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Performance of the low-latency GstLAL inspiral search towards LIGO, Virgo, and KAGRA’s fourth observing run

Physical Review D 2024年第4期109卷 042008-042008页

作者： Becca Ewing Rachael Huxford Divya Singh Leo Tsukada Chad Hanna Yun-Jing Huang Prathamesh Joshi Alvin K. Y. Li Ryan Magee Cody Messick Alex Pace Anarya Ray Surabhi Sachdev Shio Sakon Ron Tapia Shomik Adhicary Pratyusava Baral Amanda Baylor Kipp Cannon Sarah Caudill Sushant Sharma Chaudhary Michael W. Coughlin Bryce Cousins Jolien D. E. Creighton Reed Essick Heather Fong Richard N. George Patrick Godwin Reiko Harada James Kennington Soichiro Kuwahara Duncan Meacher Soichiro Morisaki Debnandini Mukherjee Wanting Niu Cort Posnansky Andrew Toivonen Takuya Tsutsui Koh Ueno Aaron Viets Leslie Wade Madeline Wade Gaurav Waratkar 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 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 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 Leonard E. Parker Center for Gravitation Cosmology and Astrophysics University of Wisconsin-Milwaukee Milwaukee Wisconsin 53201 USA School of Physics Georgia Institute of Technology Atlanta 30332 USA RESCEU The University of Tokyo Tokyo 113-0033 Japan 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 Institute of Multimessenger Astrophysics and Cosmology Missouri University of Science and Technology Physics Building 1315 N. Pine St. Rolla Missouri 65409 USA School of Physics and Astronomy University of Minnesota Minneapolis Minnesota 55455 USA Department of Physics University of Illinois Urbana Illinois 61801 USA Canadian Institute for Theoretical Astrophysics 60 St George St Toronto Ontario M5S 3H8 Canada Graduate School of Science The University of Tokyo Tokyo 113-0033 Japan Center for Gravitational Physics University of Texas at Austin Austin Texas 78712 USA LIGO Laboratory California Institute of Technology MS 100-36 Pasadena California 91125 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

GstLAL is a stream-based matched-filtering search pipeline aiming at the prompt discovery of gravitational waves from compact binary coalescences such as the mergers of black holes and neutron stars. Over the past three observation runs by the LIGO, Virgo, and KAGRA Collaboration, the GstLAL search pipeline has participated in several tens of gravitational wave discoveries. The fourth observing run (O4) is set to begin in May 2023 and is expected to see the discovery of many new and interesting gravitational wave signals which will inform our understanding of astrophysics and cosmology. We describe the current configuration of the GstLAL low-latency search and show its readiness for the upcoming observation run by presenting its performance on a mock data challenge. The mock data challenge includes 40 days of LIGO Hanford, LIGO Livingston, and Virgo strain data along with an injection campaign in order to fully characterize the performance of the search. We find an improved performance in terms of detection rate and significance estimation as compared to that observed in the O3 online analysis. The improvements are attributed to several incremental advances in the likelihood ratio ranking statistic computation and the method of background estimation.

关键词： Gravitational wave detection Astronomical black holes Binary stars

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Getting the most out of maths: How to coordinate mathematical modelling research to support a pandemic, lessons learnt from three initiatives that were part of the COVID-19 response in the UK

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Journal of Theoretical Biology 2023年 557卷 111332页

作者： Dangerfield, Ciara E. David Abrahams, I. Budd, Chris Butchers, Matt Cates, Michael E. Champneys, Alan R. Currie, Christine S.M. Enright, Jessica Gog, Julia R. Goriely, Alain Déirdre Hollingsworth, T. Hoyle, Rebecca B. INI Professional Services Isham, Valerie Jordan, Joanna Kaouri, Maha H. Kavoussanakis, Kostas Leeks, Jane Maini, Philip K. Marr, Christie Merritt, Clare Mollison, Denis Ray, Surajit Thompson, Robin N. Wakefield, Alexandra Wasley, Dawn Isaac Newton Institute to Mathematical Sciences University of Cambridge United Kingdom Joint UNIversities Pandemic and Epidemiological Research (JUNIPER) Consortium United Kingdom Department for Applied Mathematics and Theoretical Physics University of Cambridge United Kingdom Department of Mathematics University of Bath United Kingdom Department of Engineering Mathematics University of Bristol United Kingdom School of Mathematical Sciences University of Southampton United Kingdom School of Computing Science University of Glasgow United Kingdom Mathematical Institute University of Oxford United Kingdom Big Data Institute Li Ka Shing Centre for Health Information and Discovery University of Oxford United Kingdom Department of Statistical Science University College London United Kingdom Independent United Kingdom EPCC The University of Edinburgh United Kingdom Department of Actuarial Mathematics and Statistics Heriot-Watt University United Kingdom School of Mathematics and Statistics University of Glasgow United Kingdom Mathematics Institute University of Warwick United Kingdom Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research University of Warwick United Kingdom The Royal Society London United Kingdom International Centre for Mathematical Sciences University of Edinburgh & Heriot-Watt University United Kingdom

