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arXiv 2020年

作者： Schmitt, Christin Baldrati, Lorenzo Sanchez-Tejerina, Luis Schreiber, Felix Ross, Andrew Filianina, Mariia Ding, Shilei Fuhrmann, Felix Ramos, Rafael Maccherozzi, Francesco Backes, Dirk Mawass, Mohamad A. Kronast, Florian Valencia, Sergio Saitoh, Eiji Finocchio, Giovanni Kläui, Mathias Institute of Physics Johannes Gutenberg-University Mainz Mainz55128 Germany Department of Mathematical and Computer Sciences Physical Sciences and Earth Sciences University of Messina Messina98166 Italy Department of Biomedical Dental Morphological and Functional Imaging Sciences University of Messina Messina98125 Italy Graduate School of Excellence Materials Science in Mainz Mainz55128 Germany State Key Laboratory for Mesoscopic Physics School of Physics Peking University Beijing100871 China WPI-Advanced Institute for Materials Research Tohoku University Sendai980-8577 Japan Diamond Light Source Chilton Didcot OxfordshireOX11 0DE United Kingdom Helmholtz-Zentrum Berlin für Materialien und Energie Albert-Einstein-Strasse 15 BerlinD-12489 Germany Institute for Materials Research Tohoku University Sendai980-8577 Japan The Institute of AI and Beyond The University of Tokyo Tokyo113-8656 Japan Center for Spintronics Research Network Tohoku University Sendai980-8577 Japan Department of Applied Physics The University of Tokyo Tokyo113-8656 Japan

Understanding the electrical manipulation of the antiferromagnetic order is a crucial aspect to enable the design of antiferromagnetic devices working at THz frequency. Focusing on collinear insulating antiferromagnetic NiO/Pt thin films as a materials platform, we identify the crystallographic orientation of the domains that can be switched by currents and quantify the Néel vector direction changes. We demonstrate electrical switching between different T-domains by current pulses, finding that the Néel vector orientation in these domains is along [±5 ±5 19], different compared to the bulk directions. The final state of the in-plane component of the Néel vector nip after switching by current pulses j along the [1 ± 1 0] directions is nip j. By comparing the observed Néel vector orientation and the strain in the thin films, assuming that this variation arises solely from magnetoelastic effects, we quantify the order of magnitude of the magnetoelastic coupling coefficient as b0 + 2b1 = 3 × 107 J/m3. This information is key for the understanding of current-induced switching in antiferromagnets and for the design and use of such devices as active elements in spintronic devices. Copyright © 2020, The Authors. All rights reserved.

关键词： Vectors

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Physical Review B 2019年第7期99卷 075430-075430页

作者： Matthew E. Trusheim Noel H. Wan Kevin C. Chen Christopher J. Ciccarino Johannes Flick Ravishankar Sundararaman Girish Malladi Eric Bersin Michael Walsh Benjamin Lienhard Hassaram Bakhru Prineha Narang Dirk Englund Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge Massachusetts 02138 USA Department of Chemistry and Chemical Biology Harvard University Cambridge Massachusetts 02138 USA Department of Materials Science and Engineering Rensselaer Polytechnic Institute Troy New York 12180 USA College of Nanoscale Science and Engineering State University of New York Polytechnic Institute 257 Fuller Road Albany New York 12203 USA

We report on quantum emission from Pb-related color centers in diamond following ion implantation and high-temperature vacuum annealing. First-principles calculations predict a negatively charged Pb-vacancy (PbV) center in a split-vacancy configuration, with a zero-phonon transition around 2.4 eV. Cryogenic photoluminescence measurements performed on emitters in nanofabricated pillars reveal several transitions, including a prominent doublet near 520 nm. The splitting of this doublet, 5.7 THz, exceeds that reported for other group-IV centers. These observations are consistent with the PbV center, which is expected to have a combination of narrow optical transitions and stable spin states, making it a promising system for quantum network nodes.

