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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu d. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin landman, Bennett A. litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne l. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： computer vision

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Noise Effect and Stability of deep-UV Gas-Filled Fiber lasers Pumped with Ultrafast Mid-IR Pulses

Noise Effect and Stability of Deep-UV Gas-Filled Fiber Laser...

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Conference on lasers and Electro-Optics (ClEO)

作者： Abubakar I. Adamu Md. Selim Habib Callum R. Smith J. Enrique Antonio lopez Peter Uhd Jepsen Rodrigo Amezcua-Correa Ole Bang Christos Markos DTU Fotonik Technical University of Denmark Kgs. Lyngby Denmark University of Central Florida Orlando FL USA CREOL The College of Optics and Photonics University of Central Florida Orlando USA NKT Photonics Birker⊘d Denmark NORBLIS IVS Virum Denmark Department of Electrical and Computer Engineering Florida Polytechnic University USA

Noise and spectral stability of dispersive waves generated in an argon-filled hollow-core fiber pumped with 2.4 μm ultrafast pulses is presented. long-term stability and pulse-to-pulse relative intensity noise of the... 详细信息

ISBN: (数字)9781943580767

ISBN: (纸本)9781728144184

关键词： laser excitation laser stability Optical fiber dispersion laser noise Pump lasers Numerical stability

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Shear and compressibility characteristics of leachate based MSW using geotextile reinforced stone columns

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ce/papers 2018年第2-3期2卷 773-778页

作者： Shah, Manish Jose, Jerome Gandhi, Abhay Prasad, Parmeshwar L.D. College of Engineering Ahmedabad India Head & L.D. College of Engineering Ahmedabad India

This study is an attempt to enhance local municipal solid waste (MSW) of Ahmedabad city to be first used as engineering fill material based on its shear characteristics and to understand its compressibility behaviour and pore-leachate interaction infusion of geotextile reinforced stone columns is carried out through model test set-up. Focusing on the effect of leachate on compressibility and shear strength parameters, box shear test and consolidation test are performed on the MSW with addition of different concentrations of leachate at 25%, 50%, and 75% of OMC. Further to remediate compressibility issues due to post leachate-MSW interaction, stone columns will be infused. load - settlement characteristics of surface submerged MSW by infusing stone column with l/d ratio 3.75 and 2.5 and position of lateral geotextile circular disc at a distance of l/2, l/3 and l/4 respectively from the top is obtained and experimental results are compared with theoretical approach. Overall results strongly recommended to use Pirana MSW as engineered fill material. © Ernst & Sohn Verlag für Architektur und technische Wissenschaften GmbH & Co. KG, Berlin.

关键词： Compressibility

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Roadmap on data-Centric Materials Science

