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arXiv 2021年

作者： Li, Qian Li, Jianxin Sheng, Jiawei Cui, Shiyao Wu, Jia Hei, Yiming Peng, Hao Guo, Shu Wang, Lihong Beheshti, Amin Yu, Philip S. The School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China The Institute of Information Engineering Chinese Academy of Sciences Beijing100083 China The School of Cyber Security University of Chinese Academy of Sciences Beijing100083 China The School of Cyber Science and Technology Beihang University Beijing100083 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China The National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100029 China The School of Computing Macquarie University Sydney Australia The Department of Computer Science University of Illinois at Chicago Chicago60607 United States

Event extraction is a crucial research task for promptly apprehending event information from massive textual data. With the rapid development of deep learning, event extraction based on deep learning technology has become a research hotspot. Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey. This paper fills the research gap by reviewing the state-of-the-art approaches, especially focusing on the general domain event extraction based on deep learning models. We introduce a new literature classification of current general domain event extraction research according to the task definition. Afterwards, we summarize the paradigm and models of event extraction approaches, and then discuss each of them in detail. As an important aspect, we summarize the benchmarks that support tests of predictions and evaluation metrics. A comprehensive comparison among different approaches is also provided in this survey. Finally, we conclude by summarizing future research directions facing the research area. Copyright © 2021, The Authors. All rights reserved.

关键词： Extraction

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DTCMS: Dynamic traffic congestion management in Social Internet of Vehicles (SIoV)

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Internet of Things (Netherlands) 2021年 16卷

作者： Roopa, M.S. Ayesha Siddiq, S. Buyya, Rajkumar Venugopal, K.R. Iyengar, S.S. Patnaik, L.M. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bengaluru 560-001 India Cloud Computing and Distributed Systems Laboratory School of Computing and Information Systems University of Melbourne Melbourne 3053 VIC Australia Bangalore University Bengaluru 560-001 India Department of Computer Science and Engineering Florida International University Miami 33199 FL United States Consciousness Studies Program National Institute of Advanced Studies Indian Institute of Science Bengaluru 560-012 India

With the augmentation of traffic exponentially, we observe that traffic congestion does not guarantee road safety or enhance the driving experience. In the recent past, Social Internet of Vehicles (SIoV), a social network paradigm permits social relationships among every vehicle in the network or with any road infrastructure to render a radically useful environment. SIoV is beneficial for the drivers, in improving road safety, avoiding mishaps, and providing a friendly-driving experience. In this paper, we propose a traffic scheduling algorithm to gain the maximum throughput for the flow of vehicles at a road intersection with the formation of social relationships among the vehicles and with the Road Side Units (RSUs). The algorithm estimates the flow rate of vehicles for lanes at the intersections exploiting the volume of traffic moving through the given road. A condition matrix is designed for the consistent movement of traffic considering different routes on the road segments. Social relationships are devised on various aspects of travel needs for a safe, agile, and better driving experience. Simulation results illustrate the efficacy of the proposed scheme with high traffic throughput, service rate and reduce the total travelling time, delay time, and average waiting time in comparison with Dynamic Throughput Maximization Framework and Adaptive Traffic Control Algorithm. © 2020 Elsevier B.V.

关键词： Dynamic traffic control Social Internet of Vehicles (SIoV) Social relationships Traffic congestion management

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Integrating Group Homophily and Individual Personality of Topics Can Better Model Network Communities

Integrating Group Homophily and Individual Personality of To...

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IEEE International Conference on Data Mining (ICDM)

作者： Yingkui Wang Di Jin Carl Yang Jianwu Dang College of Intelligence and Computing Tianjin University Tianjin China Department of Computer Science Emory University Atlanta USA School of Information Science Japan Advanced Institute of Science and Technology Nomi Japan

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

Community detection is an important research field in the understanding of networks. The definition of network communities focuses on denser intracommunity links and sparpser intercommunity links. It cannot explain the fundamental generation mechanisms of the two types of links, which is challenging to reveal. Unfortunately, none of existing works can solve this challenge which is important for accurately modeling community structures. This paper investigates a typical category of networks which possess contents on links. Based on analyses of real networks, we get an observation that nodes with distinctive personality regarding content topics are more active across communities, while nodes without it are more active inside a community, behaving in a similar way known as homophily. This observation provides clues to the generation of intracommunity and intercommunity links. Based on above observation, this paper proposes a novel generative community detection model called GHIPT (Group Homophily and Individual Personality of Topics) by integrating group homophily and individual personality of topics. Besides deriving more precise community results by accurately modeling intracommunity and intercommunity links, GHIPT is able to identify those nodes with distinctive personality who are more willing to interact with others from different communities. It further validates that they change their community memberships more frequently. GHIPT is evaluated on two real networks, i.e., Reddit and DBLP. Experimental results show that it outperforms all the state-of-the-art baselines. In addition to case studies on above two datasets, a case study on COVID-19 dataset provides new insights to support the ongoing fight against COVID-19 pandemic.

