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arXiv 2023年

作者： LaBella, Dominic Adewole, Maruf Alonso-Basanta, Michelle Altes, Talissa Anwar, Syed Muhammad Baid, Ujjwal Bergquist, Timothy Bhalerao, Radhika Chen, Sully Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Godfrey, Devon Hilal, Fathi Familiar, Ariana Farahani, Keyvan Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Kazerooni, Anahita Fathi Kent, Collin Kirkpatrick, John Koer, Florian Van Leemput, Koen Li, Hongwei Bran Liu, Xinyang Mahtabfar, Aria McBurney-Lin, Shan McLean, Ryan Meier, Zeke Moawad, Ahmed W. Mongan, John Nedelec, Pierre Pajot, Maxence Piraud, Marie Rashid, Arif Reitman, Zachary Shinohara, Russell Takeshi Velichko, Yury Wang, Chunhao Warman, Pranav Wiggins, Walter Aboian, Mariam Albrecht, Jake Anazodo, Udunna Bakas, Spyridon Flanders, Adam Janas, Anastasia Khanna, Goldey Linguraru, Marius George Menze, Bjoern Nada, Ayman Rauschecker, Andreas M. Rudie, Jeff Tahon, Nourel Hoda Villanueva-Meyer, Javier Wiestler, Benedikt Calabrese, Evan Department of Radiation Oncology Duke University Medical Center DurhamNC United States Lab Crestview Radiology Lagos Nigeria Department of Radiation Oncology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Missouri University ColumbiaMO United States Children's National Hospital WashingtonDC United States George Washington University WashingtonDC United States University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Sage Bionetworks United States University of California San Francisco CA United States Duke University Medical Center School of Medicine United States Mayo Clinic MN United States Center for Global Health Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University Munich Germany TranslaTUM Central Institute for Translational Cancer Research Technical University of Munich Germany Children's Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States PrecisionFDA U.S. Food and Drug Administration Silver SpringMD United States Helmholtz Ai Helmholtz Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum Rechts der Isar Technical University of Munich Germany Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Department of Neuroradiology Technical University of Munich Munich Germany University of Zurich Switzerland Department of Neurosurgery Thomas Jefferson Univer

Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on multiparametric MRI (mpMRI) for diagnosis, treatment planning, and longitudinal treatment monitoring;yet automated, objective, and quantitative tools for non-invasive assessment of meningiomas on mpMRI are lacking. The BraTS meningioma 2023 challenge will provide a community standard and benchmark for state-of-the-art automated intracranial meningioma segmentation models based on the largest expert annotated multilabel meningioma mpMRI dataset to date. Challenge competitors will develop automated segmentation models to predict three distinct meningioma sub-regions on MRI including enhancing tumor, non-enhancing tumor core, and surrounding nonenhancing T2/FLAIR hyperintensity. Models will be evaluated on separate validation and held-out test datasets using standardized metrics utilized across the BraTS 2023 series of challenges including the Dice similarity coefficient and Hausdorff distance. The models developed during the course of this challenge will aid in incorporation of automated meningioma MRI segmentation into clinical practice, which will ultimately improve care of patients with meningioma. Copyright © 2023, The Authors. All rights reserved.

关键词： Tumors

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Generation of terahertz transients from Co2Fe0.4Mn0.6Si-Heusler-alloy/normal-metal nanobilayers excited by femtosecond optical pulses

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Physical Review Research 2021年第4期3卷 043025-043025页

作者： Sarah Heidtfeld Roman Adam Takahide Kubota Koki Takanashi Derang Cao Carolin Schmitz-Antoniak Daniel E. Bürgler Fangzhou Wang Christian Greb Genyu Chen Ivan Komissarov Hilde Hardtdegen Martin Mikulics Roman Sobolewski Shigemasa Suga Claus M. Schneider Research Centre Jülich Peter Grünberg Institute 52425 Jülich Germany Faculty of Physics University of Duisburg-Essen 47048 Duisburg Germany Institute for Materials Research Tohoku University Sendai 980-8577 Japan Center for Spintronics Research Network Tohoku University Sendai 980-8577 Japan Center for Science and Innovation in Spintronics (Core Research Cluster) Tohoku University Sendai 980-8577 Japan College of Physics National Demonstration Center for Experimental Applied Physics Education Qingdao University Qingdao 266071 China Materials Science Graduate Program University of Rochester Rochester New York 14627 USA Laboratory for Laser Energetics University of Rochester Rochester New York 14623 USA Department of Electrical and Computer Engineering University of Rochester Rochester New York 14627 USA Belarusian State University of Informatics and Radioelectronics Minsk 220013 Belarus Research Centre Jülich Ernst Ruska Centre for Microscopy and Spectroscopy with Electrons 52425 Jülich Germany Department of Physics and Astronomy University of Rochester Rochester New York 14627 USA The Institute of Scientific and Industrial Research Osaka University Ibaraki Osaka 567-0047 Japan Department of Physics University of California Davis Davis California 95616 USA