arXiv

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arXiv 2024年

作者： Bauer, Stefan Benner, Peter Bereau, Tristan Blum, Volker Boley, Mario Carbogno, Christian Catlow, C.A. Richard dehm, Gerhard Eibl, Sebastian Ernstorfer, Ralph Fekete, Ádám Foppa, lucas Fratzl, Peter Freysoldt, Christoph Gault, Baptiste Ghiringhelli, luca M. Giri, Sajal K. Gladyshev, Anton Goyal, Pawan Hattrick-Simpers, Jason Kabalan, lara Karpov, Petr Khorrami, Mohammad S. Koch, Christoph Kokott, Sebastian Kosch, Thomas Kowalec, Igor Kremer, Kurt leitherer, Andreas li, Yue liebscher, Christian H. logsdail, Andrew J. lu, Zhongwei luong, Felix Marek, Andreas Merz, Florian Mianroodi, Jaber R. Neugebauer, Jörg Pei, Zongrui Purcell, Thomas A.R. Raabe, dierk Rampp, Markus Rossi, Mariana Rost, Jan-Michael Saal, James Saalmann, Ulf Sasidhar, Kasturi Narasimha Saxena, Alaukik Sbailò, luigi Scheidgen, Markus Schloz, Marcel Schmidt, daniel F. Teshuva, Simon Trunschke, Annette Wei, Ye Weikum, Gerhard Xian, R. Patrick Yao, Yi Yin, Junqi Zhao, Meng Scheffler, Matthias School of Computation Information and Technology Technical University of Munich & Helmholtz AI Munich Germany Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany Institute for Theoretical Physics Heidelberg University Heidelberg Germany Thomas Lord Department of Mechanical Engineering and Materials Science Duke University DurhamNC United States Monash University Department of Data Science and AI Melbourne Australia The NOMAD Laboratory The FHI of the Max-Planck-Gesellschaft IRIS-Adlershof of the Humboldt-Universität zu Berlin Berlin Germany School of Chemistry Cardiff University Cardiff United Kingdom Max-Planck-Institut für Eisenforschung GmbH Düsseldorf Germany Max Planck Computing and Data Facility Garching Germany Institute for Optics and Atomic Physics Technical University of Berlin Berlin Germany Physics Department and IRIS-Adlershof Humboldt Universität zu Berlin Berlin Germany Max Planck Institute of Colloids and Interfaces Potsdam Germany Department of Materials Imperial College London United Kingdom Department of Materials Science and Engineering Friedrich-Alexander Universität Erlangen-Nürnberg Germany Department of Chemistry Northwestern University Illinois United States Humboldt-Universität zu Berlin Department of Physics Berlin Germany Department of Materials Science and Engineering University of Toronto Toronto Canada STFC Hartree Centre Daresbury Laboratory Daresbury Warrington United Kingdom Molecular Simulations from First Principles e.V. Berlin Germany Humboldt-Universität zu Berlin Department of Computer Science Berlin Germany Max Planck Institute for Polymer Research Mainz Germany ICFO-Institut de Ciencies Fotoniques The Barcelona Institute of Science and Technology Castelldefels Barcelona Spain Lenovo HPC Innovation Center Stuttgart Germany New York University New York United States University of Arizona Biochemistry Department Arizona United States Max Planck Institute for the Structure an

Science is and always has been based on data, but the terms ‘data-centric’ and the ‘4th paradigm’ of materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of Artificial Intelligence (AI) and its subset Machine learning (Ml), has become pivotal in addressing all these challenges. This Roadmap on data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research. © 2024, CC BY.

关键词： data Analytics

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Enhanced valley zeeman splitting in fe-doped monolayer mos2

arXiv

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arXiv 2020年

作者： li, Qi Zhao, Xiaoxu deng, longjiang Shi, Zhongtai liu, Sheng Wei, Qilin Zhang, linbo Cheng, Yingchun Zhang, li lu, Haipeng Gao, Weibo Huang, Wei Qiu, Cheng-Wei Xiang, Gang Pennycook, Stephen John Xiong, Qihua loh, Kian Ping Peng, Bo National Engineering Research Center of Electromagnetic Radiation Control Materials School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Department of Materials Science and Engineering National University of Singapore 9 Engineering Drive 1 117575 Singapore Department of Chemistry Centre for Advanced 2D Materials Graphene Research Centre National University of Singapore 117549 Singapore Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 637371 Singapore Key Laboratory of Flexible Electronics Institute of Advanced Materials Nanjing Tech University Nanjing211816 China College of Physical Science and Technology Sichuan University Chengdu610064 China Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore

The "Zeeman effect" offers unique opportunities for magnetic manipulation of the spin degree of freedom (dOF). Recently, valley Zeeman splitting, referring to the lifting of valley degeneracy, has been demonstrated in two-dimensional transition metal dichalcogenides (TMds) at liquid helium temperature. However, to realize the practical applications of valley pseudospins, the valley dOF must be controllable by a magnetic field at room temperature, which remains a significant challenge. Magnetic doping in TMds can enhance the Zeeman splitting, however, to achieve this experimentally is not easy. Here, we report unambiguous magnetic manipulation of valley Zeeman splitting at 300 K (geff = -6.4) and 10 K (geff = -11) in a CVd-grown Fe-doped MoS2 monolayer;the effective landé geff factor can be tuned to -20.7 by increasing the Fe dopant concentration, which represents an approximately fivefold enhancement as compared to undoped MoS2. Our measurements and calculations reveal that the enhanced splitting and geff factors are due to the Heisenberg exchange interaction of the localized magnetic moments (Fe 3d electrons) with MoS2 through the d-orbital hybridization. Copyright © 2020, The Authors. All rights reserved.