关键词： COVID-19 Social networking (online) Pandemics Conferences Data models Data mining

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Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge

arXiv

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arXiv 2025年

作者： Imran, Muhammad Krebs, Jonathan R. Sivaraman, Vishal Balaji Zhang, Teng Kumar, Amarjeet Ueland, Walker R. Fassler, Michael J. Huang, Jinlong Sun, Xiao Wang, Lisheng Shi, Pengcheng Rokuss, Maximilian Baumgartner, Michael Kirchhof, Yannick Maier-Hein, Klaus H. Isensee, Fabian Liu, Shuolin Han, Bing Nguyen, Bong Thanh Shin, Dong-Jin Ji-Woo, Park Choi, Mathew Uhm, Kwang-Hyun Ko, Sung-Jea Lee, Chanwoong Chun, Jaehee Kim, Jin Sung Zhang, Minghui Zhang, Hanxiao You, Xin Gu, Yun Pan, Zhaohong Liu, Xuan Liang, Xiaokun Tiefenthaler, Markus Almar-Munoz, Enrique Schwab, Matthias Kotyushev, Mikhail Epifanov, Rostislav Wodzinski, Marek Müller, Henning Qayyum, Abdul Mazher, Moona Niederer, Steven A. Wang, Zhiwei Yang, Kaixiang Ren, Jintao Korreman, Stine Sofia Gao, Yuchong Zeng, Hongye Zheng, Haoyu Zheng, Rui Yue, Jinghua Zhou, Fugen Liu, Bo Cosman, Alexander Liang, Muxuan Zhao, Chang Upchurch, Gilbert R. Ma, Jun Zhou, Yuyin Cooper, Michol A. Shao, Wei Department of Medicine University of Florida GainesvilleFL32611 United States Department of Surgery University of Florida GainesvilleFL32611 United States Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Department of Computer Information Science and Engineering University of Florida GainesvilleFL32611 United States Shanghai Jiao Tong University Shanghai China Electronic & Information Engineering School Harbin Institute of Technology [Shenzhen Shenzhen China Division of Medical Image Computing German Cancer Research Center [DKFZ Heidelberg Germany CO. LTD Beijing China MedAI Seoul Korea Republic of Korea University Seoul Korea Republic of Department of Radiation Oncology Yonsei Cancer Center Heavy Ion Therapy Research Institute Yonsei University College of Medicine Seoul Korea Republic of Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Medical University of Innsbruck Innsbruck Austria Novosibirsk State University Novosibirsk Russia AGH University of Krakow Krakow Poland Sierre Switzerland University of Geneva Geneva Switzerland The Sense Innovation and Research Center Sion Switzerland National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Hubei Wuhan China Aarhus University Department of Clinical Medicine Nordre Palle Juul-Jensens Blvd. 11 Aarhus8200 Denmark Shanghaitech University Pudong Shanghai China Image Processing Center Beihang University Beijing China Department of Computer Science and Engineering University of California Santa CruzCA95064 United States Department of Biostatistics University of Florida GainesvilleFL32611 United States University Health Network ON Canada Agronomy Department University of F

Multi-class segmentation of the aorta in computed tomography angiography (CTA) scans is essential for diagnosing and planning complex endovascular treatments for patients with aortic dissections. However, existing methods reduce aortic segmentation to a binary problem, limiting their ability to measure diameters across different branches and zones. Furthermore, no open-source dataset is currently available to support the development of multi-class aortic segmentation methods. To address this gap, we organized the AortaSeg24 MICCAI Challenge, introducing the first dataset of 100 CTA volumes annotated for 23 clinically relevant aortic branches and zones. This dataset was designed to facilitate both model development and validation. The challenge attracted 121 teams worldwide, with participants leveraging state-of-the-art frameworks such as nnU-Net and exploring novel techniques, including cascaded models, data augmentation strategies, and custom loss functions. We evaluated the submitted algorithms using the Dice Similarity Coefficient (DSC) and Normalized Surface Distance (NSD), highlighting the approaches adopted by the top five performing teams. This paper presents the challenge design, dataset details, evaluation metrics, and an in-depth analysis of the top-performing algorithms. The annotated dataset, evaluation code, and implementations of the leading methods are publicly available to support further research. All resources can be accessed at https://***. © 2025, CC BY-NC-SA.

