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arXiv 2024年

作者： Bocquet, S. Grandis, S. Bleem, L.E. Klein, M. Mohr, J.J. Schrabback, T. Abbott, T.M.C. Ade, P.A.R. Aguena, M. Alarcon, A. Allam, S. Allen, S.W. Alves, O. Amon, A. Anderson, A.J. Annis, J. Ansarinejad, B. Austermann, J.E. Avila, S. Bacon, D. Bayliss, M. Beall, J.A. Bechtol, K. Becker, M.R. Bender, A.N. Benson, B.A. Bernstein, G.M. Bhargava, S. Bianchini, F. Brodwin, M. Brooks, D. Bryant, L. Campos, A. Canning, R.E.A. Carlstrom, J.E. Rosell, A. Carnero Kind, M. Carrasco Carretero, J. Castander, F.J. Cawthon, R. Chang, C.L. Chang, C. Chaubal, P. Chen, R. Chiang, H.C. Choi, A. Chou, T.-L. Citron, R. Moran, C. Corbett Cordero, J. Costanzi, M. Crawford, T.M. Crites, A.T. da Costa, L.N. Pereira, M.E.S. Davis, C. Davis, T.M. DeRose, J. Desai, S. de Haan, T. Diehl, H.T. Dobbs, M.A. Dodelson, S. Doux, C. Drlica-Wagner, A. Eckert, K. Elvin-Poole, J. Everett, S. Everett, W. Ferrero, I. Ferté, A. Flores, A.M. Frieman, J. Gallicchio, J. García-Bellido, J. Gatti, M. George, E.M. Giannini, G. Gladders, M.D. Gruen, D. Gruendl, R.A. Gupta, N. Gutierrez, G. Halverson, N.W. Harrison, I. Hartley, W.G. Herner, K. Hinton, S.R. Holder, G.P. Hollowood, D.L. Holzapfel, W.L. Honscheid, K. Hrubes, J.D. Huang, N. Hubmayr, J. Huff, E.M. Huterer, D. Irwin, K.D. James, D.J. Jarvis, M. Khullar, G. Kim, K. Knox, L. Kraft, R. Krause, E. Kuehn, K. Kuropatkin, N. Kéruzoré, F. Lahav, O. Lee, A.T. Leget, P.-F. Li, D. Lin, H. Lowitz, A. MacCrann, N. Mahler, G. Mantz, A. Marshall, J.L. McCullough, J. McDonald, M. McMahon, J.J. Mena-Fernández, J. Menanteau, F. Meyer, S.S. Miquel, R. Montgomery, J. Myles, J. Natoli, T. Navarro-Alsina, A. Nibarger, J.P. Noble, G.I. Novosad, V. Ogando, R.L.C. Omori, Y. Padin, S. Pandey, S. Paschos, P. Patil, S. Pieres, A. Malagón, A.A. Plazas Porredon, A. Prat, J. Pryke, C. Raveri, M. Reichardt, C.L. Roberson, J. Rollins, R.P. Romero, C. Roodman, A. Ruhl, J.E. Rykoff, E.S. Saliwanchik, B.R. University Observatory Faculty of Physics Ludwig-Maximilians-Universität Scheinerstr. 1 Munich81679 Germany Universität Innsbruck Institut für Astro- und Teilchenphysik Technikerstr. 25/8 Innsbruck6020 Austria High-Energy Physics Division Argonne National Laboratory 9700 South Cass Avenue LemontIL60439 United States Kavli Institute for Cosmological Physics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Max Planck Institute for Extraterrestrial Physics Gießenbachstr. 1 Garching85748 Germany Argelander-Institut für Astronomie Auf dem Hügel 71 Bonn53121 Germany Cerro Tololo Inter-American Observatory NSF’s National Optical-Infrared Astronomy Research Laboratory Casilla 603 La Serena Chile School of Physics and Astronomy Cardiff University CardiffCF24 3YB United Kingdom Laboratório Interinstitucional de e-Astronomia - LIneA Rua Gal. José Cristino 77 RJ Rio de Janeiro20921-400 Brazil Fermi National Accelerator Laboratory P. O. Box 500 BataviaIL60510 United States Kavli Institute for Particle Astrophysics and Cosmology Stanford University 452 Lomita Mall StanfordCA94305 United States Department of Physics Stanford University 382 Via Pueblo Mall StanfordCA94305 United States SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo ParkCA94025 United States Department of Physics University of Michigan Ann ArborMI48109 United States Institute of Astronomy University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom Kavli Institute for Cosmology University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom School of Physics University of Melbourne ParkvilleVIC3010 Australia NIST Quantum Devices Group 325 Broadway Mailcode 817.03 BoulderCO80305 United States Department of Physics University of Colorado BoulderCO80309 United States The Barcelona Institute of Science and Technology Campus UAB Bellaterra Barcelona08193 Spain Institute of Cosmology and Gravitation Universit

