检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Reinke, Annika Tizabi, Minu D. Sudre, Carole H. Eisenmann, Matthias Rädsch, Tim Baumgartner, Michael Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Bankhead, Peter Benis, Arriel Blaschko, Matthew Buettner, Florian Cardoso, M. Jorge Chen, Jianxu Cheplygina, Veronika Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Engelhardt, Sandy Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hamprecht, Fred Hashimoto, Daniel A. Heckmann-Nötzel, Doreen Hirsch, Peter Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kavur, A. Emre Kenngott, Hannes Kleesiek, Jens Kleppe, Andreas Koehler, Sven Kofler, Florian Kopp-Schneider, Annette Kooi, Thijs 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 Moher, David Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Noyan, M. Alican Petersen, Jens Polat, Gorkem Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Rieke, Nicola Riegler, Michael A. Rivaz, Hassan Saez-Rodriguez, Julio Sánchez, Clara I. Schroeter, Julien Saha, Anindo Selver, M. Alper Sharan, Lalith Shetty, Shravya Smeden, Maarten V.A.N. Stieltjes, Bram Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben V.A.N. Varoquaux, Gaël Wiesenfarth, Manuel Yaniv, Ziv R. Jäger, Paul Maier-Hein, Lena Division of Intelligent Medical Systems and HI Helmholtz Imaging Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Intelligent Medical Systems Heidelberg Germany NCT Heidelberg DKFZ University Medical Center Heidelberg 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 Division of Medical Image Computing Heidelberg Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal 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 Institute of Genetics and Cancer University of Edinburgh Edinburgh United Kingdom 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 Kasteelpark Arenberg 10 - box 2441 Leuven3001 Belgium Frankfurt/Mainz DKFZ UCT Frankfurt-Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany Frankfurt Cancer Insititute Germany Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for St

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide. © 2021, CC BY.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

Sign language recognition using the fusion of image and hand landmarks through multi-headed convolutional neural network

引用

Scientific reports 2023年第1期13卷 16975页

作者： Refat Khan Pathan Munmun Biswas Suraiya Yasmin Mayeen Uddin Khandaker Mohammad Salman Ahmed A F Youssef Department of Computing and Information Systems School of Engineering and Technology Sunway University 47500 Bandar Sunway Selangor Malaysia. Department of Computer Science and Engineering BGC Trust University Bangladesh Chittagong 4381 Bangladesh. Department of Computer and Information Science Graduate School of Engineering Tokyo University of Agriculture and Technology Koganei Tokyo 184-0012 Japan. Centre for Applied Physics and Radiation Technologies School of Engineering and Technology Sunway University 47500 Bandar Sunway Selangor Malaysia. mayeenk@sunway.edu.my. Faculty of Graduate Studies Daffodil International University Daffodil Smart City Birulia Savar Dhaka 1216 Bangladesh. mayeenk@sunway.edu.my. College of Engineering and Technology American University of the Middle East Egaila Kuwait.

Sign Language Recognition is a breakthrough for communication among deaf-mute society and has been a critical research topic for years. Although some of the previous studies have successfully recognized sign language, it requires many costly instruments including sensors, devices, and high-end processing power. However, such drawbacks can be easily overcome by employing artificial intelligence-based techniques. Since, in this modern era of advanced mobile technology, using a camera to take video or images is much easier, this study demonstrates a cost-effective technique to detect American Sign Language (ASL) using an image dataset. Here, "Finger Spelling, A" dataset has been used, with 24 letters (except j and z as they contain motion). The main reason for using this dataset is that these images have a complex background with different environments and scene colors. Two layers of image processing have been used: in the first layer, images are processed as a whole for training, and in the second layer, the hand landmarks are extracted. A multi-headed convolutional neural network (CNN) model has been proposed and tested with 30% of the dataset to train these two layers. To avoid the overfitting problem, data augmentation and dynamic learning rate reduction have been used. With the proposed model, 98.981% test accuracy has been achieved. It is expected that this study may help to develop an efficient human-machine communication system for a deaf-mute society.

