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arXiv 2025年

作者： Xu, Xiao-Yue Xue, Xin Chen, Tianyu Ding, Chen Li, Tian Zhou, Haoyi Huang, He-Liang Bao, Wan-Su Henan Key Laboratory of Quantum Information and Cryptography Zhengzhou Henan450000 China 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

Noise is a major obstacle in current quantum computing, and Machine Learning for Quantum Error Mitigation (ML-QEM) promises to address this challenge, enhancing computational accuracy while reducing the sampling overheads of standard QEM methods. Yet, existing models lack physical interpretability and rely heavily on extensive datasets, hindering their scalability in large-scale quantum circuits. To tackle these issues, we introduce the Neural Noise Accumulation Surrogate (NNAS), a physics-inspired neural network for ML-QEM that incorporates the structural characteristics of quantum noise accumulation within multi-layer circuits, endowing the model with physical interpretability. Experimental results demonstrate that NNAS outperforms current methods across a spectrum of metrics, including error mitigation capability, quantum resource consumption, and training dataset size. Notably, for deeper circuits where QEM methods typically struggle, NNAS achieves a remarkable reduction of over half in errors. NNAS also demands substantially fewer training data, reducing dataset reliance by at least an order of magnitude, due to its ability to rapidly capture noise accumulation patterns across circuit layers. This work pioneers the integration of quantum process-derived structural characteristics into neural network architectures, broadly enhancing QEM's performance and applicability, and establishes an integrative paradigm that extends to various quantum-inspired neural network architectures. © 2025, CC BY.

关键词： Quantum computers

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The mechanisms that regulate neuronal pyroptosis in cerebral ischemia-reperfusion injury:current theories and recent advances

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Journal of Translational Neuroscience 2024年第1期9卷 10-14页

作者： Hui Li Lu Liu Chen Zhou Xunming Ji Department of Neurology Xuanwu HospitalCapital Medical UniversityBeijing 100053China Department of Neurosurgery Xuanwu HospitalCapital Medical UniversityBeijing 100053China Beijing Institute of Brain Disorders Laboratory of Brain DisordersMinistry of Science and TechnologyCollaborative Innovation Center for Brain DisordersBeijing Advanced Innovation Center for Big Data-Based Precision MedicineCapital Medical UniversityBeijing 100069China

Early or ultra-early pharmacological thrombolysis together with mechanical thrombectomy are key treatments for ischemic stroke,and both are aimed at vascular recanalization and improved collateral *** these methods enhance tissue perfusion in the ischemic penumbra,they also trigger complex neurotoxic reactions,including apoptosis,acidosis,ion imbalance,oxidative stress,and pyroptosis,exacerbating cerebral ischemia-reperfusion injury(CIRI).Pyroptosis,a recently discovered form of programmed cell death driven by inflammation,plays a significant role in neuronal death during *** study reviews the regulatory mechanisms of pyroptosis in CIRI.

关键词： cerebral ischemia-reperfusion injury pyroptosis connexin 43

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Domain-Invariant Feature Progressive Distillation with Adversarial Adaptive Augmentation for Low-Resource Cross-Domain NER

Domain-Invariant Feature Progressive Distillation with Adver...

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作者： Zhang, Tao Xia, Congying Liu, Zhiwei Zhao, Shu Peng, Hao Yu, Philip Department of Computer Science University of Illinois at Chicago 851 South Morgan Street ChicagoIL60607-7053 United States School of Computer Science and Technology Anhui University No. 111 Jiulong Road Hefei Anhui230601 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University No. 37 Xue Yuan Road Haidian District Beijing100191 China

Considering the expensive annotation in Named Entity Recognition (NER), Cross-domain NER enables NER in low-resource target domains with few or without labeled data, by transferring the knowledge of high-resource domains. However, the discrepancy between different domains causes the domain shift problem and hampers the performance of cross-domain NER in low-resource scenarios. In this article, we first propose an adversarial adaptive augmentation, where we integrate the adversarial strategy into a multi-task learner to augment and qualify domain adaptive data. We extract domain-invariant features of the adaptive data to bridge the cross-domain gap and alleviate the label-sparsity problem simultaneously. Therefore, another important component in this article is the progressive domain-invariant feature distillation framework. A multi-grained MMD (Maximum Mean Discrepancy) approach in the framework to extract the multi-level domain invariant features and enable knowledge transfer across domains through the adversarial adaptive data. advanced Knowledge Distillation (KD) schema processes progressively domain adaptation through the powerful pre-trained language models and multi-level domain invariant features. Extensive comparative experiments over four English and two Chinese benchmarks show the importance of adversarial augmentation and effective adaptation from high-resource domains to low-resource target domains. Comparison with two vanilla and four latest baselines indicates the state-of-the-art performance and superiority confronted with both zero-resource and minimal-resource scenarios. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Distillation