We present cosmological constraints from the abundance of galaxy clusters selected via the thermal Sunyaev-Zel’dovich (SZ) effect in South Pole Telescope (SPT) data with a simultaneous mass calibration using weak gravitational lensing data from the Dark Energy Survey (DES) and the Hubble Space Telescope (HST). The cluster sample is constructed from the combined SPT-SZ, SPTpol ECS, and SPTpol 500d surveys, and comprises 1,005 confirmed clusters in the redshift range 0.25 − 1.78 over a total sky area of 5,200 deg2. We use DES Year 3 weak-lensing data for 688 clusters with redshifts z m = 0.286 ± 0.032, σ8 = 0.817 ± 0.026, and the parameter combination σ8 (Ωm/0.3)0.25 = 0.805 ± 0.016. Our measurement of S8 ≡ σ8 √Ωm/0.3 = 0.795 ± 0.029 and the constraint from Planck CMB anisotropies (2018 TT,TE,EE+lowE) differ by 1.1σ. In combination with that Planck dataset, we place a 95% upper limit on the sum of neutrino masses ∑ mν +0.22−0.15, or a 2.2σ difference with a cosmological constant. We use the cluster abundance to measure σ8 in five redshift bins between 0.25 and 1.8, and we find the results to be consistent with structure growth as predicted by the ΛCDM model fit to Planck primary CMB data. Copyright © 2024, The Authors. All rights reserved.

关键词： Cosmology

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Analysis of the implementation of Unpad Library Management System using the Technology Acceptance Model: librarian perspective

引用

Journal of Physics: Conference Series 2019年第1期1175卷 012228-012228页

作者： Kurniasih, Nuning Rizal, Edwin Kurniawati, Nanik Sujito Sudirman, Arfin Mesran Alif, Muhammad Maulana, Yogi Sugiarto Surya Sari Faradiba Satria, Erwinsyah Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung 45363 Indonesia The National Archive of The Republic of Indonesia (ANRI) Jakarta Indonesia Department of English Education IAIN Surakarta Surakarta Indonesia Department of International Relations Universitas Padjadjaran Bandung Indonesia Department of Computer Science STIMIK Budi Darma Medan Indonesia Department of Communication Science Universitas Lambung Mangkurat Banjarmasin Indonesia Department of Business Administration Sekolah Tinggi Ilmu Politik (STISIP) Bina Putera Banjar Banjar Jawa Barat Indonesia Department of Mathematics Education Universitas Islam Malang Malang Indonesia Faculty of Teacher Training and Education Universitas Bung Hatta Padang Indonesia

In 2017 Universitas Padjadjaran introduced Unpad Library Management System (ULiMS). All faculty libraries in Unpad then gradually migrate to ULiMS. The migration from one system to another requires readiness and acceptance of all supporting components, including librarians. This study aims to analyze the implementation of ULiMS by using the Technology Acceptance Model from the perspective of the librarians. This study uses qualitative research methods with a case study approach. Primary data was obtained from the results of observations and interviews with 11 librarians from 11 faculty libraries in Unpad. Triangulation is done by triangulation theory, namely through literature review. The results showed that all faculty libraries in Unpad could receive ULiMS as a system that could integrate all library databases in Unpad. Librarians can understand the weaknesses and advantages of ULiMS. In implementing ULiMS, faculty libraries at Unpad have migrated from the old system to ULiMS. However, some faculty libraries still run the old library management system together with ULiMS to overcome the weaknesses of ULiMS. This research is expected to provide input to policymakers in implementing a new library system so that it can be well received by all relevant components, especially by librarians.