关键词： computerized tomography

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SDVADC: Secure Deduplication and Virtual Auditing of Data in Cloud

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Procedia computer science 2020年 171卷 2225-2234页

作者： Geeta C M Shreyas Raju R G Raghavendra S Rajkumar Buyya Venugopal K R S S Iyengar L M Patnaik Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bengaluru Cloud Computing and Distributed Systems (CLOUDS) Lab The University of Melbourne Australia Vice Chancellor Bangalore University Bengaluru Department of Computer Science and Engineering Florida International University USA INSA National Institute of Advanced Studies Indian Institute of Science Campus Bangalore India

Over the last few years, deploying data to cloud service for repository is an appealing passion that avoids efforts on significant information sustenance and administration. In distributed repository utilities, deduplication technique is often exploited to minimize the capacity and bandwidth necesseties of amenities by erasing repetitive data and caching only a solitary duplicate of them. Proof-of-Ownership mechanisms authorize any possessor of the identical information to approve to the distributed repository server that he possess the information in a dynamic way. In repository utilities with enormous information, the repository servers may intend to minimize the capacity of cached information, and the customers may want to examine the integrity of their information with a reasonable cost. We propose Secure Deduplication and Virtual Auditing of Data in Cloud (SDVADC) mechanism that realizes integrity auditing and deduplication of information in cloud. The mechanism supports secure deduplication of information and effective virtual auditing of the documents during the download process. In addition, the proposed mechanism lowers the burden of dataowner to audit documents by himself and there is no need to delegate auditing to the Third Party Auditor (TPA). Experimental results demonstrate that the virtual auditing has low auditing time cost relative to the existing public auditing schemes.

关键词： Cloud computing Deduplication Virtual Auditing Public Auditing Proof of Retrievability Proof of Ownership

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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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Reentrant quantum anomalous Hall effect in molecular beam epitaxy-grown MnBi2Te4 thin films

arXiv

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arXiv 2024年

作者： Li, Yuanzhao Bai, Yunhe Feng, Yang Luan, Jianli Gao, Zongwei Chen, Yang Tong, Yitian Liu, Ruixuan Chong, Su Kong Wang, Kang L. Zhou, Xiaodong Shen, Jian Zhang, Jinsong Wang, Yayu Chen, Chui-Zhen Xie, XinCheng Feng, Xiao He, Ke Xue, Qi-Kun Beijing Academy of Quantum Information Sciences Beijing100193 China State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA90095 United States Department of Physics and Astronomy University of California Los Angeles Los AngelesCA90095 United States State Key Laboratory of Surface Physics Department of Physics Fudan University Shanghai200438 China Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai200438 China Zhangjiang Fudan International Innovation Center Fudan University Shanghai201210 China Shanghai Research Center for Quantum Sciences Shanghai201315 China Collaborative Innovation Center of Advanced Microstructures Nanjing210093 China School of Physical Science and Technology Soochow University Suzhou215006 China Institute for Advanced Study Soochow University Suzhou215006 China International Center for Quantum Materials School of Physics Peking University Beijing100871 China Southern University of Science and Technology Shenzhen518055 China Frontier Science Center for Quantum Information Beijing100084 China Hefei National Laboratory Hefei230088 China

In the realm of topological quantum physics, it is widely accepted that a global bandgap is necessary to achieve quantized Hall conductance, irrespective of its origin-magnetic field, exchange coupling or other mechanisms. A material exhibiting the quantum anomalous Hall (QAH) effect had been considered equivalent to a Chern insulator, a band insulator with non-zero Chern number. However, a Chern Anderson insulator (CAI), which arises in a conducting state with exchange-induced Berry curvature splitting and disorder-induced Anderson localization, can in principle exhibit the QAH effect. In this study, we investigate intrinsic magnetic topological insulator MnBi2Te4 thin films grown by molecular beam epitaxy (MBE). We observe a reentrant QAH effect when the Fermi energy enters the valance band and magnetic field equals zero, indicating the emergence of the CAI state. The discovery opens a new avenue for realizing the QAH effect and underscores the fundamental role of both Berry curvature and Anderson localization. © 2024, CC BY-NC-ND.

