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arXiv 2025年

作者： Xue, Xin Zhou, Haoyi Chen, Tianyu Zhang, Shuai Long, Yizhou Li, Jianxin Department of Computer Science Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191 China Department of Software Beihang University Beijing100191 China Zhongguancun Laboratory Beijing100190 China

Spatial-temporal sequence forecasting (STSF) is a long-standing research problem with widespread real-world applications. Neural architecture search (NAS), which automates the neural network design, has been shown effective in tackling the STSF problem. However, the existing NAS methods for STSF focus on generating architectures in a time-consuming data-driven fashion, which heavily limits their ability to use background knowledge and explore the complicated search trajectory. Large language models (LLMs) have shown remarkable ability in decision-making with comprehensive internal world knowledge, but how it could benefit NAS for STSF remains unexplored. In this paper, we propose a novel NAS method for STSF based on LLM. Instead of directly generate architectures with LLM, We inspire the LLM’s capability with a multi-level enhancement mechanism. Specifically, on the step-level, we decompose the generation task into decision steps with powerful prompt engineering and inspire LLM to serve as instructor for architecture search based on its internal knowledge. On the instance-level, we utilize a one-step tuning framework to quickly evaluate the architecture instance and a memory bank to cumulate knowledge to improve LLM’s search ability. On the task-level, we propose a two-stage architecture search, balancing the exploration stage and optimization stage, to reduce the possibility of being trapped in local optima. Extensive experimental results demonstrate that our method can achieve competitive effectiveness with superior efficiency against existing NAS methods for STSF. © 2025, CC BY-NC-ND.

关键词： Spatio-temporal data

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Analysis on n-level quantum systems by means of a coordinate transformation

Analysis on n-level quantum systems by means of a coordinate...

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IEEE Conference on Decision and Control

作者： Huilong Xu Zibo Miao Qing Gao School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

The structural decomposition of two-level quantum systems has recently been established, related to various applications in quantum information science. As an extension to previous work on two-level quantum systems, this paper aims to investigate the structural properties of n-level quantum systems. We develop algebraic conditions under which the attainment of quantum back-action evading (BAE) measurements, the generation of decoherence-free (DF) subspaces and quantum non-demolition (QND) variables, as well as the existence of steadystate solutions for the Lindblad equations, can be realized. In particular, a specific coordinate transformation is explicitly constructed to facilitate the achievement of above-mentioned objectives, accompanied by an illustrative numerical example.

关键词： Quantum system Atmospheric measurements Quantum information science Particle measurements

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Unifying logic rules and machine learning for entity enhancing

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Science China(Information Sciences) 2020年第7期63卷 142-160页

作者： Wenfei FAN Ping LU Chao TIAN School of Informatics University of Edinburgh Shenzhen Institute of Computing Sciences Shenzhen University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Alibaba Group

This paper proposes a notion of entity enhancing, which unifies entity resolution and conflict resolution, to identify tuples that refer to the same real-world entity and at the same time, correct semantic inconsistencies. We propose to unify rule-based and machine learning(ML) methods for entity enhancing, by embedding ML classifiers as predicates in logic rules. We model entity enhancing by extending the chase. We show that the chase warrants correctness justification and the Church-Rosser property. Moreover, we settle fundamental problems associated with entity enhancing, including the enhancing, consistency, satisfiability,and implication problems, ranging from NP-complete and coNP-complete to Π~p-complete. Taken together,these provide a new theoretical framework for unifying entity resolution and conflict resolution.

关键词： logic rules machine learning entity enhancing entity resolution conflict resolution

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Epidemiology,pathogenesis,and management of coronavirus disease 2019-associated stroke

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Frontiers of Medicine 2023年第6期17卷 1047-1067页

作者： Lu Liu Chenxia Zhou Huimin Jiang Huimin Wei Yifan Zhou Chen Zhou Xunming Ji Department of Neurology Xuanwu HospitalCapital Medical UniversityBeijing 100032China Neurology and Intracranial Hypertension and Cerebral Venous Disease Center National Health Commission of ChinaXuanwu HospitalCapital Medical UniversityBeijing 100032China Beijing Institute of Brain Disorders Capital Medical UniversityBeijing 100069China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Biological Science and Medical EngineeringBeihang UniversityBeijing 100191China Department of Neurology Beijing Shjitan HospitalCapital Medical UniversityBeijing 100038China Department of Neurosurgery Xuanwu HospitalCapital Medical UniversityBeijing 100032China

The coronavirus disease 2019(COVID-19)epidemic has triggered a huge impact on healthcare,socioeconomics,and other aspects of the world over the past three *** increasing number of studies have identified a complex relationship between COVID-19 and stroke,although active measures are being implemented to prevent disease *** COVID-19 may be associated with an increased risk of stroke and increase the rates of disability and mortality,posing a serious challenge to acute stroke diagnosis,treatment,and *** review aims to provide an update on the influence of COVID-19 itself or vaccines on stroke,including arterial stroke(ischemic stroke and hemorrhagic stroke)and venous stroke(cerebral venous thrombosis).Additionally,the neurovascular mechanisms involved in SARS-CoV-2 infection and the clinical characteristics of stroke in the COVID-19 setting are *** on vaccinations,potential therapeutic approaches,and effective strategies for stroke management has been highlighted.