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First Constraints on the Epoch of Reionization Using the non-Gaussianity of the Kinematic Sunyaev-Zel’dovich Effect from the South Pole Telescope and Herschel-SPIRE Observations

arXiv

引用

arXiv 2024年

作者： Raghunathan, S. Ade, P.A.R. Anderson, A.J. Ansarinejad, B. Archipley, M. Austermann, J.E. Balkenhol, L. Beall, J.A. Benabed, K. Bender, A.N. Benson, B.A. Bianchini, F. Bleem, L.E. Bock, J. Bouchet, F.R. Bryant, L. Camphuis, E. Carlstrom, J.E. Cecil, T.W. Chang, C.L. Chaubal, P. Chiang, H.C. Chichura, P.M. Chou, T.-L. Citron, R. Coerver, A. Crawford, T.M. Crites, A.T. Cukierman, A. Daley, C. Dibert, K.R. Dobbs, M.A. Doussot, A. Dutcher, D. Everett, W. Feng, C. Ferguson, K.R. Fichman, K. Foster, A. Galli, S. Gallicchio, J. Gambrel, A.E. Gardner, R.W. Ge, F. George, E.M. Goeckner-Wald, N. Gualtieri, R. Guidi, F. Guns, S. Gupta, N. de Haan, T. Halverson, N.W. Hivon, E. Holder, G.P. Holzapfel, W.L. Hood, J.C. Hrubes, J.D. Hryciuk, A. Huang, N. Hubmayr, J. Irwin, K.D. Kéruzoré, F. Khalife, A.R. Knox, L. Korman, M. Kornoelje, K. Kuo, C.-L. Lee, A.T. Levy, K. Li, D. Lowitz, A.E. Lu, C. Maniyar, A. Martsen, E.S. McMahon, J.J. Menanteau, F. Millea, M. Montgomery, J. Moran, C. Corbett Nakato, Y. Natoli, T. Nibarger, J.P. Noble, G.I. Novosad, V. Omori, Y. Padin, S. Pan, Z. Paschos, P. Patil, S. Phadke, K.A. Prabhu, K. Pryke, C. Quan, W. Rahimi, M. Rahlin, A. Reichardt, C.L. Rouble, M. Ruhl, J.E. Saliwanchik, B.R. Schaffer, K.K. Schiappucci, E. Sievers, C. Smecher, G. Sobrin, J.A. Stark, A.A. Stephen, J. Suzuki, A. Tandoi, C. Thompson, K.L. Thorne, B. Trendafilova, C. Tucker, C. Umilta, C. Veach, T. Vieira, J.D. Viero, M.P. Wan, Y. Wang, G. Whitehorn, N. Wu, W.L.K. Yefremenko, V. Young, M.R. Zebrowski, J.A. Zemcov, M. Center for AstroPhysical Surveys National Center for Supercomputing Applications UrbanaIL61801 United States School of Physics and Astronomy Cardiff University CardiffCF24 3YB United Kingdom Fermi National Accelerator Laboratory MS209 P.O. Box 500 BataviaIL60510 United States Kavli Institute for Cosmological Physics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Department of Astronomy and Astrophysics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States School of Physics University of Melbourne ParkvilleVIC3010 Australia Department of Astronomy University of Illinois Urbana-Champaign 1002 West Green Street UrbanaIL61801 United States NIST Quantum Devices Group 325 Broadway Mailcode 817.03 BoulderCO80305 United States Department of Physics University of Colorado BoulderCO80309 United States Institut d’Astrophysique de Paris UMR 7095 CNRS & Sorbonne Université 98 bis boulevard Arago Paris75014 France High-Energy Physics Division Argonne National Laboratory 9700 South Cass Avenue. LemontIL60439 United States Kavli Institute for Particle Astrophysics and Cosmology Stanford University 452 Lomita Mall StanfordCA94305 United States Department of Physics Stanford University 382 Via Pueblo Mall StanfordCA94305 United States SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo ParkCA94025 United States California Institute of Technology PasadenaCA91125 United States Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Enrico Fermi Institute University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Department of Physics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Department of Physics McGill Space Institute McGill University 3600 Rue University MontrealQCH3A 2T8 Canada School of Mathematics Statistics & Computer Science University of KwaZulu-Natal Durban South Africa Unive