关键词： Molecular beam epitaxy

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Efficiently Computable Bounds for Magic State Distillation

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Physical Review Letters 2020年第9期124卷 090505-090505页

作者： Xin Wang Mark M. Wilde Yuan Su Institute for Quantum Computing Baidu Research Beijing 100193 China Joint Center for Quantum Information and Computer Science University of Maryland College Park Maryland 20742 USA Hearne Institute for Theoretical Physics Department of Physics and Astronomy and Center for Computation and Technology Louisiana State University Baton Rouge Louisiana 70803 USA Department of Computer Science and Institute for Advanced Computer Studies University of Maryland College Park Maryland 20742 USA

Magic-state distillation (or nonstabilizer state manipulation) is a crucial component in the leading approaches to realizing scalable, fault-tolerant, and universal quantum computation. Related to nonstabilizer state manipulation is the resource theory of nonstabilizer states, for which one of the goals is to characterize and quantify nonstabilizerness of a quantum state. In this Letter, we introduce the family of thauma measures to quantify the amount of nonstabilizerness in a quantum state, and we exploit this family of measures to address several open questions in the resource theory of nonstabilizer states. As a first application, we establish the hypothesis testing thauma as an efficiently computable benchmark for the one-shot distillable nonstabilizerness, which in turn leads to a variety of bounds on the rate at which nonstabilizerness can be distilled, as well as on the overhead of magic-state distillation. We then prove that the max-thauma can be used as an efficiently computable tool in benchmarking the efficiency of magic-state distillation, and that it can outperform previous approaches based on mana. Finally, we use the min-thauma to bound a quantity known in the literature as the “regularized relative entropy of magic.” As a consequence of this bound, we find that two classes of states with maximal mana, a previously established nonstabilizerness measure, cannot be interconverted in the asymptotic regime at a rate equal to one. This result resolves a basic question in the resource theory of nonstabilizer states and reveals a difference between the resource theory of nonstabilizer states and other resource theories such as entanglement and coherence.

关键词： Quantum benchmarking Quantum computation Quantum gates Quantum protocols Resource theories Quarks

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A Data-Centric Approach to Detecting and Mitigating Demographic Bias in Pediatric Mental Health Text: A Case Study in Anxiety Detection

arXiv

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arXiv 2024年

作者： Ive, Julia Bondaronek, Paulina Yadav, Vishal Santel, Daniel Glauser, Tracy Cheng, Tina Strawn, Jeffrey R. Agasthya, Greeshma Tschida, Jordan Choo, Sanghyun Chandrashekar, Mayanka Kapadia, Anuj J. Pestian, John University College London Institute of Health Informatics United Kingdom Queen Mary University of London School of Electronic Engineering and Computer Science London United Kingdom Division of Biomedical Informatics Cincinnati Children's Hospital Medical Center University of Cincinnati CincinnatiOH United States Advanced Computing in Health Sciences Computational Sciences in Engineering Division Oak Ridge National Laboratory United States Division of Neurology Cincinnati Children's Hospital Medical Center University of Cincinnati CincinnatiOH United States Department of Pediatrics Cincinnati Children's Hospital Medical Center University of Cincinnati CincinnatiOH United States Department of Psychiatry College of Medicine University of Cincinnati CincinnatiOH United States

Introduction Healthcare analytics and Artificial Intelligence (AI) hold transformative potential, yet AI models often inherit biases from their training data, which can exacerbate healthcare disparities, particularly among minority groups. While efforts have primarily targeted bias in structured data, mental health heavily depends on unstructured data like clinical notes, where bias and data sparsity introduce unique challenges. This study aims to detect and mitigate linguistic differences related to non-biological differences in the training data of AI models designed to assist in pediatric mental health screening. Our objectives are: (1) to assess the presence of bias by evaluating outcome parity across sex subgroups, (2) to identify bias sources through textual distribution analysis, and (3) to develop and evaluate a de-biasing method for mental health text data. Methods We examined classification parity across demographic groups, identifying biases through analysis of linguistic patterns in clinical notes. Using interpretability techniques, we assessed how gendered language influences model predictions. We then applied a data-centric de-biasing method focused on neutralizing biased terms and retaining only the salient clinical information. This methodology was tested on a model for automatic anxiety detection in pediatric patients-a crucial application given the rise in youth anxiety post-COVID-19. Results Our findings show a systematic under-diagnosis of female adolescent patients, with a 4% lower accuracy and a 9% higher False Negative Rate (FNR) compared to male patients, likely due to disparities in information density and linguistic differences in patient notes. Notes for male patients were on average 500 words longer, and linguistic similarity metrics indicated distinct word distributions between genders. Implementing our de-biasing approach reduced this diagnostic bias by up to 27%, demonstrating the approach's effectiveness in enhancing equity across dem

