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26th International Conference on Information Fusion, FUSION 2023

作者： Hiscocks, Steven Barr, Jordi Perree, Nicola Wright, James Pritchett, Henry Rosoman, Oliver Harris, Michael Gorman, Roisín Pike, Sam Carniglia, Peter Vladimirov, Lyudmil Oakes, Benedict Defence Science and Technology Laboratory Cyber and Information Systems Salisbury United Kingdom Defence Research and Development Canada Radar Sensing and Exploitation Ottawa Canada University of Liverpool Electrical Engineering & Electronics Cdt in Distributed Algorithms Liverpool United Kingdom

ISBN: (纸本)9798890344854

This paper announces version 1.0 of Stone Soup: the open-source tracking and state estimation framework. We highlight key elements of the framework and outline example applications and community *** Soup is engineered with modularity and encapsulation at its heart. This means that its many components can be put together in any number of ways to build, compare, and assure almost any type of multi-target tracking and fusion algorithm. Since its inception in 2017, it has aimed to provide the target tracking and state estimation community with an open, easy-to-deploy framework to develop and assess the performance of different types of trackers. Now, through repeated application in many use cases, implementation of a wide variety of algorithms, multiple beta releases, and contributions from the community, the framework has reached a stable *** announcing this release, we hope to encourage additional adoption and further contributions to the toolkit. We also acknowledge and express appreciation for the many contributions of time and expertise donated by the tracking and fusion community. © 2023 International Society of Information Fusion.

关键词： State estimation

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Double Deep Q Networks for Sensor Management in Space Situational Awareness 25

Double Deep Q Networks for Sensor Management in Space Situat...

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25th International Conference of Information Fusion (FUSION)

作者： Oakes, Benedict Richards, Dominic Barr, Jordi Ralph, Jason Univ Liverpool CDT Distributed Algorithms Liverpool Merseyside England STFC Hartree Ctr Artificial Intelligence Grp Daresbury England Def Sci & Technol Lab Cyber & Informat Syst Div Salisbury Wilts England Univ Liverpool Elect Engn & Elect Liverpool Merseyside England

ISBN: (数字)9781737749721

ISBN: (纸本)9781665489416

We present a novel Double Deep Q Network (DDQN) application to a sensor management problem in space situational awareness (SSA). Frequent launches of satellites into Earth orbit pose a significant sensor management challenge, whereby a limited number of sensors are required to detect and track an increasing number of objects. In this paper, we demonstrate the use of reinforcement learning to develop a sensor management policy for SSA. We simulate a controllable Earth-based telescope, which is trained to maximise the number of satellites tracked using an extended Kalman filter. The estimated state covariance matrices for satellites observed under the DDQN policy are greatly reduced compared to those generated by an alternate (random) policy. This work provides the basis for further advancements and motivates the use of reinforcement learning for SSA.

关键词： Reinforcement Learning Sensor Management Space Situational Awareness

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Dynamic Semantic-Based Spatial Graph Convolution Network for Skeleton-Based Human Action Recognition 38

Dynamic Semantic-Based Spatial Graph Convolution Network for...

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38th AAAI Conference on Artificial Intelligence, AAAI 2024

作者： Xie, Jianyang Meng, Yanda Zhao, Yitian Nguyen, Anh Yang, Xiaoyun Zheng, Yalin CDT in Distributed Algorithms School of EEECS University of Liverpool United Kingdom Department of Eye and Vision Sciences University of Liverpool Liverpool United Kingdom Department of Computer Sciences University of Liverpool Liverpool United Kingdom Cixi Institute of Biomedical Engineering Ningbo Institute of Materials Technology and Engineering CAS Cixi China Remark AI UK Limited London United Kingdom Liverpool Centre for Cardiovascular Science Liverpool United Kingdom

