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arXiv 2019年

作者： Carmichael, By Iain Calhoun, Benjamin C. Hoadley, Katherine A. Troester, Melissa A. Geradts, Joseph Couture, Heather D. Olsson, Linnea Perou, Charles M. Niethammer, Marc Hannig, Jan Marron, J.S. Department of Statistics University of Washington SeattleWA98195 United States Department of Pathology and Laboratory Medicine University of North Carolina at Chapel Hill Chapel HillNC27599 United States Department of Genetics Lineberger Comprehensive Cancer Center Computational Medicine Program University of North Carolina at Chapel Hill Chapel HillNC27599 United States Department of Epidemiology Department of Pathology and Laboratory Medicine University of North Carolina at Chapel Hill Chapel HillNC27599 United States Department of Population Sciences City of Hope National Medical Center DuarteCA91010 United States Pixel Scientia Labs RaleighNC27615 Department of Epidemiology University of North Carolina at Chapel Hill Chapel HillNC27599 United States Department of Genetics Department of Pathology and Laboratory Medicine University of North Carolina at Chapel Hill Chapel HillNC27599 United States Department of Computer Science University of North Carolina at Chapel Hill Chapel HillNC27599 United States Department of Statistics University of North Carolina at Chapel Hill Chapel HillNC27599 United States

A key challenge in modern data analysis is understanding connections between complex and differing modalities of data. For example, two of the main approaches to the study of breast cancer are histopathology (analyzing visual characteristics of tumors) and genetics. While histopathology is the gold standard for diagnostics and there have been many recent breakthroughs in genetics, there is little overlap between these two fields. We aim to bridge this gap by developing methods based on Angle-based Joint and Individual Variation Explained (AJIVE) to directly explore similarities and differences between these two modalities. Our approach exploits Convolutional Neural Networks (CNNs) as a powerful, automatic method for image feature extraction to address some of the challenges presented by statistical analysis of histopathology image data. CNNs raise issues of interpretability that we address by developing novel methods to explore visual modes of variation captured by statistical algorithms (e.g. PCA or AJIVE) applied to CNN features. Our results provide many interpretable connections and contrasts between histopathology and genetics. Copyright © 2019, The Authors. All rights reserved.

关键词： Gene expression

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SPT clusters with DES and HST weak lensing. II. Cosmological constraints from the abundance of massive halos

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Physical Review D 2024年第8期110卷 083510页

作者： S. Bocquet S. Grandis L. E. Bleem M. Klein J. J. Mohr T. Schrabback T. M. C. Abbott P. A. R. Ade M. Aguena A. Alarcon S. Allam S. W. Allen O. Alves A. Amon A. J. Anderson J. Annis B. Ansarinejad J. E. Austermann S. Avila D. Bacon M. Bayliss J. A. Beall K. Bechtol M. R. Becker A. N. Bender B. A. Benson G. M. Bernstein S. Bhargava F. Bianchini M. Brodwin D. Brooks L. Bryant A. Campos R. E. A. Canning J. E. Carlstrom A. Carnero Rosell M. Carrasco Kind J. Carretero F. J. Castander R. Cawthon C. L. Chang C. Chang P. Chaubal R. Chen H. C. Chiang A. Choi T-L. Chou R. Citron C. Corbett Moran J. Cordero M. Costanzi T. M. Crawford A. T. Crites L. N. da Costa M. E. S. Pereira C. Davis T. M. Davis J. DeRose S. Desai T. de Haan H. T. Diehl M. A. Dobbs S. Dodelson C. Doux A. Drlica-Wagner K. Eckert J. Elvin-Poole S. Everett W. Everett I. Ferrero A. Ferté A. M. Flores J. Frieman J. Gallicchio J. García-Bellido M. Gatti E. M. George G. Giannini M. D. Gladders D. Gruen R. A. Gruendl N. Gupta G. Gutierrez N. W. Halverson I. Harrison W. G. Hartley K. Herner S. R. Hinton G. P. Holder D. L. Hollowood W. L. Holzapfel K. Honscheid J. D. Hrubes N. Huang J. Hubmayr E. M. Huff D. Huterer K. D. Irwin D. J. James M. Jarvis G. Khullar K. Kim L. Knox R. Kraft E. Krause K. Kuehn N. Kuropatkin F. Kéruzoré O. Lahav A. T. Lee P.-F. Leget D. Li H. Lin A. Lowitz N. MacCrann G. Mahler A. Mantz J. L. Marshall J. McCullough M. McDonald J. J. McMahon J. Mena-Fernández F. Menanteau S. S. Meyer R. Miquel J. Montgomery J. Myles T. Natoli A. Navarro-Alsina J. P. Nibarger G. I. Noble V. Novosad R. L. C. Ogando Y. Omori S. Padin S. Pandey P. Paschos S. Patil A. Pieres A. A. Plazas Malagón A. Porredon J. Prat C. Pryke M. Raveri C. L. Reichardt J. Roberson R. P. Rollins C. Romero A. Roodman J. E. Ruhl E. S. Rykoff B. R. Saliwanchik L. Salvati C. Sánchez E. Sanchez D. Sanchez Cid A. Saro K. K. Schaffer L. F. Secco I. Sevilla-Noarbe K. Sharon E. Sheldon T. Shin C. Sievers G. Smecher M. Smith T. Somboonpanyakul M. Sommer B. Stalder A. A. Stark J. Stephen V. Straz University Observatory Faculty of Physics Universität Innsbruck Institut für Astro- und Teilchenphysik Technikerstraße 25/8 6020 Innsbruck Austria High-Energy Physics Division Argonne National Laboratory 9700 South Cass Avenue Lemont Illinois 60439 USA Kavli Institute for Cosmological Physics University of Chicago 5640 South Ellis Avenue Chicago Illinois 60637 USA Max Planck Institute for Extraterrestrial Physics Gießenbachstraße 1 85748 Garching Germany Argelander-Institut für Astronomie Auf dem Hügel 71 53121 Bonn 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 Cardiff CF24 3YB United Kingdom Laboratório Interinstitucional de e-Astronomia—LIneA Rua Gal. José Cristino 77 Rio de Janeiro Rio de Janeiro—20921-400 Brazil Fermi National Accelerator Laboratory P. O. Box 500 Batavia Illinois 60510 USA Kavli Institute for Particle Astrophysics and Cosmology Stanford University 452 Lomita Mall Stanford California 94305 USA Department of Physics Stanford University 382 Via Pueblo Mall Stanford California 94305 USA SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo Park California 94025 USA Department of Physics University of Michigan Ann Arbor Michigan 48109 USA Institute of Astronomy University of Cambridge Madingley Road Cambridge CB3 0HA United Kingdom Kavli Institute for Cosmology University of Cambridge Madingley Road Cambridge CB3 0HA United Kingdom School of Physics University of Melbourne Parkville Victoria 3010 Australia NIST Quantum Devices Group 325 Broadway Mailcode 817.03 Boulder Colorado 80305 USA Department of Physics University of Colorado Boulder Colorado 80309 USA Institut de Física d’Altes Energies (IFAE) The Barcelona Institute of Science and Technology Campus UAB 08193 Bellaterra (Barcelona) Spain Institute of Cosmology and Gravitation University of Portsmouth Portsmouth

