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Aerial Active STAR-RIS-assisted Satellite-Terrestrial Covert Communications

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arXiv 2025年

作者： Zhang, Chuang Sun, Geng Li, Jiahui Wang, Jiacheng Zhang, Ruichen Niyato, Dusit Mao, Shiwen Quek, Tony Q.S. Computer Science and Technology Jilin University Changchun130012 China Singapore University of Technology and Design Singapore487372 Singapore College of Computer Science and Technology Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849 United States Information System Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore

An integration of satellites and terrestrial networks is crucial for enhancing performance of next generation communication systems. However, the networks are hindered by the long-distance path loss and security risks in dense urban environments. In this work, we propose a satellite-terrestrial covert communication system assisted by the aerial active simultaneous transmitting and reflecting reconfigurable intelligent surface (AASTAR-RIS) to improve the channel capacity while ensuring the transmission covertness. Specifically, we first derive the minimal detection error probability (DEP) under the worst condition that the Warden has perfect channel state information (CSI). Then, we formulate an AASTAR-RIS-assisted satellite-terrestrial covert communication optimization problem (ASCCOP) to maximize the sum of the fair channel capacity for all ground users while meeting the strict covert constraint, by jointly optimizing the trajectory and active beamforming of the AASTAR-RIS. Due to the challenges posed by the complex and high-dimensional state-action spaces as well as the need for efficient exploration in dynamic environments, we propose a generative deterministic policy gradient (GDPG) algorithm, which is a generative deep reinforcement learning (DRL) method to solve the ASCCOP. Concretely, the generative diffusion model (GDM) is utilized as the policy representation of the algorithm to enhance the exploration process by generating diverse and high-quality samples through a series of denoising steps. Moreover, we incorporate an action gradient mechanism to accomplish the policy improvement of the algorithm, which refines the better state-action pairs through the gradient ascent. Simulation results demonstrate that the proposed approach significantly outperforms important benchmarks, and also validate the performance and practicality under different algorithm parameters and environment settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

Document-level Relation Extraction with Relation Correlations

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arXiv 2022年

作者： Han, Ridong Peng, Tao Wang, Benyou Liu, Lu Wan, Xiang College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China College of Software Jilin University China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China

Document-level relation extraction faces two overlooked challenges: long-tail problem and multi-label problem. Previous work focuses mainly on obtaining better contextual representations for entity pairs, hardly address the above challenges. In this paper, we analyze the co-occurrence correlation of relations, and introduce it into DocRE task for the first time. We argue that the correlations can not only transfer knowledge between data-rich relations and data-scarce ones to assist in the training of tailed relations, but also reflect semantic distance guiding the classifier to identify semantically close relations for multi-label entity pairs. Specifically, we use relation embedding as a medium, and propose two co-occurrence prediction sub-tasks from both coarse- and fine-grained perspectives to capture relation correlations. Finally, the learned correlation-aware embeddings are used to guide the extraction of relational facts. Substantial experiments on two popular DocRE datasets are conducted, and our method achieves superior results compared to baselines. Insightful analysis also demonstrates the potential of relation correlations to address the above challenges. Copyright © 2022, The Authors. All rights reserved.

关键词： Extraction

Method for Preoperative Prediction of Microvascular Invasion of Hepatocellular Carcinoma

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Method for Preoperative Prediction of Microvascular Invasion...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Chen Zhao Tian Bai Tongjia Chu Feng Wei Fa Zhang College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China Hepatobiliary and Pancreas Department General Surgery Center First Hospital of Jilin University Changchun China School of Medical Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is of great guiding significance for the formulating treatment strategies and accessing the prognosis before the surgery. However, in traditional medicine, the gold standard for the diagnosis of MVI is obtained by examining pathological images which can only be obtained by sampling and sectioning tumors after surgery. At this time, MVI results have lost the timeliness of guiding tumor resection surgery. In order to solve this problem, existing studies began to use deep learning-based methods for preoperative prediction of MVI using non-invasive imaging. Most of these methods adopt the fusion methods of multi-sequence images to predict MVI, but fail to make full use of the characteristics of multiply sequences as prior knowledge to combine into the model, resulting in no further improvement of prediction performance. So we propose a multi-sequence image difference and correlation deep learning model. The model can extract the difference and correlation information between sequences from different scales and combine them into the model. To validate proposed model, we collected a data set consists of 120 HCC patients, including 50 MVI-positive patients. Compared with existing studies, our method has greatly improved in all evaluation metrics.