In March 2020 mathematics became a key part of the scientific advice to the UK government on the pandemic response to COVID-19. Mathematical and statistical modelling provided critical information on the spread of the virus and the potential impact of different interventions. The unprecedented scale of the challenge led the epidemiological modelling community in the UK to be pushed to its limits. At the same time, mathematical modellers across the country were keen to use their knowledge and skills to support the COVID-19 modelling effort. However, this sudden great interest in epidemiological modelling needed to be coordinated to provide much-needed support, and to limit the burden on epidemiological modellers already very stretched for time. In this paper we describe three initiatives set up in the UK in spring 2020 to coordinate the mathematical sciences research community in supporting mathematical modelling of COVID-19. Each initiative had different primary aims and worked to maximise synergies between the various projects. We reflect on the lessons learnt, highlighting the key roles of pre-existing research collaborations and focal centres of coordination in contributing to the success of these initiatives. We conclude with recommendations about important ways in which the scientific research community could be better prepared for future pandemics. This manuscript was submitted as part of a theme issue on “Modelling COVID-19 and Preparedness for Future Pandemics”. © 2022 The Author(s)

关键词： COVID-19 Knowledge exchange Mathematical modelling Research co-ordination

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DSRGAN: Explicitly learning disentangled representation of underlying structure and rendering for image generation without tuple supervision

arXiv

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arXiv 2019年

作者： Hao, Guang-Yuan Yu, Hong-Xing Zheng, Wei-Shi School of Data and Computer Science Sun Yat-sen University Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Collaborative Innovation Center of High Performance Computing Nudt Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou

We focus on explicitly learning disentangled representation for natural image generation, where the underlying spatial structure and the rendering on the structure can be independently controlled respectively, yet using no tuple supervision. The setting is significant since tuple supervision is costly and sometimes even unavailable. However, the task is highly unconstrained and thus ill-posed. To address this problem, we propose to introduce an auxiliary domain which shares a common underlying-structure space with the target domain, and we make a partially shared latent space assumption. The key idea is to encourage the partially shared latent variable to represent the similar underlying spatial structures in both domains, while the two domain-specific latent variables will be unavoidably arranged to present renderings of two domains respectively. This is achieved by designing two parallel generative networks with a common Progressive Rendering Architecture (PRA), which constrains both generative networks' behaviors to model shared underlying structure and to model spatially dependent relation between rendering and underlying structure. Thus, we propose DSRGAN (GANs for Disentangling Underlying Structure and Rendering) to instantiate our method. We also propose a quantitative criterion (the Normalized Disentanglability) to quantify disentanglability. Comparison to the state-of-The-Art methods shows that DSRGAN can significantly outperform them in disentanglability. Copyright © 2019, The Authors. All rights reserved.

关键词： Rendering (computer graphics)

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Lightweight and robust representation of economic scales from satellite imagery

arXiv

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arXiv 2019年

作者： Han, Sungwon Ahn, Donghyun Cha, Hyunji Yang, Jeasurk Park, Sungwon Cha, Meeyoung School of Computing KAIST Korea Republic of Institute for Korean Regional Studies SNU Korea Republic of Data Science Group IBS Korea Republic of

Satellite imagery has long been an attractive data source providing a wealth of information regarding human-inhabited areas. While high-resolution satellite images are rapidly becoming available, limited studies have focused on how to extract meaningful information regarding human habitation patterns and economic scales from such data. We present READ, a new approach for obtaining essential spatial representation for any given district from high-resolution satellite imagery based on deep neural networks. Our method combines transfer learning and embedded statistics to efficiently learn the critical spatial characteristics of arbitrary size areas and represent such characteristics in a fixed-length vector with minimal information loss. Even with a small set of labels, READ can distinguish subtle differences between rural and urban areas and infer the degree of urbanization. An extensive evaluation demonstrates that the model outperforms state-of-the-art models in predicting economic scales, such as the population density in South Korea (R2=0.9617), and shows a high use potential in developing countries where district-level economic scales are unknown. Copyright © 2019, The Authors. All rights reserved.

