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Embedded Partitioning Real-Time Operating System Based on Microkernel

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Embedded Partitioning Real-Time Operating System Based on Mi...

IEEE International Conference on Computational science and Engineering, CSE

作者： Tanhong Chen Huiyong Li Jianwei Niu Tao Ren Guizhou Xu School of Computer Science Beihang University Hangzhou Innovation Institute Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University BEIJING ACOINFO TECHNOLOGY CO. LTD.

The embedded partitioning operating system is active in security-related fields such as avionics and automotive embedded software. The temporal and spatial isolation of the partition is the guarantee of system security. In addition, as these areas become more demanding for applications, improving the real-time performance of partitioning operating systems has become a trend. To reach this goal, we propose a solution that leverages the microkernel architecture in the embedded partitioning operating system. The microkernel ensures the determinacy of real-time scheduling policies and system calls by simplifying kernel functionality and preserving the most basic resources. Partitions built on top of the microkernel enable low-cost communication, fast context switching and mixed task scheduling. Based on the microkernel, we developed a complete real-time partitioning operating system. The system supports multi-core processors to take full advantage of the high performance of existing embedded processors. On different hardware platforms, we conducted two types of tests about interrupt response time and latency. The results show that our proposed architecture exhibits high real-time performance.

关键词： Task analysis Real-time systems Kernel Virtual machine monitors Security

Cosmology with Galaxy Cluster Properties using Machine Learning

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arXiv 2023年

作者： Qiu, Lanlan Napolitano, Nicola R. Borgani, Stefano Zhong, Fucheng Li, Xiaodong Radovich, Mario Lin, Weipeng Dolag, Klaus Tortora, Crescenzo Wang, Yang Remus, Rhea-Silvia Wu, Sirui Longo, Giuseppe School of Physics and Astronomy Sun Yat-sen University Zhuhai Campus 2 Daxue Road Tangjia Zhuhai519082 China CSST Science Center for Guangdong-Hong Kong-Macau Great Bay Area Zhuhai519082 China Department of Physics E. Pancini University Federico II Via Cinthia 6 Naples80126-I Italy Astronomy Unit Department of Physics University of Trieste via Tiepolo 11 TriesteI-34131 Italy INAF-Osservatorio Astronomico di Trieste via G. B. Tiepolo 11 TriesteI-34143 Italy IFPU Institute for Fundamental Physics of the Universe Via Beirut 2 Trieste34014 Italy INFN Instituto Nazionale di Fisica Nucleare Via Valerio 2 TriesteI-34127 Italy ICSC - Italian Research Center on High Performance Computing Big Data and Quantum Computing INAF - Osservatorio Astronomico di Padova via dell’Osservatorio 5 Padova35122 Italy Universitäts-Sternwarte Fakultät für Physik Ludwig-Maximilians-Universität München Scheinerstr.1 München81679 Germany Max-Planck-Institut für Astrophysik Karl-Schwarzschild-Straße 1 Garching85741 Germany INAF – Osservatorio Astronomico di Capodimonte Salita Moiariello 16 Napoli80131 Italy Peng Cheng Laboratory No.2 Xingke 1st Street Shenzhen518000 China

Context. Galaxy clusters are the largest gravitating structures in the universe and their mass assembly is sensitive to the underlying cosmology. Their mass function, baryon fraction, and mass distribution have been used to infer cosmological parameters, despite the presence of systematics. However, the complexity of the scaling relations among galaxy cluster properties has never been fully exploited, limiting their potential as a cosmological probe. Aims. We propose the first Machine Learning (ML) method using galaxy cluster properties from hydrodynamical simulations in different cosmologies to predict cosmological parameters combining a series of canonical cluster observables, like gas mass, gas bolometric luminosity, gas temperature, stellar mass, cluster radius, total mass, and velocity dispersion at different redshifts. Methods. The machine learning model is trained on mock "measurements" of these observable quantities from Magneticum multi-cosmology simulations to derive unbiased constraints on a set of cosmological parameters. These include the mass density parameter, Ωm, the power spectrum normalization, σ8, the baryonic density parameter, Ωb, and the reduced Hubble constant, h0. Results. We test the ML model on catalogs of a few hundred clusters taken, in turn, from each simulation and find that the ML model can correctly predict the cosmology they have been picked from. The cumulative accuracy depends on the cosmology, ranging from 21% to 75%. We demonstrate that this is sufficient to derive unbiased constraints on the main cosmological parameters with errors of the order of ∼ 14% for Ωm, ∼ 8% for σ8, ∼ 6% for Ωb, and ∼ 3% for h0. Conclusions. This proof-of-concept analysis, yet based on a limited variety of multi-cosmology simulations, shows that machine learning can efficiently map the correlations in the multi-dimensional space of the observed quantities to the cosmological parameter space and narrow down the probability that a given sample belongs to a