关键词： Atomic orbital Density functional theory Electronic structure of atoms & molecules Photoemission Quantum memories Quantum repeaters Single- and few-photon ionization & excitation

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The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI

arXiv

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arXiv 2024年

作者： Correia de Verdier, Maria Saluja, Rachit Gagnon, Louis LaBella, Dominic Baid, Ujjwall Tahon, Nourel Hoda Foltyn-Dumitru, Martha Zhang, Jikai Alafif, Maram Baig, Saif Chang, Ken D’Anna, Gennaro Deptula, Lisa Gupta, Diviya Haider, Muhammad Ammar Hussain, Ali Iv, Michael Kontzialis, Marinos Manning, Paul Moodi, Farzan Nunes, Teresa Simon, Aaron Sollmann, Nico Vu, David Adewole, Maruf Albrecht, Jake Anazodo, Udunna Chai, Rongrong Chung, Verena Faghani, Shahriar Farahani, Keyvan Kazerooni, Anahita Fathi Iglesias, Eugenio Kofler, Florian Li, Hongwei Linguraru, Marius George Menze, Bjoern Moawad, Ahmed W. Velichko, Yury Wiestler, Benedikt Altes, Talissa Basavasagar, Patil Bendszus, Martin Brugnara, Gianluca Cho, Jaeyoung Dhemesh, Yaseen Fields, Brandon K.K. Garrett, Filip Gass, Jaime Hadjiiski, Lubomir Hattangadi-Gluth, Jona Hess, Christopher Houk, Jessica L. Isufi, Edvin Layfield, Lester J. Mastorakos, George Mongan, John Nedelec, Pierre Nguyen, Uyen Oliva, Sebastian Pease, Matthew W. Rastogi, Aditya Sinclair, Jason Smith, Robert X. Sugrue, Leo P. Thacker, Jonathan Vidic, Igor Villanueva-Meyer, Javier White, Nathan S. Aboian, Mariam Conte, Gian Marco Dale, Anders Sabuncu, Mert R. Seibert, Tyler M. Weinberg, Brent Abayazeed, Aly Huang, Raymond Turk, Sevcan Rauschecker, Andreas M. Farid, Nikdokht Vollmuth, Philipp Nada, Ayman Bakas, Spyridon Calabrese, Evan Rudie, Jeffrey D. Department of Surgical Sciences Section of Neuroradiology Uppsala University Sweden Department of Radiology University of California San DiegoCA United States Department of Electical and Computer Engineering Cornell University Cornell Tech New YorkNY United States Department of Radiology Weill Cornell Medicine United States Department of Pathology and Laboratory Medicine Indiana Univeristy IN United States Department of Neuroradiology Heidelberg University Germany University of Laval Quebec CityQC Canada Department of Radiation Oncology Duke University DurhamNC United States Radiology Department University of Missouri ColumbiaMO United States Department of Electrical and Computer Engineering Duke University DurhamNC United States Department of Radiology Nassau Medical Center East MeadowNY United States Department of Radiology Stanford University CA United States Neuroimaging Unit ASST Ovest Milanese Legnano Milan Italy Ross University School of Medicine Bridgetown Barbados Department of Radiology University of California Los AngelesCA United States C.M.H. Lahore Medical College Institute of Dentistry Lahore Pakistan Department of Imaging Sciences University of Rochester Medical Center RochesterNY United States Department of Radiology Northwestern University ChicagoIL United States Department of Radiology VA San Diego Medical Center United States School of Medicine Iran University of Medical Sciences Tehran Iran Neuroradiology Department Hospital Garcia de Orta Unidade Local de Saúde de Almada Seixal EPE Almada Portugal Imaging Department Hospital da Luz Lisboa Lisbon Portugal Department of Radiation Oncology University of California IrvineCA United States Department of Diagnostic and Interventional Radiology University Hospital Ulm Ulm Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum Rechts der Isar Technical University of Munich Munich Germany Department of Radiology Scripps Clin