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 5200 deg2. We use DES Year 3 weak-lensing data for 688 clusters with redshifts z<0.95 and HST weak-lensing data for 39 clusters with 0.6

关键词： Cosmological parameters Dark energy Large scale structure of the Universe Galaxy clusters

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Ball prolate spheroidal wave functions in arbitrary dimensions

arXiv

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arXiv 2018年

作者： Zhang, Jing Li, Huiyuan Wang, Li-Lian Zhang, Zhimin School of Mathematics and Statistics Hubei Key Laboratory of Mathematical Sciences Central China Normal University Wuhan430079 China State Key Laboratory of Computer Science Laboratory of Parallel Computing Institute of Software Chinese Academy of Sciences Beijing100190 China Division of Mathematical Sciences School of Physical and Mathematical Sciences Nanyang Technological University Singapore637371 Beijing Computational Sciences and Research Center Beijing100193 China Department of Mathematics Wayne State University MI48202 United States

In this paper, we introduce the prolate spheroidal wave functions (PSWFs) of real order α > −1 on the unit ball in arbitrary dimension, termed as ball PSWFs. They are eigenfunctions of both a weighted concentration integral operator, and a Sturm-Liouville differential operator. Different from existing works on multi-dimensional PSWFs, the ball PSWFs are defined as a generalisation of orthogonal ball polynomials in primitive variables with a tuning parameter c > 0, through a "perturbation" of the Sturm-Liouville equation of the ball polynomials. From this perspective, we can explore some interesting intrinsic connections between the ball PSWFs and the finite Fourier and Hankel transforms. We provide an efficient and accurate algorithm for computing the ball PSWFs and the associated eigenvalues, and present various numerical results to illustrate the efficiency of the method. Under this uniform framework, we can recover the existing PSWFs by suitable variable *** Codes 42B37, 33E30, 33C47, 42C05, 65D20, 41A10 Copyright © 2018, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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Cross-correlation of Dark Energy Survey Year 3 lensing data with ACT and Planck thermal Sunyaev-Zel’dovich effect observations. II. Modeling and constraints on halo pressure profiles

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Physical Review D 2022年第12期105卷 123526-123526页