关键词： Deep learning Pathology Correlation Biological system modeling Surgery Predictive models Data mining

Distilling Autoregressive Models to Obtain High-Performance Non-Autoregressive Solvers for Vehicle Routing Problems with Faster Inference Speed

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arXiv 2023年

作者： Xiao, Yubin Wang, Di Li, Boyang Wang, Mingzhao Wu, Xuan Zhou, Changliang Zhou, You Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University China Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly Nanyang Technological University Singapore WeBank-NTU Joint Research Institute on Fintech Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore School of System Design and Intelligent Manufacturing Southern University of Science and Technology China

Neural construction models have shown promising performance for Vehicle Routing Problems (VRPs) by adopting either the Autoregressive (AR) or Non-Autoregressive (NAR) learning approach. While AR models produce high-quality solutions, they generally have a high inference latency due to their sequential generation nature. Conversely, NAR models generate solutions in parallel with a low inference latency but generally exhibit inferior performance. In this paper, we propose a generic Guided Non-Autoregressive knowledge Distillation (GNARKD) method to obtain high-performance NAR models having a low inference latency. GNARKD removes the constraint of sequential generation in AR models while preserving the learned pivotal components in the network architecture to obtain the corresponding NAR models through knowledge distillation. We evaluate GNARKD by applying it to three widely adopted AR models to obtain NAR VRP solvers for both synthesized and real-world instances. The experimental results demonstrate that GNARKD significantly reduces the inference time (4∼5× faster) with acceptable performance drop (2∼3%). To the best of our knowledge, this study is first-of-its-kind to obtain NAR VRP solvers from AR ones through knowledge distillation. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

AutoSTL: Automated Spatio-Temporal Multi-Task Learning

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arXiv 2023年

作者： Zhang, Zijian Zhao, Xiangyu Miao, Hao Zhang, Chunxu Zhao, Hongwei Zhang, Junbo College of Computer Science and Technology Jilin University China School of Data Science City University of Hong Kong Hong Kong Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University China Department of Computer Science Aalborg University Denmark JD Intelligent Cities Research China JD iCity JD Technology China Hong Kong Institute for Data Science City University of Hong Kong Hong Kong

Spatio-temporal prediction plays a critical role in smart city construction. Jointly modeling multiple spatio-temporal tasks can further promote an intelligent city life by integrating their inseparable relationship. However, existing studies fail to address this joint learning problem well, which generally solve tasks individually or a fixed task combination. The challenges lie in the tangled relation between different properties, the demand for supporting flexible combinations of tasks and the complex spatio-temporal dependency. To cope with the problems above, we propose an Automated Spatio-Temporal multi-task Learning (AutoSTL) method to handle multiple spatio-temporal tasks jointly. Firstly, we propose a scalable architecture consisting of advanced spatio-temporal operations to exploit the complicated dependency. Shared modules and feature fusion mechanism are incorporated to further capture the intrinsic relationship between tasks. Furthermore, our model automatically allocates the operations and fusion weight. Extensive experiments on benchmark datasets verified that our model achieves state-of-the-art performance. As we can know, AutoSTL is the first automated spatio-temporal multi-task learning method. Copyright © 2023, The Authors. All rights reserved.

关键词： Smart city

Elucidating Spatial Complex Structures from Mass Spectrometry Imaging with Deep Multimodal Model

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Elucidating Spatial Complex Structures from Mass Spectrometr...

Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Jiangnan Cui Junjie Xia Xuan Li Yan Wang Fufang Qiu Yaochen Xu Keren Xu Chunman Zuo School of Computer Science and Technology Institute of Artificial Intelligence Donghua University Shanghai China Institute of Artificial Intelligence Donghua University Shanghai China College of Computer Science and Technology Jilin University Changchun China Department of Neurosurgery Huashan Hospital Shanghai China CAS Key Laboratory of Systems Biology Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences Shanghai China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

Spatial mass spectrometry imaging (SMSI) technology has enabled the characterization of biomolecule patterns within the tissue microenvironment, yet its analyses suffer from substantial noise. The lack of effective methods for exploiting multi-view features (e.g., spatial location) has hindered their elucidation ability for tissue heterogeneity. Here, we propose MSG, a multimodal modal that integrates histology, spatial location, and MSI data, in deciphering tissue structure by graph neural network. Specifically, MSG learns low-dimensional representations by aggregating histomorphological features from neighboring tiles via graph attention autoencoder. MSG outperforms other methods in identifying spatial domains and denoising data. The subsequent domain-guided differential expression analysis of the denoised data enables the identification of ions with significantly enriched patterns within the recognized domains. MSG exhibits its versatility in handling SMSI data from various platforms.

