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arXiv 2019年

作者： Fan, Zheyong Wang, Yanzhou Gu, Xiaokun Qian, Ping Su, Yanjing Ala-Nissila, Tapio School of Mathematics and Physics Bohai University Jinzhou China QTF Centre of Excellence Department of Applied Physics Aalto University AaltoFI-00076 Finland Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing100083 China School of Mathematics and Physics University of Science and Technology Beijing Beijing100083 China Institute of Engineering Thermophysics School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China QTF Centre of Excellence Department of Applied Physics Aalto University Aalto EspooFI-00076 Finland Centre for Interdisciplinary Mathematical Modeling Department of Mathematical Sciences Loughborough University LoughboroughLeicestershireLE11 3TU United Kingdom

Silicon is an important material and many empirical interatomic potentials have been developed for atomistic simulations of it. Among them, the Tersoff potential and its variants are the most popular ones. However, all the existing Tersoff-like potentials fail to reproduce the experimentally measured thermal conductivity of diamond silicon. Here we propose a modified Tersoff potential and develop an efficient open source code called GPUGA (graphics processing units genetic algorithm) based on the genetic algorithm and use it to fit the potential parameters against energy, virial and force data from quantum density functional theory calculations. This potential, which is implemented in the efficient open source GPUMD (graphics processing units molecular dynamics) code, gives significantly improved descriptions of the thermal conductivity and phonon dispersion of diamond silicon as compared to previous Tersoff potentials and at the same time well reproduces the elastic constants. Furthermore, we find that quantum effects on the thermal conductivity of diamond silicon at room temperature are non-negligible but small: using classical statistics underestimates the thermal conductivity by about 10% as compared to using quantum statistics. Copyright © 2019, The Authors. All rights reserved.

关键词： Diamonds

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Evaluating Large Language Models for Enhancing Radiology Specialty Examination: A Comparative Study with Human Performance

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Academic Radiology 2025年

作者： Liu, Hao-Yun Chen, Shyh-Jye Wang, Weichung Lee, Chung-Hsi Hsu, Hsian-He Shen, Shu-Huei Chiou, Hong-Jen Lee, Wen-Jeng Department of Medical Imaging National Taiwan University Hospital Hsin-Chu Branch Br. No.25 Lane 442 Sec.1 Jingguo Rd Hsinchu City 300 Taiwan National Taiwan University College of Medicine No.1 Jen Ai Road Section 1 Taipei 100 Taiwan Department of Medical Imaging National Taiwan University Hospital No. 7 Zhongshan S. Rd. Zhongzheng Dist. Taipei City 100229 Taiwan Institute of Applied Mathematical Sciences Department of Mathematics and Data Science Degree Program National Taiwan University No. 1 Sec. 4 Roosevelt Rd. Taipei 106319 Taiwan Graduate Institute of Health and Biotechnology Law Taipei Medical University 250 Wuxing Street Taipei City Taiwan Department of Radiology Tri-Service General Hospital National Defense Medical Center No.325 Sec.2 Chenggong Rd. Neihu District Taipei City Taiwan Department of Radiology Taipei Veterans General Hospital No.201 Sec. 2 Shipai Rd. Beitou District Taipei City Taiwan National Yang Ming Chiao Tung University School of Medicine No. 118 Sec. 1 Zhongxiao West Road Taipei City 100 Taiwan