关键词： Cosmology

Tuning the Legacy Survey of Space and Time (LSST) Observing Strategy for Solar System science: Incremental Templates in Year 1

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arXiv 2024年

作者： Robinson, James E. Schwamb, Megan E. Jones, R. Lynne Jurić, Mario Yoachim, Peter Bolin, Bryce T. Chandler, Colin O. Chesley, Steven R. Fedorets, Grigori Fraser, Wesley C. Greenstreet, Sarah Hsieh, Henry H. McGinley, Lauren J. Merritt, Stephanie R. Opitom, Cyrielle Parejko, John K. Institute for Astronomy University of Edinburgh Royal Observatory Edinburgh Blackford Hill EdinburghEH9 3HJ United Kingdom Astrophysics Research Centre School of Mathematics and Physics Queen’s University Belfast BelfastBT7 1NN United Kingdom Rubin Observatory 950 N. Cherry Ave. TucsonAZ85719 United States Aerotek Suite 150 4321 Still Creek Drive BurnabyBCV5C6S Canada Department of Astronomy the DiRAC Institute University of Washington 3910 15th Ave NE SeattleWA98195 United States Eureka Scientific Inc. 2452 Delmer Street Suite 100 OaklandCA94602-3017 United States LSST Interdisciplinary Network for Collaboration and Computing 933 N. Cherry Avenue TucsonAZ85721 United States Department of Astronomy & Planetary Science Northern Arizona University P.O. Box 6010 FlagstaffAZ86011 United States Raw Data Speaks Initiative United States Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Dr. PasadenaCA91109 United States Finnish Centre for Astronomy with ESO University of Turku TurkuFI-20014 Finland Department of Physics University of Helsinki P.O. Box 64 Helsinki00014 Finland Herzberg Astronomy and Astrophysics Research Centre National Research Council 5071 W. Saanich Rd. VictoriaBCV9E 2E7 Canada Rubin Observatory/NSF NOIRLab 950 N. Cherry Ave TucsonAZ85719 United States Planetary Science Institute 1700 East Fort Lowell Rd. Suite 106 TucsonAZ85719 United States Department of Astronomy University of Washington 3910 15th Ave NE SeattleWA98195 United States

The Vera C. Rubin Observatory is due to commence the 10-year Legacy Survey of Space and Time (LSST) at the end of 2025. To detect transient/variable sources and identify solar system objects (SSOs), the processing pipelines require templates of the static sky to perform difference imaging. During the first year of the LSST, templates must be generated as the survey progresses, otherwise SSOs cannot be discovered nightly. The incremental template generation strategy has not been finalized;therefore, we use the Metric Analysis Framework (MAF) and a simulation of the survey cadence (one snap v4.0 10yrs) to explore template generation in Year 1. We have assessed the effects of generating templates over timescales of days-weeks, when at least four images of sufficient quality are available for ≥ 90% of the visit. We predict that SSO discoveries will begin ∼2-3 months after the start of the survey. We find that the ability of the LSST to discover SSOs in real-time is reduced in Year 1. This is especially true for detections in areas of the sky that receive fewer visits, such as the North Ecliptic Spur (NES), and in less commonly used filters, such as the u and g-bands. The lack of templates in the NES dominates the loss of real-time SSO discoveries;across the whole sky the MAF Main-Belt asteroid (MBA) discovery metric decreases by up to 63% compared to the baseline observing strategy, whereas the metric decreases by up to 79% for MBAs in the NES alone. © 2024, CC BY.

