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arXiv 2024年

作者： Li, Qian Guo, Shu Chen, Yingjia Ji, Cheng Sheng, Jiawei Li, Jianxin Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China The State Key Laboratory of Software Development Environment Beihang University Beijing100083 China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100083 China School of Mathematics Edinburgh University Edinburgh United Kingdom Institute of Information Engineering Chinese Academy of Sciences Beijing100083 China School of Cyber Security University of Chinese Academy of Sciences Beijing100083 China

Few-shot knowledge graph completion (FKGC) aims to query the unseen facts of a relation given its few-shot reference entity pairs. The side effect of noises due to the uncertainty of entities and triples may limit the few-shot learning, but existing FKGC works neglect such uncertainty, which leads them more susceptible to limited reference samples with noises. In this paper, we propose a novel uncertainty-aware few-shot KG completion framework (UFKGC) to model uncertainty for a better understanding of the limited data by learning representations under Gaussian distribution. Uncertainty representation is first designed for estimating the uncertainty scope of the entity pairs after transferring feature representations into a Gaussian distribution. Further, to better integrate the neighbors with uncertainty characteristics for entity features, we design an uncertainty-aware relational graph neural network (UR-GNN) to conduct convolution operations between the Gaussian distributions. Then, multiple random samplings are conducted for reference triples within the Gaussian distribution to generate smooth reference representations during the optimization. The final completion score for each query instance is measured by the designed uncertainty optimization to make our approach more robust to the noises in few-shot scenarios. Experimental results show that our approach achieves excellent performance on two benchmark datasets compared to its competitors. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning

arXiv

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arXiv 2024年

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3DAD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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Multi-aspect temporal network embedding: A mixture of Hawkes process view

arXiv

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arXiv 2021年

作者： Chang, Yutian Liu, Guannan Zuo, Yuan Wu, Junjie School of Economics and Management Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operations Beihang University

Recent years have witnessed the tremendous research interests in network embedding. Extant works have taken the neighborhood formation as the critical information to reveal the inherent dynamics of network structures, and suggested encoding temporal edge formation sequences to capture the historical influences of neighbors. In this paper, however, we argue that the edge formation can be attributed to a variety of driving factors including the temporal influence, which is better referred to as multiple aspects. As a matter of fact, different node aspects can drive the formation of distinctive neighbors, giving birth to the multi-aspect embedding that relates to but goes beyond a temporal scope. Along this vein, we propose a Mixture of Hawkes-based Temporal Network Embeddings (MHNE) model to capture the aspect-driven neighborhood formation of networks. In MHNE, we encode the multi-aspect embeddings into the mixture of Hawkes processes to gain the advantages in modeling the excitation effects and the latent aspects. Specifically, a graph attention mechanism is used to assign different weights to account for the excitation effects of history events, while a Gumbel-Softmax is plugged in to derive the distribution over the aspects. Extensive experiments on 8 different temporal networks have demonstrated the great performance of the multi-aspect embeddings obtained by MHNE in comparison with the state-of-the-art methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Network embeddings

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Grammars and a random generator for deterministic chain regular expressions 46

Grammars and a random generator for deterministic chain regu...

引用

46th International Conference on Current Trends in Theory and Practice of Computer Science Doctoral Student Research Forum, SOFSEM-SRF 2020

作者： Chu, Xinyu Lu, Ping Chen, Haiming State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

Deterministic regular expressions (DREs) are a core part of XML Schema and widely used in other applications. There are context-free grammars for DREs, which are not efficient for being used and also lead to the problem of being unable to generate expressions of the given length in an efficient manner. Deterministic chain regular expressions (dCHAREs) are a very practical subclass of DREs. In many practical situations, dCHAREs are more suitable than standard or deterministic regular expressions. But dCHAREs do not have a simple syntax, which puts a burden on the applications of these expressions. In this paper, we propose derivation rules and give regular grammars for dCHAREs which are more succinct than the grammars of DREs. Based on the grammars, we further design an algorithm to randomly generate dCHAREs of the given length, which fills the gap in the generation of DREs. Experimental results demonstrate that our generator is efficient in terms of running time and complexity. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Pattern matching

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Pushing AI to wireless network edge: An overview on integrated sensing, communication, and computation towards 6G

arXiv

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arXiv 2022年

作者： Zhu, Guangxu Lyu, Zhonghao Jiao, Xiang Liu, Peixi Chen, Mingzhe Xu, Jie Cui, Shuguang Zhang, Ping Shenzhen Research Institute of Big Data Shenzhen518172 China Shenzhen518172 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables33146 United States State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Peng Cheng Laboratory Shenzhen518066 China

Pushing artificial intelligence (AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things (AIoT) in the sixth-generation (6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the huge amount of data scattered at wireless network edge. In general, realizing edge intelligence corresponds to the process of sensing, communication, and computation, which are coupled ingredients for data generation, exchanging, and processing, respectively. However, conventional wireless networks design the sensing, communication, and computation separately in a task-agnostic manner, which encounters difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications such as auto-driving. This thus prompts a new design paradigm of seamless integrated sensing, communication, and computation (ISCC) in a task-oriented manner, which comprehensively accounts for the use of the data in the downstream AI applications. In view of its growing interest, this article provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art development, and shedding light on the road ahead. Copyright © 2022, The Authors. All rights reserved.

