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arXiv 2017年

作者： Li, Qianxiao Dietrich, Felix Bollt, Erik M. Kevrekidis, Ioannis G. Institute of High Performance Computing Singapore Singapore Faculty of Mathematics Technical University of Munich Germany Department of Mathematics Clarkson University United States Department of Chemical and Biological Engineering Program in Applied and Computational Mathematics Princeton University United States

Numerical approximation methods for the Koopman operator have advanced considerably in the last few years. In particular, data-driven approaches such as dynamic mode decomposition (DMD) and its generalization, the extended-DMD (EDMD), are becoming increasingly popular in practical applications. The EDMD improves upon the classical DMD by the inclusion of a flexible choice of dictionary of observables that spans a finite dimensional subspace on which the Koopman operator can be approximated. This enhances the accuracy of the solution reconstruction and broadens the applicability of the Koopman formalism. Although the convergence of the EDMD has been established, applying the method in practice requires a careful choice of the observables to improve convergence with just a finite number of terms. This is especially difficult for high dimensional and highly nonlinear systems. In this paper, we employ ideas from machine learning to improve upon the EDMD method. We develop an iterative approximation algorithm which couples the EDMD with a trainable dictionary represented by an artificial neural network. Using the Duffing oscillator and the Kuramoto Sivashinsky PDE as examples, we show that our algorithm can effectively and efficiently adapt the trainable dictionary to the problem at hand to achieve good reconstruction accuracy without the need to choose a fixed dictionary a priori. Furthermore, to obtain a given accuracy we require fewer dictionary terms than EDMD with fixed dictionaries. This alleviates an important shortcoming of the EDMD algorithm and enhances the applicability of the Koopman framework to practical problems. Copyright © 2017, The Authors. All rights reserved.

关键词： Machine learning

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Proton acceleration in thermonuclear nova explosions revealed by gamma rays (Apr, 10.1038/s41550-022-01640-z, 2022)

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NATURE ASTRONOMY 2022年第6期6卷 760-760页