关键词： Satellite imagery

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LiDAR Based Navigable Region Detection for Unmanned Surface Vehicles

LiDAR Based Navigable Region Detection for Unmanned Surface ...

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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Xiangtong Yao Yunxiao Shan Jieling Li Donghui Ma Kai Huang Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education School of Data and Computer Science Sun Yat-sen University Guangzhou China

Detection of the navigable regions for the unmanned surface vehicles (USVs) sailing on the narrow rivers is very important. Existing detection methods mostly depend on the cameras, which is sensitive to environments and cannot provide reliable navigable regions for sailing. In this paper, we propose a scheme to process 3D LiDAR data to achieve an accurate and robust navigable regions detection. We conduct field experiments in a narrow river in different scenarios to prove the performance of the proposed scheme, which reaches on average 93.8% precision and 92.7% recall.

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Energy Saving Strategy of Power System Cluster Based on Container Virtualization

Energy Saving Strategy of Power System Cluster Based on Cont...

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IEEE Asia Power and Energy Engineering Conference (APEEC)

作者： Ran Zheng Hao Wang Hai Jin Dechao Xu Yong Chen Xiaomeng Li Yufei Rao Zhenan 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 State Key Laboratory of Power Grid Safety and Energy Conservation China Electric Power Research Institute Haidian District Beijing China State Grid Henan Electric Power Company Electric Power Research Institute Zhengzhou China

ISBN: (数字)9781728167824

ISBN: (纸本)9781728167831

With the continuous development of power grids, the scale of supercomputing clusters has also gradually increased to carry a large number of power system simulation calculations, and the problem of high energy consumption has appeared. To solve this problem, we propose a container virtualization-based supercomputing cluster for power system. We analyze the impact of containers on power simulation calculations and compare the energy consumption effects of various container scheduling and migration algorithms on clusters. Experiments show that compared to virtual machines with hypervisor, which consumes massive resources and reduces performances by 28.4%, the performance degradation of container on power simulation calculation is 1.3%, which can be ignored. The energy consumption of load-concentration or resource-and-load-balance container scheduling algorithms is up to 4.0% lower and at least 2.2% lower than other algorithms. In container migration, the method combining autoregressive model with most-correlation and resource-andload-balance algorithms is better than other methods, which not only minimizes energy consumption, but also has lowest number of migrations and SLA violations. Experiments verify the feasibility and advantages of container migration in power system computing clusters.

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Bayesian data assimilation for estimating epidemic evolution: A COVID-19 study

arXiv

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arXiv 2020年

作者： Yang, Xian Wang, Shuo Xing, Yuting Li, Ling Xu, Richard Yi Da Friston, Karl J. Guo, Yike Department of Computer Science Hong Kong Baptist University Hong Kong Special Administrative Region Hong Kong Data Science Institute Imperial College London United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention China School of Computing University of Kent United Kingdom Faculty of Engineering and Information Technology University of Technology Sydney Australia Institute of Neurology University College London United Kingdom

The evolution of epidemiological parameters, such as instantaneous reproduction number Rt, is important for understanding the transmission dynamics of infectious diseases. Current estimates of time-varying epidemiological parameters often face problems such as lagging observations, averaging inference, and improper quantification of uncertainties. To address these problems, we propose a Bayesian data assimilation framework for time-varying parameter estimation. Specifically, this framework is applied to Rt estimation, resulting in the state-of-the-art 'DARt' system. With DARt, time misalignment caused by lagging observations is tackled by incorporating observation delays into the joint inference of infections and Rt;the drawback of averaging is overcome by instantaneously updating upon new observations and developing a model selection mechanism that captures abrupt changes;the uncertainty is quantified and reduced by employing Bayesian smoothing. We validate the performance of DARt and demonstrate its power in revealing the transmission dynamics of COVID-19. The proposed approach provides a promising solution for accurate and timely estimating transmission dynamics from reported data. Copyright © 2020, The Authors. All rights reserved.

关键词： Cell proliferation

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Corrigendum to “Molecular subtypes classification of breast cancer in DCE-MRI using deep features” [Expert Syst. Appl. 236 (2024) 121371]

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Expert Systems with Applications 2025年 270卷

作者： Ali M. Hasan Noor K.N. Al-Waely Hadeel K. Aljobouri Hamid A. Jalab Rabha W. Ibrahim Farid Meziane College of Medicine Al-Nahrain University Baghdad Iraq Biomedical Engineering Department College of Engineering Al-Nahrain University Iraq Faculty of Computer Science and Information Technology University of Malaya 50603 Kuala Lumpur Malaysia Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Nile Street 64001 Thi-Qar Iraq Department of Computer Science and Mathematics Lebanese American University Beirut 1102 2801 Lebanon Near East University Mathematics Research Center Department of Mathematics Near East Boulevard PC: 99138 Nicosia/Mersin 10 Turkey Data Science Research Centre School of Computing and Engineering University of Derby United Kingdom

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A holistic stream partitioning algorithm for distributed stream processing systems 20

A holistic stream partitioning algorithm for distributed str...

