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arXiv 2023年

作者： Zhu, Xusheng Chen, Wen Wu, Qingqing Li, Zhendong Li, Jun Zhang, Shunqing Ding, Ming Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China School of Electronic and Optical Engineering Nanjing University of Science Technology Nanjing210094 China Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai University Shanghai200444 China Data61 CSIRO SydneyNSW2015 Australia

In this paper, we investigate the performance of reconfigurable intelligent surface (RIS)-aided spatial shift keying (SSK) wireless communication systems with imperfect channel state information (CSI). Specifically, we study the average bit error probability (ABEP) of two RIS-SSK systems based on intelligent reflection and blind reflection modes. For the intelligent RIS-SSK scheme, we first derive the conditional pairwise error probability of the composite channel through maximum likelihood (ML) detection. Subsequently, we derive the probability density function of the combined channel. Due to the intricacies of the composite channel formulation, an exact closed-form ABEP expression is unattainable through direct derivation. To this end, we resort to employing the Gaussian-Chebyshev quadrature method to estimate the results. Additionally, we employ Q-function approximation to derive the non-exact closed-form expression in the presence of channel estimation errors. For the blind RIS-SSK scheme, we derive both closed-form ABEP expression and asymptotic ABEP expression with imperfect CSI by adopting the ML detector. To offer deeper insights, we explore the impact of discrete reflection phase shifts on the performance of the RIS-SSK system. Lastly, we extensively validate all the analytical derivations via Monte Carlo simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Channel state information

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Size-fixed group discovery via multi-constrained graph pattern matching

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Information Sciences 2025年 690卷

作者： Guliu Liu Lei Li Guanfeng Liu Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education 230601 Hefei China School of Computer Science and Information Engineering Hefei University of Technology 230601 Hefei China Department of Computing Macquarie University Sydney 2122 Australia

Multi-Constrained Graph Pattern Matching (MC-GPM) aims to match a pattern graph with multiple attribute constraints on its nodes and edges, and has garnered significant interest in various fields, including social-based e-commerce and trust-based group discovery. However, the existing MC-GPM methods do not consider situations where the number of each node in the pattern graph needs to be fixed, such as finding experts group with expert quantities and relations specified. In this paper, a Multi-Constrained Strong Simulation with the Fixed Number of Nodes (MCSS-FNN) matching model is proposed, and then a Trust-oriented Optimal Multi-constrained Path (TOMP) matching algorithm is designed for solving it. Additionally, two heuristic optimization strategies are designed, one for combinatorial testing and the other for edge matching, to enhance the efficiency of the TOMP algorithm. Empirical experiments are conducted on four real social network datasets, and the results demonstrate the effectiveness and efficiency of the proposed algorithm and optimization strategies.

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Large Language Models as Evolutionary Optimizers

Large Language Models as Evolutionary Optimizers

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Congress on Evolutionary Computation

作者： Shengcai Liu Caishun Chen Xinghua Qu Ke Tang Yew-Soon Ong A*STAR Department of Computer Science and Engineering Centre for Frontier AI Research Southern University of Science and Technology Centre for Frontier AI Research A*STAR Tianqiao & Chrissy Chen Institute Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Centre for Frontier AI Research A*STAR College of Computing and Data Science Nanyang Technological University