关键词： Pediatrics

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Hard jet substructure in a multistage approach

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Physical Review C 2024年第4期110卷 044907页

作者： Y. Tachibana A. Kumar A. Majumder A. Angerami R. Arora S. A. Bass S. Cao Y. Chen T. Dai L. Du R. Ehlers H. Elfner W. Fan R. J. Fries C. Gale Y. He M. Heffernan U. Heinz B. V. Jacak P. M. Jacobs S. Jeon Y. Ji K. Kauder L. Kasper W. Ke M. Kelsey M. Kordell, II J. Latessa Y.-J. Lee D. Liyanage A. Lopez M. Luzum S. Mak A. Mankolli C. Martin H. Mehryar T. Mengel J. Mulligan C. Nattrass D. Oliinychenko J.-F. Paquet J. H. Putschke G. Roland B. Schenke L. Schwiebert A. Sengupta C. Shen A. Silva C. Sirimanna D. Soeder R. A. Soltz I. Soudi J. Staudenmaier M. Strickland J. Velkovska G. Vujanovic X.-N. Wang R. L. Wolpert W. Zhao Yuwa Akita-city 010-1292 Japan Department of Physics McGill University Montréal Québec H3A 2T8 Canada Department of Physics and Astronomy Wayne State University Detroit Michigan 48201 USA Department of Physics University of Regina Regina Saskatchewan S4S 0A2 Canada Lawrence Livermore National Laboratory Livermore California 94550 USA Research Computing Group University Technology Solutions The University of Texas at San Antonio San Antonio Texas 78249 USA Department of Physics Duke University Durham North Carolina 27708 USA Institute of Frontier and Interdisciplinary Science Shandong University Qingdao Shandong 266237 China Laboratory for Nuclear Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Physics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Physics and Astronomy Vanderbilt University Nashville Tennessee 37235 USA Department of Physics and Astronomy University of Tennessee Knoxville Tennessee 37996 USA Physics Division Oak Ridge National Laboratory Oak Ridge Tennessee 37830 USA Department of Physics University of California Berkeley California 94270 USA Nuclear Science Division Lawrence Berkeley National Laboratory Berkeley California 94270 USA GSI Helmholtzzentrum für Schwerionenforschung 64291 Darmstadt Germany Institute for Theoretical Physics Goethe University 60438 Frankfurt am Main Germany Frankfurt Institute for Advanced Studies 60438 Frankfurt am Main Germany Cyclotron Institute Texas A&M University College Station Texas 77843 USA Department of Physics and Astronomy Texas A&M University College Station Texas 77843 USA Guangdong Provincial Key Laboratory of Nuclear Science Institute of Quantum Matter South China Normal University Guangzhou 510006 China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Southern Nuclear Science Computing Center South China Normal University Guangzhou 510006 China Department of Physics The Ohio Stat

We present predictions and postdictions for a wide variety of hard jet-substructure observables using a multistage model within the jetscape framework. The details of the multistage model and the various parameter choices are described in [Phys. Rev. C 107, 034911 (2023)]. A novel feature of this model is the presence of two stages of jet modification: a high-virtuality phase [modeled using the modular all twist transverse-scattering elastic-drag and radiation model (matter)], where modified coherence effects diminish medium-induced radiation, and a lower virtuality phase [modeled using the linear Boltzmann transport model (lbt)], where parton splits are fully resolved by the medium as they endure multiple scattering induced energy loss. Energy-loss calculations are carried out on event-by-event viscous fluid dynamic backgrounds constrained by experimental data. The uniform and consistent descriptions of multiple experimental observables demonstrate the essential role of modified coherence effects and the multistage modeling of jet evolution. Using the best choice of parameters from [Phys. Rev. C 107, 034911] (2023)], and with no further tuning, we present calculations for the medium modified jet fragmentation function, the groomed jet momentum fraction zg and angular separation rg distributions, as well as the nuclear modification factor of groomed jets. These calculations provide accurate descriptions of published data from experiments at the Large Hadron Collider. Furthermore, we provide predictions from the multistage model for future measurements at the BNL Relativistic Heavy Ion Collider.

关键词： Hard scattering Jet quenching Jets & heavy flavor physics Quark & gluon jets Quark-gluon plasma Relativistic heavy-ion collisions

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