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第43届中国控制会议

作者： Chaozhou Huang Lizhen Shao Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Shunde Innovation School University of Science and Technology Beijing

Earth observation satellites(EOS) equipped with high-resolution cameras and infrared sensors are used to observe various types of objects on the Earth's surface. When there are numerous ground targets, the available time windows for multiple EOS to observe the given task should be utilized effectively. This problem is known as the multi-satellite observation scheduling problem(MSOSP). To address the scheduling problem of dense tasks with multiple satellites, this paper establishes a bi-objective mixed integer programming model with minimizing the maximum completion time and the total weighted completion time simultaneously. Then, an improved NSGA-II algorithm(INSGA-II), which incorporates a density-based clustering algorithm called DBSCAN to enhance the diversity of solutions during the population evolution process, is proposed to solve the model. Finally, two observation scenarios are designed, and computational experiments are conducted. The results show that INSGA-II outperforms the NSGA-II algorithm in terms of C-metric and hypervolume metric.

关键词： Earth observation satellites Multi-satellite observation scheduling problem Non-dominated Sorting Genetic Algorithm

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A Logical Pattern Memory Pre-trained Model for Entailment Tree Generation

arXiv

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arXiv 2024年

作者： Yuan, Li Cai, Yi Ren, Haopeng Wang, Jiexin School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China

Generating coherent and credible explanations remains a significant challenge in the field of AI. In recent years, researchers have delved into the utilization of entailment trees to depict explanations, which exhibits a reasoning process of how a hypothesis is deduced from the supporting facts. However, existing models often overlook the importance of generating intermediate conclusions with logical consistency from the given facts, leading to inaccurate conclusions and undermining the overall credibility of entailment trees. To address this limitation, we propose the logical pattern memory pre-trained model (LMPM). LMPM incorporates an external memory structure to learn and store the latent representations of logical patterns, which aids in generating logically consistent conclusions. Furthermore, to mitigate the influence of logically irrelevant domain knowledge in the Wikipedia-based data, we introduce an entity abstraction approach to construct the dataset for pre-training LMPM. The experimental results highlight the effectiveness of our approach in improving the quality of entailment tree generation. By leveraging logical entailment patterns, our model produces more coherent and reasonable conclusions that closely align with the underlying premises. Code and data are released at https://***/YuanLi95/T5-LMPM. © 2024, CC BY-NC-ND.

关键词： Domain knowledge

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Vector Bending Sensor Based on 3D Printed Seven Core Fiber 22

Vector Bending Sensor Based on 3D Printed Seven Core Fiber

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22nd International Conference on Optical Communications and Networks, ICOCN 2024

作者： Cao, Yang Luo, Yanhua Chen, Wei Wen, Jianxiang Dong, Yanhua Wang, Tingyun Song, Zhiqiang Zhang, Xiaolei Ni, Jiasheng Wang, Jiaying Peng, Gang-Ding Chu, Yushi Zhang, Jianzhong Shanghai Institute for Advanced Communication and Data Science Shanghai University Institute of Optical Fiber Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai200444 China Shanghai Institute for Advanced Communication and Data Science Shanghai University Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai200444 China Laser Institute Qilu University of Technology Shandong Academy of Sciences Shandong Key Laboratory of Optical Fiber Sensing Technologies Jinan250014 China School of Electrical Engineering and Telecommunications University of New South Wales Photonics and Optical Communications Group SydneyNSW2052 Australia College of Physics and Optoelectronic Engineering Harbin Engineering University Key Laboratory of In-fiber Integrated Optics Ministry of Education Harbin150001 China

ISBN: (纸本)9798350367652

A novel vector bending sensor is proposed based on 3D printed seven core fiber (SCF). There are two sensing elements in the proposed sensor. One is the fiber Bragg grating (FBG) inscribed in the 3D printed SCF, and the other is the Mach-Zehnder interferometer (MZI) with the structure of single-mode fiber (SMF)-SCF-SMF. Two sensing elements exhibit significantly different responses to bending. In the curvature range of 1.115 to M-1, the curvature sensitivity of the MZI element is 22.622 nm/M-1, while that of the FBG element is 0.044 nm/M-1 in the same direction. Especially, due to the asymmetrical structure of 3D printed SCF, the proposed bending sensor clearly displays directional dependence. Given to the flexible design and manufacture of 3D printed fiber in structure and material, this study demonstrates that 3D printing fiber technology brings more opportunities for the development of advanced fiber sensors in future Internet of Things. © 2024 IEEE.