We report results from an analysis aimed at detecting the trispectrum of the kinematic Sunyaev-Zel’dovich (kSZ) effect by combining data from the South Pole Telescope (SPT) and Herschel-SPIRE experiments over a 100 deg2 field. The SPT observations combine data from the previous and current surveys, namely SPTpol and SPT-3G, to achieve depths of 4.5, 3, and 16 µK-arcmin in bands centered at 95, 150, and 220 GHz. For SPIRE, we include data from the 600 and 857 GHz bands. We reconstruct the velocity-induced large-scale correlation of the small-scale kSZ signal with a quadratic estimator that uses two cosmic microwave background (CMB) temperature maps, constructed by optimally combining data from all the frequency bands. We reject the null hypothesis of a zero trispectrum at 10.3σ level. However, the measured trispectrum contains contributions from both the kSZ and other undesired components, such as CMB lensing and astrophysical foregrounds, with kSZ being sub-dominant. We use the Agora simulations to estimate the expected signal from CMB lensing and astrophysical foregrounds. After accounting for the contributions from CMB lensing and foreground signals, we do not detect an excess kSZ-only trispectrum and use this non-detection to set constraints on reionization. By applying a prior based on observations of the Gunn-Peterson trough, we obtain an upper limit on the duration of reionization of (formula presented). We find these constraints are fairly robust to foregrounds assumptions. This trispectrum measurement is independent of, but consistent with, Planck’s optical depth measurement. This result is the first constraint on the epoch of reionization using the non-Gaussian nature of the kSZ signal. © 2024, CC BY.

关键词： Kinematics

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DeepZipper: A Novel Deep Learning Architecture for Lensed Supernovae Identification

arXiv

引用

arXiv 2021年

作者： Morgan, Robert Nord, B. Bechtol, K. González, S.J. Buckley-Geer, E. Möller, A. Park, J.W. Kim, A.G. Birrer, S. Aguena, M. Annis, J. Bocquet, S. Brooks, D. Rosell, A. Carnero Kind, M. Carrasco Carretero, J. Cawthon, R. da Costa, L.N. Davis, T.M. De Vicente, J. Doel, P. Ferrero, I. Friedel, D. Frieman, J. García-Bellido, J. Gatti, M. Gaztanaga, E. Giannini, G. Gruen, D. Gruendl, R.A. Gutierrez, G. Hollowood, D.L. Honscheid, K. James, D.J. Kuehn, K. Kuropatkin, N. Maia, M.A.G. Miquel, R. Palmese, A. Paz-Chinchón, F. Pereira, M.E.S. Pieres, A. Plazas Malagón, A.A. Reil, K. Roodman, A. Sanchez, E. Smith, M. Suchyta, E. Swanson, M.E.C. Tarle, G. To, C. Physics Department University of Wisconsin-Madison 1150 University Avenue MadisonWI53706 United States Fermi National Accelerator Laboratory P. O. Box 500 BataviaIL60510 United States Legacy Survey of Space and Time Corporation Data Science Fellowship Program United States Department of Astronomy and Astrophysics University of Chicago ChicagoIL60637 United States Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Legacy Survey of Space and Time 933 North Cherry Avenue TucsonAZ85721 United States Centre for Astrophysics & Supercomputing Swinburne University of Technology VIC3122 Australia Kavli Institute for Particle Astrophysics and Cosmology Department of Physics Stanford University StanfordCA94305 United States SLAC National Accelerator Laboratory Menlo ParkCA94025 United States Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Laboratório Interinstitucional de e-Astronomia - LIneA Rua Gal. José Cristino 77 RJ Rio de Janeiro20921-400 Brazil Faculty of Physics Ludwig-Maximilians-Universität Scheinerstr. 1 Munich81679 Germany Department of Physics & Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom Center for Astrophysical Surveys National Center for Supercomputing Applications 1205 West Clark St. UrbanaIL61801 United States Department of Astronomy University of Illinois at Urbana-Champaign 1002 W. Green Street UrbanaIL61801 United States The Barcelona Institute of Science and Technology Campus UAB Bellaterra Barcelona08193 Spain Physics Department William Jewell College LibertyMO64068 United States Observatório Nacional Rua Gal. José Cristino 77 RJ Rio de Janeiro20921-400 Brazil School of Mathematics and Physics University of Queensland BrisbaneQLD4072 Australia Madrid Spain Institute of Theoretical Astrophysics University of Oslo P.O. Box 1029 Blindern OsloNO-0315 Norway Instituto de Fisica Teorica UAM/CSIC