Rationale and Objectives: The radiology specialty examination assesses clinical decision-making, image interpretation, and diagnostic reasoning. With the expansion of medical knowledge, traditional test design faces challenges in maintaining accuracy and relevance. Large language models (LLMs) demonstrate potential in medical education. This study evaluates LLM performance in radiology specialty exams, explores their role in assessing question difficulty, and investigates their reasoning processes, aiming to develop a more objective and efficient framework for exam design. Materials and Methods: This study compared the performance of LLMs and human examinees in a radiology specialty examination. Three LLMs (GPT-4o, o1-preview, and GPT-3.5-turbo-1106) were evaluated under zero-shot conditions. Exam accuracy, examinee accuracy, discrimination index, and point-biserial correlation were used to assess LLMs’ ability to predict question difficulty and reasoning processes. The data provided by the Taiwan Radiological Society ensures comparability between AI and human performance. Results: As for accuracy, GPT-4o (88.0%) and o1-preview (90.9%) outperformed human examinees (76.3%), whereas GPT-3.5-turbo-1106 showed significantly lower accuracy (50.2%). Question difficulty analysis revealed that newer LLMs excel in solving complex questions, while GPT-3.5-turbo-1106 exhibited greater performance variability. Discrimination index and point-biserial Correlation analyses demonstrated that GPT-4o and o1-preview accurately identified key differentiating questions, closely mirroring human reasoning patterns. These findings suggest that advanced LLMs can assess medical examination difficulty, offering potential applications in exam standardization and question evaluation. Conclusion: This study evaluated the problem-solving capabilities of GPT-3.5-turbo-1106, GPT-4o, and o1-preview in a radiology specialty examination. LLMs should be utilized as tools for assessing exam question dif

关键词： Accuracy Evaluation GPT LLMs Radiology specialty examination

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Emulating the impact of additional proton–proton interactions in the ATLAS simulation by presampling sets of inelastic Monte Carlo events

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Computing and Software for Big Science 2022年第1期6卷 1-35页