ISBN: (纸本)1577358872

Graph convolutional networks (GCNs) have attracted great attention and achieved remarkable performance in skeleton-based action recognition. However, most of the previous works are designed to refine skeleton topology without considering the types of different joints and edges, making them infeasible to represent the semantic information. In this paper, we proposed a dynamic semantic-based graph convolution network (DS-GCN) for skeleton-based human action recognition, where the joints and edge types were encoded in the skeleton topology in an implicit way. Specifically, two semantic modules, the joints type-aware adaptive topology and the edge type-aware adaptive topology, were proposed. Combining proposed semantics modules with temporal convolution, a powerful framework named DS-GCN was developed for skeleton-based action recognition. Extensive experiments in two datasets, NTU-RGB+D and Kinetics-400 show that the proposed semantic modules were generalized enough to be utilized in various backbones for boosting recognition accuracy. Meanwhile, the proposed DS-GCN notably outperformed state-of-the-art methods. The code is released here https://***/davelailai/DS-GCN. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Convolution

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Stone Soup: No Longer Just an Appetiser

Stone Soup: No Longer Just an Appetiser

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International Conference on Information Fusion

作者： Steven Hiscocks Jordi Barr Nicola Perree James Wright Henry Pritchett Oliver Rosoman Michael Harris Roisín Gorman Sam Pike Peter Carniglia Lyudmil Vladimirov Benedict Oakes Cyber and Information Systems Defence Science and Technology Laboratory Salisbury UK Radar Sensing and Exploitation Defence Research and Development Canada Ottawa Canada Electrical Engineering & Electronics CDT in Distributed Algorithms University of Liverpool Liverpool UK

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Double Deep Q Networks for Sensor Management in Space Situational Awareness

arXiv

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arXiv 2022年

作者： Oakes, Benedict Richards, Dominic Barr, Jordi Ralph, Jason CDT in Distributed Algorithms University of Liverpool Liverpool United Kingdom Artificial Intelligence Group STFC Hartree Centre Daresbury United Kingdom Cyber and Information Systems Division Defence Science and Technology Laboratory United Kingdom Electrical Engineering and Electronics University of Liverpool Liverpool United Kingdom

关键词： Kalman filters

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The advantages of sub-sampling and Inpainting for scanning transmission electron microscopy

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APPLIED PHYSICS LETTERS 2023年第5期122卷 050501-050501页

作者： Browning, Nigel D. Castagna, Jony Kirkland, Angus I. Moshtaghpour, Amirafshar Nicholls, Daniel Robinson, Alex W. Wells, Jack Zheng, Yalin Univ Liverpool Mech Mat & Aerosp Eng Liverpool L69 3GH England Sivananthan Labs 590 Terr Dr Bolingbrook IL 60440 USA Univ Liverpool Distributed Algorithms CDT Liverpool L69 3GH England UKRI STFC Hartree Ctr Daresbury Lab Warrington WA4 4AD England Rosalind Franklin Inst Harwell Sci & Innovat Campus Didcot OX11 0QS England Univ Oxford Dept Mat Oxford OX2 6NN England Univ Liverpool Dept Eye & Vis Sci Liverpool L7 8TX England

Images and spectra obtained from aberration corrected scanning transmission electron microscopes (STEM) are now used routinely to quantify the morphology, structure, composition, chemistry, bonding, and optical/electronic properties of nanostructures, interfaces, and defects in many materials/biological systems. However, obtaining quantitative and reproducible atomic resolution observations from some experiments is actually harder with these ground-breaking instrumental capabilities, as the increase in beam current from using the correctors brings with it the potential for electron beam modification of the specimen during image acquisition. This beam effect is even more acute for in situ STEM observations, where the desired outcome being investigated is a result of a series of complicated transients, all of which can be modified in unknown ways by the electron beam. The aim in developing and applying new methods in STEM is, therefore, to focus on more efficient use of the dose that is supplied to the sample and to extract the most information from each image (or set of images). For STEM (and for that matter, all electron/ion/photon scanning systems), one way to achieve this is by sub-sampling the image and using Inpainting algorithms to reconstruct it. By separating final image quality from overall dose in this way and manipulating the dose distribution to be best for the stability of the sample, images can be acquired both faster and with less beam effects. In this paper, the methodology behind sub-sampling and Inpainting is described, and the potential for Inpainting to be applied to novel real time dynamic experiments will be discussed.

关键词： Scanning electron microscopy

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