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Template bank for compact binary mergers in the fourth observing run of Advanced LIGO, Advanced Virgo, and KAGRA

Physical Review D 2024年第4期109卷 044066-044066页

作者： Shio Sakon Leo Tsukada Heather Fong James Kennington Wanting Niu Chad Hanna Shomik Adhicary Pratyusava Baral Amanda Baylor Kipp Cannon Sarah Caudill Bryce Cousins Jolien D. E. Creighton Becca Ewing Richard N. George Patrick Godwin Reiko Harada Yun-Jing Huang Rachael Huxford Prathamesh Joshi Soichiro Kuwahara Alvin K. Y. Li Ryan Magee Duncan Meacher Cody Messick Soichiro Morisaki Debnandini Mukherjee Alex Pace Cort Posnansky Anarya Ray Surabhi Sachdev Divya Singh Ron Tapia Takuya Tsutsui Koh Ueno Aaron Viets Leslie Wade Madeline Wade Jonathan Wang Department of Physics The Pennsylvania State University University Park Pennsylvania 16802 USA Institute for Gravitation and the Cosmos The Pennsylvania State University University Park Pennsylvania 16802 USA Department of Physics and Astronomy University of British Columbia Vancouver British Columbia V6T 1Z4 Canada RESCEU The University of Tokyo Tokyo 113-0033 Japan Graduate School of Science The University of Tokyo Tokyo 113-0033 Japan Department of Astronomy and Astrophysics The Pennsylvania State University University Park Pennsylvania 16802 USA Institute for Computational and Data Sciences The Pennsylvania State University University Park Pennsylvania 16802 USA Leonard E. Parker Center for Gravitation Cosmology and Astrophysics University of Wisconsin-Milwaukee Milwaukee Wisconsin 53201 USA Department of Physics University of Massachusetts Dartmouth Massachusetts 02747 USA Center for Scientific Computing and Data Science Research University of Massachusetts Dartmouth Massachusetts 02747 USA Department of Physics University of Illinois Urbana Illinois 61801 USA Center for Gravitational Physics University of Texas at Austin Austin Texas 78712 USA LIGO Laboratory California Institute of Technology Pasadena California 91125 USA MIT Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwanoha Kashiwa Chiba 277-8582 Japan NASA Marshall Space Flight Center Huntsville Alabama 35811 USA Center for Space Plasma and Aeronomic Research University of Alabama in Huntsville Huntsville Alabama 35899 USA School of Physics Georgia Institute of Technology Atlanta Georgia 30332 USA Concordia University Wisconsin Mequon Wisconsin 53097 USA Department of Physics Hayes Hall Kenyon College Gambier Ohio 43022 USA Department of Physics University of Michigan Ann Arbor Michigan 48109 USA

Matched-filtering gravitational-wave search pipelines identify gravitational-wave signals by computing correlations, i.e., signal-to-noise ratios, between gravitational-wave detector data and gravitational-wave template waveforms. Intrinsic parameters, the component masses and spins, of the gravitational-wave waveforms are often stored in “template banks,” and the construction of a densely populated template bank is essential for some gravitational-wave search pipelines. This paper presents a template bank that is currently being used by the GstLAL-based compact binary search pipeline in the fourth observing run of the LIGO, Virgo, and KAGRA collaboration, and was generated with a new binary tree approach of placing templates, manifold. The template bank contains 1.8×106 sets of template parameters covering plausible neutron star and black hole systems up to a total mass of 400M⊙ with component masses between 1–200M⊙ and mass ratios between 1 and 20 under the assumption that each component object’s angular momentum is aligned with the orbital angular momentum. We validate the template bank generated with our new method, manifold, by comparing it with a template bank generated with the previously used stochastic template placement method. We show that both template banks have similar effectualness. The GstLAL search pipeline performs singular value decomposition (SVD) on the template banks to reduce the number of filters used. We describe a new grouping of waveforms that improves the computational efficiency of SVD by nearly 5 times as compared to previously reported SVD sorting schemes.

关键词： General relativity Gravitation Gravitational wave detection Gravitational waves data analysis

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HiNUMA: NUMA-Aware data Placement and Migration in Hybrid Memory Systems

HiNUMA: NUMA-Aware Data Placement and Migration in Hybrid Me...