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Triplet-Aware Graph Neural Networks for Factorized Multi-Modal Knowledge Graph Entity Alignment

SSRN

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SSRN 2023年

作者： Li, Qian Li, Jianxin Wu, Jia Peng, Xutan Ji, Cheng Peng, Hao Wang, Lihong Yu, Philip S. Beijing Advanced Innovation Center for Big Data and Brain Computing China Beijing China School of Computing Macquarie University Sydney Australia The University of Sheffield South Yorkshire United Kingdom China University of Illinois Chicago Chicago United States

Multi-Modal Entity Alignment (MMEA), aiming to discover matching entity pairs on two multi-modal knowledge graphs (MMKGs), is an essential task in knowledge graph fusion. Through mining feature information of MMKGs, entities are aligned to tackle the issue that an MMKG is incapable of effective integration. The recent attempt at neighbors and attribute fusion mainly focuses on aggregating multi-modal attributes, neglecting the structure effect with multi-modal attributes for entity alignment. This paper proposes an innovative approach, namely TriFac, to exploit embedding refinement for factorizing the original multi-modal knowledge graphs through a two-stage MMKG factorization. Notably, we propose triplet-aware graph neural networks to aggregate multi-relational features. We propose multi-modal fusion for aggregating multiple features and design three novel metrics to measure knowledge graph factorization performance on the unified factorized latent space. Empirical results indicate the effectiveness of TriFac, surpassing previous state-of-the-art models on two MMEA datasets and a power system dataset. © 2023, The Authors. All rights reserved.

关键词： Knowledge graph

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Find truth in the hands of the few:acquiring specific knowledge with crowdsourcing

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Frontiers of Computer Science 2021年第4期15卷 5-16页

作者： Tao HAN Hailong SUN Yangqiu SONG Yili FANG Xudong LIU SKLSDE Lab School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100191China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyClearwater BayHong Kong 999077China School of Computer and Information Engineering Zhejiang Gongshang UniversityHangzhou 310018China

Crowdsourcing has been a helpful mechanism to leverage human intelligence to acquire useful ***,when we aggregate the crowd knowledge based on the currently developed voting algorithms,it often results in common knowledge that may not be *** this paper,we consider the problem of collecting specific knowledge via *** the help of using external knowledge base such as WordNet,we incorporate the semantic relations between the alternative answers into a probabilisticmodel to determine which answer is more *** formulate the probabilistic model considering both worker’s ability and task’s difficulty from the basic assumption,and solve it by the expectation-maximization(EM)*** increase algorithm compatibility,we also refine our method into semi-supervised *** results show that our approach is robust with hyper-parameters and achieves better improvement thanmajority voting and other algorithms when more specific answers are expected,especially for sparse data.

关键词： crowdsourcing knowledge acquisition EM algorithm label aggregation

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ASGEA: Exploiting Logic Rules from Align-Subgraphs for Entity Alignment

arXiv

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arXiv 2024年

作者： Luo, Yangyifei Chen, Zhuo Guo, Lingbing Li, Qian Zeng, Wenxuan Cai, Zhixin Li, Jianxin School of Computer Science and Engineering Beihang University China College of Computer Science and Technology Zhejiang University China Bejing Advanced Innovation Center for Big Data and Brain Computing Beihang University China

Entity alignment (EA) aims to identify entities across different knowledge graphs that represent the same real-world objects. Recent embedding-based EA methods have achieved state-of-the-art performance in EA yet faced interpretability challenges as they purely rely on the embedding distance and neglect the logic rules behind a pair of aligned entities. In this paper, we propose the Align-Subgraph Entity Alignment (ASGEA) framework to exploit logic rules from Align-Subgraphs. ASGEA uses anchor links as bridges to construct Align-Subgraphs and spreads along the paths across KGs, which distinguishes it from the embedding-based methods. Furthermore, we design an interpretable Path-based Graph Neural Network, ASGNN, to effectively identify and integrate the logic rules across KGs. We also introduce a node-level multi-modal attention mechanism coupled with multi-modal enriched anchors to augment the Align-Subgraph. Our experimental results demonstrate the superior performance of ASGEA over the existing embedding-based methods in both EA and Multi-Modal EA (MMEA) tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Computer circuits

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Hybrid Resource Orchestration and Scheduling for Cyber-Physical-Human Systems 22

Hybrid Resource Orchestration and Scheduling for Cyber-Physi...