作者： S. Pandey M. Gatti E. Baxter J. C. Hill X. Fang C. Doux G. Giannini M. Raveri J. DeRose H. Huang E. Moser N. Battaglia A. Alarcon A. Amon M. Becker A. Campos C. Chang R. Chen A. Choi K. Eckert J. Elvin-Poole S. Everett A. Ferte I. Harrison N. Maccrann J. Mccullough J. Myles A. Navarro Alsina J. Prat R. P. Rollins C. Sanchez T. Shin M. Troxel I. Tutusaus B. Yin M. Aguena S. Allam F. Andrade-Oliveira G. M. Bernstein E. Bertin B. Bolliet J. R. Bond D. Brooks E. Calabrese A. Carnero Rosell M. Carrasco Kind J. Carretero R. Cawthon M. Costanzi M. Crocce L. N. da Costa M. E. S. Pereira J. De Vicente S. Desai H. T. Diehl J. P. Dietrich P. Doel J. Dunkley A. E. Evrard S. Ferraro I. Ferrero B. Flaugher P. Fosalba J. García-Bellido E. Gaztanaga D. W. Gerdes T. Giannantonio D. Gruen R. A. Gruendl J. Gschwend G. Gutierrez K. Herner A. D. Hincks S. R. Hinton D. L. Hollowood K. Honscheid J. P. Hughes D. Huterer B. Jain D. J. James T. Jeltema E. Krause K. Kuehn O. Lahav M. Lima M. Lokken M. S. Madhavacheril M. A. G. Maia J. J. Mcmahon P. Melchior F. Menanteau R. Miquel J. J. Mohr K. Moodley R. Morgan F. Nati M. D. Niemack L. Page A. Palmese F. Paz-Chinchón A. Pieres A. A. Plazas Malagón M. Rodriguez-Monroy A. K. Romer E. Sanchez V. Scarpine E. Schaan S. Serrano I. Sevilla-Noarbe E. Sheldon B. D. Sherwin C. Sifón M. Smith M. Soares-Santos D. Spergel E. Suchyta M. E. C. Swanson G. Tarle D. Thomas C. To T. N. Varga J. Weller E. J. Wollack Z. Xu Department of Physics and Astronomy University of Pennsylvania Philadelphia Pennsylvania 19104 USA Institute for Astronomy University of Hawai’i 2680 Woodlawn Drive Honolulu Hawai’i 96822 USA Department of Physics Columbia University New York New York 10027 USA Center for Computational Astrophysics Flatiron Institute New York New York 10010 USA Department of Astronomy/Steward Observatory University of Arizona 933 North Cherry Avenue Tucson Arizona 85721-0065 USA Institut de Física d’Altes Energies (IFAE) The Barcelona Institute of Science and Technology Campus UAB 08193 Bellaterra (Barcelona) Spain Lawrence Berkeley National Laboratory 1 Cyclotron Road Berkeley California 94720 USA Department of Physics University of Arizona Tucson Arizona 85721 USA Department of Astronomy Cornell University Ithaca New York 14853 USA Argonne National Laboratory 9700 South Cass Avenue Lemont Illinois 60439 USA Kavli Institute for Particle Astrophysics and Cosmology P. O. Box 2450 Stanford University Stanford California 94305 USA Department of Physics Carnegie Mellon University Pittsburgh Pennsylvania 15312 USA Department of Astronomy and Astrophysics University of Chicago Chicago Illinois 60637 USA Kavli Institute for Cosmological Physics University of Chicago Chicago Illinois 60637 USA Department of Physics Duke University Durham North Carolina 27708 USA Center for Cosmology and Astro-Particle Physics The Ohio State University Columbus Ohio 43210 USA Department of Physics The Ohio State University Columbus Ohio 43210 USA Santa Cruz Institute for Particle Physics Santa Cruz California 95064 USA Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive Pasadena California 91109 USA Department of Physics University of Oxford Denys Wilkinson Building Keble Road Oxford OX1 3RH United Kingdom Jodrell Bank Center for Astrophysics School of Physics and Astronomy University of Manchester Oxford Road Manchester M13 9PL United Kin

Hot, ionized gas leaves an imprint on the cosmic microwave background via the thermal Sunyaev-Zel’dovich (tSZ) effect. The cross-correlation of gravitational lensing (which traces the projected mass) with the tSZ effect (which traces the projected gas pressure) is a powerful probe of the thermal state of ionized baryons throughout the Universe and is sensitive to effects such as baryonic feedback. In a companion paper (Gatti et al. Phys. Rev. D 105, 123525 (2022)), we present tomographic measurements and validation tests of the cross-correlation between Galaxy shear measurements from the first three years of observations of the Dark Energy Survey and tSZ measurements from a combination of Atacama Cosmology Telescope and Planck observations. In this work, we use the same measurements to constrain models for the pressure profiles of halos across a wide range of halo mass and redshift. We find evidence for reduced pressure in low-mass halos, consistent with predictions for the effects of feedback from active Galactic nuclei. We infer the hydrostatic mass bias (B≡M500c/MSZ) from our measurements, finding B=1.8±0.1 when adopting the Planck-preferred cosmological parameters. We additionally find that our measurements are consistent with a nonzero redshift evolution of B, with the correct sign and sufficient magnitude to explain the mass bias necessary to reconcile cluster count measurements with the Planck-preferred cosmology. Our analysis introduces a model for the impact of intrinsic alignments (IAs) of galaxy shapes on the shear-tSZ correlation. We show that IA can have a significant impact on these correlations at current noise levels.

