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arXiv 2023年

作者： Wang, Xiaochen Zhou, Lei McAvoy, Alex Li, Aming Center for Systems and Control College of Engineering Peking University Beijing100871 China School of Automation Beijing Institute of Technology Beijing100081 China School of Data Science and Society University of North Carolina at Chapel Hill Chapel HillNC27599 United States Department of Mathematics University of North Carolina at Chapel Hill Chapel HillNC27599 United States Center for Multi-Agent Research Institute for Artificial Intelligence Peking University Beijing100871 China

Imitation is an important learning heuristic in animal and human societies. Previous explorations report that the fate of individuals with cooperative strategies is sensitive to the protocol of imitation, leading to a conundrum about how different styles of imitation quantitatively impact the evolution of cooperation. Here, we take a different perspective on the personal and external social information required by imitation. We develop a general model of imitation dynamics with incomplete information in networked systems, which unifies classical update rules including the death-birth and pairwise-comparison rule on complex networks. Under pairwise interactions, we find that collective cooperation is most promoted if individuals neglect personal information. If personal information is considered, cooperators evolve more readily with more external information. Intriguingly, when interactions take place in groups on networks with low degrees of clustering, using more personal and less external information better facilitates cooperation. Our unifying perspective uncovers intuition by examining the rate and range of competition induced by different information situations. © 2023, CC BY.

关键词： Complex networks

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Dual Prototypes Contrastive Learning for Semi-Supervised Medical Image Segmentation

SSRN

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SSRN 2024年

作者： Yue, Tianai Xu, Rongtao Wu, Jingqian Yang, Wenjie Du, Shide Wang, Changwei Johns Hopkins University Baltimore21218 United States State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The University of Hong Kong Hong Kong999077 Hong Kong College of Computer and Big Data Fuzhou University Fuzhou350108 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan250014 China

Semi-supervised techniques for medical image segmentation have demonstrated potential, effectively training models using scarce labeled data alongside a wealth of unlabeled data. Therefore, semi-supervised medical image segmentation is one of the key issues in engineering applications of medical artificial intelligence. Consistency constraints based on prototype alignment provide an intuitively sensible way to discover valuable insights from unlabeled data that can motivate segmentation performance. In this work, we propose a dual prototypes contrastive network (DPC-Net) to motivate semi-supervised medical segmentation accuracy by imposing image-level global prototype constraints and pixel-level local prototype constraints. First, we introduce a Background-Separation Global Prototype Contrastive Learning technique that utilizes the natural mutual exclusivity of foreground and background to separate the inter-class distances and encourage the segmentation network to obtain segmentation results that are more complete and do not contain background regions. Second, we design a Cross-Consistent Local Prototype Contrastive Learning techniques to extend the perturbation consistency of the two networks to the prototype's localized response to the feature map, thereby shaping a more stable intra-class prototype space and producing accurate and robust pixel-level predictions. Finally, we comprehensively evaluate our method on mainstream semi-supervised medical image segmentation benchmarks and settings, and experimental results show that our DPC-Net outperforms current state-of-the-art methods. © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Efficient Planar Pose Estimation via UWB Measurements

Efficient Planar Pose Estimation via UWB Measurements

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IEEE International Conference on Robotics and automation (ICRA)

作者： Haodong Jiang Wentao Wang Yuan Shen Xinghan Li Xiaoqiang Ren Biqiang Mu Junfeng Wu Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS) School of Data Science the Chinese University of HongKong Shenzhen P. R. China College of Control Science and Engineering and the State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou P. R. China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing P. R. China School of Mechatronic Engineering and Automation Shanghai University Shanghai P. R. China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing P. R. China

State estimation is an essential part of autonomous systems. Integrating the Ultra-Wideband (UWB) technique has been shown to correct the long-term estimation drift and bypass the complexity of loop closure detection. However, few works on robotics treat UWB as a stand-alone state estimation solution. The primary purpose of this work is to investigate planar pose estimation using only UWB range measurements. We prove the excellent property of a two-step scheme, which says we can refine a consistent estimator to be asymptotically efficient by one step of Gauss-Newton iteration. Grounded on this result, we design the GN-ULS estimator, which reduces the computation time significantly compared to previous methods and presents the possibility of using only UWB for real-time state estimation.