关键词：

来源：评论

学校读者我要写书评

暂无评论

The ultra-high-energy event KM3-230213A within the global neutrino landscape

arXiv

引用

arXiv 2025年

作者： Adriani, O. Aiello, S. Albert, A. Alhebsi, A.R. Alshamsi, M. Garre, S. Alves Ambrosone, A. Ameli, F. Andre, M. Aphecetche, L. Ardid, M. Ardid, S. Argüelles, C. Aublin, J. Badaracco, F. Bailly-Salins, L. Bardačová, Z. Baret, B. Bariego-Quintana, A. Becherini, Y. Bendahman, M. Gualandi, F. Benfenati Benhassi, M. Bennani, M. Benoit, D.M. Berbee, E. Berti, E. Bertin, V. Betti, P. Biagi, S. Boettcher, M. Bonanno, D. Bottai, S. Bouasla, A.B. Boumaaza, J. Bouta, M. Bouwhuis, M. Bozza, C. Bozza, R.M. Brânzaş, H. Bretaudeau, F. Breuhaus, M. Bruijn, R. Brunner, J. Bruno, R. Buis, E. Buompane, R. Busto, J. Caiffi, B. Calvo, D. Capone, A. Carenini, F. Carretero, V. Cartraud, T. Castaldi, P. Cecchini, V. Celli, S. Cerisy, L. Chabab, M. Chen, A. Cherubini, S. Chiarusi, T. Circella, M. Clark, R. Cocimano, R. Coelho, J.A.B. Coleiro, A. Condorelli, A. Coniglione, R. Coyle, P. Creusot, A. Cuttone, G. Dallier, R. De Benedittis, A. De Wasseige, G. Decoene, V. Deguire, P. Del Rosso, I. Di Mauro, L.S. Di Palma, I. Díaz, A.F. Diego-Tortosa, D. Distefano, C. Domi, A. Donzaud, C. Dornic, D. Drakopoulou, E. Drouhin, D. Ducoin, J.-G. Duverne, P. Dvornický, R. Eberl, T. Eckerová, E. Eddymaoui, A. van Eeden, T. Eff, M. van Eijk, D. El Bojaddaini, I. El Hedri, S. El Mentawi, S. Ellajosyula, V. Enzenhöfer, A. Ferrara, G. Filipović, M.D. Filippini, F. Franciotti, D. Fusco, L.A. Gal, T. Méndez, J. García Soto, A. Garcia Oliver, C. Gatius Geißelbrecht, N. Genton, E. Ghaddari, H. Gialanella, L. Gibson, B.K. Giorgio, E. Goos, I. Goswami, P. Gozzini, S.R. Gracia, R. Guidi, C. Guillon, B. Gutiérrez, M. Haack, C. van Haren, H. Heijboer, A. Hennig, L. Hernández-Rey, J.J. Idrissi, A. Ibnsalih, W. Idrissi Illuminati, G. Janik, O. Joly, D. de Jong, M. de Jong, P. Jung, B.J. Kalaczyński, P. Kamp, N. Keegans, J. Kikvadze, V. Kistauri, G. Kopper, C. Kouchner, A. Kovalev, Y.Y. Krupa, L. Kueviakoe, V. Kulikovskiy, V. Kvatadze, R. Labalme, M. Lahmann, R. INFN Sezione di Firenze via Sansone 1 Sesto Fiorentino50019 Italy Università di Firenze Dipartimento di Fisica e Astronomia via Sansone 1 Sesto Fiorentino50019 Italy Via Santa Sofia 64 Catania95123 Italy Université de Strasbourg CNRS IPHC UMR 7178 StrasbourgF-67000 France Université de Haute Alsace rue des Frères Lumière Mulhouse68093 Cedex France Khalifa University of Science and Technology Department of Physics PO Box 127788 Abu Dhabi United Arab Emirates Aix Marseille Univ CNRS IN2P3 CPPM Marseille France c/Catedrático José Beltrán 2 Valencia Paterna46980 Spain Università di Napoli "Federico II" Dip. Scienze Fisiche "E. Pancini" Complesso Universitario di Monte S. Angelo Via Cintia ed. G Napoli80126 Italy INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo Via Cintia ed. G Napoli80126 Italy INFN Sezione di Roma Piazzale Aldo Moro 2 Roma00185 Italy Universitat Politècnica de Catalunya Laboratori d’Aplicacions Bioacústiques Centre Tecnològic de Vilanova i la Geltrú Avda. Rambla Exposició s/n Vilanova i la Geltrú08800 Spain Subatech IMT Atlantique IN2P3-CNRS Nantes Université 4 rue Alfred Kastler - La Chantrerie BP Nantes20722 44307 France Universitat Politècnica de València Instituto de Investigación para la Gestión Integrada de las Zonas Costeras C/ Paranimf 1 Gandia46730 Spain Harvard University Department of Physics Laboratory for Particle Physics and Cosmology Lyman Laboratory 17 Oxford St. CambridgeMA02138 United States Université Paris Cité CNRS Astroparticule et Cosmologie ParisF-75013 France INFN Sezione di Genova Via Dodecaneso 33 Genova16146 Italy Università di Genova Via Dodecaneso 33 Genova16146 Italy LPC CAEN Normandie Univ ENSICAEN UNICAEN CNRS IN2P3 6 boulevard Maréchal Juin Caen14050 France Comenius University in Bratislava Department of Nuclear Physics and Biophysics Mlynska dolina F1 Bratislava842 48 Slovakia Czech Technical University in Prague Institute of Experimen