作者： Acciari, V. A. Ansoldi, S. Antonelli, L. A. Engels, A. Arbet Artero, M. Asano, K. Baack, D. Babic, A. Baquero, A. de Almeida, U. Barres Barrio, J. A. Batkovic, I. Gonzalez, J. Becerra Bednarek, W. Bellizzi, L. Bernardini, E. Bernardos, M. Berti, A. Besenrieder, J. Bhattacharyya, W. Bigongiari, C. Biland, A. Blanch, O. Bokenkamp, H. Bonnoli, G. Bosnjak, Z. Busetto, G. Carosi, R. Ceribella, G. Cerruti, M. Chai, Y. Chilingarian, A. Cikota, S. Colak, S. M. Colombo, E. Contreras, J. L. Cortina, J. Covino, S. D'Amico, G. D'Elia, V. Da Vela, P. Dazzi, F. De Angelis, A. De Lotto, B. Del Popolo, A. Delfino, M. Delgado, J. Mendez, C. Delgado Depaoli, D. Di Pierro, F. Di Venere, L. Espineira, E. Do Souto Prester, D. Dominis Donini, A. Dorner, D. Doro, M. Elsaesser, D. Ramazani, V. Fallah Alonso, L. Farina Fattorini, A. Fonseca, M. V. Font, L. Fruck, C. Fukami, S. Fukazawa, Y. Lopez, R. J. Garcia Garczarczyk, M. Gasparyan, S. Gaug, M. Giglietto, N. Giordano, F. Gliwny, P. Godinovic, N. Green, J. G. Green, D. Hadasch, D. Hahn, A. Hassan, T. Heckmann, L. Herrera, J. Hoang, J. Hrupec, D. Hutten, M. Inada, T. Ishio, K. Iwamura, Y. Martinez, I. Jimenez Jormanainen, J. Jouvin, L. Kerszberg, D. Kobayashi, Y. Kubo, H. Kushida, J. Lamastra, A. Lelas, D. Leone, F. Lindfors, E. Linhoff, L. Lombardi, S. Longo, F. Lopez-Coto, R. Lopez-Moya, M. Lopez-Oramas, A. Loporchio, S. de Oliveira Fraga, B. Machado Maggio, C. Majumdar, P. Makariev, M. Mallamaci, M. Maneva, G. Manganaro, M. Mannheim, K. Maraschi, L. Mariotti, M. Martinez, M. Aguilar, A. Mas Mazin, D. Menchiari, S. Mender, S. Micanovic, S. Miceli, D. Miener, T. Miranda, J. M. Mirzoyan, R. Molina, E. Moralejo, A. Morcuende, D. Moreno, V. Moretti, E. Nakamori, T. Nava, L. Neustroev, V. Rosillo, M. Nievas Nigro, C. Nilsson, K. Nishijima, K. Noda, K. Nozaki, S. Ohtani, Y. Oka, T. Otero-Santos, J. Paiano, S. Palatiello, M. Paneque, D. Paoletti, R. Paredes, J. M. Pavletic, L. Penil, P. Persic, M. Pihet, M. Moroni, P. G. Prada Prandini, E. Priyadarshi, C. Puljak, I. Rhode, W. Ribo, M. Rico, Universidad de La Laguna Instituto de Astrofísica de Canarias and Departamento de Astrofísica La Laguna Spain (GRID:grid.10041.34) (ISNI:***) Università di Udine and INFN Trieste Udine Italy (GRID:grid.5390.f) (ISNI:0000 0001 2113 062X) International Center for Relativistic Astrophysics (ICRA) Rome Italy (GRID:grid.5390.f) National Institute for Astrophysics (INAF) Rome Italy (GRID:grid.4293.c) (ISNI:0000 0004 1792 8585) Max-Planck-Institut für Physik Munich Germany (GRID:grid.435824.c) (ISNI:0000 0001 2375 0603) The Barcelona Institute of Science and Technology (BIST) Institut de Física d’Altes Energies (IFAE) Barcelona Spain (GRID:grid.473715.3) (ISNI:0000 0004 6475 7299) Japanese MAGIC Group: Institute for Cosmic Ray Research (ICRR) The University of Tokyo Kashiwa Japan (GRID:grid.26999.3d) (ISNI:0000 0001 2151 536X) Technische Universität Dortmund Dortmund Germany (GRID:grid.5675.1) (ISNI:0000 0001 0416 9637) Croatian MAGIC Group: University of Zagreb Faculty of Electrical Engineering and Computing (FER) Zagreb Croatia (GRID:grid.4808.4) (ISNI:0000 0001 0657 4636) Universidad Complutense de Madrid IPARCOS Institute and EMFTEL Department Madrid Spain (GRID:grid.4795.f) (ISNI:0000 0001 2157 7667) Centro Brasileiro de Pesquisas Físicas (CBPF) Rio de Janeiro Brazil (GRID:grid.418228.5) (ISNI:0000 0004 0643 8134) Università di Padova and INFN Padua Italy (GRID:grid.5608.b) (ISNI:0000 0004 1757 3470) University of Lodz Department of Astrophysics Faculty of Physics and Applied Informatics Lodz Poland (GRID:grid.10789.37) (ISNI:0000 0000 9730 2769) Università di Siena and INFN Pisa Siena Italy (GRID:grid.9024.f) (ISNI:0000 0004 1757 4641) Deutsches Elektronen-Synchrotron (DESY) Zeuthen Germany (GRID:grid.7683.a) (ISNI:0000 0004 0492 0453) ETH Zürich Zurich Switzerland (GRID:grid.5801.c) (ISNI:0000 0001 2156 2780) Instituto de Astrofísica de Andalucía-CSIC Granada Spain (GRID:grid.450285.e) (ISNI:0000 0004 1793 7043) Università di Pisa and INFN Pisa Pisa Italy (GRID:grid.5395.