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A minimax probability machine for non-decomposable performance measures

arXiv

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arXiv 2021年

作者： Luo, Junru Qiao, Hong Zhang, Bo The School of Computer Science and Artificial Intelligence Aliyun School of Big Data Changzhou University Changzhou Jiangsu province China The Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China

Imbalanced classification tasks are widespread in many real-world applications. For such classification tasks, in comparison with the accuracy rate, it is usually much more appropriate to use non-decomposable performance measures such as the Area Under the receiver operating characteristic Curve (AUC) and the Fβ measure as the classification criterion since the label class is imbalanced. On the other hand, the minimax probability machine is a popular method for binary classification problems and aims at learning a linear classifier by maximizing the accuracy rate, which makes it unsuitable to deal with imbalanced classification tasks. The purpose of this paper is to develop a new minimax probability machine for the Fβ measure, called MPMF, which can be used to deal with imbalanced classification tasks. A brief discussion is also given on how to extend the MPMF model for several other non-decomposable performance measures listed in the paper. To solve the MPMF model effectively, we derive its equivalent form which can then be solved by an alternating descent method to learn a linear classifier. Further, the kernel trick is employed to derive a nonlinear MPMF model to learn a nonlinear classifier. Several experiments on real-world benchmark datasets demonstrate the effectiveness of our new model. © 2021, CC BY.

关键词： Probability

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Learning with smooth hinge losses

arXiv

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arXiv 2021年

作者： Luo, Junru Qiao, Hong Zhang, Bo School of Computer Science and Artificial Intelligence Aliyun School of Big Data Changzhou University Changzhou Jiangsu province China Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China

Due to the non-smoothness of the Hinge loss in SVM, it is difficult to obtain a faster convergence rate with modern optimization algorithms. In this paper, we introduce two smooth Hinge losses ψG(α;σ) and ψM(α;σ) which are infinitely differentiable and converge to the Hinge loss uniformly in α as σ tends to 0. By replacing the Hinge loss with these two smooth Hinge losses, we obtain two smooth support vector machines (SSVMs), respectively. Solving the SSVMs with the Trust Region Newton method (TRON) leads to two quadratically convergent algorithms. Experiments in text classification tasks show that the proposed SSVMs are effective in real-world applications. We also introduce a general smooth convex loss function to unify several commonly-used convex loss functions in machine learning. The general framework provides smooth approximation functions to non-smooth convex loss functions, which can be used to obtain smooth models that can be solved with faster convergent optimization algorithms. © 2021, CC BY.

关键词： Classification (of information)

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Modeling and Detecting Communities in Node Attributed Networks

arXiv

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arXiv 2021年

作者： Ren, Ren Shao, Jinliang Bishop, Adrian N. Zheng, Wei Xing School of Automation Engineering University of Electronic Science and Technology of China 611731 China The Research Center on Crowd Spectrum Intelligence Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518054 China Australia Canberra Research Lab Australia The School of Computer Data and Mathematical Sciences Western Sydney University SydneyNSW2751 Australia

As a fundamental structure in real-world networks, in addition to graph topology, communities can also be reflected by abundant node attributes. In attributed community detection, probabilistic generative models (PGMs) have become the mainstream method due to their principled characterization and competitive performances. Here, we propose a novel PGM without imposing any distributional assumptions on attributes, which is superior to the existing PGMs that require attributes to be categorical or Gaussian distributed. Based on the block model of graph structure, our model incorporates the attribute by describing its effect on node popularity. To characterize the effect quantitatively, we analyze the community detectability for our model and then establish the requirements of the node popularity term. This leads to a new scheme for the crucial model selection problem in choosing and solving attributed community detection models. With the model determined, an efficient algorithm is developed to estimate the parameters and to infer the communities. The proposed method is validated from two aspects. First, the effectiveness of our algorithm is theoretically guaranteed by the detectability condition. Second, extensive experiments indicate that our method not only outperforms the competing approaches on the employed datasets, but also shows better applicability to networks with various node attributes. © 2021, CC BY.

