for Detection of Malicious Node in Wireless Sensor Networks

Comparison of SVM and Random Forest for Detection of Malicio...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Sankara Reddy Thamma E. Gurumoorthi C.Om Prakash S K Muthusundar M.S. Nidhya Balajee Maram Deloitte Consulting LLP USA Information Technology DC Technical Delivery Master II Concord North Carolina USA School of Engineering Malla Reddy University CMR University Bengaluru Dept of AI & DS Chennai Institute of Technology college Chennai School of Computer Applications Dayananda Sagar University Bangalore School of Computer Science and Artificial Intelligence SR University Ananthasagar Hasanparthy Warangal

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Wireless Sensor Networks (WSNs) are used in various contexts and offer a wide range of applications, despite being a decentralized, resource-scarce network type; this is why this study deals with the problem of malicious nodes. Identifying such nodes is important to preventing the compromise of the network and all the nodes in it. In this paper, a comparative study of two popular classifiers named Support Vector Machine (SVM) and Random Forest (RF) in order to detect hostile nodes in WSNs. By nature, a malicious node in a network will cause significant problems to the users. There are number of models also there to detect the malicious node. Based on the above-discussed parameters the paper compares these algorithms in terms of detection accuracy, computational complexity, scalability and performance measure of imbalanced data. The comparison draws on different targets such as accuracy, capacity/large scale, speed, and small sample imbalance, and noise. As stated in this paper, both SVM and Random Forest have its own strengths where the selection between them mainly depends on the network environment and the features of the malicious activity.

关键词： Support vector machines Radio frequency Wireless sensor networks Accuracy Noise Network security Feature extraction Real-time systems Random forests Tuning

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Correction: Materials laboratories of the future for alloys, amorphous, and composite materials (MRS Bulletin, (2025), 50, 2, (190-207), 10.1557/s43577-024-00846-y)

引用

MRS Bulletin 2025年 1-2页

作者： Banerjee, Sarbajit Meng, Y. Shirley Minor, Andrew M. Zhang, Minghao Zaluzec, Nestor J. Chan, Maria K.Y. Seidler, Gerald McComb, David W. Agar, Joshua Mukherjee, Partha P. Melot, Brent Chapman, Karena Guiton, Beth S. Klie, Robert F. McCue, Ian D. Voyles, Paul M. Robertson, Ian Li, Ling Chi, Miaofang Destino, Joel F. Devaraj, Arun Marquis, Emmanuelle A. Segre, Carlo U. Liu, Huinan H. Yang, Judith C. Momeni, Kasra Misra, Amit Abdolrahim, Niaz Medvedeva, Julia E. Cai, Wenjun Sehirlioglu, Alp Dizbay-Onat, Melike Mehta, Apurva Graham-Brady, Lori Maruyama, Benji Rajan, Krishna Warner, Jamie H. Taheri, Mitra L. Kalinin, Sergei V. Reeja-Jayan, B. Schwarz, Udo D. Simon, Sindee L. Brown, Craig M. Department of Chemistry and Department of Materials Science & Engineering Texas A & M University College Station United States Pritzker School of Molecular Engineering The University of Chicago Chicago United States Department of Materials Science & Engineering University of California Berkeley Berkeley United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory Berkeley United States Center for Nanoscale Materials Argonne National Laboratory Lemont United States Department of Physics University of Washington Seattle United States Department of Materials Science & Engineering The Ohio State University Columbus United States Department of Mechanical Engineering and Mechanics Drexel University Philadelphia United States School of Mechanical Engineering Purdue University West Lafayette United States Department of Chemistry University of Southern California Los Angeles United States Department of Chemistry Stony Brook University The State University of New York Stony Brook United States Department of Chemistry University of Kentucky Lexington United States Department of Physics University of Illinois at Chicago Chicago United States Department of Materials Science & Engineering Northwestern University Evanston Bangladesh Department of Materials Science & Engineering University of Wisconsin–Madison Madison United States Department of Materials Science and Engineering University of Pennsylvania Philadelphia United States Department of Mechanical Engineering and Materials Science Duke University Durham United States Department of Chemistry Creighton University Omaha United States Physical and Computational Sciences Directorate Pacific Northwest National Laboratory Richland United States Department of Materials Science & Engineering University of Michigan–Ann Arbor Ann Arbor United States Department of Physics Illinois Institute of Technology Chicago United States Department of Bioengineering and the Mater

This article was updated to correct Minghao Zhang’s affiliation from "Pritzker school of Molecular engineering, The University of Chicago, Chicago, USA" to "Pritzker school of Molecular engineering, The University of Chicago, Chicago, USA;Argonne National Laboratory, Lemont, USA." Argonne National Laboratory was left off the original publication. © The Author(s) 2025.

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