Classical novae are cataclysmic binary star systems in which the matter of a companion star is accreted on a white dwarf1,2. Accumulation of hydrogen in a layer eventually causes a thermonuclear explosion on the surface of the white dwarf3, brightening the white dwarf to ~105 solar luminosities and triggering ejection of the accumulated matter. Novae provide the extreme conditions required to accelerate particles, electrons or protons, to high energies. Here we present the detection of gamma rays by the MAGIC telescopes from the 2021 outburst of RS Ophiuchi, a recurrent nova with a red giant companion, which allowed us to accurately characterize the emission from a nova in the 60 GeV to 250 GeV energy range. The theoretical interpretation of the combined Fermi LAT and MAGIC data suggests that protons are accelerated to hundreds of gigaelectronvolts in the nova shock. Such protons should create bubbles of enhanced cosmic ray density, of the order of 10 pc, from the recurrent *** of the 2021 outburst of the nova RS Oph in very-high-energy gamma rays by the MAGIC telescopes is reported. Investigation of the gamma-ray emission provides evidence for acceleration of protons within the nova shock, which then propagate outwards to create bubbles of enhanced cosmic ray density.

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An Online Treatment Decision Aid for Men with Low-risk Prostate Cancer Eligible for Active Surveillance and Their Partners Increases the Uptake of Active Surveillance: The Navigate Randomised Controlled Trial

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European urology oncology 2025年

作者： Penelope Schofield Stephen Quinn Natalie Richards Alan White Fiona White Mark Frydenberg Suzanne Chambers Louisa G Gordon Robert Alexander 'Frank' Gardiner Declan G Murphy Matthew J Roberts John Yaxley Nathan Lawrentschuk Kevin Chu Jeremy Millar Jeremy Grummet Ilona Juraskova School of Computing Engineering and Mathematical Sciences La Trobe University Melbourne Victoria Australia Sir Peter MacCallum Department of Oncology The University of Melbourne Parkville Victoria Australia Department of Psychological Sciences Swinburne University of Technology Melbourne Victoria Australia. Electronic address: p.schofield@latrobe.edu.au. Department of Health Science and Biostatistics Swinburne University of Technology Melbourne Victoria Australia. Department of Health Sciences Research Peter MacCallum Cancer Centre Melbourne Victoria Australia. Department of Urology Cabrini Institute Cabrini Health Melbourne Victoria Australia Department of Surgery Monash University Melbourne Victoria Australia. Faculty of Health Sciences Australian Catholic University Brisbane Queensland Australia Health and Wellness Institute Edith Cowan University Perth Australia Menzies Health Institute Queensland Griffith University Southport Australia. Population Health Program Health Economics QIMR Berghofer Medical Research Institute Brisbane Queensland Australia School of Nursing Queensland University of Technology (QUT) Brisbane Queensland Australia School of Public Health The University of Queensland Brisbane Queensland Australia. The University of Queensland Brisbane Queensland Australia The University of Queensland Centre for Clinical Research Brisbane Queensland Australia The Royal Brisbane & Women's Hospital Herston Queensland Australia Edith Cowan University Perth Western Australia Griffith University Queensland Australia. Division of Cancer Surgery Peter MacCallum Cancer Centre Melbourne Victoria Australia. Department of Urology Royal Brisbane and Women's Hospital Herston Queensland Australia Department of Urology Redcliffe Hospital Redcliffe Queensland Australia. The University of Queensland Brisbane Queensland Australia. Department of Surgery The University of Melbourne Melbourne Victoria Australia Department of Urology The Ro