关键词： Population dynamics

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Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma

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Visual Computing for Industry,Biomedicine,and Art 2023年第1期6卷 329-342页

作者： Hailin Li Weiyuan Huang Siwen Wang Priya SBalasubramanian Gang Wu Mengjie Fang Xuebin Xie Jie Zhang Di Dong Jie Tian Feng Chen Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine and EngineeringBeihang UniversityBeijing 100191China CAS Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijing 100190China Department of Radiology Hainan General Hospital(Hainan Affiliated Hospital of Hainan Medical University)HaikouHainan 570311China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Department of Psychiatry Weill Cornell MedicineNew YorkNY 10065USA Department of Radiotherapy Hainan General Hospital(Hainan Affiliated Hospital of Hainan Medical University)HaikouHainan 570311China Department of Radiology Kiang Wu HospitalSanto AntónioMacao 999078China Department of Radiology Zhuhai People’s Hospital(Zhuhai Hospital Affiliated With Jinan University)ZhuhaiGuangdong 519000China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi’anShaanxi 710126China Zhuhai Precision Medical Center Zhuhai People’s HospitalZhuhaiGuangdong 519000China.

Although prognostic prediction of nasopharyngeal carcinoma (NPC) remains a pivotal research area, the role of dynamic contrast-enhanced magnetic resonance (DCE-MR) has been less explored. This study aimed to investigate the role of DCR-MR in predicting progression-free survival (PFS) in patients with NPC using magnetic resonance (MR)- and DCE-MR-based radiomic models. A total of 434 patients with two MR scanning sequences were included. The MR- and DCE-MR-based radiomics models were developed based on 289 patients with only MR scanning sequences and 145 patients with four additional pharmacokinetic parameters (volume fraction of extravascular extracellular space (ve), volume fraction of plasma space (vp), volume transfer constant (Ktrans), and reverse reflux rate constant (kep) of DCE-MR. A combined model integrating MR and DCE-MR was constructed. Utilizing methods such as correlation analysis, least absolute shrinkage and selection operator regression, and multivariate Cox proportional hazards regression, we built the radiomics models. Finally, we calculated the net reclassification index and C-index to evaluate and compare the prognostic performance of the radiomics models. Kaplan-Meier survival curve analysis was performed to investigate the model’s ability to stratify risk in patients with NPC. The integration of MR and DCE-MR radiomic features significantly enhanced prognostic prediction performance compared to MR- and DCE-MR-based models, evidenced by a test set C-index of 0.808 vs 0.729 and 0.731, respectively. The combined radiomics model improved net reclassification by 22.9%-52.6% and could significantly stratify the risk levels of patients with NPC (p = 0.036). Furthermore, the MR-based radiomic feature maps achieved similar results to the DCE-MR pharmacokinetic parameters in terms of reflecting the underlying angiogenesis information in NPC. Compared to conventional MR-based radiomics models, the combined radiomics model integrating MR and DCE-MR showed

关键词： Dynamic contrast-enhanced magnetic resonance imaging Magnetic resonance imaging Radiomics Prognostic prediction

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XAI-PSSGAN: Perception-Enhanced Spectrum Shift Generative Adversarial Network with Explainable AI System for NIR-II Fluorescence Molecular Imaging

XAI-PSSGAN: Perception-Enhanced Spectrum Shift Generative Ad...

引用

IEEE International Symposium on Biomedical Imaging

作者： Lidan Fu Binchun Lu Lingbing Li Xiaojing Shi Jie Tian Zhenhua Hu CAS Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Department of Precision Instrument Tsinghua University Beijing China Interventional Radiology Department Chinese PLA General Hospital Beijing China Beijing Advanced Innovation Center for Big Data-based Precision Medicine School of Engineering Medicine Beihang University Beijing China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and Technology Xidian University Xi'an China National Key Laboratory of Kidney Diseases Beijing China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Fluorescence imaging in the second near-infrared window (NIR-II) facilitates the real-time optical contrast for in vivo biomedical imaging. However, the detection noise is an inevitable byproduct of the real-time imaging process. Additionally, fluorescent probes currently approved for clinical use remain limited within the wavelength of NIR-IIa (1000–1300 nm). NIR-IIb (1500–1700 nm) imaging has the potential to deliver higher resolution than NIR-IIa, but relies on toxic fluorescent probes not approved for human use. To support clinical high-precision imaging, we propose a NIR-II fluorescence enhanced imaging method based on perception-enhanced spectrum shift generative adversarial network with explainable AI systems (xAI-PSSGAN). The proposed method tackles the problem of limited unpaired data training, and generates NIR-IIb resembling images with enhanced resolution and reduced noise. Experimental results demonstrate that proposed method effectively improves the imaging quality and surpasses competing alternatives, which is of great significance to promote the high-quality NIR-II imaging in clinical practice.