On February 13th, 2023, the KM3NeT/ARCA telescope detected a neutrino candidate with an estimated energy in the hundreds of PeVs. In this article, the observation of this ultra-high-energy neutrino is discussed in light of null observations above tens of PeV from the IceCube and Pierre Auger observatories. Performing a joint fit of all experiments under the assumption of an isotropic E−2 flux, the best-fit single-flavour flux normalisation is E2Φ1fν+ν¯ = 7.5 × 10−10 GeVcm−2s−1sr−1 in the 90% energy range of the KM3NeT event. Furthermore, the ultra-high-energy data are then fit together with the IceCube measurements at lower energies, either with a single power law or with a broken power law, allowing for the presence of a new component in the spectrum. The joint fit including non-observations by other experiments in the ultra-high-energy region shows a slight preference for a break in the PeV regime if the "High-Energy Starting Events" sample is included, and no such preference for the other two IceCube samples investigated. A stronger preference for a break appears if only the KM3NeT data is considered in the ultra-high-energy region, though the flux resulting from such a fit would be inconsistent with null observations from IceCube and Pierre Auger. In all cases, the observed tension between KM3NeT and other datasets is of the order of 2.5σ − 3σ, and increased statistics are required to resolve this apparent tension and better characterise the neutrino landscape at ultra-high energies. Copyright © 2025, The Authors. All rights reserved.

关键词： Neutrons

来源：评论

学校读者我要写书评

暂无评论

Risk-based decision making: estimands for sequential prediction under interventions

arXiv

引用

arXiv 2023年

作者： Luijken, Kim Morzywolek, Pawel van Amsterdam, Wouter Cinà, Giovanni Hoogland, Jeroen Keogh, Ruth Krijthe, Jesse Magliacane, Sara van Ommen, Thijs Peek, Niels Putter, Hein van Smeden, Maarten Sperrin, Matthew Wang, Junfeng Weir, Daniala Didelez, Vanessa van Geloven, Nan Department of Epidemiology Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht Netherlands Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Department of Statistics University of Washington Seattle United States Department of Medical Informatics Amsterdam University Medical Centers Amsterdam Netherlands Institute for Logic Language and Computation University of Amsterdam Amsterdam Netherlands Pacmed Amsterdam Netherlands Department of Epidemiology and Data Science Amsterdam University Medical Centers Amsterdam Netherlands Department of Medical Statistics London School of Hygiene & Tropical Medicine Keppel Street London United Kingdom Pattern Recognition and Bio-Informatics Group EEMCS Delft University of Technology Delft Netherlands Amsterdam Machine Learning Lab University of Amsterdam Amsterdam Netherlands Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Division of Informatics Imaging and Data Science Faculty of Biology Medicine and Health University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom Department of Biomedical Data Sciences Leiden University Medical Center Leiden Netherlands Division of Pharmacoepidemiology and Clinical Pharmacology Department of Pharmaceutical Sciences Utrecht University Utrecht Netherlands Department of Biometry and Data Management Leibniz Institute for Prevention Research Epidemiology - BIPS Bremen Germany Faculty of Mathematics/Computer Science University of Bremen Bremen Germany

Prediction models are used amongst others to inform medical decisions on interventions. Typically, individuals with high risks of adverse outcomes are advised to undergo an intervention while those at low risk are advised to refrain from it. Standard prediction models do not always provide risks that are relevant to inform such decisions: e.g., an individual may be estimated to be at low risk because similar individuals in the past received an intervention which lowered their risk. Therefore, prediction models supporting decisions should target risks belonging to defined intervention strategies. Previous works on prediction under interventions assumed that the prediction model was used only at one time point to make an intervention decision. In clinical practice, intervention decisions are rarely made only once: they might be repeated, deferred and re-evaluated. This requires estimated risks under interventions that can be reconsidered at several potential decision moments. In the current work, we highlight key considerations for formulating estimands in sequential prediction under interventions that can inform such intervention decisions. We illustrate these considerations by giving examples of estimands for a case study about choosing between vaginal delivery and cesarean section for women giving birth. Our formalization of prediction tasks in a sequential, causal, and estimand context provides guidance for future studies to ensure that the right question is answered and appropriate causal estimation approaches are chosen to develop sequential prediction models that can inform intervention decisions. © 2023, CC BY.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