关键词： Cosmic microwave background Cosmological parameters Gravitational lenses Large scale structure of the Universe Optical, UV, & IR astronomy

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Atacama Cosmology Telescope: Constraints on prerecombination early dark energy

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Physical Review D 2022年第12期105卷 123536-123536页

作者： J. Colin Hill Erminia Calabrese Simone Aiola Nicholas Battaglia Boris Bolliet Steve K. Choi Mark J. Devlin Adriaan J. Duivenvoorden Jo Dunkley Simone Ferraro Patricio A. Gallardo Vera Gluscevic Matthew Hasselfield Matt Hilton Adam D. Hincks Renée Hložek Brian J. Koopman Arthur Kosowsky Adrien La Posta Thibaut Louis Mathew S. Madhavacheril Jeff McMahon Kavilan Moodley Sigurd Naess Umberto Natale Federico Nati Laura Newburgh Michael D. Niemack Lyman A. Page Bruce Partridge Frank J. Qu Maria Salatino Alessandro Schillaci Neelima Sehgal Blake D. Sherwin Cristóbal Sifón David N. Spergel Suzanne T. Staggs Emilie R. Storer Alexander van Engelen Eve M. Vavagiakis Edward J. Wollack Zhilei Xu Department of Physics Columbia University New York New York 10027 USA Center for Computational Astrophysics Flatiron Institute New York New York 10010 USA School of Physics and Astronomy Cardiff University The Parade Cardiff Wales CF24 3AA United Kingdom Department of Astronomy Cornell University Ithaca New York 14853 USA Department of Physics Cornell University Ithaca New York 14853 USA Department of Physics and Astronomy University of Pennsylvania 209 South 33rd Street Philadelphia Pennsylvania 19104 USA Department of Physics Jadwin Hall Princeton University Princeton New Jersey 08544 USA Department of Astrophysical Sciences Princeton University Peyton Hall Princeton New Jersey 08544 USA Lawrence Berkeley National Laboratory One Cyclotron Road Berkeley California 94720 USA Berkeley Center for Cosmological Physics University of California Berkeley California 94720 USA Kavli Institute for Cosmological Physics University of Chicago Chicago Illinois 60637 USA Department of Physics and Astronomy University of Southern California Los Angeles California 90007 USA Astrophysics Research Centre University of KwaZulu-Natal Westville Campus Durban 4041 South Africa School of Mathematics Statistics and Computer Science University of KwaZulu-Natal Westville Campus Durban 4041 South Africa David A. Dunlap Department of Astronomy and Astrophysics University of Toronto 50 St. George St. Toronto Ontario M5S 3H4 Canada Dunlap Institute of Astronomy and Astrophysics 50 St. George St. Toronto Ontario M5S 3H4 Canada Department of Physics Yale University New Haven Connecticut 06520 USA Department of Physics and Astronomy University of Pittsburgh Pittsburgh Pennsylvania 15260 USA Université Paris-Saclay CNRS/IN2P3 IJCLab 91405 Orsay France Perimeter Institute for Theoretical Physics 31 Caroline Street N Waterloo Ontario N2L 2Y5 Canada Department of Astronomy and Astrophysics University of Chicago Chicago Illinois 60637 USA Department of Physics

The early dark energy (EDE) scenario aims to increase the value of the Hubble constant (H0) inferred from cosmic microwave background (CMB) data over that found in the standard cosmological model (ΛCDM), via the introduction of a new form of energy density in the early Universe. The EDE component briefly accelerates cosmic expansion just prior to recombination, which reduces the physical size of the sound horizon imprinted in the CMB. Previous work has found that nonzero EDE is not preferred by Planck CMB power spectrum data alone, which yield a 95% confidence level (C.L.) upper limit fEDE<0.087 on the maximal fractional contribution of the EDE field to the cosmic energy budget. In this paper, we fit the EDE model to CMB data from the Atacama Cosmology Telescope (ACT) data release 4. We find that a combination of ACT, large-scale Planck TT (similar to WMAP), Planck CMB lensing, and BAO data prefers the existence of EDE at >99.7% C.L.: fEDE=0.091−0.036+0.020, with H0=70.9−2.0+1.0 km/s/Mpc (both 68% C.L.). From a model-selection standpoint, we find that EDE is favored over ΛCDM by these data at roughly 3σ significance. In contrast, a joint analysis of the full Planck and ACT data yields no evidence for EDE, as previously found for Planck alone. We show that the preference for EDE in ACT alone is driven by its TE and EE power spectrum data. The tight constraint on EDE from Planck alone is driven by its high-ℓ TT power spectrum data. Understanding whether these differing constraints are physical in nature, due to systematics, or simply a rare statistical fluctuation is of high priority. The best-fit EDE models to ACT and Planck exhibit coherent differences across a wide range of multipoles in TE and EE, indicating that a powerful test of this scenario is anticipated with near-future data from ACT and other ground-based experiments.

