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Semantic-aided Parallel Image Transmission Compatible with Practical System

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IEEE Transactions on Wireless Communications 2025年

作者： Xu, Mingkai Wu, Yongpeng Shi, Yuxuan Xia, Xiang-Gen Debbah, Mérouane Zhang, Wenjun Zhang, Ping Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China Shanghai Jiao Tong University School of Cyber and Engineering Shanghai200240 China University of Delaware Department of Electrical and Computer Engineering NewarkDE19716 United States Khalifa University of Science and Technology KU6G Research Center PO Box Abu Dhabi127788 United Arab Emirates University Paris-Saclay CentraleSupelec Gif-sur-Yvette91192 France Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China

In this paper, we propose a novel semantic-aided image communication framework for supporting the compatibility with practical separation-based coding architectures. Particularly, the deep learning (DL)-based joint source-channel coding (JSCC) is integrated into the classical separate source-channel coding (SSCC) to transmit the images via the combination of semantic stream and image stream from DL networks and SSCC respectively, which we name as parallel-stream transmission. The positive coding gain stems from the sophisticated design of the JSCC encoder, which leverages the residual information neglected by the SSCC to enhance the learnable image features. Furthermore, a conditional rate adaptation mechanism is introduced to adjust the transmission rate of semantic stream according to residual, rendering the framework more flexible and efficient to bandwidth allocation. We also design a dynamic stream aggregation strategy at the receiver, which provides the composite framework with more robustness to signal-to-noise ratio (SNR) fluctuations in wireless systems compared to a single conventional codec. Finally, the proposed framework is verified to surpass the performance of both traditional and DL-based competitors in a large range of scenarios and meanwhile, maintains lightweight in terms of the transmission and computational complexity of semantic stream, which exhibits the potential to be applied in real systems. © 2002-2012 IEEE.

关键词： Channel coding

Learning to Detect Unacceptable Machine Translations for Downstream Tasks

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arXiv 2020年

作者： Zhang, Meng Jiang, Xin Liu, Yang Liu, Qun Huawei Noah’s Ark Lab Institute for Artificial Intelligence State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology

The field of machine translation has progressed tremendously in recent years. Even though the translation quality has improved significantly, current systems are still unable to produce uniformly acceptable machine translations for the variety of possible use cases. In this work, we put machine translation in a cross-lingual pipeline and introduce downstream tasks to define task-specific acceptability of machine translations. This allows us to leverage parallel data to automatically generate acceptability annotations on a large scale, which in turn help to learn acceptability detectors for the downstream tasks. We conduct experiments to demonstrate the effectiveness of our framework for a range of downstream tasks and translation models. Copyright © 2020, The Authors. All rights reserved.

关键词： computer aided language translation

Unveiling High-Pressure Behavior of Sc3AlC MAX Phase: A Comprehensive Theoretical Study on Structural, Mechanical, Dislocation, and Electronic Properties

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Metals 2025年第5期15卷 492-492页

作者： Junping Xi Zhipeng Wang Linkun Zhang Li Ma Pingying Tang Department of Mechanical and Automotive Engineering Science and Technology College of Hubei University of Arts and Science Xiangyang 441025 China State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Hunan University Changsha 410082 China State Key Laboratory for Powder Metallurgy Central South University Changsha 410083 China Guangxi Key Laboratory of Functional Information Materials and Intelligent Information Processing Nanning Normal University Nanning 530001 China

The structural, mechanical, dislocation, and electronic properties of the Sc3AlC MAX phase under applied pressure are investigated in detail using first-principles calculations. key parameters, including lattice parameter ratios, elastic constants, Young’s modulus, bulk modulus, shear modulus, brittle-to-ductile behavior, Poisson’s ratio, anisotropy, Cauchy pressure, yield strength, Vickers hardness, and energy factors, are systematically analyzed as a function of applied pressure. The results demonstrate that the Sc3AlC MAX phase exhibits remarkable mechanical stability within the pressure range of 0 to 60 GPa. Notably, applied pressure markedly improves its mechanical properties, such as resistance to elastic, bulk, and shear deformations. The B/G ratio suggests a tendency toward ductile behavior with increasing pressure, and the negative Cauchy pressure indicates the directional characteristics of interatomic bonding in nature. Vickers hardness and yield strength increase under pressures of 0 to 10 GPa and then decrease sharply above 50 GPa. High pressure suppresses dislocation nucleation due to the increased energy factors, along with twinning deformation. Furthermore, electronic structure analysis confirms that high pressure enhances the interatomic bonding in the Sc3AlC MAX phase, while the enhancement effect is not substantial. This study offers critical insights for designing MAX phase materials for extreme environments, advancing applications in aerospace and electronics fields.