作者： Aad, G. Abbott, B. Abbott, D.C. Abud, A. Abed Abeling, K. Abhayasinghe, D.K. Abidi, S.H. AbouZeid, O.S. Abraham, N.L. Abramowicz, H. Abreu, H. Abulaiti, Y. Hoffman, A. C. Abusleme Acharya, B.S. Achkar, B. Adam, L. Bourdarios, C. Adam Adamczyk, L. Adamek, L. Adelman, J. Adiguzel, A. Adorni, S. Adye, T. Affolder, A.A. Afik, Y. Agapopoulou, C. Agaras, M.N. Aggarwal, A. Agheorghiesei, C. Aguilar-Saavedra, J.A. Ahmad, A. Ahmadov, F. Ahmed, W.S. Ai, X. Aielli, G. Akatsuka, S. Akbiyik, M. Åkesson, T.P.A. Akilli, E. Akimov, A.V. Khoury, K. Al Alberghi, G.L. Albert, J. Verzini, M. J. Alconada Alderweireldt, S. Aleksa, M. Aleksandrov, I.N. Alexa, C. Alexopoulos, T. Alfonsi, A. Alfonsi, F. Alhroob, M. Ali, B. Ali, S. Aliev, M. Alimonti, G. Allaire, C. Allbrooke, B.M.M. Allport, P.P. Aloisio, A. Alonso, F. Alpigiani, C. Camelia, E. Alunno Estevez, M. Alvarez Alviggi, M.G. Coutinho, Y. Amaral Ambler, A. Ambroz, L. Amelung, C. Amidei, D. Santos, S. P. Amor Dos Amoroso, S. Amrouche, C.S. Anastopoulos, C. Andari, N. Andeen, T. Anders, J.K. Andrean, S.Y. Andreazza, A. Andrei, V. Anelli, C.R. Angelidakis, S. Angerami, A. Anisenkov, A.V. Annovi, A. Antel, C. Anthony, M.T. Antipov, E. Antonelli, M. Antrim, D.J.A. Anulli, F. Aoki, M. Pozo, J. A. Aparisi Aparo, M.A. Bella, L. Aperio Aranzabal, N. Ferraz, V. Araujo Arcangeletti, C. Arce, A.T.H. Arguin, J.-F. Argyropoulos, S. Arling, J.-H. Armbruster, A.J. Armstrong, A. Arnaez, O. Arnold, H. Tame, Z. P. Arrubarrena Artoni, G. Asada, H. Asai, K. Asai, S. Asbah, N.A. Asimakopoulou, E.M. Asquith, L. Assahsah, J. Assamagan, K. Astalos, R. Atkin, R.J. Atkinson, M. Atlay, N.B. Atmani, H. Atmasiddha, P.A. Augsten, K. Austrup, V.A. Avolio, G. Ayoub, M.K. Azuelos, G. Babal, D. Bachacou, H. Bachas, K. Backman, F. Bagnaia, P. Bahrasemani, H. Bailey, A.J. Bailey, V.R. Baines, J.T. Bakalis, C. Baker, O.K. Bakker, P.J. Bakos, E. Gupta, D. Bakshi Balaji, S. Balasubramanian, R. Baldin, E.M. Balek, P. Balli, F. Balunas, W.K. Balz, J. Banas, E. Bandieramonte, M. Bandyopadhyay, A. Barak, L. Barbe, W.M. Ba Department of Physics University of Adelaide Adelaide Australia Physics Department SUNY Albany Albany NY United States Department of Physics University of Alberta Edmonton AB Canada Department of Physics Ankara University Ankara Turkey Istanbul Aydin University Application and Research Center for Advanced Studies Istanbul Turkey Division of Physics TOBB University of Economics and Technology Ankara Turkey LAPP Univ. Savoie Mont Blanc CNRS/IN2P3 Annecy France High Energy Physics Division Argonne National Laboratory Argonne IL United States Department of Physics University of Arizona Tucson AZ United States Department of Physics University of Texas at Arlington Arlington TX United States Physics Department National and Kapodistrian University of Athens Athens Greece Physics Department National Technical University of Athens Zografou Greece Department of Physics University of Texas at Austin Austin TX United States Bahcesehir University Faculty of Engineering and Natural Sciences Istanbul Turkey Istanbul Bilgi University Faculty of Engineering and Natural Sciences Istanbul Turkey Department of Physics Bogazici University Istanbul Turkey Department of Physics Engineering Gaziantep University Gaziantep Turkey Institute of Physics Azerbaijan Academy of Sciences Baku Azerbaijan Institut de Física d’Altes Energies (IFAE) Barcelona Institute of Science and Technology Barcelona Spain Institute of High Energy Physics Chinese Academy of Sciences Beijing China Physics Department Tsinghua University Beijing China Department of Physics Nanjing University Nanjing China University of Chinese Academy of Science (UCAS) Beijing China Institute of Physics University of Belgrade Belgrade Serbia Department for Physics and Technology University of Bergen Bergen Norway Physics Division Lawrence Berkeley National Laboratory and University of California Berkeley CA United States Institut für Physik Humboldt Universität zu Berlin Berlin Germany Albert Ei

The accurate simulation of additional interactions at the ATLAS experiment for the analysis of proton–proton collisions delivered by the Large Hadron Collider presents a significant challenge to the computing resources. During the LHC Run 2 (2015–2018), there were up to 70 inelastic interactions per bunch crossing, which need to be accounted for in Monte Carlo (MC) production. In this document, a new method to account for these additional interactions in the simulation chain is described. Instead of sampling the inelastic interactions and adding their energy deposits to a hard-scatter interaction one-by-one, the inelastic interactions are presampled, independent of the hard scatter, and stored as combined events. Consequently, for each hard-scatter interaction, only one such presampled event needs to be added as part of the simulation chain. For the Run 2 simulation chain, with an average of 35 interactions per bunch crossing, this new method provides a substantial reduction in MC production CPU needs of around 20%, while reproducing the properties of the reconstructed quantities relevant for physics analyses with good accuracy. © 2022, The Author(s).