The Influence of Waste Tire Powder on the Properties of Waste Tire Powder/Polypropylene Plastic Composite

引用

IOP Conference Series: Materials Science and Engineering 2021年第1期1092卷

作者： Huei Ruey Ong Wan Mohd Eqhwan Iskandar Emyra Ezzaty Masiren Md. Maksudur Rahman Khan Ridzuan Ramli Rohaya Mohamed Halim Muhammad Khairul Anuar Mohamed Faculty of Engineering & Technology DRB-HICOM University of Automotive Malaysia Peramu Jaya Industrial Area 26607 Pekan Pahang MALAYSIA Department of Chemical Engineering College of Engineering Universiti Malaysia Pahang Lebuhraya Tun Razak 26300 Kuantan Pahang MALAYSIA Malaysian Palm Oil Board (MPOB) No. 6 Persiaran Institusi Bandar Baru Bangi 43000 Kajang Selangor MALAYSIA Centre for Mathematical Science College of Computing & Applied Science Universiti Malaysia Pahang Lebuhraya Tun Razak 26300 Kuantan Pahang MALAYSIA

The use of waste tire powder (WTP) as a polypropylene (PP) property enhancer in plastic composite has been prepared. Scanning electron microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR) and mechanical testing have been used to measure the properties of PP/WTP. Sample morphology shows that there are no impurities in post-fracture images on both PP and PP/WTP. Apart from that, it can be seen that the surface of PP / WTP is slightly smooth. PP and PP/WTP are nearly similar in the FTIR spectrum. During tensile and flexural tests, the PP/WTP value drops as the WTP amount increases. For elongation break and impact testing, PP/WTP at 40% WTP exhibits highest strength value compare to other samples. In all cases, WTP with smallest range of size (250–500 µm) shows better mechanical performance compare to other sizes. PP/WTP plastic composite has a highly great potential to be used in most plastic manufacturing industry and sustain the green environment by taking the advantage of waste tire powder.

关键词：

来源：评论

学校读者我要写书评

暂无评论

AI-based Reconstruction for Fast MRI – A Systematic Review and Meta-analysis

arXiv

引用

arXiv 2021年

作者： Chen, Yutong Schönlieb, Carola-Bibiane Liò, Pietro Leiner, Tim Dragotti, Pier Luigi Wang, Ge Rueckert, Daniel Firmin, David Yang, Guang National Heart & Lung Institute Imperial College London LondonSW3 6NP United Kingdom Cardiovascular Research Centre Royal Brompton Hospital LondonSW3 6NP United Kingdom Faculty of Biology University of Cambridge CambridgeCB2 1RX United Kingdom Department of Applied Mathematics and Theoretical Physics University of Cambridge CambridgeCB3 0WA United Kingdom Department of Computer Science and Technology University of Cambridge CambridgeCB3 0FD United Kingdom Utrecht University Medical Centre Utrecht3584 CX Netherlands Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2BU United Kingdom Biomedical Imaging Center Rensselaer Polytechnic Institute TroyNY12180 United States Department of Computing Imperial College London LondonSW7 2BU United Kingdom Institute for Medical Informatics Statistics and Epidemiology Technical University of Munich Munich81675 Germany

Compressed sensing (CS) has been playing a key role in accelerating the magnetic resonance imaging (MRI) acquisition process. With the resurgence of artificial intelligence, deep neural networks and CS algorithms are being integrated to redefine the state of the art of fast MRI. The past several years have witnessed substantial growth in the complexity, diversity, and performance of deep learning-based CS techniques that are dedicated to fast MRI. In this meta-analysis, we systematically review the deep learning-based CS techniques for fast MRI, describe key model designs, highlight breakthroughs, and discuss promising directions. We have also introduced a comprehensive analysis framework and a classification system to assess the pivotal role of deep learning in CS-based acceleration for MRI. © 2021, CC BY-NC-ND.