关键词： Solar system

Complementary time-frequency domain networks for dynamic parallel MR image reconstruction

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arXiv 2020年

作者： Qin, Chen Duan, Jinming Hammernik, Kerstin Schlemper, Jo Küstner, Thomas Botnar, René Prieto, Claudia Price, Anthony N. Hajnal, Joseph V. Rueckert, Daniel Institute for Digital Communications School of Engineering University of Edinburgh Edinburgh United Kingdom Department of Computing Imperial College London London United Kingdom School of Computer Science University of Birmingham Birmingham United Kingdom Institute for AI and Informatics Klinikum Rechts der Isar Technical University of Munich Munich Germany Hyperfine Research Inc. GuilfordCT United States School of Biomedical Engineering and Imaging Sciences King's College London St. Thomas' Hospital London United Kingdom Department of Diagnostic and Interventional Radiology Medical Image and Data Analysis University Hospital of Tuebingen Tuebingen Germany

Purpose To introduce a novel deep learning based approach for fast and high-quality dynamic multi-coil MR reconstruction by learning a complementary time-frequency domain network that exploits spatio-temporal correlations simultaneously from complementary domains. Theory and Methods Dynamic parallel MR image reconstruction is formulated as a multi-variable minimisation problem, where the data is regularised in combined temporal Fourier and spatial (x-f ) domain as well as in spatio-temporal image (x-t) domain. An iterative algorithm based on variable splitting technique is derived, which alternates among signal de-aliasing steps in x-f and x-t spaces, a closed-form point-wise data consistency step and a weighted coupling step. The iterative model is embedded into a deep recurrent neural network which learns to recover the image via exploiting spatio-temporal redundancies in complementary domains. Results Experiments were performed on two datasets of highly undersampled multi-coil short-axis cardiac cine MRI scans. Results demonstrate that our proposed method outperforms the current state-of-the-art approaches both quantitatively and qualitatively. The proposed model can also generalise well to data acquired from a different scanner and data with pathologies that were not seen in the training set. Conclusion The work shows the benefit of reconstructing dynamic parallel MRI in complementary time-frequency domains with deep neural networks. The method can effectively and robustly reconstruct high-quality images from highly undersampled dynamic multi-coil data (16× and 24× yielding 15s and 10s scan times respectively) with fast reconstruction speed (2.8s). This could potentially facilitate achieving fast single-breath-hold clinical 2D cardiac cine imaging. © 2020, CC BY.

关键词： Magnetic resonance imaging

Power Supply Noise Aware Task Scheduling on Homogeneous 3D MPSoCs Considering the Thermal Constraint

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Journal of Computer science & Technology 2018年第5期33卷 966-983页

作者： Ying-Lin Zhao Jian-Lei Yang Wei-Sheng Zhao Aida Todri-Sanial Yuan-Qing Cheng Fert Beijing Research Institute Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing 100191 China School of Electronic and Information Engineering Beihang University Beijing 100191 China Qingdao Research Institute Beihang University Qingdao 266041 China School of Computer Science and Engineering Beihang University Beijing 100191 China Laboratory of Informaties Robotics and Mieroeleetronics University of Montpellier Montpellier 34095 France National Center for Scientific Research MontpeUier 34095 France

Thanks to the emerging 3D integration technology, The multiprocessor system on chips （MPSoCs） can now integrate more IP cores on chip with improved energy efficiency. However, several severe challenges also rise up for 3D ICs due to the die-stacking architecture. Among them, power supply noise becomes a big concern. In the paper, we investigate power supply noise （PSN） interactions among different cores and tiers and show that PSN variations largely depend on task assignments. On the other hand, high integration density incurs a severe thermal issue on 3D ICs. In the paper, we propose a novel task scheduling framework considering both the PSN and the thermal issue. It mainly consists of three parts. First, we extract current stimuli of running tasks by analyzing their power traces derived from architecture level simulations. Second, we develop an efficient power delivery network （PDN） solver to evaluate PSN magnitudes efficiently. Third, we propose a heuristic algorithm to solve the formulated task scheduling problem. Compared with the state-of-the-art task assignment algorithm, the proposed method can reduce PSN by 12% on a 2 × 2 × 2 3D MPSoCs and by 14% on a 3 × 3 × 3 3D MPSoCs. The end-to-end task execution time also improves as much as 5.5% and 7.8% respectively due to the suppressed PSN.