BACKGROUND AND OBJECTIVE:Evidence suggests that curative treatment for low-risk prostate cancer (LRPC) has no survival benefits over active surveillance (AS); thus, treatment choice becomes a value-sensitive decision. Decision aids (DAs) have the potential to facilitate this process, yet no DA has been tailored to the Australian health care system or population. This study aims to evaluate the impact of an online DA (Navigate) on the uptake of AS, quality of life, and decision-making in Australia. METHODS:This parallel-group, prospective, randomised controlled trial recruited men (from May 2017 to May 2021) from participating cancer centres, via self-referral, or via clinician referral. The inclusion criteria were the following: a recent LRPC diagnosis, no decision on treatment, and clinical suitability for AS. Partners could also enrol. Assessments were undertaken at baseline (before decision) and after baseline (1, 2, and 6 mo). Participants were randomised 1:1 to Navigate (online DA, intervention) or a national prostate cancer website (usual care), stratified by the site/recruitment method. Partners were allocated to the group matching their respective partners. The primary outcome was self-reported uptake of AS for first-line treatment at 1 mo. The secondary outcomes included decision-making preparedness; decisional conflict, regret, and satisfaction; illness communication; and prostate cancer-specific quality of life. Intention-to-treat analyses were conducted. KEY FINDINGS AND LIMITATIONS:Of the 619 patients referred, those eligible (n = 302) were randomised to either Navigate (n = 153) or usual care (n = 149), with no significant between-group differences at baseline. The proportion of men self-reporting AS versus another treatment was 90.6% (Navigate) versus 79.0% (usual care; p = 0.008). Navigate participants also reported greater decision-making preparedness (p < 0.001). Partners were allocated to Navigate (n = 70) or usual care (n = 49); no significant be

关键词： Active surveillance Decision support techniques Domestic partners Internet-based intervention Prostate cancer Randomised controlled trial

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Multi-partition time aggregation for Markov Chains

Multi-partition time aggregation for Markov Chains

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IEEE Annual Conference on Decision and Control

作者： Edilson F. Arruda Marcelo D. Fragoso Fabricio O. Ourique Industrial Engineering Program Federal University of Rio de Janeiro Rio de Janeiro RJ Brazil Center for Systems and Control National Laboratory for Scientific Computing Petrópolis RJ Brazil Federal University of Santa Catarina Araranguá SC Brazil

Motivated by Markov decision processes, this paper introduces a form of embedding for Markov chains which is based on the partition of the state space into a manageable number of subsets, with the aim of enabling a decomposition algorithm for calculating long-term costs and probabilities. The decomposition enables the decision maker to derive the long term distribution by making use of evaluations in the domain of the partitions, which presents reduced cardinality with respect to the original state space and hence yields reduced computational effort.

关键词： Markov processes Trajectory Frequency modulation Aerospace electronics Partitioning algorithms Transient analysis Absorption

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Harmonized-Multinational qEEG norms (HarMNqEEG)

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NEUROIMAGE 2022年 256卷 119190-119190页

作者： Li, Min Wang, Ying Lopez-Naranjo, Carlos Hu, Shiang Reyes, Ronaldo Cesar Garcia Paz-Linares, Deirel Areces-Gonzalez, Ariosky Hamid, Aini Ismafairus Abd Evans, Alan C. Savostyanov, Alexander N. Calzada-Reyes, Ana Villringer, Arno Tobon-Quintero, Carlos A. Garcia-Agustin, Daysi Yao, Dezhong Dong, Li Aubert-Vazquez, Eduardo Reza, Faruque Razzaq, Fuleah Abdul Omar, Hazim Abdullah, Jafri Malin Galler, Janina R. Ochoa-Gomez, John F. Prichep, Leslie S. Galan-Garcia, Lidice Morales-Chacon, Lilia Valdes-Sosa, Mitchell J. Trondle, Marius Zulkifly, Mohd Faizal Mohd Rahman, Muhammad Riddha Bin Abdul Milakhina, Natalya S. Langer, Nicolas Rudych, Pavel Koenig, Thomas Virues-Alba, Trinidad A. Lei, Xu Bringas-Vega, Maria L. Bosch-Bayard, Jorge F. Valdes-Sosa, Pedro Antonio [a]The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China [b]Cuban Center for Neurocience La Habana Cuba [c]McGill Centre for Integrative Neuroscience Ludmer Centre for Neuroinformatics and Mental Health Montreal Neurological Institute Canada [d]Department of Neurosciences School of Medical Sciences Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [e]Brain and Behaviour Cluster School of Medical Sciences Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [f]Hospital Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [g]Humanitarian Institute Novosibirsk State University Novosibirsk 630090 Russia [h]Laboratory of Psychophysiology of Individual Differences Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine Novosibirsk 630117 Russia [i]Laboratory of Psychological Genetics at the Institute of Cytology and Genetics Siberian Branch of the Russian Academy of Sciences Novosibirsk 630090 Russia [j]University of Pinar del Río “Hermanos Saiz Montes de Oca” Pinar del Río Cuba [k]Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany [l]Department of Cognitive Neurology University Hospital Leipzig Leipzig Germany [m]Center for Stroke Research Charité-Universitätsmedizin Berlin Berlin Germany [n]Grupo Neuropsicología y Conducta - GRUNECO Faculty of Medicine Universidad de Antioquia Colombia [o]Research Department Institución Prestadora de Servicios de Salud IPS Universitaria Colombia [p]The Cuban center aging longevity and health Havana Cuba [q]Research Unit of NeuroInformation Chinese Academy of Medical Sciences Chengdu 2019RU035 China [r]School of Electrical Engineering Zhengzhou University Zhengzhou 4500