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IEEE International Conference on Computer Design: VLSI in Computers and Processors, (ICCD)

作者： Zhuohui Duan Haikun Liu Xiaofei Liao Hai Jin Wenbin Jiang Yu Zhang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Non-uniform memory access (NUMA) architectures feature asymmetrical memory access latencies on different CPU nodes. Hybrid memory systems composed of non-volatile memory (NVM) and DRAM further diversify memory access latencies due to the relatively large performance gap between NVM and DRAM. Traditional NUMA memory management policies fail to manage hybrid memories effectively and may even hurt application performance. In this paper, we present HiNUMA, a new NUMA abstraction for memory allocation and migration in hybrid memory systems. HiNUMA advocates NUMA topologyaware hybrid memory allocation policies for the initial data placement. HiNUMA also proposes a new NUMA balancing mechanism called HANB for memory migration at runtime. HANB considers both data access frequency and memory bandwidth utilization to reduce the cost of memory accesses in hybrid memory systems. We evaluate the performance of HiNUMA with several typical workloads. Experimental results show that HiNUMA can effectively utilize hybrid memories, and deliver much higher application performance than conventional NUMA memory management policies and other state-of-the-art work.

关键词： Nonvolatile memory Random access memory Bandwidth Memory management Resource management Task analysis Memory architecture

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Disentangling high-order mechanisms and high-order behaviours in complex systems

arXiv

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arXiv 2022年

作者： Rosas, Fernando E. Mediano, Pedro A.M. Luppi, Andrea I. Varley, Thomas F. Lizier, Joseph T. Stramaglia, Sebastiano Jensen, Henrik J. Marinazzo, Daniele Data Science Institute Imperial College London United Kingdom Centre for Psychedelic Research Imperial College London United Kingdom Centre for Complexity Science Imperial College London United Kingdom Department of Psychology University of Cambridge United Kingdom Department of Psychology Queen Mary University of London United Kingdom University Division of Anaesthesia University of Cambridge United Kingdom Department of Clinical Neurosciences University of Cambridge United Kingdom Leverhulme Centre for the Future of Intelligence University of Cambridge United Kingdom Department of Psychological & Brain Sciences Indiana University BloomingtonIN United States School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Centre for Complex Systems Faculty of Engineering The University of Sydney Australia Dipartimento Interateneo di Fisica Università Degli Studi di Bari Aldo Moro Italy INFN Sezione di Bari Italy Department of Mathematics Imperial College London United Kingdom Institute of Innovative Research Tokyo Institute of Technology Japan Department of Data Analysis Ghent University Belgium

Battiston et al. (arXiv:2110.06023) provide a comprehensive overview of how investigations of complex systems should take into account interactions between more than two elements, which can be modelled by hypergraphs and studied via topological data analysis. Following a separate line of enquiry, a broad literature has developed information-theoretic tools to characterize high-order interdependencies from observed data. While these could seem to be competing approaches aiming to address the same question, in this correspondence we clarify that this is not the case, and that a complete account of higher-order phenomena needs to embrace both. Copyright © 2022, The Authors. All rights reserved.

关键词： Large scale systems

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An Early Stage Researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

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Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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Asymmetric scattering behaviors of spin wave dependent on magnetic vortex chirality

arXiv

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arXiv 2020年

作者： Zhang, Xue-Feng Shim, Je-Ho Ma, Xiao-Ping Song, Cheng Yu, Haiming Piao, Hong-Guang Hubei Engineering Research Center of Weak Magnetic-Field Detection China Three Gorges University Yichang443002 China Department of Physics College of Science Yanbian University Yanji133002 China School of Materials Science and Engineering Tsinghua University Beijing100084 China Fert Beijing Institute School of Integrated Circuit Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

In this letter, an asymmetric spin wave scattering behaviors caused by vortex chirality are investigated in cross-shaped ferromagnetic system. In the system, four scattering behaviors are found, 1) asymmetric skew scattering, depending on the polarity of vortex core, 2) back scattering (reflection), depending on the vortex core stiffness, 3) side deflection scattering, depending on structural symmetry of the vortex circulation, and 4) geometrical scattering, depending on waveguide structure. The first and second scattering behaviors are attributed to nonlinear topological magnon spin Hall effect related to magnon spin-transfer torque effect, which has value for magnonic exploration and application. Copyright © 2020, The Authors. All rights reserved.