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Heterogeneous multireference alignment for images with application to 2-D classification in single particle reconstruction

arXiv

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arXiv 2018年

作者： Ma, Chao Bendory, Tamir Boumal, Nicolas Sigworth, Fred Singer, Amit Program in Applied and Computational Mathematics Princeton University PrincetonNJ United States Department of Mathematics Princeton University PrincetonNJ United States Department of Cellular and Molecular Physiology Yale University School of Medicine New HavenCT United States

Motivated by the task of 2-D classification in single particle reconstruction by cryo-electron microscopy (cryo-EM), we consider the problem of heterogeneous multireference alignment of images. In this problem, the goal is to estimate a (typically small) set of target images from a (typically large) collection of observations. Each observation is a rotated, noisy version of one of the target images. For each individual observation, neither the rotation nor which target image has been rotated are known. As the noise level in cryo-EM data is high, clustering the observations and estimating individual rotations is challenging. We propose a framework to estimate the target images directly from the observations, completely bypassing the need to cluster or register the images. The framework consists of two steps. First, we estimate rotation-invariant features of the images, such as the bispectrum. These features can be estimated to any desired accuracy, at any noise level, provided sufficiently many observations are collected. Then, we estimate the images from the invariant features. Numerical experiments on synthetic cryo-EM datasets demonstrate the effectiveness of the method. Ultimately, we outline future developments required to apply this method to experimental data. Copyright © 2018, The Authors. All rights reserved.

关键词： Alignment

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The Athena++ Adaptive Mesh Refinement Framework: Design and Magnetohydrodynamic Solvers

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Astrophysical Journal, Supplement Series 2020年第1期249卷

作者： Stone, James M. Tomida, Kengo White, Christopher J. Felker, Kyle G. Department of Astrophysical Sciences Princeton University Princeton 08544 NJ United States Program in Applied and Computational Mathematics Princeton University Princeton 08544 NJ United States Department of Earth and Space Science Osaka University Toyonaka Osaka 560-0043 Japan Kavli Institute for Theoretical Physics University of California Santa Barbara Santa Barbara 93107 CA United States School of Natural Sciences Institute for Advanced Study Princeton 08544 NJ United States Astronomical Institute Tohoku University Sendai Miyagi 980-8578 Japan Argonne National Laboratory Lemont 60439 IL United States

The design and implementation of a new framework for adaptive mesh refinement calculations are described. It is intended primarily for applications in astrophysical fluid dynamics, but its flexible and modular design enables its use for a wide variety of physics. The framework works with both uniform and nonuniform grids in Cartesian and curvilinear coordinate systems. It adopts a dynamic execution model based on a simple design called a "task list"that improves parallel performance by overlapping communication and computation, simplifies the inclusion of a diverse range of physics, and even enables multiphysics models involving different physics in different regions of the calculation. We describe physics modules implemented in this framework for both nonrelativistic and relativistic magnetohydrodynamics (MHD). These modules adopt mature and robust algorithms originally developed for the Athena MHD code and incorporate new extensions: support for curvilinear coordinates, higher-order time integrators, more realistic physics such as a general equation of state, and diffusion terms that can be integrated with super-time-stepping algorithms. The modules show excellent performance and scaling, with well over 80% parallel efficiency on over half a million threads. The source code has been made publicly available. © 2020. The American Astronomical Society. All rights reserved..

关键词： Astronomy software Magnetohydrodynamics

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Correction to: Air change rate effects on the airborne diseases spreading in Underground Metro wagons

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Environmental science and pollution research international 2021年第24期28卷 31908页

作者： Mostafa El-Salamony Ahmed Moharam Amr Guaily Mohammed A Boraey Aerospace Department College of Engineering Peking University Beijing 100871 China. elsalamony.mostafa@***. Smart Engineering Systems Research Center (SESC) Nile University Sheikh Zayed City 12588 Egypt. Department of Engineering Mathematics and Physics Faculty of Engineering Cairo University Giza 12613 Egypt. Mechanical Engineering Program School of Engineering and Applied Sciences Nile University Sheikh Zayed City 12588 Egypt. Mechanical Power Engineering Department Zagazig University Zagazig 44519 Egypt.