关键词： Artificial intelligence

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Effects of Dynamic-Win-Stay-Lose-Learn model with voluntary participation in social dilemma

arXiv

引用

arXiv 2021年

作者： Shi, Zhenyu Wei, Wei Feng, Xiangnan Zhang, Ruizhi Zheng, Zhiming School of Mathematical Sciences Beihang University Beijing China Key Laboratory of Mathematics Informatics Behavioral Semantics Ministry of Education China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang China Peng Cheng Laboratory ShenzhenGuangdong China

In recent years, Win-Stay-Lose-Learn rule has attracted wide attention as an effective strategy updating rule, and voluntary participation is proposed by introducing a third strategy in Prisoner's dilemma game. Some researche show that combining Win-Stay-Lose-Learn rule with voluntary participation could promote cooperation mor significantly under moderate temptation values, however, cooperators' survival under high aspiration levels and high temptation values is still a challenging problem. In this paper, inspired by Achievement Motivation Theory a Dynamic-Win-Stay-Lose-Learn rule with voluntary participation is investigated, where a dynamic aspiration process is introduced to describe the co-evolution of individuals' strategies and aspirations. It is found tha cooperation is extremely promoted and defection is almost extinct in our model, even when the initial aspiration levels and temptation values are high. The combination of dynamic aspiration and voluntary participation play an active role since loners could survive under high initial aspiration levels and they will expand stably becaus of their fixed payoffs. The robustness of our model is also discussed and some adverse structures are found which should be alerted in the evolutionary process. Our work provides a more realistic model and shows tha cooperators may prevail defectors in an unfavorable initial environment. Copyright © 2021, The Authors. All rights reserved.

关键词： Game theory

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Unsupervised content-preserving transformation for optical microscopy

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Light(Science & Applications) 2021年第3期10卷 390-400页

作者： Xinyang Li Guoxun Zhang Hui Qiao Feng Bao Yue Deng Jiamin Wu Yangfan He Jingping Yun Xing Lin Hao Xie Haoqian Wang Qionghai Dai Department of Automation Tsinghua UniversityBeijing 100084China Tsinghua Shenzhen International Graduate School Tsinghua UniversityShenzhen 518055China Institute for Brain and Cognitive Sciences Tsinghua UniversityBeijing 100084China School of Astronautics Beihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100191China Department of Pathology Sun Yat-sen University Cancer CenterGuangzhou 510060China State Key Laboratory of Oncology in South China Guangzhou 510060China Collaborative Innovation Center for Cancer Medicine Guangzhou 510060China Beijing Innovation Center for Future Chips Tsinghua UniversityBeijing 100084China

The development of deep learning and open access to a substantial collection of imaging data together provide a potential solution for computational image transformation,which is gradually changing the landscape of optical imaging and biomedical ***,current implementations of deep learning usually operate in a supervised manner,and their reliance on laborious and error-prone data annotation procedures remains a barrier to more general ***,we propose an unsupervised image transformation to facilitate the utilization of deep learning for optical microscopy,even in some cases in which supervised models cannot be *** the introduction of a saliency constraint,the unsupervised model,named Unsupervised content-preserving Transformation for Optical Microscopy(UTOM);can learn the mapping between two image domains without requiring paired training data while avoiding distortions of the image *** shows promising performance in a wide range of biomedical image transformation tasks,including in silico histological staining,fluorescence image restoration,and virtual fluorescence *** evaluations reveal that UTOM achieves stable and high-fidelity image transformations across different imaging conditions and *** anticipate that our framework will encourage a paradigm shift in training neural networks and enable more applications of artificial intelligence in biomedical imaging.

关键词： transformation preserving image

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Collective firing patterns of neuronal networks with short-term synaptic plasticity

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Physical Review E 2021年第2期103卷 022312-022312页

作者： Chong-Yang Wang Ji-Qiang Zhang Zhi-Xi Wu Jian-Yue Guan Lanzhou Center for Theoretical Physics and Key Laboratory of Theoretical Physics of Gansu Province Lanzhou University Lanzhou Gansu 730000 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China School of Physics and Electronic-Electrical Engineering Ningxia University Yinchuan 750021 China

We investigate the occurrence of synchronous population activities in a neuronal network composed of both excitatory and inhibitory neurons and equipped with short-term synaptic plasticity. The collective firing patterns with different macroscopic properties emerge visually with the change of system parameters, and most long-time collective evolution also shows periodic-like characteristics. We systematically discuss the pattern-formation dynamics on a microscopic level and find a lot of hidden features of the population activities. The bursty phase with power-law distributed avalanches is observed in which the population activity can be either entire or local periodic-like. In the purely spike-to-spike synchronous regime, the periodic-like phase emerges from the synchronous chaos after the backward period-doubling transition. The local periodic-like population activity and the synchronous chaotic activity show substantial trial-to-trial variability, which is unfavorable for neural code, while they are contrary to the stable periodic-like phases. We also show that the inhibitory neurons can promote the generation of cluster firing behavior and strong bursty collective firing activity by depressing the activities of postsynaptic neurons partially or wholly.