关键词： Compressed sensing

来源：评论

学校读者我要写书评

暂无评论

Middle East and North African Health Informatics Association (MENAHIA): A New Opportunity for the Region

引用

Yearbook of Medical Informatics 2019年第1期28卷 292-296页

作者： Riyad Alshammari Najeeb Al-Shorbaji Zakiuddin Ahmed Zaid Al-Hamadan Abdulwahhab Alshammari Osama El Hassan Dari Alhuwail Salim Al Salmi Eiman Al-Jafar Hassan Ghazal College of Public Health and Health Informatics King Saud bin Abdulaziz University for Health Sciences Riyadh Saudi Arabia King Abdullah International Medical Research Center Riyadh Saudi Arabia Ministry of the National Guard- Health Affairs Riyadh Saudi Arabia President of the Saudi Health Informatics Association President of the eHealth Development Association of Jordan Program Director RAHAH King Saud University Project Director Riphah Institute of Health Improvement & Safety CEO Digital Care Pakistan Associate Professor Jordan University of Science and Technology College of Applied Medical Sciences King Saud bin Abdulaziz University for Health Sciences Riyadh Saudi Arabia Vice-Chair of Emirates Health Informatics Society Head of eHealth section at Dubai Health Authority Dubai United Arab Emirates Department of Information Science College of Computing Sciences and Engineering Kuwait University Kuwait Health Informatics Unit Dasman Diabetes Institute Kuwait Associate Deanship for Health Information Management Program Oman College of Health Sciences Muscat Sultanate of Oman Department of Health Informatics and Information Management Faculty of Allied Health Sciences Kuwait University Kuwait. National Center for Scientific and Technological Research Rabat and President of the Moroccan Society for Telemedicine and eHealth Rabat Morocco

During the 2018 IMIA General Assembly (GA) held on Oct 2018 in Sri Lanka, a new organization, the Middle East and North African Health Informatics Association (MENAHIA) was established as a regional non-governmental organization to promote Health Informatics in the region. Health Informatics activities in applications, education, and research have been growing in the Middle East and North African region for many years. Over the past year, health informatics initiatives within the region focused on the education sector and the workforce. The need to enhance the education delivery and methods is essential in promoting Health Informatics in the region as well as establishing master programs in many countries within the region. This paper provides an overview of the variety of initiatives that are occurring in the region and are endorsed by MENAHIA.

关键词： Saudi Arabia Pakistan Jordan United Arab Emirates Morocco Kuwait Oman Bahrain Health Informatics the Middle East

来源：评论

学校读者我要写书评

暂无评论

25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

引用

BMC NEUROSCIENCE 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

关键词：

来源：评论

学校读者我要写书评

暂无评论

Spin-current-mediated rapid magnon localisation and coalescence after ultrafast optical pumping of ferrimagnetic alloys

arXiv

引用

arXiv 2018年

作者： Iacocca, E. Liu, T-M. Reid, A.H. Fu, Z. Ruta, S. Granitzka, P.W. Jal, E. Bonetti, S. Gray, A.X. Graves, C.E. Kukreja, R. Chen, Z. Higley, D.J. Chase, T. Le Guyader, L. Hirsch, K. Ohldag, H. Schlotter, W.F. Dakovski, G.L. Coslovich, G. Hoffmann, M.C. Carron, S. Tsukamoto, A. Savoini, M. Kirilyuk, A. Kimel, A.V. Rasing, T. Stöhr, J. Evans, R.F.L. Ostler, T. Chantrel, R.W. Hoefer, M.A. Silva, T.J. Dürr, H.A. Department of Applied Mathematics University of Colorado BoulderCO80309 United States National Institute of Standards and Technology BoulderCO80305 United States Department of Physics Division for Theoretical Physics Chalmers University of Technology Gothenburg412 96 Sweden SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo ParkCA94025 United States School of Physics Science and Engineering Tongji University Shanghai200092 China Department of Physics University of York YorkYO10 5DD United Kingdom Spectroscopy and Coherent Scattering European X-Ray Free-Electron Laser Facility GmbH Holzkoppel 4 Schenefeld22869 Germany Department of Electronics and Computer Science Nihon University 7-24-1 Narashino-dai Funabashi Chiba274-8501 Japan Radboud University Institute for Molecules and Materials Heyendaalseweg 135 AJ Nijmegen6525 Netherlands Université de Liège Physique des Matériaux et Nanostructures Liège Sart TilmanB-4000 Belgium Faculty of Arts Computing Engineering and Sciences Sheffield Hallam University Howard Street SheffieldS1 1WB United Kingdom Department of Physics and Astronomy Uppsala University Box 516 Uppsala751 20 Sweden