关键词： Training Image resolution Explainable AI Statistical analysis Noise Imaging Fluorescence Generative adversarial networks Real-time systems Probes

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Monitoring System of Seismo Dynamics of Rocks for Recording Earthquake Precursors 15

Monitoring System of Seismo Dynamics of Rocks for Recording ...

引用

15th International Scientific-Technical Conference on Actual Problems of Electronic Instrument Engineering, APEIE 2021

作者： Zhmud, V.A. Dimitrov, L.V. Mansurova, M.E. Semibalamut, V.M. Fomin, Yu. N. Stukach, O.V. Novosibirsk State Technical University Institute of Laser Physics SB RAS Russia Dept. of Automation Novosibirsk Russia Technical University of Sofia Mechanical Engineering Faculty Sofia Bulgaria Al-Farabi Kazakh National University Department of Artificial Intelligence and Big Data Almaty Kazakhstan Siberian Branch of the Federal State Budgetary Institution of Science of the Federal Research Center Unified Geophysical Service of the Russian Academy of Sciences Novosibirsk Russia Novosibirsk State Technical University Dept. of Information Defense Novosibirsk Russia

ISBN: (纸本)9781665434089

Internet of things (IoT) and smart city technologies are closely related to citizen security issues. One of the important components of safety is to reduce damage from man-made and natural disasters. In this area, the most important direction is the continuous monitoring of rock vibrations under the influence of tidal forces. These technologies, with minor changes, can be used to monitor the state of complex technological structures, such as dams, bridges, large buildings. It is known that continuous recording of micro deformations of rocks under the action of gravitational forces from the Sun and the Moon due to a change in their location in relation to the Earth's surface allows one to obtain additional information about the possible future earthquakes. The corresponding meters are based on two helium-neon (He-Ne) lasers. They contain a rather complicated and insufficiently reliable system for automatic phase and frequency tuning of the slave laser to the master. Insufficient accuracy of such phase feedback control generates errors in measurement. This paper proposes to abandon such variants of the devices based on the use of two lasers. The structure based on the only laser has been proposed. This requires metrological justification, as well as the development of appropriate optical schemes, design development and change of the electronic part. Nevertheless, such a modification seems appropriate. The reasons for this lie in a clear increase in reliability and accuracy, a decrease in cost and size. © 2021 IEEE.

关键词： Earthquakes

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Quadratic sparse Gaussian graphical model estimation method for massive variables 29

Quadratic sparse Gaussian graphical model estimation method ...

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29th International Joint Conference on artificial intelligence, IJCAI 2020

作者： Zhang, Jiaqi Wang, Meng Li, Qinchi Wang, Sen Chang, Xiaojun Wang, Beilun College of Software Engineering Southeast University China School of Computer Science and Engineering Southeast University China School of Artificial Intelligence Southeast University China Electrical Engineering and Automation YOUPEI College Yancheng Institute of Technology China School of Information Technology and Electrical Engineering University of Queensland Australia Department of Data Science and AI Monash University Australia

ISBN: (纸本)9780999241165

We consider the problem of estimating a sparse Gaussian Graphical Model with a special graph topological structure and more than a million variables. Most previous scalable estimators still contain expensive calculation steps (e.g., matrix inversion or Hessian matrix calculation) and become infeasible in high-dimensional scenarios, where p (number of variables) is larger than n (number of samples). To overcome this challenge, we propose a novel method, called Fast and Scalable Inverse Covariance Estimator by Thresholding (FST). FST first obtains a graph structure by applying a generalized threshold to the sample covariance matrix. Then, it solves multiple block-wise subproblems via element-wise thresholding. By using matrix thresholding instead of matrix inversion as the computational bottleneck, FST reduces its computational complexity to a much lower order of magnitude (O(p2)). We show that FST obtains the same sharp convergence rate O(p(log max{p, n}/n) as other state-of-the-art methods. We validate the method empirically, on multiple simulated datasets and one real-world dataset, and show that FST is two times faster than the four baselines while achieving a lower error rate under both Frobenius-norm and max-norm. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Inverse problems

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