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Uncertainty-Guided Lung Nodule Segmentation with Feature-Aware Attention

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arXiv 2021年

作者： Yang, Han Sheny, Lu Zhang, Mengke Wang, Qiuli School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China School of Big Data and Software Engineering Chongqing University Chongqing China

Since radiologists have different training and clinical experiences, they may provide various segmentation annotations for a lung nodule. Conventional studies choose a single annotation as the learning target by default, but they waste valuable information of consensus or disagreements ingrained in the multiple annotations. This paper proposes an Uncertainty-Guided Segmentation Network (UGS-Net), which learns the rich visual features from the regions that may cause segmentation uncertainty and contributes to a better segmentation result. With an Uncertainty-Aware Module, this network can provide a Multi-Confidence Mask (MCM), pointing out regions with different segmentation uncertainty levels. Moreover, this paper introduces a Feature-Aware Attention Module to enhance the learning of the nodule boundary and density differences. Experimental results show that our method can predict the nodule regions with different uncertainty levels and achieve superior performance in LIDC-IDRI dataset. © 2021, CC BY.

关键词： Biological organs

Explaining Agent Behavior in Agent-Based Modeling Using Subgroup Discovery

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SSRN

SSRN 2023年

作者： Romero, Elena Carmona del Jesús, Cristóbal José Cordón, Oscar del Jesus, María José Damas, Sergio Chica, Manuel Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain Dept. of Computer Science and Artificial Intelligence DECSAI University of Granada Granada18071 Spain Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Jaen Jaen23071 Spain Leicester School of Pharmacy De Montfort University LeicesterLEI 9BH United Kingdom Dept. of Computer Science University of Jaen Jaen23071 Spain Dept. of Software Engineering University of Granada Granada18071 Spain School of Electrical Engineering and Computing The University of Newcastle CallaghanNSW2308 Australia

Agent-based modeling (ABM) is a popular tool for simulating complex systems. In addition to the key performance indicators ABM provides as main outputs of the modeling process, it also generates valuable information about the agents' roadmap, but the large volume of generated data is difficult to analyze and understand. Explainable Artificial Intelligence (XAI) techniques can improve the transparency and interpretability of these models. Subgroup Discovery (SD) is a useful tool for XAI that can facilitate the analysis of the data with respect to a variable of interest. In this contribution, we propose a methodology for applying SD to ABM for extracting useful knowledge of agents behavior from the model output. We compare the performance of different SD algorithms and show the effectiveness of our methodology through a case study in the marketing area. We analyze the obtained set of rules from the point of view of a marketing expert. The results show that our methodology can provide a deeper understanding of consumer behavior and market dynamics, providing potential areas of opportunity for marketers. © 2023, The Authors. All rights reserved.

关键词： Consumer behavior

Revisiting dimensionality reduction techniques for visual cluster analysis: An empirical study

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arXiv 2021年

作者： Xia, Jiazhi Zhang, Yuchen Song, Jie Chen, Yang Wang, Yunhai Liu, Shixia School of Computer Science and Engineering Central South University China I4 data School of Computer Science and Technology Shandong University China School of Software Tsinghua University China

Dimensionality Reduction (DR) techniques can generate 2D projections and enable visual exploration of cluster structures of high-dimensional datasets. However, different DR techniques would yield various patterns, which significantly affect the performance of visual cluster analysis tasks. We present the results of a user study that investigates the influence of different DR techniques on visual cluster analysis. Our study focuses on the most concerned property types, namely the linearity and locality, and evaluates twelve representative DR techniques that cover the concerned properties. Four controlled experiments were conducted to evaluate how the DR techniques facilitate the tasks of 1) cluster identification, 2) membership identification, 3) distance comparison, and 4) density comparison, respectively. We also evaluated users' subjective preference of the DR techniques regarding the quality of projected clusters. The results show that: 1) Non-linear and Local techniques are preferred in cluster identification and membership identification;2) Linear techniques perform better than non-linear techniques in density comparison;3) UMAP (Uniform Manifold Approximation and Projection) and t-SNE (t-Distributed Stochastic Neighbor Embedding) perform the best in cluster identification and membership identification;4) NMF (Nonnegative Matrix Factorization) has competitive performance in distance comparison;5) t-SNLE (t-Distributed Stochastic Neighbor Linear Embedding) has competitive performance in density comparison. Copyright © 2021, The Authors. All rights reserved.