We generated pulses of electromagnetic radiation in the terahertz (THz) frequency range by optical excitation of Co2Fe0.4Mn0.6Si (CFMS)/normal-metal (NM) bilayer structures. The CFMS is a Heusler alloy showing a band gap in one spin channel and is therefore a half metal. We compared the THz emission efficiency in a systematic manner for four different CFMS/NM bilayers, where NM was either Pt, Ta, Cr, or Al. Our measurements show that the THz intensity is highest for a Pt capping. We also demonstrate the tunability of the THz amplitude by varying the magnetic field for all four bilayers. We attribute the THz generation to the inverse spin Hall effect. In order to investigate the role of the interface in THz generation, we measured the spin mixing conductance for each CFMS/NM bilayer using a ferromagnetic resonance method. We found that the spin-orbit coupling cannot completely describe the THz generation in the bilayers and that the spin transmission efficiency of the CFMS/NM interface and the spin diffusion length, as well as the oxidation of the NM layer, play crucial roles in the THz emission process.

关键词： Inverse spin Hall effect Magnetism Spin injection Spin pumping Spintronics Ferromagnetic resonance Terahertz time-domain spectroscopy

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Artificial Intelligence, Bioinformatics, and Pathology

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Advances in Molecular Pathology 2022年第1期5卷 e1-e24页

作者： Joshua Levy Yunrui Lu Marietta Montivero Ojas Ramwala Jason McFadden Carly Miles Adam Gilbert Diamond Ramya Reddy Ram Reddy Taylor Hudson Zarif Azher Akash Pamal Sameer Gabbita Tess Cronin Abdol Aziz Ould Ismail Tarushii Goel Sanjay Jacob Anish Suvarna Sumanth Ratna Jason Zavras Louis Vaickus Department of Pathology and Laboratory Medicine Emerging Diagnostic and Investigative Technologies Dartmouth Health Lebanon NH USA Department of Dermatology Dartmouth Health Lebanon NH USA Department of Epidemiology Dartmouth College Geisel School of Medicine Hanover NH USA Program in Quantitative Biomedical Sciences Dartmouth College Geisel School of Medicine Hanover NH USA Dartmouth College Geisel School of Medicine Hanover NH USA Department of Biomedical Informatics and Medical Education University of Washington Seattle WA USA Undiagnosed Diseases Program National Human Genome Research Institute Bethesda MD USA Department of Computer Science University of Michigan Ann Arbor MI USA Hanover High School Hanover NH USA Thomas Jefferson High School for Science and Technology Alexandria VA USA Department of Computer Science University of Virginia Charlottesville VA USA University of New Hampshire Durham NH USA White River Junction VA Medical Center White River Junction VT USA Massachusetts Institute of Technology Cambridge MA USA Case Western Reserve University Cleveland OH USA Yale University New Haven CT USA Broad Institute of MIT and Harvard Cambridge MA USA

The advent of complex molecular assays and anatomic imaging platforms has enabled unprecedented characterization of disease pathogenesis. However, challenges remain in formulating diagnostic and prognostic tests that can effectively navigate and synthesize the massive treasure troves of information now available in modern pathology laboratories. Artificial intelligence (AI), and, in particular, machine learning, are pattern mining algorithms that can efficiently uncover biomarkers and risk factors personalized to the individual. These technologies will also have the benefit of providing efficiency gains, resolving ambiguity, and reducing the unnecessary resource expenditures in the Pathology setting. Adoption of AI technologies in these settings can be improved by engaging pathologists on how AI can be meaningfully used in their practice and through dissemination and communication of these core concepts and their potential benefits. In this work, we summarize emerging application areas for AI in the fields of Anatomic and Molecular Pathology.