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ESAE: Evolutionary Strategy-Based Architecture Evolution 14th

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ESAE: Evolutionary Strategy-Based Architecture Evolution

14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Gu, Xue Meng, Ziyao Liang, Yanchun Xu, Dong Huang, Han Han, Xiaosong Wu, Chunguo Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education School of Computer Zhuhai College of Jilin University Zhuhai519041 China Department of Electrical Engineering and Computer Science Informatics Institute and Christopher S. Bond Life Sciences Center University of Missouri ColumbiaMO65211 United States School of Software Engineering South China University of Technology GuangZhou510006 China

ISBN: (纸本)9789811534249

Although deep neural networks (DNNs) play important roles in many fields, the architecture design of DNNs can be challenging due to the difficulty of input data representation, the huge number of parameters and the complex layer relationships. To overcome the obstacles of architecture design, we developed a new method to generate the optimal structure of DNNs, named Evolutionary Strategy-based Architecture Evolution (ESAE), consisting of a bi-level representation and a probability distribution learning approach. The bi-level representation encodes architectures in the gene and parameter levels. The probability distribution learning approach ensures the efficient convergence of the architecture searching process. By using Fashion-MNIST and CIFAR-10, the effectiveness of the proposed ESAS is verified. The evolved DNNs, starting from a trivial initial architecture with one single convolutional layer, achieved the accuracies of 94.48% and 93.49% on Fashion-MNIST and CIFAR-10, respectively, and require remarkably less hardware costs in terms of GPUs and running time, compared with the existing state-of-the-art manual screwed architectures. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

A Survey of Sum-Product Networks Structural Learning

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SSRN

SSRN 2022年

作者： Xia, Riting Zhang, Yan Liu, Xueyan Yang, Bo School of Artificial Intelligence Jilin University Jilin Changchun130012 China School of Computer Science and Technology Jilin University Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineer Jilin University Ministry of Education Jilin Changchun130012 China College of Communication Engineering Jilin University Jilin Changchun130012 China

Sum-product networks (SPNs) in deep probabilistic models have made great progress in computer vision, robotics, neuro-symbolic artificial intelligence, natural language processing, probabilistic programming languages, and other fields. Compared with probabilistic graphical models and deep probabilistic models, SPNs can balance the tractability and expressive efficiency. In addition, SPNs remain more interpretable than deep neural models. The expressiveness and complexity of SPNs depend on its own structure. Thus, how to design an effective SPN structure learning algorithm that can balance expressiveness and complexity has become a hot research topic in recent years. In this paper, we review SPN structure learning comprehensively, including the motivation of SPN structure learning, a systematic review of related theories, the proper categorization of different SPN structure learning algorithms, several evaluation approaches and some helpful online resources. Moreover, we discuss some open issues and research directions for SPN structure learning. To our knowledge, this is the first survey to focus specifically on SPN structure learning, and we hope to provide useful references for researchers in related fields. © 2022, The Authors. All rights reserved.

关键词： Efficiency

Bi-classifier with neighborhood aggregation for unsupervised domain adaptation

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Information Sciences 2025年 718卷

作者： Lü, Shuai Zhang, Xinyu Li, Zongze Li, Jingyao Kang, Meng Key Laboratory of Symbolic Computation and Knowledge Engineering (Jilin University) Ministry of Education Changchun 130012 China College of Computer Science and Technology Jilin University Changchun 130012 China College of Software Jilin University Changchun 130012 China

Recently, adversarial learning with bi-classifier has been confirmed to be effective in unsupervised domain adaptation, which can effectively promote the alignment of different distributions across domains. Most previous methods with bi-classifier exploit the output disagreement of the bi-classifier to learn transferable representations. However, only considering the disagreement between two classifiers cannot guarantee that the model performs well in the target domain. The main reason is that the model does not consider the local structure of the target domain data. To address this problem, we propose a Bi-Classifier with Neighborhood Aggregation (BCNA). Specifically, we provide two pseudo-labels for a target sample based on the nearest neighbor aggregation strategy. Then, we propose a self-supervised loss with two pseudo-labels based on confidence weight. In addition, we also propose a novel consistency regularization loss for bi-classifier. Unlike previous methods with bi-classifier, we achieve the goal of minimizing the disagreement between two classifiers from the perspective of different views of the sample. Our method can increase the discriminability of the model while ensuring the feature transferability. Extensive experiments show that our method has achieved state-of-the-art performance on three datasets that are widely used in unsupervised domain adaptation. © 2025 Elsevier Inc.

关键词： Bi-classifier Consistency regularization Neighborhood aggregation Self-supervision Unsupervised domain adaptation