关键词： Cosmic microwave background Cosmological parameters Dark energy Evolution of the Universe

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Author Correction: Community-wide hackathons to identify central themes in single-cell multi-omics

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Genome biology 2021年第1期22卷 246页

作者： Kim-Anh Lê Cao Al J Abadi Emily F Davis-Marcisak Lauren Hsu Arshi Arora Alexis Coullomb Atul Deshpande Yuzhou Feng Pratheepa Jeganathan Melanie Loth Chen Meng Wancen Mu Vera Pancaldi Kris Sankaran Dario Righelli Amrit Singh Joshua S Sodicoff Genevieve L Stein-O'Brien Ayshwarya Subramanian Joshua D Welch Yue You Ricard Argelaguet Vincent J Carey Ruben Dries Casey S Greene Susan Holmes Michael I Love Matthew E Ritchie Guo-Cheng Yuan Aedin C Culhane Elana Fertig Melbourne Integrative Genomics School of Mathematics and Statistics University of Melbourne Melbourne Australia. kimanh.lecao@nimelb.edu.au. Melbourne Integrative Genomics School of Mathematics and Statistics University of Melbourne Melbourne Australia. McKusick-Nathans Institute of the Department of Genetic Medicine Johns Hopkins School of Medicine Baltimore MD USA. Data Science Dana-Farber Cancer Institute Boston MA USA. Biostatistics Harvard TH Chan School of Public Health Boston MA USA. Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA. Centre de Recherches en Cancérologie de Toulouse (INSERM) Université Paul Sabatier III Toulouse France. Cancer Convergence Institute and Division of Quantitative Sciences Department of Oncology Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University School of Medicine Baltimore MD USA. Department of Mathematics and Statistics McMaster University Hamilton Canada. Bavarian Center for Biomolecular Mass Spectrometry (BayBioMS) School of Life Sciences Technical University of Munich Munich Germany. Department of Biostatistics UNC Chapel Hill NC USA. Barcelona Supercomputing Center Barcelona Spain. Department of Statistics University of Wisconsin Madison WI USA. Department of Statistical Sciences University of Padova Padova PD Italy. Department of Pathology and Laboratory Medicine University of British Columbia Vancouver BC Canada. PROOF Centre of Excellence Vancouver BC Canada. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. Department of Biomedical Engineering University of Michigan Ann Arbor MI USA. Department of Neuroscience Johns Hopkins University Baltimore MD USA. Kavli Neuroscience Discovery Institute Johns Hopkins University Baltimore MD USA. Klarman Cell Observatory Broad Institute of MIT and Harvard Cambridge MA USA. Department of Computer Science and Engineering University of Michiga

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The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients

arXiv

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arXiv 2021年

作者： Baheti, Bhakti Chakrabarty, Satrajit Akbari, Hamed Bilello, Michel Wiestler, Benedikt Schwarting, Julian Calabrese, Evan Rudie, Jeffrey Abidi, Syed Mousa, Mina Villanueva-Meyer, Javier Fields, Brandon K.K. Kofler, Florian Shinohara, Russell Takeshi Iglesias, Juan Eugenio Mok, Tony C.W. Chung, Albert C.S. Wodzinski, Marek Jurgas, Artur Marini, Niccolò Atzori, Manfredo Müller, Henning Grosbröhmer, Christoph Siebert, Hanna Hansen, Lasse Heinrich, Mattias P. Canalini, Luca Klein, Jan Gerken, Annika Heldmann, Stefan Hering, Alessa Hahn, Horst K. Meng, Mingyuan Bi, Lei Feng, Dagan Kim, Jinman Zeineldinal, Ramy A. Karar, Mohamed E. Mathis-Ullrich, Franziska Burgert, Oliver Abderezaei, Javid Pionteck, Aymeric Chopra, Agamdeep Kurt, Mehmet Yan, Kewei Yan, Yonghong Tang, Zhe Ma, Jianqiang Nasser, Sahar Almahfouz Kurian, Nikhil Cherian Meena, Mohit Shamsi, Saqib Sethi, Amit Tustison, Nicholas J. Avants, Brian B. Cook, Philip Gee, James C. Tian, Lin Greer, Hastings Niethammer, Marc Hoopesar, Andrew Hoffmannas, Malte Dalcaar, Adrian V. Christodoulidis, Stergios Estiene, Théo Vakalopoulou, Maria Paragios, Nikos Marcus, Daniel S. Davatzikos, Christos Sotiras, Aristeidis Menze, Bjoern Bakas, Spyridon Waldmannstetter, Diana Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO United States GE HealthCare San RamonCA United States University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Mallinckrodt Institute of Radiology Washington University School of Medicine St. LouisMO United States Institute for Informatics Data Science & Biostatistics Washington University School of Medicine St. LouisMO United States Department of Bioengineering Santa Clara University Santa ClaraCA United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Computer Science TUM School of Computation Information and Technology Technical University of Munich Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Munich Germany Department of Nuclear Medicine University Hospital Regensburg Regensburg Germany University Hospital Regensburg Regensburg Germany Department of Radiology & Biomedical Imaging University of California San FranciscoCA United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Radiology University of California San DiegoCA United States Helmholtz AI Helmholtz Munich Neuherberg Germany Penn Statistics in Imaging and Visualization Center Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Philadelphia United States Massachusetts General Hospital Harvard Medical School United States Centre for Medical Imaging Computing University College London United Kingdom The Hong Kong University of Science and Technology Hong Kong University of Applied Sciences West

