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arXiv 2019年

作者： Liang, Junjie Xu, Dongkuan Sun, Yiwei Honavar, Vasant Artificial Intelligence Research Laboratory Pennsylvania State University College of Information Sciences and Technology Pennsylvania State University Department of Computer Science and Engineering Pennsylvania State University Institute of Computational and Data Sciences Pennsylvania State University

We consider the problem of learning predictive models from longitudinal data, consisting of irregularly repeated, sparse observations from a set of individuals over time. Such data often exhibit longitudinal correlation (LC) (correlations among observations for each individual over time), cluster correlation (CC) (correlations among individuals that have similar characteristics), or both. These correlations are often accounted for using mixed effects models that include fixed effects and random effects, where the fixed effects capture the regression parameters that are shared by all individuals, whereas random effects capture those parameters that vary across individuals. However, the current state-of-the-art methods are unable to select the most predictive fixed effects and random effects from a large number of variables, while accounting for complex correlation structure in the data and non-linear interactions among the variables. We propose Longitudinal Multi-Level Factorization Machine (LMLFM), to the best of our knowledge, the first model to address these challenges in learning predictive models from longitudinal data. We establish the convergence properties, and analyze the computational complexity, of LMLFM. We present results of experiments with both simulated and real-world longitudinal data which show that LMLFM outperforms the state-of-the-art methods in terms of predictive accuracy, variable selection ability, and scalability to data with large number of variables. The code and supplemental material is available at https://***/junjieliang672/LMLFM. Copyright © 2019, The Authors. All rights reserved.

关键词： Random processes

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A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations

arXiv

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arXiv 2024年

作者： Garrucho, Lidia Kushibar, Kaisar Reidel, Claire-Anne Joshi, Smriti Osuala, Richard Tsirikoglou, Apostolia Bobowicz, Maciej del Riego, Javier Catanese, Alessandro Gwoździewicz, Katarzyna Cosaka, Maria-Laura Abo-Elhoda, Pasant M. Tantawy, Sara W. Sakrana, Shorouq S. Shawky-Abdelfatah, Norhan O. Abdo-Salem, Amr Muhammad Kozana, Androniki Divjak, Eugen Ivanac, Gordana Nikiforaki, Katerina Klontzas, Michail E. García-Dosdá, Rosa Gulsun-Akpinar, Meltem Lafcı, Oğuz Mann, Ritse Martín-Isla, Carlos Prior, Fred Marias, Kostas Starmans, Martijn P.A. Strand, Fredrik Díaz, Oliver Igual, Laura Lekadir, Karim Barcelona Spain Department of Oncology-Pathology Karolinska Institutet Stockholm Sweden Institute of Machine Learning in Biomedical Imaging Helmholtz Center Munich Munich Germany School of Computation Information and Technology Technical University of Munich Munich Germany 2nd Dept. of Radiology Medical University of Gdansk Gdansk Poland Parc Taulí Hospital Universitari Sabadell Spain Hospital Germans Trias i Pujol Badalona Spain Centro Mamario Instituto Alexander Fleming Buenos Aires Argentina Department of Diagnostic & Interventional Radiology and Molecular Imaging Faculty of Medicine Ain Shams University Cairo Egypt Department of Radiology University Hospital of Heraklion Stavrakia Greece Department of Diagnostic and Interventional Radiology University Hospital Dubrava Zagreb Croatia University of Zagreb School of Medicine Zagreb Croatia Computational BioMedicine Laboratory Institute of Computer Science Foundation for Research and Technology—Hellas Heraklion Greece Department of Radiology School of Medicine University of Crete Heraklion Greece Medical Imaging and Radiology Universitary and Politechnic Hospital La Fe Valencia Spain Department of Radiology Hacettepe University Faculty of Medicine Sihhiye Ankara Turkey Department of Biomedical Imaging and Image-guided Therapy Medical University of Vienna Vienna Austria Department of Radiology and Nuclear Medicine Radboud University Medical Center Netherlands University of Arkansas for Medical Sciences Little RockAR United States Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Greece Heraklion Greece Department of Radiology and Nuclear Medicine Erasmus MC Cancer Institute University Medical Center Rotterdam Rotterdam Netherlands Department of Pathology Erasmus MC Cancer Institute University Medical Center Rotterdam Rotterdam Netherlands Breast Radiology Karolinska University Hospital Stockholm Sweden Passeig Lluís Companys 23 Barcelona Sp