关键词： Mach-Zehnder interferometers

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Dynamic Graph Pattern Matching in Medical knowledge Graphs 4

Dynamic Graph Pattern Matching in Medical Knowledge Graphs

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4th International Conference on data Intelligence and Security, ICDIS 2022

作者： Du, Xun Tao, Zhenchao Li, Lei School of Computer Science and Information Engineering Hefei University of Technology Hefei China The First Affiliated Hospital University of Science and Technology of China Hefei China Hefei University of Technology Ministry of Education Key Laboratory of Knowledge Engineering with Big Data Hefei China

ISBN: (数字)9781665459686

ISBN: (纸本)9781665459693

As the study of graph pattern matching (GPM) attracts more and more scholars, the research of GPM in the medical field begins to emerge, from protein structure analysis to breast cancer classification diagnosis, and GPM is more and more widely adopted. However, in medical knowledge graphs (MKG), when the pattern graph dynamically changes, to obtain the matching results of the changed pattern graph, the traditional method has to carry out global matching instead of local matching, which causes a waste of resources and takes a lot of time. To quickly return the matching results of the dynamic pattern graph in MKGs, in this paper, we introduce a caching mechanism to cache the previous matching results and propose a new longest common path matching (M-LCPM) algorithm based on multithreading. The algorithm only performs partial matching on the changed pattern graph and can quickly obtain the matching result. Considering that the M-LCPM algorithm cannot get matching results when the pattern graph changes completely and there may be missing matching subgraphs when the M-LCPM algorithm performs matching, we optimize the M-LCPM algorithm and propose the M-LCPM-Enhance algorithm. Experimental results on two data sets show that our dynamic algorithm is more efficient than existing algorithms. © 2022 IEEE.

关键词： graph pattern matching longest common path medical knowledge graphs

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Computing PUR of Zero-Dimensional Ideals of Breadth at Most One

引用

Journal of Systems Science & Complexity 2021年第6期34卷 2396-2409页

作者： PAN Jian SHANG Baoxin LI Zhe ZHANG Shugong School of Mathematics Key Laboratory of Symbolic Computation and Knowledge Engineering(Ministry of Education)Jilin UniversityChangchun 130012China College of Science Northeast Electric Power UniversityJilin 132012China School of Science Changchun University of Science and TechnologyChangchun 130022China

In this paper,for a zero-dimensional polynomial ideal I,the authors prove that k[x_(1),x_(2),…,x_(n)]/I is cyclic if and only if the breadth of I is 0 or ***,the authors present a new algorithm to compute polynomial ... 详细信息

关键词： Cyclic basis Grobner basis polynomial univariate representation separating element

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Global and Adaptive Local Label Correlation for Multi-label Learning with Missing Labels

Global and Adaptive Local Label Correlation for Multi-label ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Qingxia Jiang Peipei Li Yuhong Zhang Xuegang Hu Key Laboratory of Knowledge Engineering with Big Data (the ministry of Education) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui Province China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei Anhui Province China

Label missing is a major challenge in multi-label learning. Many existing methods try to use label correlation to recover ground-truth labels, but they only focus on the label correlation within the original label space, however, the label correlation learned in this way is incomplete. Thus, inspired $b$ y the matrix adaptive column correlation, we propose a method to continuously adjust the label correlation matrix while the labels are filled in by adaptive column correlation learning method. Specifically, to reduce the impact of the missing label s on label correlation, the label space is firstly completed through manifold regularization while learning the local label information by adaptive column correlation learning in the complemented label space. Secondly, the global label correlation is utilized by adding a low-rank constraint to the entire label space. Finally, by jointly taking advantage of the global and adaptive local label correlation, our proposed approach achieves superior performance on both synthetic and real-world data sets from diverse domains compared to state-of-the art baselines.

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Neural-network-based prescribed-time tracking control for flexible-link manipulators

Neural-network-based prescribed-time tracking control for fl...

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第43届中国控制会议

作者： Guoxu Jing Yuling Li Jie Dong School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education Shunde Innovation School University of Science and Technology Beijing

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This paper addresses the prescribed-time control problem for flexible-link manipulators. First, we use radial basis function neural networks to handle the system uncertainties. On this basis, we propose a prescribed-time neural network trajectory tracking controller based on the temporal scale transformation method. The stability of the closed-loop systems is analyzed, and it is shown that the tracking error eventually converges to the small neighborhood around the origin within a given settling time T. Finally, the simulation results demonstrate effectiveness of the proposed control scheme.