Large-scale astronomical surveys have the potential to capture data on large numbers of strongly gravitationally lensed supernovae (LSNe). To facilitate timely analysis and spectroscopic follow-up before the supernova fades, an LSN needs to be identified soon after it begins. To quickly identify LSNe in optical survey datasets, we designed ZipperNet, a multi-branch deep neural network that combines convolutional layers (traditionally used for images) with long short-term memory (LSTM) layers (traditionally used for time series). We tested ZipperNet on the task of classifying objects from four categories – no lens, galaxy-galaxy lens, lensed type Ia supernova, lensed core-collapse supernova – within high-fidelity simulations of three cosmic survey data sets – the Dark Energy Survey (DES), Rubin Observatory’s Legacy Survey of Space and Time (LSST), and a Dark Energy Spectroscopic Instrument (DESI) imaging survey. Among our results, we find that for the LSST-like dataset, ZipperNet classifies LSNe with a receiver operating characteristic area under the curve of 0.97, predicts the spectroscopic type of the lensed supernovae with 79% accuracy, and demonstrates similarly high performance for LSNe 1-2 epochs after first detection. We anticipate that a model like ZipperNet, which simultaneously incorporates spatial and temporal information, can play a significant role in the rapid identification of lensed transient systems in cosmic survey experiments. Copyright © 2021, The Authors. All rights reserved.

关键词： Supernovae

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Kinetic Monte Carlo simulations of helium cluster nucleation in tungsten with preexisting vacancies

Kinetic Monte Carlo simulations of helium cluster nucleation...

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作者： Yang, Zhangcan Blondel, Sophie Hammond, Karl D. Wirth, Brian D. Huazhong University of Science and Technology Department of Nuclear Engineering and Technology Wuhan430074 China University of Tennessee Department of Nuclear Engineering KnoxvilleTN37996 United States Oak Ridge National Laboratory Computer Sciences and Mathematics Division Oak RidgeTN37831 United States University of Missouri Department of Chemical Engineering ColumbiaMO65211 United States University of Missouri Nuclear Engineering Program ColumbiaMO65211 United States Oak Ridge National Laboratory Nuclear Science and Engineering Dir. Oak RidgeTN37831 United States