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Evolutionary algorithms (EAs) have achieved remarkable success in tackling complex combinatorial optimization problems. However, EAs often demand carefully-designed operators with the aid of domain expertise to achieve satisfactory performance. In this work, we present the first study on large language models (LLMs) as evolutionary combinatorial optimizers. The main advantage is that it requires minimal domain knowledge and human efforts, as well as no additional training of the model. This approach is referred to as LLM-driven EA (LMEA). Specifically, in each generation of the evolutionary search, LMEA instructs the LLM to select parent solutions from current population, and perform crossover and mutation to generate offspring solutions. Then, LMEA evaluates these new solutions and include them into the population for the next generation. LMEA is equipped with a self-adaptation mechanism that controls the temperature of the LLM. This enables it to balance between exploration and exploitation and prevents the search from getting stuck in local optima. We investigate the power of LMEA on the classical traveling salesman problems (TSPs) widely used in combinatorial optimization research. Notably, the results show that LMEA performs competitively to traditional heuristics in finding high-quality solutions on TSP instances with up to 20 nodes. Additionally, we also study the effectiveness of LLM-driven crossover/mutation and the self- adaptation mechanism in evolutionary search. In summary, our results reveal the great potentials of LLMs as evolutionary optimizers for solving combinatorial problems. We hope our research shall inspire future explorations on LLM-driven EAs for complex optimization challenges.

关键词： Training Large language models Computational modeling Sociology Evolutionary computation Traveling salesman problems Temperature control

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Discrimination-Aware Domain Adversarial Neural Network

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Journal of Computer Science & Technology 2020年第2期35卷 259-267页

作者： Yun-Yun Wang Jian-Min Gu Chao Wang Song-Can Chen Hui Xue College of Computer Science and Engineering Nanjing University of Posts and Telecommunications Nanjing 210046China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing 210046China College of Computer Science and Technology/College of Artificial Intelligence Nanjing University of Aeronautics and AstronauticsNanjing 210023China Key Laboratory of Pattern Analysis and Machine Intelligence Ministry of Industry and Information Technology Nanjing 210023China School of Computer Science and Engineering Southeast UniversityNanjing 210096China

The domain adversarial neural network(DANN)methods have been successfully proposed and attracted much attention *** DANNs,a discriminator is trained to discriminate the domain labels of features generated by a generator,whereas the generator attempts to confuse it such that the distributions between domains are *** a result,it actually encourages the whole alignment or transfer between domains,while the inter-class discriminative information across domains is not *** this paper,we present a Discrimination-Aware Domain Adversarial Neural Network(DA2NN)method to introduce the discriminative information or the discrepancy of inter-class instances across domains into deep domain ***2NN considers both the alignment within the same class and the separation among different classes across domains in knowledge transfer via multiple *** results show that DA2NN can achieve better classification performance compared with the DANN methods.

关键词： adversarial learning inter-class separation deep neural network discrimination-aware domain adaptation

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Boosting Fine-Grained Visual Anomaly Detection with Coarse-knowledge-Aware Adversarial Learning 39

Boosting Fine-Grained Visual Anomaly Detection with Coarse-K...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Fang, Qingqing Su, Qinliang Lv, Wenxi Xu, Wenchao Yu, Jianxing School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Department of Computing The Hong Kong Polytechnic University Hong Kong School of Artificial Intelligence Sun Yat-sen University Guangdong China Pazhou Lab Guangzhou 510330 China

ISBN: (纸本)157735897X

Many unsupervised visual anomaly detection methods train an auto-encoder to reconstruct normal samples and then leverage the reconstruction error map to detect and localize the anomalies. However, due to the powerful modeling and generalization ability of neural networks, some anomalies can also be well reconstructed, resulting in unsatisfactory detection and localization accuracy. In this paper, a small coarsely-labeled anomaly dataset is first collected. Then, a coarse-knowledge-aware adversarial learning method is developed to align the distribution of reconstructed features with that of normal features. The alignment can effectively suppress the auto-encoder’s reconstruction ability on anomalies and thus improve the detection accuracy. Considering that anomalies often only occupy very small areas in anomalous images, a patch-level adversarial learning strategy is further developed. Although no patch-level anomalous information is available, we rigorously prove that by simply viewing any patch features from anomalous images as anomalies, the proposed knowledge-aware method can also align the distribution of reconstructed patch features with the normal ones. Experimental results on four medical datasets and two industrial datasets demonstrate the effectiveness of our method in improving the detection and localization performance. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Adversarial machine learning