关键词： Stochastic systems

Representative Local Feature Mining for Few-Shot Learning

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Representative Local Feature Mining for Few-Shot Learning

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Kun Yan Lingbo Liu Jun Hou Ping Wang School of Software and Microelectronics Peking University School of Data and Computer Science Sun Yat-Sen University SenseTime Research National Engineering Research Center for Software Engineering Peking University Key Laboratory of High Confidence Software Technologies (PKU) Ministry of Education

Few-shot learning aims to recognize unseen images of new classes with only a few training examples. While great progress has been made with deep learning technology, most metric-based works rely on the measurement based on global feature representation of images, which is sensitive to background factors due to the scarcity of training data. Given this, we propose a novel method that chooses representative local features to facilitate few-shot learning. Specifically, we propose a "task-specific guided" strategy to mine local features that are task-specific and discriminative. For each task, we first mine representative local features for labeled images by a loss guided mechanism. Then these local features are used to guide a classifier to mine representative local features for unlabeled images. In this way, task-specific representative local features can be selected for better classification. We empirically show our method can effectively alleviate the negative effect introduced by background factors. Extensive experiments on two few-shot benchmarks show the effectiveness of the proposed method.

关键词： Training Measurement Learning systems Deep learning Image recognition Conferences Training data

Driving Behavior Safety Assessment in Edge Computing Using Multitask Discriminative TS Fuzzy Model

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IEEE Transactions on Consumer Electronics 2024年

作者： Gu, Yi Chen, Zhiyu Wang, Lu Shen, Jinsong Bashir, Ali Kashif Wang, Wei Jiangnan University School of Artificial Intelligence and Computer Science Wuxi214122 China Jiangnan University Engineering Research Center of Intelligent Technology for Healthcare Ministry of Education Wuxi214122 China Chongqing University School of Big Data and Software Engineering Chongqing400044 China Jiangsu Keli Internet Information Technology Co. Ltd Jingjiang214500 China Manchester Metropolitan University Department of Computing and Mathematics United Kingdom Woxsen University Woxsen School of Business Hyderabad502 345 India Lebanese American University Department of Computer Science and Mathematics Beirut Lebanon Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China

Driving behavior safety analysis in edge computing involves the real-time collection and processing of vehicle driving data. Fuzzy models have advantages in analyzing driving behavior due to their excellent ability to handle uncertain information. This study develops a multitask discriminative Takagi-Sugeno fuzzy model (MD-TS-FM) for driving behavior safety assessment. To comprehensively analyze the correlation and differences in multitask driving behaviors, the designed consequent part consists of two parts: task-shared part reflects the shared intrinsic structure information that are consistent across different driving tasks, and task-specific part reflects distinct characteristics and variations specific to each task, allowing the model to address the unique aspects of different driving behaviors. Accordingly, the task-shared consequent part is characterized by low-rank property, which mines global structural information within the fuzzy space;whereas the task-specific consequent part exhibits sparsity, which removes non-discriminative and irrelevant information. This sparsity ensures that the model focuses on the most critical features for each specific task. Furthermore, a discriminative diversity term is introduced to enhance the diversity between tasks, which explores the consistent information of task-shared consequents while reducing the overlap of task-specific consequents. Experimental results indicate that the MD-TS-FM model can be effectively applied to driving behavior safety assessment. © 1975-2011 IEEE.

关键词： driving behavior safety assessment edge computing fuzzy model multitask learning

Deep learning-based simultaneous bioavailability assessment and speciation analysis of dissolved organic copper

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Chinese Chemical Letters 2025年第05期 679-684页

作者： Zhaojing Huang Hao Li Jiayi Luo Shunxing Li Ming Zhao Fengjiao Liu Haijiao Xie College of Chemistry and Chemical Engineering Xiamen University College of Chemistry Chemical Engineering & Environmental ScienceMinnan Normal University College of Computer and Data Science/College of Software Fuzhou University School of Energy and Environmental Engineering University of Science and Technology Beijing Fujian Province Key Laboratory of Modern Analytical Science and Separation Technology Fujian Province University Key Laboratory of Pollution Monitoring and ControlMinnan Normal University Hangzhou Yanqu Information Technology Company of Limited Liability

Algal copper uptake(i.e., Cu bioavailability) in the euphotic zone plays a vital role in algal photosynthesis and respiration, affecting the primary productivity and the source and sink of atmospheric *** Cu uptake is controlled by natural dissolved organic Cu(DOCu) speciation(i.e., complexed with the dissolved organic matter) that conventionally could be tested by model prediction or molecular-level characterizations in the lab, while DOCu uptake are hardly directly assessed. Thus, the new chemistrybiology insight into the mechanisms of the Cu uptake process in algae is urgent. The DOCu speciation transformation(organic DOCu to free Cu(II) ions), enzymatic reduction-induced valence change(reduction of free Cu(II) to Cu(I) ions), and algal Cu uptake at the algae-water interface are imitated. Herein,an intelligent system with DOCu colorimetric sensor is developed for real-time monitoring of newly generated Cu(I) ions. Deep learning with whole sample image-based characterization and powerful feature extraction capabilities facilitates colorimetric measurement. In this context, the Cu bioavailability with 7kinds of organic ligands(e.g., amino acids, organic acids, carbohydrates) can be predicted by the mimetic intelligent biosensor within 15.0 min, i.e., the DOCu uptake and speciation is successfully predicted and streamlined by the biomimetic approach.