关键词： SARS-CoV-2 ischemic stroke stroke hemorrhagic stroke cerebral venous thrombosis vaccination

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V2I Based Environment Perception for Autonomous Vehicles at Intersections

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China Communications 2021年第7期18卷 1-12页

作者： Xuting Duan Hang Jiang Daxin Tian Tianyuan Zou Jianshan Zhou Yue Cao Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems&Safety ControlSchool of Transportation Science and EngineeringBeihang UniversityBeijing 100191China School of Cyber Science and Engineering Wuhan UniversityWuhan 430072China

In recent years,autonomous driving technology has made good progress,but the noncooperative intelligence of vehicle for autonomous driving still has many technical bottlenecks when facing urban road autonomous driving challenges.V2I(Vehicle-to-Infrastructure)communication is a potential solution to enable cooperative intelligence of vehicles and *** this paper,the RGB-PVRCNN,an environment perception framework,is proposed to improve the environmental awareness of autonomous vehicles at intersections by leveraging V2I communication *** framework integrates vision feature based on *** normal distributions transform(NDT)point cloud registration algorithm is deployed both on onboard and roadside to obtain the position of the autonomous vehicles and to build the local map objects detected by roadside multi-sensor system are sent back to autonomous vehicles to enhance the perception ability of autonomous vehicles for benefiting path planning and traffic efficiency at the *** field-testing results show that our method can effectively extend the environmental perception ability and range of autonomous vehicles at the intersection and outperform the PointPillar algorithm and the VoxelRCNN algorithm in detection accuracy.

关键词： V2I environmental perception autonomous vehicles 3D objects detection

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Machine-learning-assisted intelligent synthesis of UiO-66(Ce):Balancing the trade-off between structural defects and thermal stability for e hydrogenation of Dicyclopentadiene

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Materials Genome Engineering Advances 2024年第3期2卷 96-106页

作者： Jing Lin Tao Ban Tian Li Ye Sun Shenglan Zhou Rushuo Li Yanjing Su Jitti Kasemchainan Hongyi Gao Lei Shi Ge Wang Beijing Key Laboratory of Function Materials for Molecule&Structure Construction Beijing Advanced Innovation Center for Materials Genome EngineeringSchool of Materials Science and EngineeringUniversity of Science and Technology BeijingBeijingChina Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and EngineeringBeihang UniversityBeijingChina Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and TechnologySchool of Materials Science and EngineeringUniversity of Science and Technology BeijingBeijingChina Department of Chemical Technology Chulalongkorn UniversityBangkokThailand

Metal-organic frameworks(MOFs),renowned for structural diversity and design flexibility,exhibit potential in ***,the pursuit of higher catalytic activity through defects often compromises stability,requiring a delicate *** trial-and-error method for optimizing synthesis parameters within the complex chemical space is ***,taking the typical MOF UiO-66(Ce)as an illustrative example,a closed loop workflow is built,which integrates ma-chine learning(ML)-assissted prediction,multi-objective optimization(MOO)and experimental preparation to synergistically optimize the defect content and thermal stability of UiO-66(Ce)for efficient hydrogenation of dicyclopentadiene(DCPD).An automatic data extraction program ensures data accuracy,establishing a high-quality *** is employed to explore the intricate synthesis-structure-property correlations,enabling precise delineation of pure-phase subspace and accurate predictions of *** two iterations,MOO model identifies optimal protocols for high defect content(>40%)and thermal stability(>300℃).The optimized UiO-66(Ce)exhibits superior catalytic performance in hydroge-nation of DCPD,validating the precision and reliability of our *** ML-assisted approach offers a valuable paradigm for solving the trade-off riddle in materials field.

关键词： defect content machine learning metal-organic frameworks multi-objective optimization thermal stability

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Prompt-based Unifying Inference Attack on Graph Neural Networks 39

Prompt-based Unifying Inference Attack on Graph Neural Netwo...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wei, Yuecen Fu, Xingcheng Liu, Lingyun Sun, Qingyun Peng, Hao Hu, Chunming School of Software Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University China

ISBN: (纸本)157735897X

Graph neural networks (GNNs) provide important prospective insights in applications such as social behavior analysis and financial risk analysis based on their powerful learning capabilities on graph data. Nevertheless, GNNs' predictive performance relies on the quality of task-specific node labels, so it is common practice to improve the model's generalization ability in the downstream execution of decision-making tasks through pre-training. Graph prompting is a prudent choice but risky without taking measures to prevent data leakage. In other words, in high-risk decision scenarios, prompt learning can infer private information by accessing model parameters trained on private data (publishing model parameters in pre-training, i.e., without directly leaking the raw data, is a tacitly accepted trend). However, myriad graph inference attacks necessitate tailored module design and processing to enhance inference capabilities due to variations in supervision signals. In this paper, we propose a novel Prompt-based unifying Inference Attack framework on GNNs, named ProIA. Specifically, ProIA retains the crucial topological information of the graph during pre-training, enhancing the background knowledge of the inference attack model. It then utilizes a unified prompt and introduces additional disentanglement factors in downstream attacks to adapt to task-relevant knowledge. Finally, extensive experiments show that ProIA enhances attack capabilities and demonstrates remarkable adaptability to various inference attacks. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Graph neural networks