Gliomas are the most common malignant primary brain tumors in adults and one of the deadliest types of cancer. There are many challenges in treatment and monitoring due to the genetic diversity and high intrinsic heterogeneity in appearance, shape, histology, and treatment response. Treatments include surgery, radiation, and systemic therapies, with magnetic resonance imaging (MRI) playing a key role in treatment planning and post-treatment longitudinal assessment. The 2024 Brain Tumor Segmentation (BraTS) challenge on post-treatment glioma MRI will provide a community standard and benchmark for state-of-the-art automated segmentation models based on the largest expert-annotated post-treatment glioma MRI dataset. Challenge competitors will develop automated segmentation models to predict four distinct tumor sub-regions consisting of enhancing tissue (ET), surrounding non-enhancing T2/fluid-attenuated inversion recovery (FLAIR) hyperintensity (SNFH), non-enhancing tumor core (NETC), and resection cavity (RC). Models will be evaluated on separate validation and test datasets using standardized performance metrics utilized across the BraTS 2024 cluster of challenges, including lesion-wise Dice Similarity Coefficient and Hausdorff Distance. Models developed during this challenge will advance the field of automated MRI segmentation and contribute to their integration into clinical practice, ultimately enhancing patient care. © 2024, CC BY.

关键词： Magnetic resonance imaging

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A Comparative Evaluation of SteamVR Tracking and the OptiTrack System for Medical Device Tracking

A Comparative Evaluation of SteamVR Tracking and the OptiTra...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Tim Ameler Kai Blohme Lilith Brandt Raphael Brüngel Alice Hensel Lisa Huber Francis Kuper Jessica Swoboda Maren Warnecke Michaela Warzecha Daniel Heß Johannes Frömke Annette Schmitz-Stolbrink Christoph M. Friedrich Department of Computer Science University of Applied Sciences and Arts Dortmund (FH Dortmund) Dortmund Germany Institut für die Digitalisierung von Arbeits- und Lebenswelten (IDiAL) University of Applied Sciences and Arts Dortmund (FH Dortmund) Dortmund Germany Cardiac Thoracic Vascular Surgery St.-Johannes-Hospital Dortmund Dortmund Germany Pediatric Radiology Klinikum Dortmund Dortmund Germany Institute for Medical Informatics University Hospital Essen Essen Germany

Tracking of medical devices can be used in diverse situations, e.g., training as well as image guidance for surgery and surgery planning. Therefore, position and orientation of a device, for instance, an ultrasound probe, need to be identified as precisely as possible. This enables correct representation of digital 3D models in medical image processing platforms such as 3D Slicer or MevisLab. In this manuscript, a comparative evaluation of the low-cost Swept Angle Laser Tracking (SALT) system SteamVR Tracking and the multi-camera-based Opti-Track System is presented. Their potential for medical device tracking is demonstrated in the use case of ultrasound probe tracking for simulation purposes. An evaluation of tracking errors is performed using a Universal Robotics UR5 industrial robot under non-laboratory conditions, involving common issues such as reflections and occlusions. A discussion on the tracking accuracy of both systems is given. The communication of tracking data is established for 3D Slicer and MeVisLab with the use of the PLUS Toolkit via the OpenIGTLink protocol.

关键词： Ultrasonic imaging Servers Probes Three-dimensional displays Robots Solid modeling Software

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Preface of the 3rd international workshop on (Meta)Modelling for Healthcare Systems (MMHS 2018) 3

Preface of the 3rd international workshop on (Meta)Modelling...

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3rd International Workshop on (Meta)Modelling for Healthcare Systems, MMHS 2018

作者： Rutle, Adrian Lamo, Yngve MacCaull, Wendy Iovino, Ludovico Department of Computing Mathematics and Physics Western Norway University of Applied Sciences Bergen Norway Department of Mathematics Statistics and Computer Science St. Francis Xavier University Antigonish Canada Gran Sasso Science Institute V.le Francesco Crispi L’Aquila Italy