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HMDST: A Hybrid Model-data Driven Approach for Spatio-Temporally Consistent Video Inpainting

HMDST: A Hybrid Model-Data Driven Approach for Spatio-Tempor...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Li Fang Kaijun Zou Zhiye Chen Long Ye State Key Laboratory of Media Convergence and Communication Communication University of China Beijing China School of Information and Communication Engineering Communication University of China Beijing China Guangdong South New Media Inc. Guangzhou China School of Data Science and Intelligent Media Communication University of China Beijing China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Video inpainting fills in the missing regions in videos, which should be coherent and natural. Conventional model-driven methods with manual priors may produce blurry, distorted or inconsistent results due to the absence of high-level semantics. While data-driven approaches like deep learning directly learn mappings from observations to target videos, they may face challenges in quality, generalization, and robustness. Existing methods use optical flow to model the motion and context between frames, but the flow estimation may be inaccurate or unstable in the missing regions. This paper introduces a hybrid model-data driven approach for spatio-temporally consistent video inpainting. It combines prior knowledge of imaging and deep prior learned through training, and employs elaborately designed modules to accurately model the motion and contextual information. Our network can be trained end-to-end, leading to a more efficient and effective inpainting process. Extensive experiments demonstrate the superiority of our method qualitatively and quantitatively.

关键词： Training Semantics Imaging Optical distortion Manuals Streaming media Robustness

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Fourier neural operator for large eddy simulation of compressible Rayleigh-Taylor turbulence

arXiv

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arXiv 2024年

作者： Luo, Tengfei Li, Zhijie Yuan, Zelong Peng, Wenhui Liu, Tianyuan Wang, Liangzhu Wang, Jianchun Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Turbulence Research and Applications Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen518055 China Harbin Engineering University Qingdao Innovation and Development Base Qingdao266000 China The Hong Kong Polytechnic University Department of Applied Mathematics 999077 Hong Kong College of Engineering Peking University Beijing100091 China Concordia University Centre for Zero Energy Building Studies Department of Building Civil and Environmental Engineering MontrealH3G 1M8 Canada

The Fourier neural operator (FNO) framework is applied to the large eddy simulation (LES) of three-dimensional compressible Rayleigh-Taylor (RT) turbulence with miscible fluids at Atwood number At = 0.5, stratification parameter Sr = 1.0, and Reynolds numbers Re = 10000 and 30000. The FNO model is first used for predicting three-dimensional compressible turbulence. The different magnitudes of physical fields are normalized using root mean square values for an easier training of FNO models. In the a posteriori tests, the FNO model outperforms the velocity gradient model (VGM), the dynamic Smagorinsky model (DSM), and implicit large eddy simulation (ILES) in predicting various statistical quantities and instantaneous structures, and is particularly superior to traditional LES methods in predicting temperature fields and velocity divergence. Moreover, the computational efficiency of the FNO model is much higher than that of traditional LES methods. FNO models trained with short-time, low Reynolds number data exhibit a good generalization performance on longer-time predictions and higher Reynolds numbers in the a posteriori tests. Copyright © 2024, The Authors. All rights reserved.

关键词： Reynolds number

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Medical Scene Graphs and Reasoning

Medical Scene Graphs and Reasoning

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Chuxue Cao Yiming He Yuzhen Chen Chunli Song Hao Ling Renchu Guan Xiaoyue Feng The Key Laboratory for Symbolic Computation and Knowledge Engineering of the Ministry of Education College of Software Jilin University Changchun China The Second Hospital of Jilin University Changchun China The Key Laboratory for Symbolic Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)9781665429825