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An overview on the designs of distributed observers in LTI multi-agent systems

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Science China(Technological Sciences) 2021年第11期64卷 2337-2346页

作者： PEI YingQing GU HaiBo LIU KeXin LU JinHu Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain ComputingBeihang UniversityBeijing 100191China

With the rapid development of information technologies and cloud computing, sensor networks play an increasingly important role in our society. Over the past few decades, distributed observer theory has attracted unprecedented attention due to its wide potential applications in different areas. Meanwhile, various approaches and algorithms have been proposed and investigated. The design of distributed observers is one of the frontier topics of system and control research, which has the significant theoretical values and broad application prospects. This paper attempts to review the representative models and the corresponding approaches for distributed observer design in linear time-invariant(LTI) systems. Firstly, the research backgrounds and main advances of distributed observer designs are briefly reviewed. Then, recent results of distributed observer designs for discrete-time and continuous-time LTI multi-agent systems(MASs) are introduced in detail, respectively. Finally, the prospects and the future work directions of the design of distributed observers are put forward. The main purpose of this paper is to promote the emerging topic on the designs of distributed observers, with focuses on the interdisciplinary interest from technological sciences.

关键词： multi-agent system linear time-invarient system distributed observer consensus

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Cooperative Channel Assignment for VANETs Based on Dual Reinforcement Learning

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Computers, Materials & Continua 2021年第2期66卷 2127-2140页

作者： Xuting Duan Yuanhao Zhao Kunxian Zheng Daxin Tian Jianshan Zhou Jian Gao Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191China School of Transportation Science and Engineering Beihang UniversityBeijing100191China Department of Engineering and Design University of SussexBrightonBN19RHUK Research Institute of Highway Ministry of Transport Beijing100088China

Dynamic channel assignment(DCA)is significant for extending vehicular ad hoc network(VANET)capacity and mitigating ***,the un-known global state information and the lack of centralized control make channel assignment performances a challenging task in a distributed vehicular direct communication *** our preliminary field test for communication under V2X scenario,we find that the existing DCA technology cannot fully meet the communication performance requirements of *** order to improve the communication performance,we firstly demonstrate the feasibility and potential of reinforcement learning(RL)method in joint channel selection decision and access fallback adaptation design in this ***,a dual reinforcement learning(DRL)-based cooperative DCA(DRL-CDCA)mechanism is ***,DRL-CDCA jointly optimizes the decision-making behaviors of both the channel selection and back-off adaptation based on a multi-agent dual reinforcement learning ***,nodes locally share and incorporate their individual rewards after each communication to achieve regional consistency *** results show that the proposed DRL-CDCA can better reduce the one-hop packet delay,improve the packet delivery ratio on average when compared with two other existing mechanisms.

关键词： Vehicular ad hoc networks reinforcement learning dynamic channel assignment

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A Low-Code Development Framework for Constructing Industrial Apps 15th

A Low-Code Development Framework for Constructing Industrial...

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15th CCF Conference on Computer Supported Cooperative Work and Social computing, Chinese CSCW 2020

作者： Wang, Jingyue Qi, Binhang Zhang, Wentao Sun, Hailong SKLSDE Lab School of Computer Science and Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191 China

ISBN: (纸本)9789811625398

With the advent of the Industry 4.0, intelligent manufacturing has become a technological highland to conquer in the process of enterprise digitalization. As the core competitiveness of intelligent manufacturing, industrial apps, with new features such as customization and lightweight, has emerged as a new type of industrial software. Traditional development methods and tools can hardly meet the large demand of industrial software on account of its long development cycles while low-code development can greatly improve the productivity of industrial software, lower the barriers and reduce costs for development. Therefore, the research and application of low-code development for industrial apps has received much attention. Industrial Internet platforms such as Siemens, OutSystems have successively launched low-code tools. However, there is still a lack of an open, unified low-code development framework in industry. In response to the above problems, we propose a low-code framework to develop industrial apps quickly and easily, which paves the way for leveraging the crowd intelligence of worldwide developers to improve the productivity of developing industrial apps. Based on BPMN2.0 and Apache Activiti engine, this framework provides drag-and-drop process design, one-click process deployment and operation, data monitoring and other functions. In this paper, we present a prototype system of a low-code development framework and demonstrate its functions through a use case of developing a predictive maintenance application. Finally, the aircraft turbine life data is used to verify the effectiveness of the system. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Productivity

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