MMHS 2018 was arranged as a joint workshop with the HealthTech seminar at Western Norway University of applied sciences, Bergen, Norway, June 13-15. It was the third workshop in the MMHS workshop series focusing on modeling and metamodeling for healthcare systems. E-health is one of the fastest growing ICT markets. According to Mordor Intelligence the market share for e-health was estimated as USD 124 billion in 2016 and is projected to reach USD 244 billion by 2021, growing at a compound annual growth rate of 14.56% during the forecast period. Major software and ICT infrastructure companies are currently investing for this growth. Several modelling approaches have been successfully implemented in the healthcare domain, such as business process modelling, domain specific languages for representation of health resources (HL7 FHIR), and conceptual modelling with archetypes (OPEN EHR), However, there is still a huge potential for metamodelling and model based software engineering to tackle problems such as software interoperability, formalization and simulation of work processes and clinical guidelines, domain specific abstractions, etc. The goal of this workshop was to create a forum for healthcare system developers and researchers on the one side and modeling researchers and the modeling community on the other. © 2018 CEUR-WS. All rights reserved.

关键词： Interoperability

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Detection of Malfunctional Smart Electricity Meters Based on Deep Learning of Electricity Usage Data

arXiv

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arXiv 2019年

作者： Liu, Ming Liu, Dongpeng Sun, Guangyu Zhao, Yi Wang, Duolin Liu, Fangxing Fang, Xiang He, Qing Xu, Dong Key Laboratory for Applied Statistics of Moe and School of Mathematics and Statistics Northeast Normal University Changchun130024 China Department of Applied Mathematics School of Mathematics and Statistics Sciences Changchun University of Technology Changchun130012 China Department of Electrical Engineering and Computer Science Christopher S. Bond Life Sciences Center University of Missouri ColumbiaMO65211 United States Co. Ltd Beijing100084 China National Institute of Metrology Beijing100029 China

Detecting malfunctional smart meters based on electricity usage and targeting them for replacement can save significant resources. For this purpose, we developed a novel deep-learning method for malfunctional smart meter detection based on long short-term memory (LSTM) and a modified convolutional neural network (CNN). Our method uses LSTM to predict the reading of a master meter based on data collected from submeters. If the predicted value is significantly different from master meter reading data over a period of time, the diagnosis part will be activated, classifying every submeter to identify the malfunctional submeter based on CNN. We propose a time series-recurrence plot (TS-RP) CNN, by combining the sequential raw data of electricity and its recurrence plots in the phase space as dual input branches of CNN. By combining this time sequential (TS) raw data with the recurrence plots (RP), we found that the classification performance was much better than when using the sequential raw data only. We compared our method with several classical methods, including the elastic net and gradient boosting regression methods, which show that our method performs better. To the best of our knowledge, our TS-RP CNN is the first method to apply deep learning in malfunctional meter detection. It is also relatively unique in the way it combines sequential data and its phase-space transformation as the dual input for general sequential data classification. This method is not only useful for increasing the service life span of smart meters, preventing unnecessary replacement, but it also provides a general method for managing other instruments of sequential data. Copyright © 2019, The Authors. All rights reserved.

关键词： Smart meters

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Author Correction: Multidimensional nanoscopic chiroptics

Nature Reviews Physics

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Nature Reviews Physics 2022年 138页

作者： Yang Chen Qing Zhang Cheng-Wei Qiu Wei Du Oscar Ávalos-Ovando Alexander O. Govorov Jing Wu Qing-Hua Xu Na Liu Hiromi Okamoto Qihua Xiong Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore CAS Key Laboratory of Mechanical Behaviour and Design of Materials Department of Precision Machinery and Precision Instrumentation University of Science and Technology of China Hefei China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore Singapore Institute of Functional Nano and Soft Materials (FUNSOM) Soochow University Suzhou China Department of Physics and Astronomy Ohio University Athens OH USA Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu China Institute of Materials Research and Engineering (IMRE) Agency for Science Technology and Research (A*STAR) Singapore Singapore Department of Chemistry National University of Singapore Singapore Singapore 2nd Physics Institute University of Stuttgart Stuttgart Germany Max Planck Institute for Solid State Research Stuttgart Germany Institute for Molecular Science and the Graduate University for Advanced Studies (SOKENDAI) Okazaki Aichi Japan State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics Tsinghua University Beijing China Beijing Academy of Quantum Information Sciences (BAQIS) Beijing China