关键词： landforms

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deep learning Hamiltonians from disordered Image data in Quantum Materials

arXiv

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arXiv 2022年

作者： Basak, S. Banguero, M. Alzate Burzawa, l. Simmons, F. Salev, P. Aigouy, l. Qazilbash, M.M. Schuller, I.K. Basov, d.N. Zimmers, A. Carlson, E.W. Department of Physics and Astronomy Purdue University West LafayetteIN47907 United States Purdue Quantum Science and Engineering Institute West LafayetteIN47907 United States Laboratoire de Physique et d'Étude des Matériaux Espci Paris Psl Université Cnrs Sorbonne Université Paris75005 France Department of Computer Science Purdue University West LafayetteIN47907 United States Department of Physics and Astronomy University of Denver DenverCO80208 United States Department of Physics Center for Advanced Nanoscience University of California-San Diego La Jolla CA92093 United States Department of Physics College of William and Mary WilliamsburgVA23187 United States Department of Physics Columbia University New YorkNY10027 United States

The capabilities of image probe experiments are rapidly expanding, providing new information about quantum materials on unprecedented length and time scales. Many such materials feature inhomogeneous electronic properties with intricate pattern formation on the observable surface. This rich spatial structure contains information about interactions, dimensionality, and disorder - a spatial encoding of the Hamiltonian driving the pattern formation. Image recognition techniques from machine learning are an excellent tool for interpreting information encoded in the spatial relationships in such images. Here, we develop a deep learning framework for using the rich information available in these spatial correlations in order to discover the underlying Hamiltonian driving the patterns. We first vet the method on a known case, scanning near-field optical microscopy on a thin film of VO2. We then apply our trained convolutional neural network architecture to new optical microscope images of a different VO2film as it goes through the metal-insulator transition. We find that a two-dimensional Hamiltonian with both interactions and random field disorder is required to explain the intricate, fractal intertwining of metal and insulator domains during the transition. This detailed knowledge about the underlying Hamiltonian paves the way to using the model to control the pattern formation via, e.g., tailored hysteresis protocols. We also introduce a distribution-based confidence measure on the results of a multi-label classifier, which does not rely on adversarial training. In addition, we propose a new machine learning based criterion for diagnosing a physical system's proximity to criticality. Copyright © 2022, The Authors. All rights reserved.

关键词： Image recognition

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2B3C: 2 Box 3 Crop of Facial Image for Gender Classification with Convolutional Networks

arXiv

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arXiv 2018年

作者： Gajjar, Vandit Department of Electronics and Communication Engineering and Computer Vision Group L. D. College of Engineering Ahmedabad India

In this paper, we tackle the classification of gender in facial images with deep learning. Our convolutional neural networks (CNN) use the VGG-16 architecture [1] and are pretrained on ImageNet for image classification. Our proposed method (2B3C) first detects the face in the facial image, increases the margin of a detected face by 50%, cropping the face with two boxes three crop schemes (left, Middle, and Right crop) and extracts the CNN predictions on the cropped schemes. The CNN of our method is fine-tuned on the Adience and lFW with gender annotations. We show the effectiveness of our method by achieving 90.8% classification on Adience and achieving competitive 95.3% classification accuracy on lFW dataset. In addition, to check the true ability of our method, our gender classification system has a frame rate of 7-10 fps (frames per seconds) on a GPU considering real-time scenarios. Copyright © 2018, The Authors. All rights reserved.

关键词： Crops

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Annular dielectric resonator-based antenna for multi-frequency GNSS applications: details on the engineered surface insert

Annular dielectric resonator-based antenna for multi-frequen...

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European Conference on Antennas and Propagation

作者： Slobodan Jovi? Michel Clénet Yahia M. M. Antar Defence R&D Canada Ottawa Research Centre Ottawa Canada Electrical and Computer Engineering Dept. Royal Military College of Canada Kingston Canada

This paper focuses on an engineered surface used in a novel compact antenna concept for multi-frequency Global Navigation Satellite Systems (GNSS) application and possibly l-band satellite communications. The antenna covers frequencies from 1.15 to 1.62 GHz and can be used as either a dual-band or wideband antenna. It is based on an annular ring dielectric resonator (dR) with a conformal cylindrical engineered surface inserted against the dR inner diameter. The antenna is excited using 4 ports fed in quadrature to maximize the circular polarization bandwidth and beamwidth. The engineered surface contributes to the antenna gain and impedance bandwidth enhancements and affects the undesired coupling between the feeding ports. It has been separately investigated to better understand its impact on the overall antenna performance. Some results of that study are provided in this paper.