Artificial Intelligence (AI) research in breast cancer Magnetic Resonance Imaging (MRI) faces challenges due to limited expert-labeled segmentations. To address this, we present a multicenter dataset of 1506 pretreatment T1-weighted dynamic contrast-enhanced MRI cases, including expert annotations of primary tumors and non-mass-enhanced regions. The dataset integrates imaging data from four collections in The Cancer Imaging Archive (TCIA), where only 163 cases with expert segmentations were initially available. To facilitate the annotation process, a deep learning model was trained to produce preliminary segmentations for the remaining cases. These were subsequently corrected and verified by 16 breast cancer experts (averaging 9 years of experience), creating a fully annotated dataset. Additionally, the dataset includes 49 harmonized clinical and demographic variables, as well as pre-trained weights for a baseline nnU-Net model trained on the annotated data. This resource addresses a critical gap in publicly available breast cancer datasets, enabling the development, validation, and benchmarking of advanced deep learning models, thus driving progress in breast cancer diagnostics, treatment response prediction, and personalized care. Copyright © 2024, The Authors. All rights reserved.

关键词： Dynamic contrast enhanced MRI

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Benchmark problems for transcranial ultrasound simulation: Intercomparison of compressional wave models

arXiv

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arXiv 2022年

作者： Aubry, Jean-Francois Bates, Oscar Boehm, Christian Pauly, Kim Butts Christensen, Douglas Cueto, Carlos Gélat, Pierre Guasch, Lluis Jaros, Jiri Jing, Yun Jones, Rebecca Li, Ningrui Marty, Patrick Montanaro, Hazael Neufeld, Esra Pichardo, Samuel Pinton, Gianmarco Pulkkinen, Aki Stanziola, Antonio Thielscher, Axel Treeby, Bradley Van't Wout, Elwin Physics for Medicine Paris INSERM U1273 ESPCI Paris PSL University CNRS UMR 8063 France Department of Bioengineering Imperial College London Exhibition Road LondonSW7 2AZ United Kingdom Institute of Geophysics ETH Zürich Sonneggstrasse 5 Zürich8092 Switzerland Department of Radiology Stanford University StanfordCA United States Department of Biomedical Engineering Department of Electrical and Computer Engineering University of Utah United States Department of Surgical Biotechnology Division of Surgery and Interventional Science University College London LondonNW3 2PF United Kingdom Earth Science and Engineering Department Imperial College London London United Kingdom Centre of Excellence IT4Innovations Faculty of Information Technology Brno University of Technology Bozetechova 2 Brno612 00 Czech Republic Graduate Program in Acoustics The Pennsylvania State University University Park PA16802 United States Joint Department of Biomedical Engineering University of North Carolina at Chapel Hill North Carolina State University NC United States Department of Electrical Engineering Stanford University StanfordCA United States Zurich Switzerland Laboratory for Acoustics / Noise control Empa Swiss Federal Laboratories for Materials Science and Technology Dubendorf Switzerland Zurich Switzerland Radiology and Clinical Neurosciences Departments Cumming School of Medicine University of Calgary Canada Department of Applied Physics University of Eastern Finland Kuopio70211 Finland Department of Medical Physics and Biomedical Engineering University College London Gower Street LondonWC1E 6BT United Kingdom Technical University of Denmark Denmark Danish Research Center for Magnetic Resonance Copenhagen University Hospital Hvidovre Denmark Institute for Mathematical and Computational Engineering School of Engineering and Faculty of Mathematics Pontificia Universidad Catolica de Chile Santiago Chile

