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Leader-Following Consensus of Stochastic Dynamical Multi-Agent Systems Under PI Control

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Leader-Following Consensus of Stochastic Dynamical Multi-Age...

Annual Conference of Industrial Electronics Society

作者： Haibo Gu Kexin Liu Jinhu Lu Zhang Ren School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

ISBN: (数字)9781728154145

ISBN: (纸本)9781728154152

With the development of swarm intelligence in last decades, consensus problem of multi-agent systems has been attracting much attention. To reveal the inherent mechanism of leader-following consensus in multi-agent systems with stochastic dynamics, PI control protocols are designed in this paper. Owing to the stochastic analysis techniques, algebraic graph theory, and constructing appropriate Lyapunov function, it is proved that the leader-following consensus of nonlinear stochastic dynamical multi-agent systems can be reached in mean square. Sufficient condition are deduced to select the PI control protocol parameters. Finally, the theoretical results are demonstrated through a simulation example.

关键词： Protocols Topology Stochastic processes Network topology Multi-agent systems Laplace equations Eigenvalues and eigenfunctions

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

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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

Attend and Select: A Segment Selective Transformer for Microblog Hashtag Generation

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arXiv 2021年

作者： Mao, Qianren Li, Xi Liu, Bang Guo, Shu Hao, Peng Li, Jianxin Wang, Lihong Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China The State Key Laboratory of Software Development Environment Beihang University Beijing100191 China RALI Mila University of Montreal United Kingdom CENCERT/CC Beijing210016 China

Hashtag generation aims to generate short and informal topical tags from a microblog post, in which tokens or phrases form the hashtags. These tokens or phrases may originate from primary fragmental textual pieces (e.g., segments) in the original text and are separated into different segments. However, conventional sequence-to-sequence generation methods are hard to filter out secondary information from different textual granularity and are not good at selecting crucial tokens. Thus, they are suboptimal in generating more condensed hashtags. In this work, we propose a modified Transformer-based generation model with adding a segments-selection procedure for the original encoding and decoding phases. The segments-selection phase is based on a novel Segments Selection Mechanism (SSM) to model different textual granularity on global text, local segments, and tokens, contributing to generate condensed hashtags. Specifically, it first attends to primary semantic segments and then transforms discontinuous segments from the source text into a sequence of hashtags by selecting crucial tokens. Extensive evaluations on the two datasets reveal our approach's superiority with significant improvements to the extraction and generation baselines. The code and datasets are available at https://***/OpenSUM/HashtagGen. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics

Discriminative features matter: Multi-layer bilinear pooling for camera localization 30

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Discriminative features matter: Multi-layer bilinear pooling...

30th British Machine Vision Conference, BMVC 2019

作者： Wang, Xin Wang, Xiang Wang, Chen Bai, Xiao Wu, Jing Hancock, Edwin Robert School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Jiangxi Research Institute Beihang University Beijing China School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Department of Computer Science University of York York United Kingdom

Deep learning based camera localization from a single image has been explored recently since these methods are computationally efficient. However, existing methods only provide general global representations, from which an accurate pose estimation can not be reliably derived. We claim that effective feature representations for accurate pose estimation shall be both "informative" (focusing on geometrically meaningful regions) and "discriminative" (accounting for different poses of similar images). Therefore, we propose a novel multi-layer factorized bilinear pooling module for feature aggregation. Specifically, informative features are selected via bilinear pooling, and discriminative features are highlighted via multi-layer fusion. We develop a new network for camera localization using the proposed feature pooling module. The effectiveness of our approach is demonstrated by experiments on an outdoor Cambridge Landmarks dataset and an indoor 7 Scenes dataset. The results show that focusing on discriminative features significantly improves the network performance of camera localization in most cases. Codes will be available soon. © 2019. The copyright of this document resides with its authors.

关键词： Deep learning

Adaptive hybrid transaction processing with hardware transaction memory 21

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Adaptive hybrid transaction processing with hardware transac...

21st IEEE International Conference on High Performance computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on data Science and Systems, HPCC/SmartCity/DSS 2019

作者： He, Tao Li, Jianxin Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China SKLSDE Lab Beihang University Beijing China

ISBN: (纸本)9781728120584

Many performance-sensitive software systems gain benefit from the availability of advanced hardware features, and many research works have been transited to engineering reality. For instance, restricted hardware transaction memory (HTM), a hardware feature providing hardware atomicity and isolation guarantees for transactional execution, has gained much attention in the community of database system and becomes a new kind of approach for concurrency control of transaction processing. Previous works show that hardware transactional memory is a very promising mechanism when processing typical OLTP workloads and scales well on multi-core machines. However, the high transaction abort rate caused by capacity overflow is also the major trouble when applying hardware transactional memory in transaction execution under high-contention workloads. Many methods, such as falling back to lock-based approaches, have been proposed to improve the performance when applying hardware transactional memory to transaction execution. In this paper, we analyze the penalty of abort and fall back when hardware transactional memory fails to process transactions. Based on these analyses, we exploit the strengths of hardware transactional memory with an adaptive hybrid mechanism to improve both the throughput and latency of transaction processing on the multicore systems. Our experiment on TPC-C shows that our adaptive design can remarkably improve the throughput and latency of in-memory transaction processing. © 2019 IEEE.