关键词： MAX phase first-principles calculations mechanical properties energy factor high pressure

Learning Purified Feature Representations from Task-irrelevant Labels

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arXiv 2021年

作者： Li, Yinghui Wang, Chen Li, Yangning Zheng, Hai-Tao Shen, Ying Tsinghua Shenzhen International Graduate School Tsinghua University China Department of Computer Science and Technology Tsinghua University China School of Intelligent Systems Engineering Sun-Yat Sen University China Peng Cheng Laboratory China

Learning an empirically effective model with generalization using limited data is a challenging task for deep neural networks. In this paper, we propose a novel learning framework called Purified Learning to exploit task-irrelevant features extracted from task-irrelevant labels when training models on small-scale datasets. Particularly, we purify feature representations by using the expression of task-irrelevant information, thus facilitating the learning process of classification. Our work is built on solid theoretical analysis and extensive experiments, which demonstrate the effectiveness of Purified Learning. According to the theory we proved, Purified Learning is model-agnostic and doesn’t have any restrictions on the model needed, so it can be combined with any existing deep neural networks with ease to achieve better performance. © 2021, CC BY.

关键词： Deep neural networks

Temporal residual self-attention based radar target trajectory prediction

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Temporal residual self-attention based radar target trajecto...

The 2nd International Conference on Distributed Sensing and intelligent systems (ICDSIS 2021)

作者： J. Jin Y. Fan Y. Liu J. Zhang F. Zou Z. Lyu School of Computer Science and Information Engineering Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei Anhui 230601 China Department of Medical Devices Zhejiang Pharmaceutical College Ningbo China 76203 China

With the increasing popularity of unmanned aerial vehicles (UAVs) and the continuous development of the commercial airline industry, anomalous and illegal flights occur frequently, which imposes a great threat on the aviation. Accurate prediction of UAV trajectories is a great demand to ensure the safety of airspace. Existing popular ground target trajectory prediction methods cannot be directly applied to the trajectory prediction of UAVs, since the trajectories of flying targets are significantly different from the trajectories of ground targets. The flight trajectory consists of a time series of radar data, and the attention mechanism can improve the processing performance of the time-series data. However, the traditional attention mechanism only considers the attention of each data point. In this paper, we propose a self-attention mechanism based on temporal residuals, which not only considers attention of track points, but also implements attention distribution of track data on the time domain to better represent the global dependence of the current prediction moment on all track points. On the basis of the time residual self-attention mechanism, we propose a track prediction model based on time Residual Self-Attention (TRSA). We conduct experiments on the flight data provided by global flight data services website VariFlight. Experimental results show that model TRSA can effectively improve the target trajectory performance in terms of root mean squared error (RMSE) and mean absolute error (MAE).

关键词： autonomous aerial vehicles deep learning (artificial intelligence) mean square error methods radar computing radar theory radar tracking time series

Nonparaxiality-triggered Landau-Zener transition in topological photonic waveguides

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arXiv 2022年

作者： Xie, An Zhou, Shaodong Xi, Kelei Ding, Li Pan, Yiming Ke, Yongguan Wang, Huaiqiang Zhuang, Songlin Cheng, Qingqing Shanghai Key Laboratory of Modern Optical System School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Physics Department Solid State Institute Technion Haifa32000 Israel Guangdong Provincial Key Laboratory of Quantum Metrology and Sensing School of Physics and Astronomy Sun Yat-Sen University Zhuhai Campus Zhuhai519082 China National Laboratory of Solid State Microstructures School of Physics Nanjing University Nanjing210093 China State Key Laboratory of Terahertz and Millimeter Waves City University of Hong Kong Hong Kong

Photonic lattices have been widely used for simulating quantum physics, owing to the similar evolutions of paraxial waves and quantum particles. However, nonparaxial wave propagations in photonic lattices break the paradigm of the quantum-optical analogy. Here, we reveal that nonparaxiality exerts stretched and compressed forces on the energy spectrum in the celebrated Aubry-André-Harper model. By exploring the mini-gaps induced by the finite size of the different effects of nonparaxiality, we experimentally present that the expansion of one band gap supports the adiabatic transfer of boundary states while Landau-Zener transition occurs at the narrowing of the other gap, whereas identical transport behaviors are expected for the two gaps under paraxial approximation. Our results not only serve as a foundation of future studies of dynamic state transfer but also inspire applications leveraging nonparaxial transitions as a new degree of freedom. © 2022, CC BY.

关键词： Optical lattices

CT Scan Synthesis for Promoting computer-Aided Diagnosis Capacity of COVID-19 16th

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CT Scan Synthesis for Promoting Computer-Aided Diagnosis Cap...