computational models of acoustic wave propagation are frequently used in transcranial ultrasound therapy, for example, to calculate the intracranial pressure field or to calculate phase delays to correct for skull distortions. To allow intercomparison between the different modeling tools and techniques used by the community, an international working group was convened to formulate a set of numerical benchmarks. Here, these benchmarks are presented, along with intercomparison results. Nine different benchmarks of increasing geometric complexity are defined. These include a single-layer planar bone immersed in water, a multi-layer bone, and a whole skull. Two transducer configurations are considered (a focused bowl and a plane piston), giving a total of 18 permutations of the benchmarks. Eleven different modeling tools are used to compute the benchmark results. The models span a wide range of numerical techniques, including the finite-difference time-domain method, angular-spectrum method, pseudospectral method, boundary-element method, and spectral-element method. Good agreement is found between the models, particularly for the position, size, and magnitude of the acoustic focus within the skull. When comparing results for each model with every other model in a cross comparison, the median values for each benchmark for the difference in focal pressure and position are less than 10% and 1 mm, respectively. The benchmark definitions, model results, and intercomparison codes are freely available to facilitate further comparisons. Copyright © 2022, The Authors. All rights reserved.

关键词： Bone

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Drug3D-DTI: Improved Drug-target Interaction Prediction by Incorporating Spatial Information of Small Molecules

Drug3D-DTI: Improved Drug-target Interaction Prediction by I...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhirui Liao Xiaodi Huang Hiroshi Mamitsuka Shanfeng Zhu School of Computer Science Fudan University Shanghai China Institute of Artificial Intelligence Biomedicine Nanjing University Nanjing China School of Computing Mathematics and Engineering Charles Sturt University Albury NSW Australia Bioinformatics Center Institute for Chemical Research Kyoto University Uji Kyoto Japan Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) Ministry of Education China MOE Frontiers Center for Brain Science Fudan University Shanghai China Zhangjiang Fudan International Innovation Center Shanghai China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai China

ISBN: (纸本)9781665429825

A number of machine learning (ML) approaches for drug discovery have been available that rely only on sequential (1D) and planar (2D) information without effectively using the 3D information for generating features of drugs. However, 3D information of small molecules can reflect relative position of atoms more directly, which affects molecular properties. In this work, we present a new deep learning model called Drug3D-DTI for drug-target interaction prediction. Drug3D-DTI takes advantage of molecular spatial information, i.e., atom proximity in three-dimensional (3D) structures. We comprehensively evaluated the performance of Drug3D-DTI on two datasets with two tasks of regression and classification. In particular, we compared Drug3D-DTI with several existing methods including the two cutting-edge methods for compound-protein interaction prediction. From the experimental results, Drug3D-DTI clearly outperformed other methods under all settings. Further, this performance improvement was validated by ablation experiments and a case study. The implementation of Drug3D-DTI is available at (https://***/zhiruiliao/Drug3D-DTI).

关键词： Drugs Proteins Deep learning Solid modeling Three-dimensional displays Conferences Predictive models

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WaveNano:a signal-level nanopore base-caller via simultaneous prediction of nucleotide labels and move labels through bi-directional WaveNets

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Frontiers of Electrical and Electronic engineering in China 2018年第4期6卷 359-368页

作者： Sheng Wang Zhen Li Yizhou YU Xin Gao Computational Bioscience Research Center (CBRC) King Abdullah University of Science and Technology (KAUST)Thuwa23955-6900Kingdom of Saudi Arabia Department of Computer Science University of Hong KongHong Kong SAR 999077China School of Science and Engineering Shenzhen Research Institute of Big DataThe Chinese University of Hong Kong Shenzhen 518172China

Background The Oxford MinION nanopore sequencer is the recently appealing third-generation genome sequencing device that is portable and no larger than a cellphone. Despite the benefits of MinION to sequence ultra-long reads in real-time, the high error rate of the existing base-calling methods, especially indels (insertions and deletions), prevents its use in a variety of *** In this paper, we show that such indel errors are largely due to the segmentation process on the input electrical current signal from MinION. All existing methods conduct segmentation and nucleotide label prediction in a sequential manner, in which the errors accumulated in the first step will irreversibly influence the final base-calling. We further show that the indel issue can be significantly reduced via accurate labeling of nucleotide and move labels directly from the raw signal, which can then be efficiently learned by a bi-directionalWaveNet model simultaneously through feature sharing. Our bi-directional WaveNet model with residual blocks and skip connections is able to capture the extremely long dependency in the raw signal. Taking the predicted move as the segmentation guidance, we employ the Viterbi decoding to obtain the final base-calling results from the smoothed nucleotide probability *** Our proposed base-caller, WaveNano, achieves good performance on real MinION sequencing data from Lambda *** The signal-level nanopore base-callerWaveNano can obtain higher base-calling accuracy, and generate fewer insertions/deletions in the base-called sequences.