A Correction to this paper has been published: https://***/10.1007/s11356-021-13405-8

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The SRG/eROSITA All-Sky Survey: Dark Energy Survey Year 3 Weak Gravitational Lensing by eRASS1 selected Galaxy Clusters

arXiv

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arXiv 2024年

作者： Grandis, S. Ghirardini, V. Bocquet, S. Garrel, C. Mohr, J.J. Liu, A. Kluge, M. Kimmig, L. Reiprich, T.H. Alarcon, A. Amon, A. Artis, E. Bahar, Y.E. Balzer, F. Bechtol, K. Becker, M.R. Bernstein, G. Bulbul, E. Campos, A. Carnero Rosell, A. Carrasco Kind, M. Cawthon, R. Chang, C. Chen, R. Chiu, I. Choi, A. Clerc, N. Comparat, J. Cordero, J. Davis, C. Derose, J. Diehl, H.T. Dodelson, S. Doux, C. Drlica-Wagner, A. Eckert, K. Elvin-Poole, J. Everett, S. Ferte, A. Gatti, M. Giannini, G. Giles, P. Gruen, D. Gruendl, R.A. Harrison, I. Hartley, W.G. Herner, K. Huff, E.M. Kleinebreil, F. Kuropatkin, N. Leget, P.F. Maccrann, N. McCullough, J. Merloni, A. Myles, J. Nandra, K. Navarro-Alsina, A. Okabe, N. Pacaud, F. Pandey, S. Prat, J. Predehl, P. Ramos, M. Raveri, M. Rollins, R.P. Roodman, A. Ross, A.J. Rykoff, E.S. Sanchez, C. Sanders, J. Schrabback, T. Secco, L.F. Seppi, R. Sevilla-Noarbe, I. Sheldon, E. Shin, T. Troxel, M. Tutusaus, I. Varga, T.N. Wu, H. Yanny, B. Yin, B. Zhang, X. Zhang, Y. Alves, O. Bhargava, S. Brooks, D. Burke, D.L. Carretero, J. Costanzi, M. da Costa, L.N. Pereira, M.E.S. De Vicente, J. Desai, S. Doel, P. Ferrero, I. Flaugher, B. Friedel, D. Frieman, J. García-Bellido, J. Gutierrez, G. Hinton, S.R. Hollowood, D.L. Honscheid, K. James, D.J. Jeffrey, N. Lahav, O. Lee, S. Marshall, J.L. Menanteau, F. Ogando, R.L.C. Pieres, A. Plazas Malagón, A.A. Romer, A.K. Sanchez, E. Schubnell, M. Smith, M. Suchyta, E. Swanson, M.E.C. Tarle, G. Weaverdyck, N. Weller, J. Universität Innsbruck Institut für Astro- und Teilchenphysik Technikerstr. 25/8 Innsbruck6020 Austria Max Planck Institute for Extraterrestrial Physics Giessenbachstrasse 1 Garching85748 Germany Faculty of Physics Ludwig-Maximilians-Universität München Scheinerstr. 1 Munich81679 Germany Universität Bonn Auf dem Hügel 71 Bonn53121 Germany Kavli Institute for Cosmology University of Cambridge Mading- ley Road CambridgeCB3 0HA United Kingdom Institute of Astronomy University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom Physics Department University of Wisconsin-Madison MadisonWI53706 United States Argonne National Laboratory 9700 South Cass Avenue LemontIL60439 United States Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Department of Physics Carnegie Mellon University PittsburghPA15312 United States Instituto de Astrofisica de Canarias Tenerife La LagunaE-38205 Spain Laboratório Interinstitucional de e-Astronomia - LIneA Rua Gal. José Cristino 77 RJ Rio de Janeiro20921-400 Brazil Universidad de La Laguna Dpto. Astrofísica Tenerife La LagunaE-38206 Spain Center for Astrophysical Surveys National Center for Supercom- puting Applications 1205 West Clark St. UrbanaIL61801 United States Department of Astronomy University of Illinois at Urbana-Champaign 1002 W. Green Street UrbanaIL61801 United States Physics Department William Jewell College LibertyMO64068 United States Department of Astronomy and Astrophysics University of Chicago ChicagoIL60637 United States Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Department of Physics Duke University DurhamNC27708 United States Department of Physics National Cheng Kung University Tainan70101 Taiwan NASA Goddard Space Flight Center 8800 Greenbelt Rd Green- belt MD20771 United States IRAP Université de Toulouse CNRS UPS CNES Toulouse France Jodrell Bank Center f