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A Bayesian estimation method for variational phase-field fracture problems

arXiv

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arXiv 2019年

作者： Khodadadian, Amirreza Noii, Nima Parvizi, Maryam Abbaszadeh, Mostafa Wick, Thomas Heitzinger, Clemens Wiedner Hauptstraße 8-10 Vienna1040 Austria Faculty of Mathematics and Computer Sciences Amirkabir University of Technology No. 424 Hafez Ave. 15914 Tehran Iran Institute of Applied Mathematics Leibniz University Hannover Welfengarten 1 Hanover30167 Germany School of Mathematical and Statistical Sciences Arizona State University TempeAZ85287 United States

In this work, we propose a parameter estimation framework for fracture propagation problems. The fracture problem is described by a phase-field method. Parameter estimation is realized with a Bayesian framework. Here, the focus is on uncertainties arising in the solid material parameters and the critical energy release rate. A reference value (obtained on a sufficiently small mesh) as the replacement of measurement will be chosen, and their posterior distribution is obtained. Due to time- and mesh dependency of the problem, the computational costs can be high. Using Bayesian inversion, we solve the problem on a relatively coarse mesh and fit the parameters. The obtained load-displacement curve that is usually the target function is matched with the reference values. Finally, our algorithmic approach is substantiated with several numerical examples. Copyright © 2019, The Authors. All rights reserved.

关键词： Bayesian networks

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Detection of the [O I] 63 µm emission line from the z = 6.04 quasar J2054-0005

arXiv

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arXiv 2024年

作者： Ishii, Nozomi Hashimoto, Takuya Ferkinhoff, Carl Rybak, Matus Inoue, Akio K. Michiyama, Tomonari Donevski, Darko Fujimoto, Seiji Salak, Dragan Kuno, Nario Matsuo, Hiroshi Mawatari, Ken Tamura, Yoichi Izumi, Takuma Nagao, Tohru Nakazato, Yurina Osone, Wataru Sugahara, Yuma Usui, Mitsutaka Wakasugi, Koki Yajima, Hidenobu Bakx, Tom J.L.C. Fudamoto, Yoshinobu Meyer, Romain A. Walter, Fabian Yoshida, Naoki Graduate School of Pure and Applied Sciences University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8571 Japan Setagaya Business Square 4-10-1 Yoga Setagaya-ku Tokyo158-0097 Japan Tomonaga Center for the History of the Universe University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8571 Japan Department of Physics Winona State University WinonaMN55987 United States Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Mekelweg 4 Delft2628 CD Netherlands Leiden Observatory Leiden University P.O. Box 9513 Leiden2300 RA Netherlands SRON - Netherlands Institute for Space Research Niels Bohrweg 4 CA Leiden2333 Netherlands Department of Physics School of Advanced Science and Engineering Faculty of Science and Engineering Waseda University 3-4-1 Okubo Shinjuku Tokyo169-8555 Japan Waseda Research Institute for Science and Engineering Faculty of Science and Engineering Waseda University 3-4-1 Okubo Shinjuku Tokyo169-8555 Japan Faculty of Information Science Shunan University 843-4-2 Gakuendai Shunanshi Yamaguchi745-8566 Japan National Center for Nuclear Research Pasteura 7 Warsaw02-093 Poland SISSA Via Bonomea 265 Trieste34136 Italy Department of Astronomy The University of Texas at Austin AustinTX78712 United States Institute for the Advancement of Higher Education Hokkaido University Kita 17 Nishi 8 Kita-ku Hokkaido Sapporo060-0817 Japan Department of Cosmosciences Graduate School of Science Hokkaido University Kita 10 Nishi 8 Kita-ku Hokkaido Sapporo060-0810 Japan National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Department of Physics Graduate School of Science Nagoya University Furo Chikusa Nagoya Aichi464-8602 Japan Research Center for S ace and Cosmic Evolution Ehime Universit 2-5 Bunk o-cho Matsu ama Ehime Ja an 790-8577 China Department of Physics The University of Tokyo 7-3-1 Hongo Bunkyo