Medical scene graph contributes to cognitive tasks such as question answering. An automatic medical scene graph generator can annotate medical images with scene graphs conveniently. An end-to-end model is proposed, which contains an automatic medical scene graph generator based on medical images and surgery records. The novel model can extract symptom information of the images from surgery records via name entity recognition then convert it into scene graphs. The content and format of the scene graph are pre-defined according to the features of the images. A medical scene graph (MSG) dataset is also produced. The dataset contains 116 scene graphs where each graph includes 6 to 30 entities. Experiments show that our model performs well on making scene graphs of CT coronary angiography. Significantly, the scene graphs have the type, state, location, and index of arteries. To verify the practicability of the MSG dataset, We construct an intelligent model that can generate key diagnostic results. In addition, our medical scene graph generator can be applied to other medical areas to annotate different kinds of medical images intelligently. And the MSG dataset can directly be used in other cognitive visual tasks.

关键词： Visualization Computed tomography Angiography Surgery Medical services Generators Task analysis

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CT-CAD: Context-Aware Transformers for End-to-End Chest Abnormality Detection on X-Rays

CT-CAD: Context-Aware Transformers for End-to-End Chest Abno...

引用

2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Kong, Qiran Wu, Yirui Yuan, Chi Wang, Yongli Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing China College of Computer and Information Hohai University Nanjing China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9781665401265

Supervised based deep learning methods have achieved great success in medical image analysis domain. Essentially, most of them could be further improved by exploring and embedding context knowledge for accuracy boosting. Moreover, they generally suffer from slow convergency and high computing cost, which prevents their usage in a practical scenario. To tackle these problems, we present CT-CAD, context-aware transformers for end-to-end chest abnormality detection on X-Ray images. The proposed method firstly constructs a context-aware feature extractor, which enlarges receptive fields to encode multi-scale context information via an iterative feature fusion scheme and dilated context encoding blocks. Afterwards, deformable transformer detector are built for category classification and location regression, where their deformable attention block attend to a small set of key sampling points, thus allowing the transformer to focus on feature subspace and accelerate convergence speed. Through comparative experiments on Vinbig Chest and Chest Det10 datasets, the proposed CT-CAD demonstrates its effectiveness and outperforms the existing methods in mAP and training epoches. © 2021 IEEE.

关键词： Iterative methods

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data Governance: Status, Technologies, Applications and Development Trends 8

Data Governance: Status, Technologies, Applications and Deve...

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8th International Conference on data Science in Cyberspace, DSC 2023

作者： Zhu, Haiyang Chen, Xiaofeng Li, Suyan Wang, Yejin Fang, Xinwei Zhu, Minfeng Zhejiang University Wuchan Zhongda Digital Technology Co. Ltd. The State Key Lab of Cad&cg Hangzhou China Zhejiang University Hangzhou Qulian Technology Co. Ltd. The State Key Laboratory of Blockchain and Data Security Hangzhou China Zhejiang Institute of Mechanical & Electrical Engineering Hangzhou China Wuchan Zhongda Digital Technology Co. Ltd. Hangzhou China School of Medicine Zhejiang University Sir Run Run Shaw Hospital Department of Internet and Artificial Intelligence Hangzhou China Zhejiang University Hangzhou China

ISBN: (纸本)9798350331035

data governance involves legal, policy, technologies, and standards measures to ensure security, accuracy, integrity, consistency, and accessibility of data, with the primary objective of unlocking its latent value. In this context, the study initiates by scrutinizing the present status of data governance, subsequently identifying pivotal challenges through the lens of computer sciences. Moreover, to effectively grapple with the intricacies of high-dimensional data, data incompletion, and data redundancy within the realm of data governance, we present a compendium of data governance technology methodologies, encompassing data dimensionality reduction, data imputation, data deduplication, and data visualization. Further enhancing our discourse, we shed light on the typical scenarios of large-scale supply chain enterprises, elucidating data analysis and data intelligence applications. The convergence of these insights seeks to provide a comprehensive overview both data governance technology research and real-world application. © 2023 IEEE.

关键词： Blockchain data Deduplication data Dimensionality Reduction data Governance data imputation data Intelligence

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