The object kinetic Monte Carlo code Kinetic Simulations Of Microstructure Evolution (KSOME) was used to study the subsurface helium clustering behavior in tungsten as a function of temperature, helium implantation rate, and vacancy concentration. The simulations evaluated helium implantation fluxes from 1022 to 1026 m-2 · s-1 at temperatures from 473 to 1473 K for 100-eV helium ions implanted below tungsten surfaces and for vacancy concentrations between 1 and 50 parts per million. Such vacancy concentrations far exceed thermodynamic equilibrium values but are consistent with supersaturated concentrations expected during concurrent, or preexisting, neutron irradiation. The thermodynamics and kinetic parameters to describe helium diffusion and clustering are input to KSOME based on values obtained from atomistic simulation results. These kinetic Monte Carlo results clearly delineate two different regimes of helium cluster nucleation, one dominated by helium self-trapping at high implantation rates and lower temperatures and one where helium-vacancy trapping dominates the helium cluster nucleation at lower implantation rates and higher temperatures. The transition between these regimes has been mapped as a function of implantation rate, temperature, and vacancy concentration and can provide guidance to understand the conditions under which neutron irradiation effects may contribute to subsurface gas nucleation in tungsten plasma-facing components. © 2017 American Nuclear Society.

关键词： Kinetics

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The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI

arXiv

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arXiv 2023年

作者： Moawad, Ahmed W. Janas, Anastasia Baid, Ujjwal Ramakrishnan, Divya Saluja, Rachit Ashraf, Nader Jekel, Leon Amiruddin, Raisa Adewole, Maruf Albrecht, Jake Anazodo, Udunna Aneja, Sanjay Anwar, Syed Muhammad Bergquist, Timothy Calabrese, Evan Chiang, Veronica Chung, Verena Conte, Gian Marco Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Kazerooni, Anahita Fathi Kofler, Florian Krantchev, Kiril LaBella, Dominic Van Leemput, Koen Li, Hongwei Bran Linguraru, Marius George Link, Katherine E. Liu, Xinyang Maleki, Nazanin Meier, Zeke Menze, Bjoern H. Moy, Harrison Osenberg, Klara Piraud, Marie Reitman, Zachary Shinohara, Russel Takeshi Tahon, Nourel hoda Nada, Ayman Velichko, Yuri S. Wang, Chunhao Wiestler, Benedikt Wiggins, Walter Shafique, Umber Willms, Klara Avesta, Arman Bousabarah, Khaled Chakrabarty, Satrajit Gennaro, Nicolo Holler, Wolfgang Kaur, Manpreet LaMontagne, Pamela Lin, MingDe Lost, Jan Marcus, Daniel S. Maresca, Ryan Merkaj, Sarah Nada, Ayaman Pedersen, Gabriel Cassinelli von Reppert, Marc Sotiras, Aristeidis Teytelboym, Oleg Tillmans, Niklas Westerhoff, Malte Youssef, Ayda Godfrey, Devon Floyd, Scott Rauschecker, Andreas Villanueva-Meyer, Javier Pflüger, Irada Cho, Jaeyoung Bendszus, Martin Brugnara, Gianluca Cramer, Justin Perez-Carillo, Gloria Guzman J. Johnson, Derek R. Kam, Anthony Kwan, Benjamin Yin Ming Lai, Lillian Lall, Neil U. Memon, Fatima Patro, Satya Narayana Petrovic, Bojan So, Tiffany Y. Thompson, Gerard Wu, Lei Schrickel, E. Brooke Bansal, Anu Barkhof, Frederik Besada, Cristina Chu, Sammy Druzgal, Jason Dusoi, Alexandru Farage, Luciano Feltrin, Fabricio Fong, Amy Fung, Steve H. Gray, R. Ian Ikuta, Ichiro Iv, Michael Postma, Alida A. Mahajan, Amit Joyner, David Krumpelman, Chase Letourneau-Guillon, Laurent Lincoln, Christie M. Maros, Mate E. Miller, Elka Morón, Fanny Nimchinsky, Esther A. Ozsarlak, Ozkan Patel, Uresh Rohatgi, Saurabh Saha, Atin Sayah, Anousheh Schwartz, Eric D. Shih, Robert Shiroishi, Mark Trinity health Mid Atlantic Hosiptals DarbyPA United States Department of Radiology and Biomedical Imaging Yale School of Medicine New HavenCT United States Division of Computational Pathology Department of Pathology and Laboratory Medicine School of Medicine Indiana University IndianapolisIN United States Department of Electical and Computer Engineering Cornell University Cornell Tech New YorkNY United States Department of Radiology Weill Cornell Medicine New YorkNY United States ImagineQuant Children’s Hospital of Philadelphia PhiladelphiaPA United States College of Medicine Alfaisal University Riyadh Saudi Arabia DKFZ Division of Translational Neurooncology The WTZ German Cancer Consortium DKTK Partner Site University Hospital Essen Essen Germany Medical Artificial Intelligence Lab Crestview Radiology Lagos Nigeria Sage Bionetworks SeattleWA United States Montreal Neurological Institute McGill University Montreal Canada lab Crestview Radiology Lagos Nigeria Department of Therapeutic Radiology Yale School of Medicine New HavenCT United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Department of Radiology Mayo Clinic RochesterMN United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Neurosurgery Yale School of Medicine New HavenCT United States Center for Global Health Perelman School of Medicine University of Pennsylvania PA United States Department of Informatics Technical University Munich Germany Children’s Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Children’s National Hospital WashingtonDC United States PrecisionFDA U.S. Food and Drug Administration S