关键词： MPSoCs power supply noise （PSN） power delivery network （PDN） task scheduling algorithm temperature 3D

Environmental, health and economic impact assessment of clean heating renovation: Evidence from 88 cities in northern China

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Environmental Impact Assessment Review 2025年 115卷

作者： Huang, Jingting Wang, Huili Liu, Zhaoyin Zhang, Li Gao, Hanyu Wang, Zhe Zheng, Yixuan Chen, Xiaojun Zhao, Lin Guo, Shangmin Cai, Bofeng Li, Linyan Department of Data Science College of Computing City University of Hong Kong SAR Hong Kong China Center for Carbon Neutrality Chinese Academy of Environmental Planning Beijing100043 China School of the Environment and Safety Engineering Jiangsu University Zhenjiang212013 China Department of Chemical and Biological Engineering Hong Kong University of Science and Technology Clear Water Bay Hong Kong Department of Civil and Environmental Engineering Hong Kong University of Science and Technology Clear Water Bay Hong Kong State Environmental Protection Key Laboratory of Environmental Pollution and Greenhouse Gases Co-control Chinese Academy of Environmental Planning Beijing100043 China State Grid Liaoning Electric Power Company Limited Economic Research Institute Shenyang110000 China Department of Infectious Diseases and Public Health Jockey Club College of Veterinary Medicine and Life Sciences City University of Hong Kong SAR Hong Kong China

To address environmental issues related to heating, the Chinese government launched the "Clean Heating Plan in the Northern Region" as a national energy transition strategy, renovating heating systems for over 30 million households in 88 cities. With the completion of five implementation phases by March 2024, this study presents an evaluation framework to comprehensively assess the environmental, health, and economic impacts of the policy. On-site surveys were conducted annually, monitoring the advancements of clean heating renovations in over 100,000 households across these cities. Combined with air pollution metrics, meteorological data, and city statistical records, a Difference-in-Differences (DID) model with continuous treatment was used to evaluate the environmental benefits associated with the incremental adoption of clean heating in each city. Subsequently, a health impact assessment and cost-benefit analysis were conducted to analyze changes in all-cause mortality and their associated economic benefits. Results indicated an average PM2.5 reduction of 10.31 μg/m3 (95 % CI: [9.25, 11.37]) per heating season and an annual reduction of 1573 in all-cause mortality (95 % CI: [630, 2518]). The economic benefits of these improvements were estimated at 717.1 billion CNY, contributing to an average benefit-cost ratio of 1.48. The proposed evaluation framework serves as a structured method for evaluating the environmental, health, and economic impacts of the energy policy and provides robust evidence to support implementing similar initiatives. © 2025 Elsevier Inc.

关键词： Cost benefit analysis

THE TEMPO SURVEY II: science CASES LEVERAGED FROM A PROPOSED 30-DAY TIME DOMAIN SURVEY OF THE ORION NEBULA WITH THE NANCY GRACE ROMAN SPACE TELESCOPE