关键词： Neuronal dynamics Population dynamics Synapses Synchronization Collective dynamics Neuronal avalanches Neuronal networks Integrate-and-fire model

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Science as Exploration in a Knowledge Landscape: Tracing Hotspots or Seeking Opportunity?

arXiv

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arXiv 2023年

作者： Liu, Feifan Zhang, Shuang Xia, Haoxiang Institute of Systems Engineering Dalian University of Technology No.2 Linggong Road Liaoning Dalian116024 China Center for Big Data and Intelligent Decision-Making Dalian University of Technology Linggong Road Liaoning Dalian116024 China Institute for Advanced Intelligence Dalian University of Technology Linggong Road Liaoning Dalian116024 China Key Laboratory of Social Computing and Cognitive Intelligence Ministry of Education of China Linggong Road Liaoning Dalian116024 China

The selection of research topics by scientists can be viewed as an exploration process conducted by individuals with cognitive limitations traversing a complex cognitive landscape influenced by both individual and social factors. While existing theoretical investigations have provided valuable insights, the intricate and multifaceted nature of modern science hinders the implementation of empirical experiments. This study leverages advancements in deep learning techniques to investigate the patterns and dynamic mechanisms of topic-transition among scientists. By constructing the knowledge space across 6 large-scale disciplines, we depict the trajectories of scientists' topic transitions within this space, measuring the flow and distance of research regions across different sub-spaces. Our findings reveal a predominantly conservative pattern of topic transition at the individual level, with scientists primarily exploring local knowledge spaces. Furthermore, simulation modeling analysis identifies research intensity, driven by the concentration of scientists within a specific region, as the key facilitator of topic transition. Conversely, the knowledge distance between fields serves as a significant barrier to exploration. Notably, despite potential opportunities for breakthrough discoveries at the intersection of subfields, empirical evidence suggests that these opportunities do not exert a strong pull on scientists, leading them to favor familiar research areas. Our study provides valuable insights into the exploration dynamics of scientific knowledge production, highlighting the influence of individual cognition, social factors, and the intrinsic structure of the knowledge landscape itself. These findings offer a framework for understanding and potentially shaping the course of scientific progress. © 2023, CC BY-NC-ND.

关键词： Deep learning

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The Temporal Structural Pattern in Scientific Collaborative Behavior from the Perspective of Complex Network 23rd

The Temporal Structural Pattern in Scientific Collaborative...

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23rd International Symposium on Knowledge and Systems Sciences, KSS 2024

作者： Zholdoshbaeva, Elina Zhang, Shuang Liu, Feifan Xia, Haoxiang Institute of Systems Engineering Dalian University of Technology No. 2 Linggong Road Liaoning Dalian116024 China Center for Big Data and Intelligent Decision-Making Dalian University of Technology Linggong Road Liaoning Dalian116024 China School of Business East China University of Science and Technology 130 Meilong Road Xuhui District Shanghai200237 China Institute for Advanced Intelligence Dalian University of Technology Linggong Road Liaoning Dalian116024 China Key Laboratory of Social Computing and Cognitive Intelligence Ministry of Education of China Linggong Road Liaoning Dalian116024 China

ISBN: (纸本)9789819601776

The rapid advancement of science has underscored the importance of collaboration among scientists. While many studies have explored the structure of collaboration networks, there is a lack of comprehensive quantitative research on the temporal patterns of these collaborations. This study addresses this gap by analyzing temporal scientific collaboration networks in physics, spanning from 1960 to 2021, using complex network theories and methods. By constructing five-year temporal collaborative networks and employing metrics such as network diameter, shortest path, and clustering coefficient, this research reveals an increase in connectivity and small-world characteristics over time. Additionally, community detection highlights a shift towards specialized collaboration patterns and increased cross-community interactions. The study also uncovers growing centrality and social stratification within the research system, with the "rich club" phenomenon indicating strong and dynamic collaborations among highly connected scientists. Temporal analysis shows that while ultra-high connectivity scientists engage in fluctuating relationships, medium-high connectivity scientists maintain more stable collaborations. These findings provide insights into the evolution of collaborative behaviors and inform science policy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Collaborative learning

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