关键词： nanopore sequencing bi-directional WaveNets base-calling third generation sequencing deep learning

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Asian diversity in human immune cells

引用

Cell 2025年第8期188卷 2288-2306.e24页

作者： Kock, Kian Hong Tan, Le Min Han, Kyung Yeon Ando, Yoshinari Jevapatarakul, Damita Chatterjee, Ankita Lin, Quy Xiao Xuan Buyamin, Eliora Violain Sonthalia, Radhika Rajagopalan, Deepa Tomofuji, Yoshihiko Sankaran, Shvetha Park, Mi-So Abe, Mai Chantaraamporn, Juthamard Furukawa, Seiko Ghosh, Supratim Inoue, Gyo Kojima, Miki Kouno, Tsukasa Lim, Jinyeong Myouzen, Keiko Nguantad, Sarintip Oh, Jin-Mi Rayan, Nirmala Arul Sarkar, Sumanta Suzuki, Akari Thungsatianpun, Narita Venkatesh, Prasanna Nori Moody, Jonathan Nakano, Masahiro Chen, Ziyue Tian, Chi Zhang, Yuntian Tong, Yihan Tan, Crystal T.Y. Tizazu, Anteneh Mehari Loh, Marie Hwang, You Yi Ho, Roger C. Larbi, Anis Ng, Tze Pin Won, Hong-Hee Wright, Fred A. Villani, Alexandra-Chloé Park, Jong-Eun Choi, Murim Liu, Boxiang Maitra, Arindam Pithukpakorn, Manop Suktitipat, Bhoom Ishigaki, Kazuyoshi Okada, Yukinori Yamamoto, Kazuhiko Carninci, Piero Chambers, John C. Hon, Chung-Chau Matangkasombut, Ponpan Charoensawan, Varodom Majumder, Partha P. Shin, Jay W. Park, Woong-Yang Prabhakar, Shyam Genome Institute of Singapore (GIS) Agency for Science STAR) 60 Biopolis Street Genome Singapore 138672 Singapore Samsung Genome Institute Samsung Medical Center Seoul 06351 South Korea Laboratory for Advanced Genomics Circuit RIKEN Center for Integrative Medical Sciences (IMS) 1-7-22 Suehiro-cho Tsurumi-ku Kanagawa Yokohama 230-0045 Japan Laboratory for Transcriptome Technology RIKEN Center for IMS 1-7-22 Suehiro-cho Tsurumi-ku Kanagawa Yokohama 230-0045 Japan Single-cell omics and Systems Biology of Diseases (scSyBiD) Research Unit Faculty of Science Mahidol University Bangkok 10400 Thailand Department of Microbiology Faculty of Science Mahidol University Bangkok 10400 Thailand John C. Martin Centre for Liver Research and Innovations Sonarpur Kolkata 700150 India Laboratory for Systems Genetics RIKEN Center for IMS 1-7-22 Suehiro-cho Tsurumi-ku Kanagawa Yokohama 230-0045 Japan Department of Statistical Genetics of Medicine Osaka University 2-2 Yamadaoka Osaka Suita 565-0871 Japan Integrated Frontier Research for Medical Science Division Institute for Open and Transdisciplinary Research Initiatives Osaka University 2-2 Yamadaoka Osaka Suita 565-0871 Japan Samsung Advanced Institute for Health Sciences & Technology Sungkyunkwan University Seoul 06351 South Korea Laboratory for Autoimmune Diseases RIKEN Center for IMS 1-7-22 Suehiro-cho Tsurumi-ku Kanagawa Yokohama 230-0045 Japan Department of Biochemistry Faculty of Science Mahidol University Bangkok 10400 Thailand Integrative Computational BioScience Center Mahidol University Nakhon Pathom 73170 Thailand Biotechnology Research and Innovation Council National Institute of Biomedical Genomics West Bengal Kalyani 741251 India Laboratory for Genome Information Analysis RIKEN Center for IMS 1-7-22 Suehiro-cho Tsurumi-ku Kanagawa Yokohama 230-0045 Japan Department of Allergy and Rheumatology of Medicine The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8654 Japan D