This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects " and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

关键词： Quantitative EEG EEG cross-spectrum Riemannian geometry Batch effects Z-score Harmonization Malnutrition Covid induced brain dysfunction Developmental Brain Chart

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An interactive dashboard to track themes, development maturity, and global equity in clinical artificial intelligence research

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The Lancet. Digital health 2022年第4期4卷 e212-e213页

作者： Joe Zhang Stephen Whebell Jack Gallifant Sanjay Budhdeo Heather Mattie Piyawat Lertvittayakumjorn Maria Del Pilar Arias Lopez Beatrice J Tiangco Judy W Gichoya Hutan Ashrafian Leo A Celi James T Teo Institute of Global Health Innovation Imperial College London London UK Department of Critical Care King's College Hospital NHS Foundation Trust London UK. Electronic address: joe.zhang@imperial.ac.uk. Department of Critical Care Townsville University Hospital Queensland Health Townsville QLD Australia. Department of Surgery Imperial College Healthcare NHS Foundation Trust London UK Centre for Human and Applied Physiological Sciences King's College London London UK. Department of Neurology National Hospital for Neurology and Neurosurgery London UK Department of Clinical and Movement Neurosciences University College London London UK. Department of Biostatistics Harvard T H Chan School of Public Health Harvard University Cambridge MA USA. Department of Computing Imperial College London London UK. SATI-Q Program Argentine Society of Intensive Care Buenos Aires Argentina. National Institute of Health College of Medicine University of the Philippines Metro Manila Philippines Division of Medicine The Medical City Pasig City Philippines. Department of Radiology Emory University School of Medicine Atlanta Georgia USA. Preemptive Medicine and Health Security Initiative Flagship Pioneering Cambridge MA USA. Department of Biostatistics Harvard T H Chan School of Public Health Harvard University Cambridge MA USA Institute for Medical Engineering & Science Massachusetts Institute of Technology Cambridge MA USA Department of Medicine Beth Israel Deaconess Medical Centre Boston MA USA. Department of Neurology King's College Hospital NHS Foundation Trust London UK London Medical Imaging & AI Centre Guy's and St Thomas' Hospital London UK.

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Alginate Microcapsule Technology and Impacts on Cell Therapy Development

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Microscopy and Microanalysis 2017年第S1期23卷 1214-1215页

作者： Marwa Belhaj Vinal Menon Barbel Rohrer Jay Potts Department of Cell Biology and Anatomy School of Medicine University of South Carolina Columbia USA. Department of Ophthalmology Medical University of South Carolina Charleston South Carolina USA. Ralph H. Johnson VA Medical Center Charleston South Carolina USA. Biomedical Engineering Program College of Engineering and Computing University of South Carolina Columbia USA.

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Spatial-Temporal pattern analysis and prediction of air quality in Taiwan

Spatial-Temporal pattern analysis and prediction of air qual...