Registration of longitudinal brain Magnetic Resonance Imaging (MRI) scans containing pathologies is challenging due to dramatic changes in tissue appearance. Although there has been considerable progress in developing general-purpose medical image registration techniques, they have not yet attained the requisite precision and reliability for this task, highlighting its inherent complexity. Here we describe the Brain Tumor Sequence Registration (BraTS-Reg) challenge, as the first public benchmark environment for deformable registration algorithms focusing on estimating correspondences between pre-operative and follow-up scans of the same patient diagnosed with a diffuse brain glioma. The challenge was conducted in conjunction with both the IEEE International Symposium on Biomedical Imaging (ISBI) 2022 and the International Conference on Medical Image Computing and computer-Assisted Intervention (MICCAI) 2022. The BraTS-Reg data comprise de-identified multi-institutional multi-parametric MRI (mpMRI) scans, curated for size and resolution according to a canonical anatomical template, and divided into training, validation, and testing sets. Clinical experts annotated ground truth (GT) landmark points of anatomical locations distinct across the temporal domain. The training data with their GT annotations, were publicly released to enable the development of registration algorithms. The validation data, without their GT annotations, were also released to allow for algorithmic evaluation prior to the testing phase, which only allowed submission of containerized algorithms for evaluation on hidden hold-out testing data. Quantitative evaluation and ranking was based on the Median Euclidean Error (MEE), Robustness, and the determinant of the Jacobian of the displacement field. The top-ranked methodologies yielded similar performance across all evaluation metrics and shared several methodological commonalities, including pre-alignment, deep neural networks, inverse consistency

关键词： Magnetic resonance imaging

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A community-powered search of machine learning strategy space to find NMR property prediction models

arXiv

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arXiv 2020年

作者： Bratholm, Lars A. Gerrard, Will Anderson, Brandon Bai, Shaojie Choi, Sunghwan Dang, Lam Hanchar, Pavel Howard, Addison Huard, Guillaume Kim, Sanghoon Kolter, Zico Kondor, Risi Kornbluth, Mordechai Lee, Youhan Lee, Youngsoo Mailoa, Jonathan P. Nguyen, Thanh Tu Popovic, Milos Rakocevic, Goran Reade, Walter Song, Wonho Stojanovic, Luka Thiede, Erik H. Tijanic, Nebojsa Torrubia, Andres Willmott, Devin Butts, Craig P. Glowacki, David R. Participants, Kaggle School of Chemistry University of Bristol Cantock’s Close BristolBS8 1TS United Kingdom School of Mathematics University of Bristol Fry Building Woodland Road BristolBS1 1UG United Kingdom Dept. of Computer Science University of Bristol Merchant Venturer’s Building BristolBS8 1UB United Kingdom Intangible Realities Laboratory University of Bristol Cantock’s Close BristolBS8 1TS United Kingdom Bosch Center for Artificial Intelligence PittsburghPA15222 United States Carnegie Mellon University PittsburghPA15213 United States National Institute of Supercomputing and Network Korea Institute of Science and Technology Information 245 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of BNP Paribas Cardif Fyusion Inc. Kaggle Google Inc. Mountain View United States Ebay Korea 34F Gangnam Finance Center 152 Teheran Ro Gangnam Gu Seoul Korea Republic of Department of Computer Science University of Chicago 5730 S Ellis Ave ChicagoIL60637 United States Department of Statistics University of Chicago 5747 S Ellis Ave ChicagoIL60637 United States Center for Computational Mathematics Flatiron Institute 162 5th Ave. New YorkNY10010 United States Bosch Research and Technology Center CambridgeMA02139 United States Department of Chemical and Biomolecular Engineering Korea Advanced Institute of Science and Technology 291 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of 111 Daedeok-daero 989beon-gil Yuseong-gu Daejeon Korea Republic of MINDS AND COMPANY 2621 Nambusunhwan-ro Gangnam-gu Seoul06267 Korea Republic of Totient Inc Sindjeliceva 9 Belgrade11000 Serbia KAIST Web Security & Privacy Lab 291 Daehak-ro Yuseong-guDaejeon34141 Korea Republic of *** Alicante Spain

The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML strategies to a particular domain, it can be difficult to assess in advance what strategy to adopt within a vast space of possibilities. Here we outline the results of an online community-powered effort to swarm search the space of ML strategies and develop algorithms for predicting atomic-pairwise nuclear magnetic resonance (NMR) properties in molecules. Using an open-source dataset, we worked with Kaggle to design and host a 3-month competition which received 47,800 ML model predictions from 2,700 teams in 84 countries. Within 3 weeks, the Kaggle community produced models with comparable accuracy to our best previously published ‘in-house’ efforts. A meta-ensemble model constructed as a linear combination of the top predictions has a prediction accuracy which exceeds that of any individual model, 7-19x better than our previous state-of-the-art. The results highlight the potential of transformer architectures for predicting quantum mechanical (QM) molecular properties. Copyright © 2020, The Authors. All rights reserved.