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20th International Conference on Parallel and Distributed computing, Applications and Technologies, PDCAT 2019

作者： Li, Kejian Liu, Gang Lu, Minhua Guangdong Province Key Laboratory of Popular High Performance Computers Guangdong Province Engineering Center of China-made High Performance Data Computing System College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Biomedical Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9781728126166

The performances of modern distributed stream processing systems are critically affected by the distribution of the load across workers. Skewed data streams in real world are very common and pose a great challenge to these systems, especially for stateful applications. Key splitting, which allows a single key to be routed to multiple workers, is a great idea to achieve good balance of load in the cluster. However, it comes with the cost of increased memory consumption and computation overhead as well as network communication. In this paper, we present a new definition of metric to model the cost of key splitting for intra-operator parallelism in stream processing systems and provide a novel perspective to reduce replication factor while keeping both overall load imbalance and processing latency low. Similar to previous work, our approach treats the head and the tail of the distribution differently in order to reduce memory requirements. For the head, it uses our proposed notion of regional load imbalance to decide dynamically whether to make one more worker responsible for the heavy hitter or not. For the tail, it simply uses hash partitioning to keep the size of the routing table for the head as small as possible. Extensive experimental evaluation demonstrates that our approach provides superior performance compared to the state-of-the-art partitioning algorithms in terms of load imbalance, replication factor and latency over different levels of skewed stream distributions. © 2019 IEEE.

关键词： Distributed parameter control systems

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Extending black-hole remnant surrogate models to extreme mass ratios

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Physical Review D 2023年第8期108卷 084015-084015页

作者： Matteo Boschini Davide Gerosa Vijay Varma Cristóbal Armaza Michael Boyle Marceline S. Bonilla Andrea Ceja Yitian Chen Nils Deppe Matthew Giesler Lawrence E. Kidder Prayush Kumar Guillermo Lara Oliver Long Sizheng Ma Keefe Mitman Peter James Nee Harald P. Pfeiffer Antoni Ramos-Buades Mark A. Scheel Nils L. Vu Jooheon Yoo Dipartimento di Fisica “G. Occhialini ” Universitá degli Studi di Milano-Bicocca Piazza della Scienza 3 20126 Milano Italy Max Planck Institute for Gravitational Physics (Albert Einstein Institute) Am Mühlenberg 1 Potsdam 14476 Germany INFN Sezione di Milano-Bicocca Piazza della Scienza 3 20126 Milano Italy School of Physics and Astronomy & Institute for Gravitational Wave Astronomy University of Birmingham Birmingham B15 2TT United Kingdom Department of Mathematics Center for Scientific Computing and Data Science Research University of Massachusetts Dartmouth Massachusetts 02747 USA Cornell Center for Astrophysics and Planetary Science Cornell University Ithaca New York 14853 USA Nicholas and Lee Begovich Center for Gravitational-Wave Physics and Astronomy California State University Fullerton Fullerton California 92831 USA TAPIR 350-17 California Institute of Technology 1200 E California Boulevard Pasadena California 91125 USA Department of Physics Cornell University Ithaca New York 14853 USA International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bangalore 560089 India

Numerical-relativity surrogate models for both black-hole merger waveforms and remnants have emerged as important tools in gravitational-wave astronomy. While producing very accurate predictions, their applicability is limited to the region of the parameter space where numerical-relativity simulations are available and computationally feasible. Notably, this excludes extreme mass ratios. We present a machine-learning approach to extend the validity of existing and future numerical-relativity surrogate models toward the test-particle limit, targeting in particular the mass and spin of postmerger black-hole remnants. Our model is trained on both numerical-relativity simulations at comparable masses and analytical predictions at extreme mass ratios. We extend the gaussian-process-regression model NRSur7dq4Remnant, validate its performance via cross validation, and test its accuracy against additional numerical-relativity runs. Our fit, which we dub NRSur7dq4EmriRemnant, reaches an accuracy that is comparable to or higher than that of existing remnant models while providing robust predictions for arbitrary mass ratios.

关键词： Classical black holes Gravitational wave sources Gravitational waves Astronomical black holes Numerical relativity

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