Context. Number counts of galaxy clusters across redshift are a powerful cosmological probe, if a precise and accurate reconstruction of the underlying mass distribution is performed - a challenge called mass calibration. With the advent of wide and deep photometric surveys, weak gravitational lensing by clusters has become the method of choice to perform this measurement. Aims. We measure and validate the weak gravitational lensing (WL) signature in the shape of galaxies observed in the first 3 years of the Dark Energy Survey (DES Y3) caused by galaxy clusters and groups selected in the first all-sky survey performed by SRG (Spectrum Roentgen Gamma)/eROSITA (eRASS1). These data are then used to determine the scaling between X-ray photon count rate of the clusters and their halo mass and redshift. Methods. We empirically determine the degree of cluster member contamination in our background source sample. The individual cluster shear profiles are then analysed with a Bayesian population model that self-consistently accounts for the lens sample selection and contamination, and includes marginalization over a host of instrumental and astrophysical systematics. To quantify the accuracy of the mass extraction of that model, we perform mass measurements on mock cluster catalogs with realistic synthetic shear profiles. This allows us to establish that hydro-dynamical modelling uncertainties at low lens redshifts (z © 2024, CC BY.

关键词： Calibration

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Reactive particle-tracking solutions to a benchmark problem on heavy metal cycling in lake sediments

arXiv

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arXiv 2019年

作者： Schmidt, Michael J. Pankavich, Stephen D. Navarre-Sitchler, Alexis Engdah, Nicholas B. Bolster, Diogo Benson, David A. Department of Civil and Environmental Engineering and Earth Sciences University of Notre Dame Notre DameIN46556 United States Department of Applied Mathematics and Statistics Colorado School of Mines GoldenCO80401 United States Hydrologic Science and Engineering Program Department of Geology and Geological Engineering Colorado School of Mines GoldenCO80401 United States Department of Civil and Environmental Engineering Washington State University PullmanWA99164 United States

Geochemical systems are known to exhibit highly variable spatiotemporal behavior. This may be observed both in non-smooth concentration curves in space for a single sampling time and also in variability between samples taken from the same location at different times. However, most models that are designed to simulate these systems provide only single-solution smooth curves and fail to capture the noise and variability seen in the data. We apply a recently developed reactive particle-tracking method to a system that displays highly-complex geochemical behavior. When the method is made to most closely resemble a corresponding Eulerian method, in its unperturbed form, we see near-exact match between solutions of the two models. More importantly, we consider two approaches for perturbing the model and find that the spatially-perturbed condition is able to capture a greater degree of the variability present in the data. This method of perturbation is a task to which particle methods are uniquely suited and Eulerian models are not well-suited. Additionally, because of the nature of the algorithm, noisy spatial gradients can be highly resolved by a large number of mobile particles, and this incurs negligible computational cost, as compared to expensive chemistry calculations. Copyright © 2019, The Authors. All rights reserved.