关键词： broadband antenna dielectric resonator antenna GNSS antenna l-band compact antenna circular polarization engineered surface periodic structures EBG

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A survey on morphological assessment of knee articular cartilage from MR images

A survey on morphological assessment of knee articular carti...

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2018 International Conference on Smart Systems and Inventive Technology, ICSSIT 2018

作者： Mahendrakar, Pavan Biradar, Shankremma Naik, Sakku Arjunwad, Shruti Khatavi, Rajashree Department of Computer Science and Engineering B.L.D.E.A's V. P. Dr. P G. Halakatti College of Engineeringand Technology Vijayapur Karnataka586103 India

ISBN: (纸本)9781538658734

Nowadays, articular disorders and musculoskeletal diseases are one of the major health issues and attack usually the older generations. The human knee joint is commonly infected by osteoarthritis (OA) disease. OA can affect the joints of the spine, fingers, thumbs, hips, knees, and toes, but it is most prevalent in the knee and hip joints. Here we are concentrating only on Knee joints. Knee Osteoarthritis is a serious, excruciating & potentially life-threatening joint disease, may lead to permanent disability. It can be detected by measuring changes in knee internal tissues such as cartilage, meniscus and Subchondral Cartilage. For detecting knee osteoarthritis, Magnetic Resonance Imaging is performed to obtain a detailed picture of knee joint and change are measured from MR images. MRI is a test that uses powerful magnets, radio waves, and a make to comprehensive pictures inside our *** main objective is to develop a fully automated non-contrast MRI application for detection and Segmentation of knee bones. The proposed approach is based on Convolution Neural Network to achieve a robust and perfect segmentation of even highly pathological knee structures. © 2018 IEEE.

关键词： Cartilage

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An Interaction and Merger in a Massive Multiple System Create a Magnetic Field in a Massive Star

An Interaction and Merger in a Massive Multiple System Creat...

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作者： Frost, Abigail J. Sana, Hugues Mahy, laurent Wade, Gregg Barron, James le Bouquin, Jean-Baptiste Mérand, Antoine Schneider, Fabian R.N. Shenar, Tomer Barbá, Rodolfo H. Bowman, dominic M. Fabry, Matthias Farhang, Amin Marchant, Pablo Morrell, Nidia I. Smoker, Jonathan V. European Southern Observatory Alonso de Cordova 3107 Vitacura Santiago Chile Institute of Astronomy KU Leuven Leuven3001 Belgium Royal Observatory of Belgium BrusselsB-1180 Belgium Department of Physics & Space Science Royal Military College of Canada KingstonONK7K 0C6 Canada Department of Physics Engineering & Astronomy Queen's University KingstonONK7L 3N6 Canada Université Grenoble Alpes Centre National de la Recherche Scientifique Institut de Planétologie et d'Astrophysique de Grenoble GrenobleF-38000 France European Southern Observatory Headquarters Garching bei München85748 Germany Heidelberger Institut für Theoretische Studien Heidelberg69118 Germany Astronomisches Rechen-Institut Zentrum für Astronomie der Universität Heidelberg Heidelberg69120 Germany The School of Physics and Astronomy Tel Aviv University Tel Aviv69978 Israel Departamento de Física y Astronomía Universidad de la Serena La Serena Chile School of Mathematics Statistics and Physics Newcastle University Newcastle upon TyneNE1 7RU United Kingdom Villanova University Dept. of Astrophysics and Planetary Science 800 E Lancaster Ave VillanovaPA19085 United States School of Astronomy Institute for Research in Fundamental Sciences Tehran19395-5531 Iran Las Campanas Observatory Carnegie Observatories La Serena Chile UK Astronomy Technology Centre Royal Observatory EdinburghEH9 3HJ United Kingdom

When stars are gravitationally bound in a binary or higher order multiple system there is a chance they can interact, enabling mass and momentum transfer. Such interactions can be life-changing events for the stars involved as they can change their internal mixing, final mass and rotational speeds. In these proceedings, we describe our recent work which provides evidence that an interaction in a previous triple system caused a merger, the product of which is a magnetic star. This created the system as it is seen today - a massive binary system surrounded by an enriched ejecta nebula where only one of the massive stars is magnetic. © 2024 Societe Royale des Sciences de liege. All rights reserved.

关键词： Momentum transfer

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