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arXiv 2024年

作者： Soares-Furtado, Melinda Limbach, Mary Anne Vanderburg, Andrew Bally, John Becker, Juliette Rosen, Anna L. Bouma, Luke G. Vos, Johanna M. Howell, Steve B. Beatty, Thomas G. Best, William M.J. Cody, Anne Marie Distler, Adam D’Onghia, Elena Heller, René Hensley, Brandon S. Hinkel, Natalie R. Jackson, Brian Kounkel, Marina Kraus, Adam Mann, Andrew W. Marston, Nicholas T. Robberto, Massimo Rodriguez, Joseph E. Steffen, Jason H. Teske, Johanna K. Townsend, Richard Yarza, Ricardo Youngblood, Allison Department of Astronomy University of Wisconsin-Madison 475 N. Charter St. MadisonWI53706 United States Department of Physics Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA02139 United States Department of Astronomy University of Michigan Ann ArborMI48109 United States Center for Astrophysics and Space Astronomy Astrophysical and Planetary Sciences Department University of Colorado BoulderCO80389 United States Department of Astronomy San Diego State University San DiegoCA92182 United States Computational Science Research Center San Diego State University San DiegoCA92182 United States Department of Astronomy MC 249-17 California Institute of Technology PasadenaCA91125 United States School of Physics Trinity College Dublin The University of Dublin Dublin 2 Ireland NASA Ames Research Center Moffett FieldCA94035 United States Department of Astronomy The University of Texas at Austin AustinTX78712 United States SETI Institute 189 N. Bernardo Ave #200 Mountain ViewCA94043 United States Max Planck Institute for Solar System Research Justus-von-Liebig-Weg 3 Göttingen37077 Germany Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive PasadenaCA91109 United States Physics & Astronomy Department Louisiana State University Baton RougeLA70803 United States Department of Physics Boise State University BoiseID83725-1570 United States Department of Physics & Astronomy Vanderbilt University NashvilleTN37235 United States Department of Physics & Astronomy The University of North Carolina at Chapel Hill Chapel HillNC27599-3255 United States Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States Johns Hopkins University 3400 N. Charles St. BaltimoreMD21218 United States Center for Data Intensive and Time Domain Astronomy Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Un

The TEMPO (Transiting Exosatellites, Moons, and Planets in Orion) Survey is a proposed 30-day observational campaign using the Nancy Grace Roman Space Telescope. By providing deep, high-resolution, short-cadence infrared photometry of a dynamic star-forming region, TEMPO will investigate the demographics of exosatellites orbiting free-floating planets and brown dwarfs — a largely unexplored discovery space. Here, we present the simulated detection yields of three populations: extrasolar moon analogs orbiting free-floating planets, exosatellites orbiting brown dwarfs, and exoplanets orbiting young stars. Additionally, we outline a comprehensive range of anticipated scientific outcomes accompanying such a survey. These science drivers include: obtaining observational constraints to test prevailing theories of moon, planet, and star formation;directly detecting widely separated exoplanets orbiting young stars;investigating the variability of young stars and brown dwarfs;constraining the low-mass end of the stellar initial mass function;constructing the distribution of dust in the Orion Nebula and mapping evolution in the near-infrared extinction law;mapping emission features that trace the shocked gas in the region;constructing a dynamical map of Orion members using proper motions;and searching for extragalactic sources and transients via deep extragalactic observations reaching a limiting magnitude of mAB = 29.7 mag (F146 filter). © 2024, CC BY.