关键词： Heavy metals

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Science with the Daksha High Energy Transients Mission

arXiv

引用

arXiv 2022年

作者： Bhalerao, Varun Sawant, Disha Pai, Archana Tendulkar, Shriharsh Vadawale, Santosh Bhattacharya, Dipankar Rana, Vikram Adalja, Hitesh Kumar L. Anupama, G.C. Bala, Suman Banerjee, Smaranika Basu, Judhajeet Belatikar, Hrishikesh Beniamini, Paz Bhaganagare, Mahesh Bhaskar, Ankush Bhattacharjee, Soumyadeep Bose, Sukanta Cenko, Brad Chanda, Mehul Vijay Dewangan, Gulab Dixit, Vishal Dutta, Anirban Gawade, Priyanka Ghodgaonkar, Abhijeet Goyal, Shiv Kumar Gunasekaran, Suresh Hemanth, Manikantan Hotokezaka, Kenta Iyyani, Shabnam Guruprasad, P.J. Kasliwal, Mansi Koyande, Jayprakash G. Kulkarni, Salil Kutty, A.P.K. Ladiya, Tinkal Mahapatra, Suddhasatta Marla, Deepak Mate, Sujay Mehla, Advait Mithun, N.P.S. More, Surhud Mote, Rakesh Mukherjee, Dipanjan Narang, Sanjoli Narendranath, Shyama Nema, Ayush Nimbalkar, Sudhanshu Nissanke, Samaya Palit, Sourav Patel, Jinaykumar Patel, Arpit Paul, Biswajit Pradeep, Priya Ramachandran, Prabhu Roy, Kinjal Saiguhan, B.S. Bharath Saji, Joseph Saleem, M. Saraogi, Divita Sastry, Parth Shanmugam, M. Sharma, Piyush Shetye, Amit Singh, Nishant Singh, Shreeya Singhal, Akshat Sreekumar, S. Sridhar, Srividhya Srinivasan, Rahul Tallur, Siddharth Tiwari, Neeraj K. Vadladi, Amrutha Lakshmi Vaishnava, C.S. Vishwakarma, Sandeep Waratkar, Gaurav Department of Physics IIT Bombay Powai Mumbai400076 India Department of Astronomy and Astrophysics Tata Institute of Fundamental Research Mumbai400005 India Physical Research Laboratory Navrangpura Ahmedabad380009 India Ashoka University Department of Physics Haryana Sonepat131029 India Raman Research Institute C. V. Raman Avenue Sadashivanagar Bengaluru560080 India Indian Institute of Astrophysics 2nd Block 100 Feet Rd Koramangala Bengaluru560034 India Tohoku University Aoba Sendai980-0845 Japan Stockholm University AlbaNova StockholmSE 10691 Sweden Department of Electrical Engineering IIT Bombay Powai Mumbai400076 India Department of Natural Sciences The Open University of Israel P.O Box 808 Ra'anana4353701 Israel Department of Physics Indian Institute of Science Karnataka Bengaluru560012 India Inter-University Center for Astronomy and Astrophysics Maharashtra Pune411007 India University of Maryland College Park United States Interdisciplinary Program in Climate Studies IIT Bombay Powai Mumbai400076 India Department of Aerospace Engineering IIT Bombay Powai Mumbai400076 India University of Tokyo Bunkyo City Tokyo113-8654 Japan School of Physics IISER Kerala Thiruvananthapuram695551 India Division of Physics Mathematics and Astronomy California Institute of Technology PasadenaCA91125 United States Department of Mechanical Engineering IIT Bombay Powai Mumbai400076 India Space Astronomy Group U R Rao Satellite Centre ISRO Bengaluru560037 India Nikhef Science Park 105 Amsterdam1098 XG Netherlands Department of Mechanical and Aerospace Engineering The University of Texas at Arlington ArlingtonTX76019 United States Vikram Sarabhai Space Centre Kochuveli Thiruvananthapuram695022 India Chennai Mathematical Institute Tamilnadu Siruseri603103 India School of Physics and Astronomy University of Minnesota MinneapolisMN55455 United States Department of Chemical Engineering IIT Bombay Powai Mumbai400076 Ind