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Multi-objective Aerial IRS-assisted ISAC Optimization via Generative AI-enhanced Deep Reinforcement Learning

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arXiv 2025年

作者： Xie, Wenwen Sun, Geng Wang, Jiacheng Du, Hongyang Kang, Jiawen Huang, Kaibin Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering The University of Hong Kong 999077 Hong Kong School of Automation Guangdong University of Technology Guangzhou510006 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Integrated sensing and communication (ISAC) has garnered substantial research interest owing to its pivotal role in advancing the development of next-generation (6G) wireless networks. However, achieving a performance balance between communication and sensing in the dual-function radar communication (DFRC)-based ISAC system remains a significant challenge. In this paper, an aerial intelligent reflecting surface (IRS)-assisted ISAC system is explored, where a base station (BS) supports dual-functional operations, enabling both data transmission for multiple users and sensing for a blocked target, with the channel quality enhanced by an IRS mounted on the unmanned aerial vehicle (UAV). Moreover, we formulate an integrated communication, sensing, and energy efficiency multi-objective optimization problem (CSEMOP), which aims to maximize the communication rate of the users and the echo rate of the target, while minimizing UAV propulsion energy consumption by jointly optimizing the BS beamforming matrix, IRS phase shifts, the flight velocity and angle of the UAV. Considering the non-convexity, trade-off, and dynamic nature of the formulated CSEMOP, we propose a generative diffusion model-based deep deterministic policy gradient (GDMDDPG) method to solve the problem. Specifically, the diffusion model is incorporated into the actor network of DDPG to improve the action quality, with noise perturbation mechanism for better exploration and recent prioritized experience replay (RPER) sampling mechanism for enhanced training efficiency. Simulation results indicate that the GDMDDPG method delivers superior performance compared to the existing methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Multiobjective optimization

Applying hybrid clustering in pulsar candidate sifting with multi-modality for FAST survey

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arXiv 2023年

作者： You, Zi-Yi Pan, Yun-Rong Ma, Zhi Zhang, Li Xiao, Shuo Zhang, Dan-Dan Dang, Shi-Jun Zhao, Ru-Shuang Wang, Pei Dong, Ai-Jun Jiang, Jia-Tao Leng, Ji-Bing Li, Wei-An Li, Si-Yao School of Physics and Electronic Science Guizhou Normal University Guiyang550025 China College of Big Data and Information Engineering Guizhou University Guiyang550025 China CAS Key Laboratory of FAST National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550025 China School of Big data and Computer Science Guizhou Normal University Guiyang550025 China Guizhou Software Engineering Research Center Guiyang550000 China Key Laboratory of Information and Computing Guizhou Province Guizhou Normal University Guiyang550001 China

Pulsar search is always the basis of pulsar navigation, gravitational wave detection and other research topics. Currently, the volume of pulsar candidates collected by Five-hundred-meter Aperture Spherical radio Telescope (FAST) shows an explosive growth rate that has brought challenges for its pulsar candidate filtering System. Particularly, the multi-view heterogeneous data and class imbalance between true pulsars and non-pulsar candidates have negative effects on traditional single-modal supervised classification methods. In this study, a multi-modal and semi-supervised learning based pulsar candidate sifting algorithm is presented, which adopts a hybrid ensemble clustering scheme of density-based and partition-based methods combined with a feature-level fusion strategy for input data and a data partition strategy for parallelization. Experiments on both HTRU (The High Time Resolution Universe Survey) 2 and FAST actual observation data demonstrate that the proposed algorithm could excellently identify the pulsars: On HTRU2, the precision and recall rates of its parallel mode reach 0.981 and 0.988. On FAST data, those of its parallel mode reach 0.891 and 0.961, meanwhile, the running time also significantly decrease with the increment of parallel nodes within limits. So, we can get the conclusion that our algorithm could be a feasible idea for large scale pulsar candidate sifting of FAST drift scan observation. Copyright © 2023, The Authors. All rights reserved.

关键词： Clustering algorithms

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