关键词： Artificial intelligence technologies Artificial neural networks Machine learning algorithms Virtual staining Spatial omics Bioinformatics Image analysis

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Improving Students Skills to Solve Elementary Equations in K-12 programs Using an Intelligent Tutoring System

Improving Students Skills to Solve Elementary Equations in K...

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Frontiers in Education (FIE) Conference

作者： Arcanjo Miguel Mota Lopes José Francisco de Magalhães Netto Ricardo A. L. de Souza Andreza Bastos Mourão Thais Oliveira Almeida Dhanielly P. R. de Lima Post-Graduate Program in Informatics (PPGI) Institute of Computing (IComp) Federal University of Amazonas (UFAM) Manaus AM Brazil Institute of Exact Sciences (ICE) Federal University of Amazonas (UFAM) Manaus AM Brazil University of State of Amazonas (UEA) Post-Graduate Program in Informatics (PPGI) Institute of Computing (IComp) Federal University of Amazonas (UFAM) Manaus AM Brazil Department of Science Computer Federal University of Roraima Boa Vista RR Brazil

ISBN: (数字)9781728117461

ISBN: (纸本)9781728117478

This full paper of research to practice category describes an experiment using an Intelligent Tutoring System to support teaching and learning that evolve solutions of first-degree equations. The difficulties encountered in solving algebra problems can make students consider math and related science a difficult task. We propose a Tutor to promote the learning of this class of problems. In this article, we describe the functioning and architecture of the tutor composed by the Interface, Student Model, Tutorial Strategies and Domain Model. Also, a Virtual Assistant was included to motivate students in the learning process. The tutoring strategies that comprise the tutor are projected from models found in the literature. To approximate the interactions of the tutor with the actions of the teachers, Lev Vygotsky's Concept of Zone Proximal Development was used. The methodology used in this project was the case study applied in a real-life context, obtaining a combination of quantitative and qualitative evidence about technical and pedagogical usability that show the degree of satisfaction of the students regarding the proposed approach.

关键词： Education Mathematical model informatics Abstract algebra Cognition

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OPTIMADE, an API for exchanging materials data

arXiv

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arXiv 2021年

作者： Andersen, Casper W. Armiento, Rickard Blokhin, Evgeny Conduit, Gareth J. Dwaraknath, Shyam Evans, Matthew L. Fekete, Ádám Gopakumar, Abhijith Gražulis, Saulius Merkys, Andrius Mohamed, Fawzi Oses, Corey Pizzi, Giovanni Rignanese, Gian-Marco Scheidgen, Markus Talirz, Leopold Toher, Cormac Winston, Donald Aversa, Rossella Choudhary, Kamal Colinet, Pauline Curtarolo, Stefano Di Stefano, Davide Draxl, Claudia Er, Suleyman Esters, Marco Fornari, Marco Giantomassi, Matteo Govoni, Marco Hautier, Geoffroy Hegde, Vinay Horton, Matthew K. Huck, Patrick Huhs, Georg Hummelshøj, Jens Kariryaa, Ankit Kozinsky, Boris Kumbhar, Snehal Liu, Mohan Marzari, Nicola Morris, Andrew J. Mostofi, Arash A. Persson, Kristin A. Petretto, Guido Purcell, Thomas Ricci, Francesco Rose, Frisco Scheffler, Matthias Speckhard, Daniel Uhrin, Martin Vaitkus, Antanas Villars, Pierre Waroquiers, David Wolverton, Chris Wu, Michael Yang, Xiaoyu École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Materials Design and Informatics Unit Department of Physics Chemistry and Biology Linköping University Sweden Tilde Materials Informatics Straßmannstraße 25 Berlin10249 Germany Materials Platform for Data Science Sepapaja 6 Tallinn15551 Estonia Theory of Condensed Matter Group Cavendish Laboratory 19 J.J. Thomson Avenue CambridgeCB3 0HE United Kingdom Lawrence Berkeley Labs BerkeleyCA United States Chemin des Étoiles 8 Louvain-la-Neuve1348 Belgium Department of Physics King's College London London United Kingdom Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Institute of Biotechnology Life Science Center Vilnius University Saulétekio av. 7 VilniusLT-10257 Lithuania Institute of Computer Science Faculty of Mathematics and Informatics Vilnius University Naugarduko g. 24 VilniusLT-03225 Lithuania Fritz-Haber-Institut der Max-Planck-Gesellschaft Faradayweg 4-6 Berlin14195 Germany Center for Autonomous Materials Design Duke University DurhamNC27708 United States Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Humboldt-Universität zu Berlin Institut für Physik and IRIS Adlershof Berlin12489 Germany École Polytechnique Fédérale de Lausanne Sion1951 Switzerland Trieste34136 Italy Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany Materials Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Ansys 300 Rustat House 62 Clifton Rd CambridgeCB1 7EG United Kingdom De Zaale 20 Eindhoven5612 AJ Netherlands Department of Physics and Science of Advanced Materials Program Central Michigan University Mount PleasantMI48859 United States Argonne National Laboratory LemontIL60439 United States Thayer School of Engineering Dartmouth College HanoverNH03755 United States Los AltosCA940