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well-annotated, diverse d

关键词： Image segmentation

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Optimal Learning for Sequential Decision Making for Expensive Cost Functions with Stochastic Binary Feedbacks

arXiv

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arXiv 2017年

作者： Wang, Yingfei Wang, Chu Powell, Warren Department of Computer Science Princeton University PrincetonNJ08540 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Operations Research and Financial Engineering Princeton University PrincetonNJ08544 United States

We consider the problem of sequentially making decisions that are rewarded by "successes" and "failures" which can be predicted through an unknown relationship that depends on a partially controllable vector of attributes for each instance. The learner takes an active role in selecting samples from the instance pool. The goal is to maximize the probability of success in either offline (training) or online (testing) phases. Our problem is motivated by real-world applications where observations are time consuming and/or expensive. We develop a knowledge gradient policy using an online Bayesian linear classifier to guide the experiment by maximizing the expected value of information of labeling each alternative. We provide a finite-time analysis of the estimated error and show that the maximum likelihood estimator based produced by the KG policy is consistent and asymptotically normal. We also show that the knowledge gradient policy is asymptotically optimal in an offline setting. This work further extends the knowledge gradient to the setting of contextual bandits. We report the results of a series of experiments that demonstrate its efficiency. Copyright © 2017, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

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Compression for quantum population coding

arXiv

引用

arXiv 2017年

作者： Yang, Yuxiang Bai, Ge Chiribella, Giulio Hayashi, Masahito Department of Computer Science University of Hong Kong Pokfulam Road Hong Kong HKU Shenzhen Institute of Research and Innovation Kejizhong 2nd Road Shenzhen China Department of Computer Science University of Oxford Parks Road Oxford United Kingdom Canadian Institute for Advanced Research CIFAR Program in Quantum Information Science Department of Computer Science University of Hong Kong Pokfulam Road Hong Kong Graduate School of Mathematics Nagoya University Furocho Chikusaku Nagoya464-860 Japan Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 117542 Singapore

We study the compression of n quantum systems, each prepared in the same state belonging to a given parametric family of quantum states. For a family of states with f independent parameters, we devise an asymptotically faithful protocol that requires a hybrid memory of size (f/2) log n, including both quantum and classical bits. Our construction uses a quantum version of local asymptotic normality and, as an intermediate step, solves the problem of compressing displaced thermal states of n identically prepared modes. In both cases, we show that (f/2) log n is the minimum amount of memory needed to achieve asymptotic faithfulness. In addition, we analyze how much of the memory needs to be quantum. We find that the ratio between quantum and classical bits can be made arbitrarily small, but cannot reach zero: unless all the quantum states in the family commute, no protocol using only classical bits can be faithful, even if it uses an arbitrarily large number of classical bits. Copyright © 2017, The Authors. All rights reserved.

关键词： Quantum optics

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Retracted: Efforts to Overcome Cyber Crime Actions in Indonesia

Retracted: Efforts to Overcome Cyber Crime Actions in Indone...