关键词： Nuclear magnetic resonance

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The Atacama Cosmology Telescope: Mitigating the impact of extragalactic foregrounds for the DR6 CMB lensing analysis

arXiv

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arXiv 2023年

作者： MacCrann, Niall Sherwin, Blake D. Qu, Frank J. Namikawa, Toshiya Madhavacheril, Mathew S. Abril-Cabezas, Irene An, Rui Austermann, Jason E. Battaglia, Nicholas Battistelli, Elia S. Beall, James A. Bolliet, Boris Bond, J. Richard Cai, Hongbo Calabrese, Erminia Coulton, William R. Darwish, Omar Duff, Shannon M. Duivenvoorden, Adriaan J. Dunkley, Jo Farren, Gerrit S. Ferraro, Simone Golec, Joseph E. Guan, Yilun Han, Dongwon Hervías-Caimapo, Carlos Hill, J. Colin Hilton, Matt Hložek, Renée Hubmayr, Johannes Kim, Joshua Li, Zack Kosowsky, Arthur Louis, Thibaut McMahon, Jeff Marques, Gabriela A. Moodley, Kavilan Naess, Sigurd Niemack, Michael D. Page, Lyman Partridge, Bruce Schaan, Emmanuel Sehgal, Neelima Sifón, Cristóbal Wollack, Edward J. Salatino, Maria Ullom, Joel N. Van Lanen, Jeff Van Engelen, Alexander Wenzl, Lukas Damtp Centre for Mathematical Sciences University of Cambridge Wilberforce Road CambridgeCB3 OWA United Kingdom Kavli Institute for Cosmology Cambridge Madingley Road CambridgeCB3 0HA United Kingdom Utias The University of Tokyo Kashiwa277-8583 Japan Department of Physics and Astronomy University of Pennsylvania 209 South 33rd Street PhiladelphiaPA19104 United States Department of Physics and Astronomy University of Southern California Los AngelesCA90089 United States Nist Quantum Sensors Group 325 Broadway BoulderCO80305 United States Department of Astronomy Cornell University IthacaNY14853 United States Sapienza-University of Rome Physics Department Piazzale Aldo Moro 5 RomeI-00185 Italy Canadian Institute for Theoretical Astrophysics University of Toronto TorontoONM5S 3H8 Canada Department of Physics and Astronomy University of Pittsburgh PittsburghPA15260 United States School of Physics and Astronomy Cardiff University The Parade Wales CardiffCF24 3AA Canada Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Université de Genève Département de Physique Théorique Centre for Astroparticle Physics 24 quai Ernest-Ansermet Genève 4CH-1211 Switzerland Joseph Henry Laboratories of Physics Princeton University Jadwin Hall PrincetonNJ08544 United States Department of Astrophysical Sciences Princeton University Peyton Hall PrincetonNJ08544 United States Lawrence Berkeley National Laboratory One Cyclotron Road BerkeleyCA94720 United States Department of Physics University of Chicago 5720 South Ellis Avenue ChicagoIL60637 United States Kavli Institute for Cosmological Physics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Dunlap Institute for Astronomy and Astrophysics University of Toronto 50 St. George St. TorontoONM5S 3H4 Canada Instituto de Astrofísica Centro de Astro-Ingeniería Facultad de Física Pontificia Universidad Católica de

We investigate the impact and mitigation of extragalactic foregrounds for the CMB lensing power spectrum analysis of Atacama Cosmology Telescope (ACT) data release 6 (DR6) data. Two independent microwave sky simulations are used to test a range of mitigation strategies. We demonstrate that finding and then subtracting point sources, finding and then subtracting models of clusters, and using a profile bias-hardened lensing estimator, together reduce the fractional biases to well below statistical uncertainties, with the inferred lensing amplitude, Alens, biased by less than 0.2σ. We also show that another method where a model for the cosmic infrared background (CIB) contribution is deprojected and high frequency data from Planck is included has similar performance. Other frequency-cleaned options do not perform as well, incurring either a large noise cost, or resulting in biased recovery of the lensing spectrum. In addition to these simulation-based tests, we also present null tests performed on the ACT DR6 data which test for sensitivity of our lensing spectrum estimation to differences in foreground levels between the two ACT frequencies used, while nulling the CMB lensing signal. These tests pass whether the nulling is performed at the map or bandpower level. The CIB-deprojected measurement performed on the DR6 data is consistent with our baseline measurement, implying contamination from the CIB is unlikely to significantly bias the DR6 lensing spectrum. This collection of tests gives confidence that the ACT DR6 lensing measurements and cosmological constraints presented in companion papers to this work are robust to extragalactic foregrounds. © 2023, CC BY.