The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification. © 2021, CC BY.

关键词： Specifications

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Democratising Knowledge Representation with BioCypher

arXiv

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arXiv 2022年

作者： Lobentanzer, Sebastian Aloy, Patrick Baumbach, Jan Bohar, Balazs Charoentong, Pornpimol Danhauser, Katharina Doğan, Tunca Dreo, Johann Dunham, Ian Fernandez-Torras, Adrià Gyori, Benjamin M. Hartung, Michael Hoyt, Charles Tapley Klein, Christoph Korcsmaros, Tamas Maier, Andreas Mann, Matthias Ochoa, David Pareja-Lorente, Elena Popp, Ferdinand Preusse, Martin Probul, Niklas Schwikowski, Benno Sen, Bünyamin Strauss, Maximilian T. Turei, Denes Ulusoy, Erva Heidrun Wodke, Judith Andrea Saez-Rodriguez, Julio Heidelberg University Faculty of Medicine Heidelberg University Hospital Institute for Computational Biomedicine Bioquant Heidelberg Germany The Barcelona Institute of Science and Technology Catalonia Barcelona Spain Catalonia Barcelona Spain Institute for Computational Systems Biology University of Hamburg Germany Earlham Institute Norwich United Kingdom Biological Research Centre Szeged Hungary Heidelberg University Im Neuenheimer Feld 267 Heidelberg69120 Germany Im Neuenheimer Feld 460 Heidelberg69120 Germany Department of Pediatrics Dr. von Hauner Children’s Hospital University Hospital LMU Munich Germany Biological Data Science Lab Department of Computer Engineering Hacettepe University Ankara Turkey Department of Bioinformatics Graduate School of Health Sciences Hacettepe University Ankara Turkey Computational Systems Biomedicine Lab Department of Computational Biology Institut Pasteur Université Paris Cité Paris France Bioinformatics and Biostatistics Hub Institut Pasteur Université Paris Cité Paris France Wellcome Genome Campus Cambridgeshire HinxtonCB10 1SD United Kingdom Open Targets Wellcome Genome Campus Cambridgeshire HinxtonCB10 1SD United Kingdom Laboratory of Systems Pharmacology Harvard Medical School Boston United States Imperial College London London United Kingdom Quadram Institute Bioscience Norwich United Kingdom Proteomics Program Novo Nordisk Foundation Centre for Protein Research University of Copenhagen Copenhagen Denmark Department of Proteomics and Signal Transduction Max Planck Institute of Biochemistry Martinsried Germany Im Neuenheimer Feld 460 Heidelberg69120 Germany Neuherberg Germany Medical Informatics Laboratory University Medicine Greifswald Germany

Standardising the representation of biomedical knowledge among all researchers is an insurmountable task, hindering the effectiveness of many computational methods. To facilitate harmonisation and interoperability despite this fundamental challenge, we propose to standardise the framework of knowledge graph creation instead. We implement this standardisation in BioCypher, a FAIR (findable, accessible, interoperable, reusable) framework to transparently build biomedical knowledge graphs while preserving provenances of the source data. Mapping the knowledge onto biomedical ontologies helps to balance the needs for harmonisation, human and machine readability, and ease of use and accessibility to nonspecialist researchers. We demonstrate the usefulness of this framework on a variety of use cases, from maintenance of task-specific knowledge stores, to interoperability between biomedical domains, to on-demand building of task-specific knowledge graphs for federated learning. BioCypher (https://***) frees up valuable developer time;we encourage further development and usage by the community. © 2022, CC BY-NC-ND.