关键词： Flexible-link manipulator prescribed-time control temporal scale transformation neural networks

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In-Context Decision Transformer: Reinforcement Learning via Hierarchical Chain-of-Thought

arXiv

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arXiv 2024年

作者： Huang, Sili Hu, Jifeng Chen, Hechang Sun, Lichao Yang, Bo School of Artificial Intelligence Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Lehigh University BethlehemPA United States

In-context learning is a promising approach for offline reinforcement learning (RL) to handle online tasks, which can be achieved by providing task prompts. Recent works demonstrated that in-context RL could emerge with self-improvement in a trial-and-error manner when treating RL tasks as an across-episodic sequential prediction problem. Despite the self-improvement not requiring gradient updates, current works still suffer from high computational costs when the across-episodic sequence increases with task horizons. To this end, we propose an In-context Decision Transformer (IDT) to achieve self-improvement in a high-level trial-and-error manner. Specifically, IDT is inspired by the efficient hierarchical structure of human decision-making and thus reconstructs the sequence to consist of high-level decisions instead of low-level actions that interact with environments. As one high-level decision can guide multi-step low-level actions, IDT naturally avoids excessively long sequences and solves online tasks more efficiently. Experimental results show that IDT achieves state-of-the-art in long-horizon tasks over current in-context RL methods. In particular, the online evaluation time of our IDT is 36× times faster than baselines in the D4RL benchmark and 27× times faster in the Grid World benchmark. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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IF-Font: ideographic description sequence-following font generation 24

IF-Font: ideographic description sequence-following font gen...

引用

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Xinping Chen Xiao Ke Wenzhong Guo Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou China and Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China

ISBN: (纸本)9798331314385

Few-shot font generation (FFG) aims to learn the target style from a limited number of reference glyphs and generate the remaining glyphs in the target font. Previous works focus on disentangling the content and style features of glyphs, combining the content features of the source glyph with the style features of the reference glyph to generate new glyphs. However, the disentanglement is challenging due to the complexity of glyphs, often resulting in glyphs that are influenced by the style of the source glyph and prone to artifacts. We propose IF-Font, a novel paradigm which incorporates Ideographic Description Sequence (IDS) instead of the source glyph to control the semantics of generated glyphs. To achieve this, we quantize the reference glyphs into tokens, and model the token distribution of target glyphs using corresponding IDS and reference tokens. The proposed method excels in synthesizing glyphs with neat and correct strokes, and enables the creation of new glyphs based on provided IDS. Extensive experiments demonstrate that our method greatly outperforms state-of-the-art methods in both one-shot and few-shot settings, particularly when the target styles differ significantly from the training font styles. The code is available at https://***/Stareven233/IF-Font.

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Few-Shot Fine-Grained Classification of Histological Images

Few-Shot Fine-Grained Classification of Histological Images

引用

Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Yingdong Jiang Jing Huang Zihe Jin Leqi Shen Ziyi Zhang Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Software Jilin University Jilin University ChangChun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Jilin University ChangChun China College of Computer Science and Technology Jilin University ChangChun China

Histological image classification plays a crucial role in cancer diagnosis. However, the acquisition of well-labeled histological images is prohibitively expensive, and obtaining rare abnormal samples is challenging. Therefore, applying few-shot learning methods to histological image classification tasks holds significant clinical value. Nevertheless, existing research predom-inantly relies on coarse-grained image classification approaches based on natural image datasets, which struggle to address the fine-grained challenges encountered in histological image classification, such as intra-class diversity and inter-class similarity. To tackle this issue, this study proposes a novel few-shot fine-grained classification method for histological images, named “Category-Aware Feature Map Reconstruction Network.” This method employs channel weights to localize the differences between inter-class and intra-class regions, composed of intra-class channel weights and inter-class channel weights, collectively referred to as category-aware weights. Specifically, intra-class channel weights indicate the matching degree of salient regions within the support set of a particular class, while inter-class channel weights represent the degree of containing distinct information between classes. The category-aware weights are utilized to transform the support feature maps and query feature maps, generating feature maps that capture differentiating details between categories. Finally, the distance between the transformed query feature map and support feature map is calculated to achieve probabilistic predictions for the categories. On a histological few-shot dataset, this method achieves an accuracy of 90.23% using ResNet-12 as the feature extractor, surpassing the baseline model by 5.24% and outperforming other few-shot methods by at least 10% in the 5-way 10-shot experimental setting. The proposed method exhibits exceptional performance on histological image few-shot datasets, playing a

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