关键词： Orbits

Decoding the Molecular Universe - Workshop Report

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arXiv 2023年

作者： Metz, Thomas O. Adkins, Joshua N. Armentrout, Peter B. Chain, Patrick Chu, Fanny Corley, Courtney D. Cort, John R. Denis, Elizabeth Drell, Daniel Duncan, Katherine R. Ewing, Robert G. Fernandez, Facundo M. Fiehn, Oliver Garg, Neha Grimme, Stefan Henry, Christopher Hettich, Robert L. Kind, Tobias Linington, Roger G. Miller, Gary W. Northen, Trent Overdahl, Kirsten Patrinos, Ari Raftery, Daniel Rigor, Paul Smith, Richard D. Sobus, Jon Teeguarden, Justin Vertes, Akos Waters, Katrina Webb-Robertson, Bobbie-Jo Williams, Antony Wishart, David Biological Sciences Division Earth and Biological Sciences Directorate Pacific Northwest National Laboratory RichlandWA United States Department of Chemistry University of Utah Salt Lake CityUT United States Biosciences Division Chemical Earth and Life Sciences Directorate Los Alamos National Laboratory Los AlamosNM United States Nuclear Chemistry and Biology Division National Security Directorate Pacific Northwest National Laboratory RichlandWA United States AI and Data Analytics Division Pacific Northwest National Laboratory RichlandWA United States Office of Biological and Environmental Research U.S. Department of Energy WashingtonDC United States University of Strathclyde Strathclyde Institute of Pharmacy and Biomedical Sciences Glasgow United Kingdom School of Chemistry and Biochemistry Petit Institute for Bioengineering and Bioscience Georgia Institute of Technology AtlantaGA United States University of California Davis West Coast Metabolomics Center DavisCA United States School of Chemistry and Biochemistry Petit Institute for Bioengineering and Bioscience Center for Microbial Dynamics and Infection Georgia Institute of Technology AtlantaGA United States Mulliken Center for Theoretical Chemistry Clausius Institute for Physical and Theoretical Chemistry University of Bonn Germany Data Science and Learning Division Argonne National Laboratory LemontIL United States Biosciences Division Oak Ridge National Laboratory Oak RidgeTN United States Enveda Biosciences BoulderCO United States Department of Chemistry Simon Fraser University BurnabyBC Canada Department of Environmental Health Sciences Mailman School of Public Health Columbia University New YorkNY United States Environmental Genomics and System Biology Division Lawrence Berkeley National Laboratory CA United States Metabolomics Core Facility National Institute of Environmental Health Sciences Research Triangle Park NC United States Novim Group Santa BarbaraCA United States Nor

On August 9-10, 2023, a workshop was convened at the Pacific Northwest National Laboratory (PNNL) in Richland, WA that brought together a group of internationally recognized experts in metabolomics, natural products discovery, chemical ecology, chemical and biological threat assessment, cheminformatics, computational chemistry, cloud computing, artificial intelligence, and novel technology development. These experts were invited to assess the value and feasibility of a grand-scale project to create new technologies that would allow the identification and quantification of all small molecules, or to "decode the molecular universe." The Decoding the Molecular Universe project would extend and complement the success of the Human Genome Project (HGP) by developing new capabilities and technologies to measure small molecules – defined as non-protein, non-polymer molecules less than ~1500 Daltons – of any origin and generated in biological systems or produced abiotically. The workshop was inspired by recognition that these new technologies would drive the next revolution in multiple fields of strategic importance to the nation: biological sciences, medical sciences, national security, chemistry, and the bio-economy. The workshop was supported by PNNL’s investment to advance the field of computational mass spectrometry in support of Department of Energy and Department of Homeland Security missions via a project named the m/q Initiative. The workshop was modeled on similar workshops and related efforts held in the mid-1980s that were the catalyst for the HGP. Workshop participants and observers included representatives from academia, government agencies, industry, and national laboratories whose research involves or where the success of programs and mission areas depends heavily on the study or measurement of such small molecules. Primary motivations for the workshop were a nearly universal need – across scientific disciplines and applied and basic research domains – to ide

关键词： Proteins

Introduction to voice presentation attack detection and recent advances

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arXiv 2019年

作者： Sahidullah, Md Delgado, Héctor Todisco, Massimiliano Kinnunen, Tomi Evans, Nicholas Yamagishi, Junichi Lee, Kong-Aik School of Computing University of Eastern Finland Finland Inria France Department of Digital Security EURECOM France National Institute of Informatics Japan University of Edinburgh United Kingdom Data Science Research Laboratories NEC Corporation Japan

Over the past few years significant progress has been made in the field of presentation attack detection (PAD) for automatic speaker recognition (ASV). This includes the development of new speech corpora, standard evaluation protocols and advancements in front-end feature extraction and back-end classifiers. The use of standard databases and evaluation protocols has enabled for the first time the meaningful benchmarking of different PAD solutions. This chapter summarises the progress, with a focus on studies completed in the last three years. The article presents a summary of findings and lessons learned from two ASVspoof challenges, the first community-led benchmarking efforts. These show that ASV PAD remains an unsolved problem and that further attention is required to develop generalised PAD solutions which have potential to detect diverse and previously unseen spoofing attacks. Copyright © 2019, The Authors. All rights reserved.

关键词： Benchmarking