关键词： Chirality

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Spatial-Temporal Graph Convolutional Network for Video-Based Person Re-Identification

Spatial-Temporal Graph Convolutional Network for Video-Based...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Jinrui Yang Wei-Shi Zheng Qize Yang Ying-Cong Chen Qi Tian School of Data and Computer Science Sun Yat-sen University China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing China Peng Cheng Laboratory Shenzhen China Chinese University of Hong Kong Hong Kong China The Huawei Noah's Ark Lab China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

While video-based person re-identification (Re-ID) has drawn increasing attention and made great progress in recent years, it is still very challenging to effectively overcome the occlusion problem and the visual ambiguity problem for visually similar negative samples. On the other hand, we observe that different frames of a video can provide complementary information for each other, and the structural information of pedestrians can provide extra discriminative cues for appearance features. Thus, modeling the temporal relations of different frames and the spatial relations within a frame has the potential for solving the above problems. In this work, we propose a novel Spatial-Temporal Graph Convolutional Network (STGCN) to solve these problems. The STGCN includes two GCN branches, a spatial one and a temporal one. The spatial branch extracts structural information of a human body. The temporal branch mines discriminative cues from adjacent frames. By jointly optimizing these branches, our model extracts robust spatial-temporal information that is complementary with appearance information. As shown in the experiments, our model achieves state-of-the-art results on MARS and DukeMTMC-VideoReID datasets.

关键词： Feature extraction Computational modeling Robustness Optical computing Optical imaging Computer vision Cameras

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Biomechanics-informed Neural Networks for Myocardial Motion Tracking in MRI

arXiv

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arXiv 2020年

作者： Qin, Chen Wang, Shuo Chen, Chen Qiu, Huaqi Bai, Wenjia Rueckert, Daniel Department of Computing Imperial College London London United Kingdom Institute for Digital Communications School of Engineering University of Edinburgh Edinburgh United Kingdom Data Science Institute Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom

Image registration is an ill-posed inverse problem which often requires regularisation on the solution space. In contrast to most of the current approaches which impose explicit regularisation terms such as smoothness, in this paper we propose a novel method that can implicitly learn biomechanics-informed regularisation. Such an approach can incorporate application-specific prior knowledge into deep learning based registration. Particularly, the proposed biomechanics-informed regularisation leverages a variational autoencoder (VAE) to learn a manifold for biomechanically plausible deformations and to implicitly capture their underlying properties via reconstructing biomechanical simulations. The learnt VAE regulariser then can be coupled with any deep learning based registration network to regularise the solution space to be biomechanically plausible. The proposed method is validated in the context of myocardial motion tracking on 2D stacks of cardiac MRI data from two different datasets. The results show that it can achieve better performance against other competing methods in terms of motion tracking accuracy and has the ability to learn biomechanical properties such as incompressibility and strains. The method has also been shown to have better generalisability to unseen domains compared with commonly used L2 regularisation schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Motion analysis

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Perturbative quantum simulation

arXiv

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arXiv 2021年

作者： Sun, Jinzhao Endo, Suguru Lin, Huiping Hayden, Patrick Vedral, Vlatko Yuan, Xiao Center on Frontiers of Computing Studies Peking University Beijing100871 China Clarendon Laboratory University of Oxford Parks Road OxfordOX1 3PU United Kingdom Quantum Advantage Research Beijing100080 China NTT Computer & Data Science Laboratories NTT Corporation Musashino Tokyo180-8585 Japan School of Computer Science Peking University Beijing100871 China Stanford Institute for Theoretical Physics Stanford University StanfordCA94305 United States Centre for Quantum Technologies National University of Singapore Singapore117543 Singapore

Approximation based on perturbation theory is the foundation for most of the quantitative predictions of quantum mechanics, whether in quantum many-body physics, chemistry, quantum field theory or other domains. Quantum computing provides an alternative to the perturbation paradigm, yet state-of-the-art quantum processors with tens of noisy qubits are of limited practical utility. Here, we introduce perturbative quantum simulation, which combines the complementary strengths of the two approaches, enabling the solution of large practical quantum problems using limited noisy intermediate-scale quantum hardware. The use of a quantum processor eliminates the need to identify a solvable unperturbed Hamiltonian, while the introduction of perturbative coupling permits the quantum processor to simulate systems larger than the available number of physical qubits. We present an explicit perturbative expansion that mimics the Dyson series expansion and involves only local unitary operations, and show its optimality over other expansions under certain conditions. We numerically benchmark the method for interacting bosons, fermions, and quantum spins in different topologies, and study different physical phenomena, such as information propagation, charge-spin separation, and magnetism, on systems of up to 48 qubits only using an 8 + 1 qubit quantum hardware. We experimentally demonstrate our scheme on the IBM quantum cloud, verifying its noise robustness and illustrating its potential for benchmarking large quantum processors with smaller ones. Copyright © 2021, The Authors. All rights reserved.