The relationships of human diversity with biomedical phenotypes are pervasive yet remain understudied, particularly in a single-cell genomics context. Here, we present the Asian Immune Diversity Atlas (AIDA), a multi-national single-cell RNA sequencing (scRNA-seq) healthy reference atlas of human immune cells. AIDA comprises 1,265,624 circulating immune cells from 619 donors, spanning 7 population groups across 5 Asian countries, and 6 controls. Though population groups are frequently compared at the continental level, we found that sub-continental diversity, age, and sex pervasively impacted cellular and molecular properties of immune cells. These included differential abundance of cell neighborhoods as well as cell populations and genes relevant to disease risk, pathogenesis, and diagnostics. We discovered functional genetic variants influencing cell-type-specific gene expression, which were under-represented in non-Asian populations, and helped contextualize disease-associated variants. AIDA enables analyses of multi-ancestry disease datasets and facilitates the development of precision medicine efforts in Asia and beyond. © 2025 The Authors

关键词： eQTL genetic diversity genetics genomics healthy baseline human diversity immune cells precision medicine single-cell RNA sequencing sub-continental diversity

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Parallel Dispatch:A New Paradigm of Electrical Power System Dispatch

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IEEE/CAA Journal of Automatica Sinica 2018年第1期5卷 311-319页

作者： Jun Jason Zhang Fei-Yue Wang Qiang Wang Dazhi Hao Xiaojing Yang David Wenzhong Gao Xiangyang Zhao Yingchen Zhang Department of Electrical and Computer Engineering Ritchie School of Engineering and Computer Science University of Denver Denver 80210 CO USA State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Science (SKL-MCCS CASIA) Beijing 100190 China Research Center of Computational Experiments and Parallel Systems The National University of Defense Technology Changsha 410073 China State Grid Tianjin Electric Power Company Tianjin 300010 China School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China National Renewable Energy Laboratory Golden CO 80401 USA IEEE

Modern power systems are evolving into sociotechnical systems with massive complexity, whose real-time operation and dispatch go beyond human capability. Thus,the need for developing and applying new intelligent power system dispatch tools are of great practical significance. In this paper, we introduce the overall business model of power system dispatch, the top level design approach of an intelligent dispatch system, and the parallel intelligent technology with its dispatch applications. We expect that a new dispatch paradigm,namely the parallel dispatch, can be established by incorporating various intelligent technologies, especially the parallel intelligent technology, to enable secure operation of complex power grids,extend system operators' capabilities, suggest optimal dispatch strategies, and to provide decision-making recommendations according to power system operational goals.

关键词： ACP knowledge automation power dispatch parallel dynamic programming parallel intelligence paralle learning situational awareness

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Cosmological baryon spread and impact on matter clustering in CAMELS