引用

2018 Workshop on Multidisciplinary and Its Applications: Applied mathematics, computer science, Information Systems, and Information Technology, WMA-Mathcomtech 2018

作者： Hatta, Muhammad Rajamanickam, Ramalinggam Abdullah, Dahlan Hartono, H. Bunga, Marten Adji, Hari Sapto Amiruddin, A. Djanggih, Hardianto Hipan, Nasrun Salle, S. Salmawati, S. Azis, Abdul Wahab, Mirnawanti Karinda, Kisman Saleh, A.A. Sudarsana, I Ketut Purnomo, Agung Anam, Fatkul Rianita, Dian Department of Criminal Law Universitas Malikusssaleh Aceh Indonesia Faculty of Law National University of Malaysia Selangor Malaysia Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Law Universitas Gorontalo Indonesia Department of Law Universitas Tompotika Luwuk Banggai Indonesia Department of Law Universitas Muhammadiyah Luwuk Banggai Indonesia Department of Law Universitas Muslim Indonesia Indonesia Department of State Adminstration Sekolah Tinggi Ilmu Administrasi YAPPI Makassar Indonesia Department of Management Sekolah Tinggi Ilmu Ekonomi AMKOP Makassar Indonesia Department of Goverment Studies Universitas Muhammadiyah Luwuk Banggai Indonesia Statistics Financial and Social Sciences Research Group Indonesia Department of Religious Education Institut Hindu Dharma Negeri Denpasar Denpasar Indonesia Entrepreneurship Program Bina Nusantara Institute of Creative Technology Malang Indonesia Department of Mathematics Education Universitas Wijaya Kusuma Surabaya Indonesia Department of Administration University of Lancang Kuning Riau Indonesia

The presence of the internet can penetrate the boundaries between countries and accelerate the spread and exchange of information throughout the world. Although there are many positive impacts of internet use, there are also negative impacts that disturb the community. One can easily become a criminal (cyber crime) only by creating blogs, accounts, applications, programs, sites in various mass media and social media to commit fraud, data theft, illegal use of credit cards, the spread of pornographic content, online gambling, online prostitution, hate speech, radicalism, terrorism and so on. To overcome crime in the virtual space, the government has issued Law No. 19 of 2016 concerning Amendments to Law No. 11 of 2008 concerning Information and Electronic Transactions. But not all internet users know and understand the substance of the law. Therefore, the government, employers and institutions that are engaged in information and communication technology have the responsibility to socialize the law to the public to prevent crime in the virtual space. © Published under licence by IOP Publishing Ltd.

关键词： computer crime

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Lamé Parameter Estimation from Static Displacement Field Measurements in the Framework of Nonlinear Inverse Problems

arXiv

引用

arXiv 2017年

作者： Hubmer, Simon Sherina, Ekaterina Neubauer, Andreas Scherzer, Otmar Johannes Kepler University Linz Doctoral Program Computational Mathematics Altenbergerstraße 69 LinzA-4040 Austria Technical University of Denmark Department of Applied Mathematics and Computer Science Asmussens Allé Kongens Lyngby 2800 Denmark Johannes Kepler University Linz Industrial Mathematics Institute Altenbergerstraße 69 LinzA-4040 Austria University of Vienna Computational Science Center Oskar Morgenstern-Platz 1 Vienna1090 Austria Johann Radon Institute Linz Altenbergerstraße 69 LinzA-4040 Austria

We consider a problem of quantitative static elastography, the estimation of the Lamé parameters from internal displacement field data. This problem is formulated as a nonlinear operator equation. To solve this equation, we investigate the Landweber iteration both analytically and numerically. The main result of this paper is the verification of a nonlinearity condition in an infinite dimensional Hilbert space context. This condition guarantees convergence of iterative regularization methods. Furthermore, numerical examples for recovery of the Lamé parameters from displacement data simulating a static elastography experiment are *** Codes 65J22, 65J15, 74G75 Copyright © 2017, The Authors. All rights reserved.

关键词： Inverse problems

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