关键词： Concurrency control

Robust Optimization for Quantum Reinforcement Learning Control using Partial Observations

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arXiv 2022年

作者： Jiang, Chen Pan, Yu Wu, Zheng-Guang Gao, Qing Dong, Daoyi State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou310027 China The School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China The School of Engineering and Information Technology University of New South Wales CanberraACT2600 Australia

The current quantum reinforcement learning control models often assume that the quantum states are known a priori for control optimization. However, full observation of quantum state is experimentally infeasible due to the exponential scaling of the number of required quantum measurements on the number of qubits. In this paper, we investigate a robust reinforcement learning method using partial observations to overcome this difficulty. This control scheme is compatible with near-term quantum devices, where the noise is prevalent and predetermining the dynamics of quantum state is practically impossible. We show that this simplified control scheme can achieve similar or even better performance when compared to the conventional methods relying on full observation. We demonstrate the effectiveness of this scheme on examples of quantum state control and quantum approximate optimization algorithm. It has been shown that high-fidelity state control can be achieved even if the noise amplitude is at the same level as the control amplitude. Besides, an acceptable level of optimization accuracy can be achieved for QAOA with noisy control Hamiltonian. This robust control optimization model can be trained to compensate the uncertainties in practical quantum computing. © 2022, CC BY.

关键词： Robust control

Generating disentangled arguments with prompts: A simple event extraction framework that works

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arXiv 2021年

作者： Si, Jinghui Peng, Xutan Li, Chen Xu, Haotian Li, Jianxin Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China State Key Laboratory of Software Development Environment Beihang University China Alibaba Group China Department of Computer Science The University of Sheffield United Kingdom

Event Extraction bridges the gap between text and event signals. Based on the assumption of trigger-argument dependency, existing approaches have achieved state-of-the-art performance with expert-designed templates or complicated decoding constraints. In this paper, for the first time we introduce the prompt-based learning strategy to the domain of Event Extraction, which empowers the automatic exploitation of label semantics on both input and output sides. To validate the effectiveness of the proposed generative method, we conduct extensive experiments with 11 diverse baselines. Empirical results show that, in terms of F1 score on Argument Extraction, our simple architecture is stronger than any other generative counterpart and even competitive with algorithms that require template engineering. Regarding the measure of recall, it sets new overall records for both Argument and Trigger Extractions. We hereby recommend this framework to the community, with the code publicly available at https://***/GDAP. Copyright © 2021, The Authors. All rights reserved.

关键词： Extraction

A Survey on Deep Learning Event Extraction: Approaches and Applications

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arXiv 2021年

作者： Li, Qian Li, Jianxin Sheng, Jiawei Cui, Shiyao Wu, Jia Hei, Yiming Peng, Hao Guo, Shu Wang, Lihong Beheshti, Amin Yu, Philip S. The School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China The Institute of Information Engineering Chinese Academy of Sciences Beijing100083 China The School of Cyber Security University of Chinese Academy of Sciences Beijing100083 China The School of Cyber Science and Technology Beihang University Beijing100083 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China The National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100029 China The School of Computing Macquarie University Sydney Australia The Department of Computer Science University of Illinois at Chicago Chicago60607 United States

Event extraction is a crucial research task for promptly apprehending event information from massive textual data. With the rapid development of deep learning, event extraction based on deep learning technology has become a research hotspot. Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey. This paper fills the research gap by reviewing the state-of-the-art approaches, especially focusing on the general domain event extraction based on deep learning models. We introduce a new literature classification of current general domain event extraction research according to the task definition. Afterwards, we summarize the paradigm and models of event extraction approaches, and then discuss each of them in detail. As an important aspect, we summarize the benchmarks that support tests of predictions and evaluation metrics. A comprehensive comparison among different approaches is also provided in this survey. Finally, we conclude by summarizing future research directions facing the research area. Copyright © 2021, The Authors. All rights reserved.

关键词： Extraction

Fast Subspace Clustering Based on the Kronecker Product

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Fast Subspace Clustering Based on the Kronecker Product

International Conference on Pattern Recognition

作者： Lei Zhou Xiao Bai Liang Zhang Jun Zhou Edwin Hancock School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Jiangxi Research Institute Beihang University Beijing China School of Information and Communication Technology Griffith University Nathan Australia University of York York U.K.

Subspace clustering is a useful technique for many computer vision applications in which the intrinsic dimension of high-dimensional data is often smaller than the ambient dimension. Spectral clustering, as one of the main approaches to subspace clustering, often takes on a sparse representation or a low-rank representation to learn a block diagonal self-representation matrix for subspace generation. However, existing methods require solving a large scale convex optimization problem with a large set of data, with computational complexity reaches O(N³) for N data points. Therefore, the efficiency and scalability of traditional spectral clustering methods can not be guaranteed for large scale datasets. In this paper, we propose a subspace clustering model based on the Kronecker product. Due to the property that the Kronecker product of a block diagonal matrix with any other matrix is still a block diagonal matrix, we can efficiently learn the representation matrix which is formed by the Kronecker product of k smaller matrices. By doing so, our model significantly reduces the computational complexity to O(kN³/^k). Furthermore, our model is general in nature, and can be adapted to different regularization based subspace clustering methods. Experimental results on two public datasets show that our model significantly improves the efficiency compared with several state-of-the-art methods. Moreover, we have conducted experiments on synthetic data to verify the scalability of our model for large scale datasets.

关键词： Adaptation models Computational modeling Scalability Clustering methods Face recognition Memory management data models