We present the science case for the proposed Daksha high energy transients mission. Daksha will comprise of two satellites covering the entire sky from 1 keV to > 1 MeV. The primary objectives of the mission are to discover and characterize electromagnetic counterparts to gravitational wave source;and to study Gamma Ray Bursts (GRBs). Daksha is a versatile all-sky monitor that can address a wide variety of science cases. With its broadband spectral response, high sensitivity, and continuous all-sky coverage, it will discover fainter and rarer sources than any other existing or proposed mission. Daksha can make key strides in GRB research with polarization studies, prompt soft spectroscopy, and fine time-resolved spectral studies. Daksha will provide continuous monitoring of X-ray pulsars. It will detect magnetar outbursts and high energy counterparts to Fast Radio Bursts. Using Earth occultation to measure source fluxes, the two satellites together will obtain daily flux measurements of bright hard X-ray sources including active galactic nuclei, X-ray binaries, and slow transients like Novae. Correlation studies between the two satellites can be used to probe primordial black holes through lensing. Daksha will have a set of detectors continuously pointing towards the Sun, providing excellent hard X-ray monitoring data. Closer to home, the high sensitivity and time resolution of Daksha can be leveraged for the characterization of Terrestrial Gamma-ray Flashes. © 2022, CC BY.

关键词： Gravity waves

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The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

arXiv

引用

arXiv 2023年

作者： Kazerooni, Anahita Fathi Khalili, Nastaran Liu, Xinyang Haldar, Debanjan Jiang, Zhifan Anwar, Syed Muhammed Albrecht, Jake Adewole, Maruf Anazodo, Udunna Anderson, Hannah Bagheri, Sina Baid, Ujjwal Bergquist, Timothy Borja, Austin J. Calabrese, Evan Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Haldar, Shuvanjan Iglesias, Juan Eugenio Janas, Anastasia Johansen, Elaine Jones, Blaise V. Kofler, Florian LaBella, Dominic Lai, Hollie Anne Van Leemput, Koen Li, Hongwei Bran Maleki, Nazanin McAllister, Aaron S. Meier, Zeke Menze, Bjoern Moawad, Ahmed W. Nandolia, Khanak K. Pavaine, Julija Piraud, Marie Poussaint, Tina Prabhu, Sanjay P. Reitman, Zachary Rodriguez, Andres Rudie, Jeffrey D. Sanchez-Montano, Mariana Shaikh, Ibraheem Salman Shah, Lubdha M. Sheth, Nakul Shinohara, Russel Taki Tu, Wenxin Viswanathan, Karthik Wang, Chunhao Ware, Jeffrey B. Wiestler, Benedikt Wiggins, Walter Zapaishchykova, Anna Aboian, Mariam Bornhorst, Miriam de Blank, Peter Deutsch, Michelle Fouladi, Maryam Hoffman, Lindsey Kann, Benjamin Lazow, Margot Mikael, Leonie Nabavizadeh, Ali Packer, Roger Resnick, Adam Rood, Brian Vossough, Arastoo Bakas, Spyridon Linguraru, Marius George Children’s Hospital of Philadelphia PhiladelphiaPA United States Department of Neurosurgery University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Department of Neurosurgery Thomas Jefferson University Hospital PA United States Sage Bionetworks United States Lab Crestview Radiology Lagos Nigeria McGill University MontrealQC Canada Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Neurosurgery The University of Southern California CA United States Department of Radiology Duke University Medical Center United States Mayo Clinic MN United States Center for Global Health Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States Biomedical Engineering Rutgers University New BrunswickNJ United States Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Yale University New HavenCT United States PrecisionFDA U.S. Food and Drug Administration Silver SpringMD United States Cincinnati Children’s Hospital Medical Center United States Helmholtz AI Helmholtz Munich Germany Department of Radiation Oncology Duke University Medical Center United States Department of Radiology Children’s Health Orange County CA United States Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Department of Radiol

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors. © 2023, CC BY.

关键词： Tumors

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