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22nd IEEE International Conference on High Performance computing and Communications, 18th IEEE International Conference on Smart City and 6th IEEE International Conference on data Science and Systems, HPCC-SmartCity-DSS 2020

作者： Zhu, Jianyong Wang, Xu Wo, Tianyu Hu, Chunming Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing China

ISBN: (纸本)9781728176499

Recently Cyber-Physical-Human Systems (CPHS) have been attracted much attention. Unlike existing computing paradigms such as Cloud computing and Edge computing, CPHS usually comprise many types of hybrid resources such as software and hardware in the cloud layer, network devices and edge servers in the network layer, as well as the physical devices and human participation. Such hybrid resource makes it challenging to efficient resource scheduling for CPHS. Previous studies on CPHS often focus on improving scheduling efficiency for specific application scenarios and thus are limited for other CPHS. In this work, we present a unified hybrid resource scheduling framework for CPHS, including a hybrid resource model for multiple types of resource providers and participants in CPHS, a hybrid resource orchestration tool that can describe our hybrid resource model as the resource requirements, and a scheduling mechanism to satisfy the resource requirements especially by considering the resource constraints and crosslayer scheduling in CPHS. We perform the case study of typical CPHS scenarios and show that our hybrid resource model and scheduling framework is effective, and provide a new view for improving the resource provision efficiency in CPHS. © 2020 IEEE.

关键词： Network layers

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Motion Forecasting for Autonomous Vehicles: A Survey

arXiv

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arXiv 2025年

作者： Shi, Jianxin Chen, Jinhao Wang, Yuandong Sun, Li Liu, Chunyang Xiong, Wei Wo, Tianyu Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Information Engineering College Capital Normal University Beijing100048 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China Didi Chuxing Beijing100094 China

In recent years, the field of autonomous driving has attracted increasingly significant public interest. Accurately forecasting the future behavior of various traffic participants is essential for the decision-making of Autonomous Vehicles (AVs). In this paper, we focus on both scenario-based and perception-based motion forecasting for AVs. We propose a formal problem formulation for motion forecasting and summarize the main challenges confronting this area of research. We also detail representative datasets and evaluation metrics pertinent to this field. Furthermore, this study classifies recent research into two main categories: supervised learning and self-supervised learning, reflecting the evolving paradigms in both scenario-based and perception-based motion forecasting. In the context of supervised learning, we thoroughly examine and analyze each key element of the methodology. For self-supervised learning, we summarize commonly adopted techniques. The paper concludes and discusses potential research directions, aiming to propel progress in this vital area of AV technology. © 2025, CC BY.

关键词： Self-supervised learning

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Fast Robot Hierarchical Exploration Based on Deep Reinforcement Learning

Fast Robot Hierarchical Exploration Based on Deep Reinforcem...

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International Wireless Communications and Mobile computing Conference, IWCMC

作者： Shun Zuo Jianwei Niu Lu Ren Zhenchao Ouyang State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beihang University Beijing China Beihang Hangzhou Innovation Institute Yuhang Beihang University Beijing China

This paper investigates the use of reinforcement learning for autonomous exploration in an unknown environment. Autonomous exploration is crucial in many situations, such as urban search, security inspection, environmental mapping, etc. Traditional approaches focused on frontiers are unlikely to span a variety of enormously complex scenarios. Convergence is a little more difficult for learning-based approaches, which can adapt to many different environments. Consequently, a hierarchical exploration framework is built using frontier information. We propose a reinforcement learning-based local decision exploration model that uses deep neural networks to learn the optimal strategy from the environment. To prevent falling into local optimization, we also suggest a global rescue module to assist the robot in returning to the proper exploration track. Compared with other hierarchical methods, the framework is more effective and resilient in many contexts, greatly decreasing the total completion time and path length.

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Personalized Federated Learning with Collaborative Aggregation Networks for Multi-Site brain Disorder Diagnosis

Personalized Federated Learning with Collaborative Aggregati...

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Industrial Automation, Robotics and Control Engineering (IARCE), International Conference on

作者： Qian Si Yang Li School of Cyber Science and Technology Beihang University Beijing China Department of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Virtual Reality Technology and Systems Advanced Institute of Information Technology Peking University Beihang University Beijing China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

In multi-site brain disease diagnosis studies, traditional centralized training methods necessitate sharing medical data, posing significant privacy risks. Federated learning (FL) offers a privacy-preserving solution by enabling global model training through aggregating locally trained models from multiple data centers without sharing raw data. However, current FL approaches rely on a server-based network topology, where central server failure disrupts training. Additionally, data heterogeneity across sites often slows convergence and reduces accuracy. To overcome these issues, we introduce a decentralized personalized federated learning collaborative aggregation network (pFedCAN). This framework has two core components: (1) separating local models into shared and personalized layers, and (2) forming a collaborative aggregation network via similarity detection in the shared layers. Specifically, each center trains its local model, then separates it into shared and personalized layers. The shared layer is exchanged with other centers, while the personalized layer remains local. data centers analyze similarities in received shared layers to build a collaborative network, where shared layers from similar centers are aggregated to refine the model. This approach flexibly adapts to varying levels of data heterogeneity, enhancing model training efficiency. Validation on public datasets, ABIDE I and ADHD, shows that the proposed method outperforms current leading techniques.

关键词： Training Adaptation models data centers Federated learning Service robots Collaboration brain modeling data models Servers Medical diagnostic imaging

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