关键词： Cosmology

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Evidence for large baryonic feedback at low and intermediate redshifts from kinematic Sunyaev-Zel’dovich observations with ACT and DESI photometric galaxies

arXiv

引用

arXiv 2024年

作者： Hadzhiyska, B. Ferraro, S. Guachalla, B. Ried Schaan, E. Aguilar, J. Ahlen, S. Battaglia, N. Bond, J.R. Brooks, D. Calabrese, E. Choi, S.K. Claybaugh, T. Coulton, W.R. Dawson, K. Devlin, M. Dey, B. Doel, P. Duivenvoorden, A.J. Dunkley, J. Farren, G.S. Font-Ribera, A. Forero-Romero, J.E. Gallardo, P.A. Gaztañaga, E. Gontcho, S. Gontcho Gralla, M. Le Guillou, L. Gutierrez, G. Guy, J. Hill, J.C. Hložek, R. Honscheid, K. Juneau, S. Kehoe, R. Kisner, T. Kremin, A. Landriau, M. Liu, R.H. Louis, T. MacCrann, N. de Macorra, A. Madhavacheril, M. Manera, M. Meisner, A. Miquel, R. Moodley, K. Moustakas, J. Mroczkowski, T. Naess, S. Newman, J. Niemack, M.D. Niz, G. Page, L. Palanque-Delabrouille, N. Partridge, B. Percival, W.J. Prada, F. Qu, F.J. Rossi, G. Sanchez, E. Schlegel, D. Schubnell, M. Sehgal, N. Seo, H. Sifón, C. Spergel, D. Sprayberry, D. Staggs, S. Tarlé, G. Vargas, C. Vavagiakis, E.M. Weaver, B.A. Wollack, E.J. Zhou, R. Zou, H. Miller Institute for Basic Research in Science University of California BerkeleyCA94720 United States Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Berkeley Center for Cosmological Physics Department of Physics University of California BerkeleyCA94720 United States Department of Physics Stanford University StanfordCA94305-4085 United States Kavli Institute for Particle Astrophysics and Cosmology 382 Via Pueblo Mall StanfordCA94305-4060 United States SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo ParkCA94025 United States Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Physics Dept. Boston University 590 Commonwealth Avenue BostonMA02215 United States Department of Astronomy Cornell University IthacaNY14853 United States Canadian Institute for Theoretical Astrophysics TorontoONM5S 3H8 Canada Department of Physics & Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom School of Physics and Astronomy Cardiff University The Parade CardiffCF24 3AA United Kingdom Department of Physics and Astronomy University of California RiversideCA92521 United States Kavli Institute for Cosmology Cambridge Madingley Road CambridgeCB3 0HA United Kingdom DAMTP Centre for Mathematical Sciences University of Cambridge Wilberforce Road CambridgeCB3 OWA United Kingdom Department of Physics and Astronomy The University of Utah 115 South 1400 East Salt Lake CityUT84112 United States Department of Physics and Astronomy University of Pennsylvania 209 South 33rd Street PhiladelphiaPA United States University of Pittsburgh 3941 O’Hara Street PittsburghPA15260 United States Center for Computational Astrophysics Flatiron Institute New YorkNY10010 United States Joseph Henry Laboratories of Physics Jadwin Hall Princeton University PrincetonNJ08544 United States Department of Astrophysical Sciences Princeton University Peyton Ha

Recent advances in cosmological observations have provided an unprecedented opportunity to investigate the distribution of baryons relative to the underlying matter. In this work, we robustly show that the gas is much more extended than the dark matter at 40σ and the amount of baryonic feedback at z 1 strongly disfavors low-feedback models such as that of state-of-the-art hydrodynamical simulation IllustrisTNG compared with high-feedback models such as that of the original Illustris simulation. This has important implications for bridging the gap between theory and observations and understanding galaxy formation and evolution. Furthermore, a better grasp of the baryon-dark matter link is critical to future cosmological analyses, which are currently impeded by our limited knowledge of baryonic feedback. Here, we measure the kinematic Sunyaev-Zel’dovich (kSZ) effect from the Atacama Cosmology Telescope (ACT), stacked on the luminous red galaxy (LRG) sample of the Dark Energy Spectroscopic Instrument (DESI) imaging survey. This is the first analysis to use photometric redshifts for reconstructing galaxy velocities. Due to the large number of galaxies comprising the DESI imaging survey, this is the highest signal-to-noise stacked kSZ measurement to date: we detect the signal at 13σ and find that the gas is more spread out than the dark matter at ∼40σ. Our work opens up the possibility to recalibrate large hydrodynamical simulations using the kSZ effect. In addition, our findings point towards a way of alleviating inconsistencies between weak lensing surveys and cosmic microwave background (CMB) experiments such as the ‘low S8’ tension, and shed light on long-standing enigmas in astrophysics such as the ‘missing baryon’ problem. Copyright © 2024, The Authors. All rights reserved.

关键词： Galaxies

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