We report the highest-redshift detection of [O I] 63 µm from a luminous quasar, J2054-0005, at z = 6.04 based on the Atacama Large Millimeter/sub-millimeter Array Band 9 observations. The [O I] 63 µm line luminosity is (4.5 ± 1.5) × 109 L☉, corresponding to the [O I] 63 µm-to-far-infrared luminosity ratio of ≈ 6.7 × 10−4, which is consistent with the value obtained in the local universe. Remarkably, [O I] 63 µm is as bright as [C II] 158 µm, resulting in the [O I]-to-[C II] line luminosity ratio of 1.3 ± 0.5. Based on a careful comparison of the luminosity ratios of [O I] 63 µm, [C II] 158 µm, and dust continuum emission to models of photo-dissociation regions, we find that J2054-0005 has a gas density log(nH/cm−3)= 3.7 ± 0.3 and an incident far-ultraviolet radiation field of log(G/G0) = 3.0 ± 0.1, showing that [O I] 63 µm serves as an important coolant of the dense and warm gas in J2054-0005. A close examination of the [O I] and [C II] line profiles suggests that the [O I] line may be partially self-absorbed, however deeper observations are needed to verify this conclusion. Regardless, the gas density and incident radiation field are in a broad agreement with the values obtained in nearby star-forming galaxies and objects with [O I] 63 µm observations at z = 1 − 3 with the Herschel Space Observatory. These results demonstrate the power of ALMA high-frequency observations targeting [O I] 63 µm to examine the properties of photo-dissociation regions in high-redshift galaxies. © 2024, CC0.

关键词： Density of gases

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ChaLearn looking at people: IsoGD and ConGD large-scale RGB-D gesture recognition

arXiv

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arXiv 2019年

作者： Wan, Jun Lin, Chi Wen, Longyin Li, Yunan Miao, Qiguang Escalera, Sergio Anbarjafari, Gholamreza Guyon, Isabelle Guo, Guodong Li, Stan Z. National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China JD Finance Mountain ViewCA United States University of Southern California Los AngelesCA90089-0911 United States School of Computer Science and Technology Xidian University & Xi'an Key Laboratory of Big Data and Intelligent Vision 2nd South Taibai Road Xi'an710071 China Universitat de Barcelona Computer Vision Center Spain iCV Lab Institute of Technology University of Tartu Estonia Faculty of Engineering Hasan Kalyoncu University Gaziantep Turkey Institute of Digital Technologies Loughborough University London United Kingdom ChaLearn United States University Paris-Saclay France institute of Deep Learning Baidu Research National Engineering Laboratory for Deep Learning Technology and Application China

The ChaLearn large-scale gesture recognition challenge has been run twice in two workshops in conjunction with the International Conference on Pattern Recognition (ICPR) 2016 and International Conference on computer vision (ICCV) 2017, attracting more than 200 teams around the world. This challenge has two tracks, focusing on isolated and continuous gesture recognition, respectively. This paper describes the creation of both benchmark datasets and analyzes the advances in large-scale gesture recognition based on these two datasets. We discuss the challenges of collecting large-scale ground-truth annotations of gesture recognition, and provide a detailed analysis of the current state-of-the-art methods for large-scale isolated and continuous gesture recognition based on RGB-D video sequences. In addition to recognition rate and mean jaccard index (MJI) as evaluation metrics used in our previous challenges, we also introduce the corrected segmentation rate (CSR) metric to evaluate the performance of temporal segmentation for continuous gesture recognition. Furthermore, we propose a bidirectional long short-term memory (Bi-LSTM) baseline method, determining the video division points based on the skeleton points extracted by convolutional pose machine (CPM). Experiments demonstrate that the proposed Bi-LSTM outperforms the state-of-the-art methods with an absolute improvement of 8.1% (from 0.8917 to 0.9639) of CSR. Copyright © 2019, The Authors. All rights reserved.

关键词： Long short-term memory

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