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IEEE International Conference on Ubi-Media computing

作者： Ping-Wei Soh Kai-Hsiang Chen Jen-Wei Huang Hone-Jay Chu Institute of Computer and Communication Engineering National Cheng Kung University Tainan Taiwan PhD Program for Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Tainan Taiwan Department of Electrical Engineering National Cheng Kung University Tainan Taiwan Department of Geomatics National Cheng Kung University Tainan Taiwan

This study explores the spatial-temporal patterns of particulate matter (PM) in Taiwan. Probability map of PM and daily patterns are discussed in this study. Data mining provides more detailed spatial-temporal information for PM variations and trends. The proposed model will show that data mining provides a relatively high goodness of fit and sufficient space-time explanatory power, particularly air pollution frequency and affect areas. In the proposed model, a method using Dynamic Time Warping is proposed to analyse temporal similarity between stations. The proposed model can eliminate global effect on a single station through the performance of multiple stations. The proposed model will further be used for prediction of PM2.5. The prediction results will discuss the spatial-temporal relations between stations. This study will investigate the distribution of PM and its cyclicality.

关键词： Predictive models Time series analysis Atmospheric modeling Air pollution Conferences Data mining

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Data Classification combining Self-Organizing Maps and Informative Nearest Neighbor

Data Classification combining Self-Organizing Maps and Infor...

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International Joint Conference on Neural Networks

作者： Leandro J. Moreira Leandro A. Silva Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University Sao Paulo Brazil Graduate Program in Electrical Engineering & Computing Mackenzie Presbyterian University Sao Paulo Brazil

ISBN: (纸本)9781509006212

The task of classifying data has been addressed in various works, and has been utilized in various areas of application, such as medicine, industry, marketing, financial market and many others. This work will present a data classifier proposal that combines the SOM (Self-Organizing Map) neural network with INN (Informative Nearest Neighbors). The combination of these two algorithms will be called in this work as SOM-INN. The classification will be done in a two steps task: in the first the SOM has a functionality to reduce the dataset through a process that utilizes the winning neuron concept and, in the second step, the dataset objects selected will be utilized as reference for the INN algorithm to decide about the classification utilizing the most informative object of the reduced dataset. Experiments using 14 public datasets will be made comparing classic classification algorithms from the indicators of accuracy and time consumed in the classification process. The results obtained show that the proposed SOM-INN algorithm, when compared with the other classifiers, presents better accuracy rates in databases where the border region does not have the object classes well distributed. However, its main differential is in the classification time, which is extremely important for real applications.

关键词： Classification algorithms Training Neurons Proposals Prototypes Measurement Self-organizing feature maps self-organising feature maps Sorting algorithm Nearest neighbor Neurons PROTOTYPE Functional training Dataset Pharmaceutical Industry pubs data classification GRHL3 gene

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Fourier phase retrieval: Uniqueness and algorithms

arXiv

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arXiv 2017年

作者： Bendory, Tamir Beinerty, Robert Eldarz, Yonina C. Program in Applied and Computational Mathematics Princeton University PrincetonNJ United States Institute of Mathematics and Scientific Computing University of Graz Heinrichstraße 36 Graz8010 Austria Andrew and Erna Viterbi Faculty of Electrical Engineering Technion - Israel Institute of Technology Haifa Israel

The problem of recovering a signal from its phaseless Fourier transform measurements, called Fourier phase retrieval, arises in many applications in engineering and science. Fourier phase retrieval poses fundamental theoretical and algorithmic challenges. In general, there is no unique mapping between a one-dimensional signal and its Fourier magnitude and therefore the problem is ill-posed. Additionally, while almost all multidimensional signals are uniquely mapped to their Fourier magnitude, the performance of existing algorithms is generally not well-understood. In this chapter we survey methods to guarantee uniqueness in Fourier phase retrieval. We then present different algorithmic approaches to retrieve the signal in practice. We conclude by outlining some of the main open questions in this field. Copyright © 2017, The Authors. All rights reserved.

关键词： Fourier transforms

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