Sub-picosecond magnetisation manipulation via femtosecond optical pumping has attracted wide attention ever since its original discovery in 1996. However, the spatial evolution of the magnetisation is not yet well understood, n part due to the difficulty in experimentally probing such rapid dynamics. Here, we find evidence of rapid magnetic order recovery in materials with perpendicular magnetic anisotropy via nonlinear magnon processes. We identify both localisation and coalescence regimes, whereby localised magnetic textures nucleate and subsequently evolve in accordance with a power law formalism. Coalescence is observed for optical excitations both above and below the switching threshold. Simulations indicate that the ultrafast generation of noncollinear magnetisation via optical pumping establishes exchange-mediated spin currents with an equivalent 100% spin polarised charge current density of 108A/cm2. Such large spin currents precipitate rapid recovery of magnetic order after optical pumping. These processes suggest an ultrafast optical route for the stabilization of desired meta-stable states, e.g., solated skyrmions. Copyright © 2018, The Authors. All rights reserved.

关键词： Optical pumping

来源：评论

学校读者我要写书评

暂无评论

Toward the End-to-End Optimization of Particle Physics Instruments with Differentiable Programming: a White Paper

arXiv

引用

arXiv 2022年

作者： Dorigo, Tommaso Giammanco, Andrea Vischia, Pietro Aehle, Max Bawaj, Mateusz Boldyrev, Alexey de Castro Manzano, Pablo Derkach, Denis Donini, Julien Edelen, Auralee Fanzago, Federica Gauger, Nicolas R. Glaser, Christian Baydin, Atılım G. Heinrich, Lukas Keidel, Ralf Kieseler, Jan Krause, Claudius Lagrange, Maxime Lamparth, Max Layer, Lukas Maier, Gernot Nardi, Federico Pettersen, Helge E.S. Ramos, Alberto Ratnikov, Fedor Röhrich, Dieter de Austri, Roberto Ruiz del Árbol, Pablo Martínez Ruiz Savchenko, Oleg Simpson, Nathan Strong, Giles C. Taliercio, Angela Tosi, Mia Ustyuzhanin, Andrey Zaraket, Haitham Istituto Nazionale di Fisica Nucleare Sezione di Padova Italy Université Catholique de Louvain Belgium Scientific Computing Technische Universität Kaiserslautern Germany Università di Perugia INFN Sezione di Perugia Italy HSE University Russia Université Clermont Auvergne Laboratoire de Physique de Clermont CNRS IN2P3 France SLAC National Accelerator Laboratory United States Department of Physics and Astronomy Uppsala University Sweden Department of Computer Science University of Oxford United Kingdom Physik-Department Technische Universität München Germany Center for Technology and Transfer University of Applied Sciences Worms Germany CERN Switzerland NHETC Dept. of Physics and Astronomy Rutgers University United States Università di Napoli "Federico II" Italy Germany Università degli Studi di Padova Italy Department of Oncology and Medical Physics Haukeland University Hospital Norway Instituto de Física Corpuscular UV-CSIC Spain Department of Physics and Technology University of Bergen Norway Instituto de Física de Cantabria UC-CSIC Spain Lund University Sweden Multi-Disciplinary Physics Laboratory Optics and Fiber Optics Group Faculty of Sciences Lebanese University Lebanon Lebanon

The full optimization of the design and operation of instruments whose functioning relies on the interaction of radiation with matter is a superhuman task, given the large dimensionality of the space of possible choices for geometry, detection technology, materials, data-acquisition, and information-extraction techniques, and the interdependence of the related parameters. On the other hand, massive potential gains in performance over standard, "experience-driven" layouts are in principle within our reach if an objective function fully aligned with the final goals of the instrument is maximized by means of a systematic search of the configuration space. The stochastic nature of the involved quantum processes make the modeling of these systems an intractable problem from a classical statistics point of view, yet the construction of a fully differentiable pipeline and the use of deep learning techniques may allow the simultaneous optimization of all design parameters. In this document we lay down our plans for the design of a modular and versatile modeling tool for the end-to-end optimization of complex instruments for particle physics experiments as well as industrial and medical applications that share the detection of radiation as their basic ingredient. We consider a selected set of use cases to highlight the specific needs of different applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Medical applications

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：