关键词： Hamiltonians

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The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions

arXiv

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arXiv 2023年

作者： Ma, Jun Xie, Ronald Ayyadhury, Shamini Ge, Cheng Gupta, Anubha Gupta, Ritu Gu, Song Zhang, Yao Lee, Gihun Kim, Joonkee Lou, Wei Li, Haofeng Upschulte, Eric Dickscheid, Timo de Almeida, José Guilherme Wang, Yixin Han, Lin Yang, Xin Labagnara, Marco Gligorovski, Vojislav Scheder, Maxime Rahi, Sahand Jamal Kempster, Carly Pollitt, Alice Espinosa, Leon Mignot, Tâm Middeke, Jan Moritz Eckardt, Jan-Niklas Li, Wangkai Li, Zhaoyang Cai, Xiaochen Bai, Bizhe Greenwald, Noah F. Van Valen, David Weisbart, Erin Cimini, Beth A. Cheung, Trevor Brück, Oscar Bader, Gary D. Wang, Bo Peter Munk Cardiac Centre University Health Network TorontoON Canada Department of Laboratory Medicine and Pathobiology University of Toronto TorontoON Canada Vector Institute TorontoON Canada Department of Molecular Genetics University of Toronto TorontoON Canada Donnelly Centre University of Toronto TorontoON Canada Princess Margaret Cancer Centre University Health Network TorontoON Canada School of Medicine and Pharmacy Ocean University of China Qingdao China New Delhi India Laboratory Oncology Dr. BRA-IRCH All India Institute of Medical Sciences New Delhi India Department of Image Reconstruction Nanjing Anke Medical Technology Co. Ltd. Nanjing China Shanghai Artificial Intelligence Laboratory Shanghai China Graduate School of AI KAIST Seoul Korea Republic of Shenzhen Research Institute of Big Data Shenzhen China Shenzhen China Helmholtz AI Research Center Jülich Jülich Germany Faculty of Mathematics and Natural Sciences Institute of Computer Science Heinrich Heine University Düsseldorf Düsseldorf Germany Hinxton United Kingdom Champalimaud Foundation - Centre for the Unknown Lisbon Portugal Department of Bioengineering Stanford University Palo AltoCA United States Tandon School of Engineering New York University New YorkNY United States School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China Lausanne Switzerland School of Biological Sciences University of Reading Reading United Kingdom Laboratoire de Chimie Bactérienne CNRS Université Aix Marseille UMR Institut de Microbiologie de la Méditerranée Marseille France Department of Internal Medicine I University Hospital Dresden Technical University Dresden Dresden Germany Else Kroener Fresenius Center for Digital Health Technical University Dresden Dresden Germany Department of Automation University of Science and Technology of China Hefei China Institute of Advanced Technology University of Science and Technology of China Hefei Chi

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multi-modality cell segmentation benchmark, comprising over 1500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging. © 2023, CC BY-NC-ND.

关键词： Deep learning

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ros: Secure cross-channel transfers via channel hub

arXiv

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arXiv 2019年

作者： Ye, YongJie Zhang, Jingjing Wu, Weigang Luo, Xiapu Cao, Jiannong School of Data and Computer Science Sun Yat-sen University Guangzhou China Department of Computing Hong Kong Polytechnic University Hong Kong

—The payment channel, which allows two parties to perform micropayments without involving the blockchain, has become a promising proposal to improve the scalability of decentralized ledgers such as Bitcoin and Ethereum. Payment channels have been extended to the payment network, through which users can utilize existing channels as intermediary links to route coins to others. However, routing payments through multiple channels bears nontrivial overheads. It requires every intermediary channel to lock a portion of its available capacity until the payment is settled. This may lead to deadlock in a concurrent situation. The intermediary nodes in a payment path may also charge fees for routing a payment. The longer the routing path, the more serious the above problems. In this paper, we design and develop a novel off-chain system to shorten the routing path for the payment network. In particular, we propose the channel hub, which is an extension of the payment hub, to allows transferring coins directly from one payment channel to another within the same hub. That is, the channel hub can be viewed as a shortcut device for the underlying payment network. We design a new protocol named Boros to perform secure off-chain cross-channel transfers through the channel hub. We not only present the security definition of the Boros protocol formally but also prove its security using the UC-framework. To demonstrate the feasibility of the Boros protocol, we develop a proof-of-concept prototype running on the Ethereum. Our evaluation shows that our system can effectively shorten the off-chain routing path. Copyright © 2019, The Authors. All rights reserved.

关键词： Blockchain

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