16th International Conference on intelligent Computing, ICIC 2020

作者： Li, Heng Hu, Yan Li, Sanqian Lin, Wenjun Liu, Peng Higashita, Risa Liu, Jiang School of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China Tomey Corporation Nagoya451-0051 Japan Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen518055 China Ningbo Institute of Industrial Technology Chinese Academy of Sciences Ningbo China

ISBN: (纸本)9783030608019

Nowadays, with the rapid spread of Corona Virus Disease 2019 (COVID-19), this epidemic has become a threatening risk for global public health. Medical workers and researchers all over the world are struggling against the novel coronavirus in the front line. Because the computed tomography (CT) images from infected patients exposure characteristic abnormalities, automatic CT analyzers based on AI-based algorithms are extensively employed as effective weapons to aid clinicians. However, unbalanced data and lack of annotations obstruct AI-based algorithms applying in aided diagnosis because of their low performance. Therefore, in order to solve the above problems, a general-purpose solution is proposed to synthesize COVID-19 CT scans from non-COVID-19 data for providing high-quality negative-positive paired CT scans. Particularly, we introduce an elastic registration algorithm of CT images to manufacture paired training data. Then, a conditional Generative Adversarial Networks (GANs) based image-to-image translation model is implemented to synthesize COVID-19 CT scans from non-COVID-19 data. The effectiveness of our proposed algorithm used in COVID-19 aided diagnosis is verified in the experiments, and the identification and detection capacities of the classification models have been enhanced with the generated CT scans. Specifically, the precise lesion location is achieved by the generated data with a weakly supervised algorithm of class activation mapping (CAM). The model and code of this paper are publicly available at https://***/lihengbit/Synthesis-of-COVID-19-CT-Scan. © 2020, Springer Nature Switzerland AG.

关键词： computer aided diagnosis

MaxMatch: Semi-Supervised Learning with Worst-Case Consistency

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arXiv 2022年

作者： Jiang, Yangbangyan Li, Xiaodan Chen, Yuefeng He, Yuan Xu, Qianqian Yang, Zhiyong Cao, Xiaochun Huang, Qingming State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China Security Department of Alibaba Group Hangzhou311121 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Shenzhen518107 China University of Chinese Academy of Sciences Beijing101408 China Peng Cheng Laboratory Shenzhen518055 China

In recent years, great progress has been made to incorporate unlabeled data to overcome the inefficiently supervised problem via semi-supervised learning (SSL). Most state-of-the-art models are based on the idea of pursuing consistent model predictions over unlabeled data toward the input noise, which is called consistency regularization. Nonetheless, there is a lack of theoretical insights into the reason behind its success. To bridge the gap between theoretical and practical results, we propose a worst-case consistency regularization technique for SSL in this paper. Specifically, we first present a generalization bound for SSL consisting of the empirical loss terms observed on labeled and unlabeled training data separately. Motivated by this bound, we derive an SSL objective that minimizes the largest inconsistency between an original unlabeled sample and its multiple augmented variants. We then provide a simple but effective algorithm to solve the proposed minimax problem, and theoretically prove that it converges to a stationary point. Experiments on five popular benchmark datasets validate the effectiveness of our proposed method. Copyright © 2022, The Authors. All rights reserved.

关键词： Image classification

An integrated GIS-based multivariate adaptive regression splines-cat swarm optimization for improving the accuracy of wildfire susceptibility mapping

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Geocarto International 2023年第1期38卷

作者： Hai, Tao Theruvil Sayed, Biju Majdi, Ali Zhou, Jincheng Sagban, Rafid Band, Shahab S. Mosavi, Amir School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Guizhou Duyun China Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Selangor Shah Alam Malaysia Department of Computer Science Dhofar University Salalah Oman Department of Building and Construction Technologies Engineering Al-Mustaqbal University College Hilla Iraq Department of Computer Technology Engineering Technical Engineering College Al-Ayen University Thi-Qar Iraq Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou Taiwan John von Neumann Faculty of Informatics Obuda University Budapest Hungary German Research Center for Artificial Intelligence Oldenburg Germany Institute of the Information Society University of Public Service Budapest Hungary

A hybrid machine learning method is proposed for wildfire susceptibility mapping. For modeling a geographical information system (GIS) database including 11 influencing factors and 262 fire locations from 2013 to 2018 is used for developing an integrated multivariate adaptive regression splines (MARS). The cat swarm optimization (CSO) algorithm tunes the parameters of the MARS in order to generate accurate susceptibility maps. From the Pearson correlation results, it is observed that land use, temperature, and slope angle have strong correlation with the fire severity. The results demonstrate that the prediction capability of the MARS-CSO model outperforms model tree, reduced error pruning tree and MARS. The resulting wildfire risk map using MARS-CSO reveals that 20% of the study areas is categorized in the very low wildfire risk class, whereas 40% is under the very high class of fire hazard. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： artificial intelligence cat swarm optimization geospatial machine learning natural hazard Wildfire susceptibility