关键词： Ontology

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A Neuro-Inspired Approach to Solve a Simultaneous Location and Mapping Task Using Shared Information in Multiple Robots Systems 15

A Neuro-Inspired Approach to Solve a Simultaneous Location a...

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15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018

作者： Menezes, Matheus Chaves De Freitas, Edison Pignaton Cheng, Sen De Oliveira, Alexandre Cesar Muniz De Almeida Ribeiro, Paulo Rogerio Graduate Program on Computer Science Federal University of Maranhão Sao Luis Brazil Computational Neuroscience Ruhr University Bochum Institute for Neural Computation Bochum Germany Department of Informatics Federal University of Maranhão Sao Luis Maranhao Brazil Department of Computer Engineering Federal University of Maranhão Sao Luis Maranhao Brazil

ISBN: (纸本)9781538695821

This paper presents a neuro-inspired mapping approach that uses partial information shared by multiple robots to reduce the time to create a map of an entire environment. Robots using a neurobiologically inspired algorithm, namely RatSLAM, map an environment sharing video information among themselves. RatSLAM, which is based on the navigation system present in the hippocampus of rodents' brain, has been widely used on simultaneous localization and mapping (SLAM) problem. This proposal has been able to generate suitable maps mainly when there is redundant information, e.g. a scene is seen more than once, since this fact activates local view cells that inject activity inside the pose cells via an excitatory link. The work here reported extends this approach by merging partial information acquired by multiple robots. The results from the performed experiments show that the final map built by two robots with shared information is similar to one built by two robots performing the same mapping task individually, i.e. without sharing information. However, the time spent to generate the whole map with the proposed shared approach was smaller than the one without the shared information. Thus, the current approach allows creating a complete map of an environment within a reduced time using multiple robots. © 2018 IEEE.

关键词： Mapping

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Federated Learning Enables Big Data for Rare Cancer Boundary Detection

arXiv

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arXiv 2022年

作者： Pati, Sarthak Baid, Ujjwal Edwards, Brandon Sheller, Micah Wang, Shih-Han Reina, G. Anthony Foley, Patrick Gruzdev, Alexey Karkada, Deepthi Davatzikos, Christos Sako, Chiharu Ghodasara, Satyam Bilello, Michel Mohan, Suyash Vollmuth, Philipp Brugnara, Gianluca Preetha, Chandrakanth J. Sahm, Felix Maier-Hein, Klaus Zenk, Maximilian Bendszus, Martin Wick, Wolfgang Calabrese, Evan Rudie, Jeffrey Villanueva-Meyer, Javier Cha, Soonmee Ingalhalikar, Madhura Jadhav, Manali Pandey, Umang Saini, Jitender Garrett, John Larson, Matthew Jeraj, Robert Currie, Stuart Frood, Russell Fatania, Kavi Huang, Raymond Y. Chang, Ken Balana, Carmen Capellades, Jaume Puig, Josep Trenkler, Johannes Pichler, Josef Necker, Georg Haunschmidt, Andreas Meckel, Stephan Shukla, Gaurav Liem, Spencer Alexander, Gregory S. Lombardo, Joseph Palmer, Joshua D. Flanders, Adam E. Dicker, Adam P. Sair, Haris I. Jones, Craig K. Venkataraman, Archana Jiang, Meirui So, Tiffany Y. Chen, Cheng Heng, Pheng Ann Dou, Qi Kozubek, Michal Lux, Filip Michálek, Jan Matula, Petr Keřkovský, Miloš Kopřivová, Tereza Dostál, Marek Vybíhal, Václav Vogelbaum, Michael A. Mitchell, J. Ross Farinhas, Joaquim Maldjian, Joseph A. Yogananda, Chandan Ganesh Bangalore Pinho, Marco C. Reddy, Divya Holcomb, James Wagner, Benjamin C. Ellingson, Benjamin M. Cloughesy, Timothy F. Raymond, Catalina Oughourlian, Talia Hagiwara, Akifumi Wang, Chencai To, Minh-Son Bhardwaj, Sargam Chong, Chee Agzarian, Marc Falcão, Alexandre Xavier Martins, Samuel B. Teixeira, Bernardo C.A. Sprenger, Flávia Menotti, David Lucio, Diego R. LaMontagne, Pamela Marcus, Daniel Wiestler, Benedikt Kofler, Florian Ezhov, Ivan Metz, Marie Jain, Rajan Lee, Matthew Lui, Yvonne W. McKinley, Richard Slotboom, Johannes Radojewski, Piotr Meier, Raphael Wiest, Roland Derrick, Murcia Fu, Eric Haas, Rourke Thompson, John Ormond, David Ryan Badve, Chaitra Sloan, Andrew E. Vadmal, Vachan Waite, Kristin Colen, Rivka R. Pei, Linmin Ak, Murat Srinivasan, Ashok Bapuraj, J. Rajiv Rao, Arvind Wang, Nicholas Yoshiaki, Ota Moritani, Tos University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University of Munich Bavaria Munich Germany Intel Corporation Santa ClaraCA United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Heidelberg Germany Department of Neuropathology Heidelberg University Hospital Heidelberg Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany Neurology Clinic Heidelberg University Hospital Heidelberg Germany Department of Radiology & Biomedical Imaging University of California San Francisco San FranciscoCA United States Symbiosis Center for Medical Image Analysis Symbiosis International University Maharashtra Pune India Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Karnataka Bangalore India Department of Radiology School of Medicine and Public Health University of Wisconsin MadisonWI United States Department of Medical Physics School of Medicine and Public Health University of Wisconsin MadisonWI United States Department of Neuroradiology Clinical Neuroscience Center University Hospital Zurich University of Zurich Zurich Switzerland Leeds Teaching Hospitals Trust Department of Radiology Leeds United Kingdom Department of Radiology Brigham and Women's Hospital Harvard Medical School BostonMA United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Catalan Institute of Oncology Badalona Spain Consorci MAR Parc de Salut de Barcelona Catalonia Spain Josep Trueta University Hospital Girona Spain