arXiv

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arXiv 2023年

作者： Gebhardt, Matthew Anglés-Alcázar, Daniel Borrow, Josh Genel, Shy Villaescusa-Navarro, Francisco Ni, Yueying Lovell, Christopher Nagai, Daisuke Davé, Romeel Marinacci, Federico Vogelsberger, Mark Hernquist, Lars Department of Physics University of Connecticut 196 Auditorium Road U-3046 StorrsCT06269-3046 United States Center for Computational Astrophysics Flatiron Institute 162 Fifth Avenue New YorkNY10010 United States Department of Physics Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA02139 United States Institute for Computational Cosmology Department of Physics Durham University South Road DurhamDH1 3LE United Kingdom Columbia Astrophysics Laboratory Columbia University 550 West 120th Street New YorkNY10027 United States Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08544 United States Harvard-Smithsonian Center for Astrophysics 60 Garden Street CambridgeMA02138 United States McWilliams Center for Cosmology Department of Physics Carnegie Mellon University PittsburghPA15213 United States Institute of Cosmology and Gravitation University of Portsmouth Burnaby Road PortsmouthPO1 3FX United Kingdom Centre for Astrophysics Research School of Physics Engineering & Computer Science University of Hertfordshire HatfieldAL10 9AB United Kingdom Department of Physics Yale University New HavenCT06520 United States Institute for Astronomy University of Edinburgh Royal Observatory EdinburghEH9 3HJ United Kingdom Department of Physics & Astronomy University of the Western Cape Cape Town7535 South Africa South African Astronomical Observatories Observatory Cape Town7925 South Africa Dipartimento di Fisica e Astronomia Augusto Righi Universit‘a di Bologna via Gobetti 93/2 Bologna40129 Italy The NSF AI Institute for Artificial Intelligence and Fundamental Interactions Massachusetts Institute of Technology CambridgeMA United States

We quantify the cosmological spread of baryons relative to their initial neighboring dark matter distribution using thousands of state-of-the-art simulations from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project. We show that dark matter particles spread relative to their initial neighboring distribution owing to chaotic gravitational dynamics on spatial scales comparable to their host dark matter halo. In contrast, gas in hydrodynamic simulations spreads much further from the initial neighboring dark matter owing to feedback from supernovae (SNe) and Active Galactic Nuclei (AGN). We show that large-scale baryon spread is very sensitive to model implementation details, with the fiducial SIMBA model spreading ∼40% of baryons >1 Mpc away compared to ∼10% for the IllustrisTNG and ASTRID models. Increasing the efficiency of AGN-driven outflows greatly increases baryon spread while increasing the strength of SNe-driven winds can decrease spreading due to non-linear coupling of stellar and AGN feedback. We compare total matter power spectra between hydrodynamic and paired N-body simulations and demonstrate that the baryonic spread metric broadly captures the global impact of feedback on matter clustering over variations of cosmological and astrophysical parameters, initial conditions, and galaxy formation models. Using symbolic regression, we find a function that reproduces the suppression of power by feedback as a function of wave number (k) and baryonic spread up to k ∼ 10 ℎ Mpc−1 while highlighting the challenge of developing models robust to variations in galaxy formation physics implementation. Copyright © 2023, The Authors. All rights reserved.

关键词： Active Galactic Nuclei

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Leveraging randomized compiling for the QITE algorithm

arXiv

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arXiv 2021年

作者： Ville, Jean-Loup Morvan, Alexis Hashim, Akel Naik, Ravi K. Lu, Marie Mitchell, Bradley Kreikebaum, John-Mark O’Brien, Kevin P. Wallman, Joel J. Hincks, Ian Emerson, Joseph Smith, Ethan Younis, Ed Iancu, Costin Santiago, David I. Siddiqi, Irfan Quantum Nanoelectronics Laboratory Dept. of Physics University of California at Berkeley BerkeleyCA94720 United States Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Inst. for Quantum Computing Dept. of Applied Mathematics University of Waterloo WaterlooONN2L 3G1 Canada Quantum Benchmark Inc. KitchenerONN2H 5G5 Canada

The success of the current generation of Noisy Intermediate-Scale Quantum (NISQ) hardware shows that quantum hardware may be able to tackle complex problems even without error correction. One outstanding issue is that of coherent errors arising from the increased complexity of these devices. These errors can accumulate through a circuit, making their impact on algorithms hard to predict and mitigate. Iterative algorithms like Quantum Imaginary Time Evolution are susceptible to these errors. This article presents the combination of both noise tailoring using Randomized Compiling and error mitigation with a purification. We also show that Cycle Benchmarking gives an estimate of the reliability of the purification. We apply this method to the Quantum Imaginary Time Evolution of a Transverse Field Ising Model and report an energy estimation and a ground state infidelity both below 1%. Our methodology is general and can be used for other algorithms and platforms. We show how combining noise tailoring and error mitigation will push forward the performance of NISQ devices. © 2021, CC BY.

关键词： Benchmarking

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