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25,256 MRI scans from 6,314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing. © 2022, CC BY.

关键词： Tumors

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Rainfall forecasting using PSPline and rice production with ocean-atmosphere interaction 2

Rainfall forecasting using PSPline and rice production with ...

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2nd International Conference on Eco Engineering Development 2018, ICEED 2018

作者： Caraka, Rezzy Eko Supatmanto, Budi Darmawan Tahmid, Muhammad Soebagyo, Joko Mauludin, M. Ali Iskandar, Akbar Pardamean, Bens Bina Nusantara University Jakarta11480 Indonesia Indonesia Indonesia Agency for Meteorology Climatology and Geophysics Indonesia College of Technology Wastukancana Purwakarta Indonesia Laboratory of Sociology and Extension Padjadjaran University Indonesia Informatics Engineering STMIK AKBA Makassar Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

The role of climate can be affected by plants. The weather can accelerate and multiply the existence of various plant pests and diseases, accelerate the growth and development of grass among plants, and encourage the emergence of infection and significant damage to plants. The elements of climate that affect the growth of plants are one of them is rainfall. In this paper, we performed the simulation using the non-parametric penalized spline (PSPLINE) method and studied the effect on rice production in Lampung. It can be concluded that the increasing fluctuation, frequency, and intensity of climate anomalies in the last decade caused by the ENSO phenomenon have an impact on changes in distribution patterns, intensity, and period of the wet season so that the start of the rainy season and the dry season becomes too late. As a result, there is a seasonal shift from normal average conditions that can ultimately have severe implications for food crops. In a nutshell penalized spline gives high accuracy with R 2 = 96.227% and MAPE = 1.62%. © 2018 Institute of Physics Publishing. All rights reserved.

关键词： Rain

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Hybrid cryptosystem multi-power RSA with N=PmQ and VMPC

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IOP Conference Series: Materials science and Engineering 2020年第1期725卷

作者： U Erdiansyah MKM Nasution Sawaluddin Master Program (S2) of Informatics Engineering Faculty of Computer Science and Information Technology University of Sumatera Utara Medan Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences University of Sumatera Utara Medan Indonesia

In digital world, data and information is very valuable and much targeted by unauthorized parties. One way to secure messages is cryptography. Cryptography itself is a branch of science in concealing messages. In further development a hybrid cryptosystem was formed which combines symmetrical and asymmetric cryptography, which in this paper uses VMPC and Multi-Power.. The RSA Multi-Power algorithm is an asymmetric algorithm that is excellent at message encryption but requires a long process time because it uses very complicated calculations. While VMPC has advantages in the process with fast time and relatively small size of ciphertext. Here we have analyzed the performance of various variants of RSA that have been hybridized with VMPC and given the results.

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