Machine learning identification of multiple-state OAM superpositions detected with spatial mode sensors

2021 ◽  
Author(s):  
Jaime E. Cisternas ◽  
Javier Espinoza ◽  
Jaime A. Anguita
Keyword(s):  
Machine Learning ◽  
Spatial Mode ◽  
Multiple State

scholarly journals Spatial Mode Correction of Single Photons Using Machine Learning

2021 ◽  
pp. 2000103
Author(s):  
Narayan Bhusal ◽  
Sanjaya Lohani ◽  
Chenglong You ◽  
Mingyuan Hong ◽  
Joshua Fabre ◽  
...  
Keyword(s):  
Machine Learning ◽  
Single Photons ◽  
Spatial Mode

scholarly journals Front Cover: Spatial Mode Correction of Single Photons Using Machine Learning (Adv. Quantum Technol. 3/2021)

2021 ◽  
Vol 4 (3) ◽  
pp. 2170031
Author(s):  
Narayan Bhusal ◽  
Sanjaya Lohani ◽  
Chenglong You ◽  
Mingyuan Hong ◽  
Joshua Fabre ◽  
...  
Keyword(s):  
Machine Learning ◽  
Single Photons ◽  
Spatial Mode ◽  
Front Cover

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning

Phase Transitions in Machine Learning

2009 ◽  
Author(s):  
Lorenza Saitta ◽  
Attilio Giordana ◽  
Antoine Cornuejols
Keyword(s):  
Machine Learning ◽  
Phase Transitions

Understanding Machine Learning

2009 ◽  
Author(s):  
Shai Shalev-Shwartz ◽  
Shai Ben-David
Keyword(s):  
Machine Learning

State-of-the-art pulmonary arterial imaging – Part 1

VASA ◽  
2018 ◽  
Vol 47 (5) ◽  
pp. 345-359 ◽  
Author(s):  
Yuki Tanabe ◽  
Luis Landeras ◽  
Abed Ghandour ◽  
Sasan Partovi ◽  
Prabhakar Rajiah
Keyword(s):  
State Of The Art ◽  
Pulmonary Arteries ◽  
Pulmonary Angiography ◽  
Imaging Modalities ◽  
Nuclear Medicine Imaging ◽  
Multiple State ◽  
Pulmonary Arterial ◽  
Magnetic Resonance Imaging Mri ◽  
Acquired Abnormalities ◽  
Arterial Imaging

Abstract. The pulmonary arteries are affected by a variety of congenital and acquired abnormalities. Multiple state-of-the art imaging modalities are available to evaluate these pulmonary arterial abnormalities, including computed tomography (CT), magnetic resonance imaging (MRI), echocardiography, nuclear medicine imaging and catheter pulmonary angiography. In part one of this two-part series on state-of-the art pulmonary arterial imaging, we review these imaging modalities, focusing particularly on CT and MRI. We also review the utility of these imaging modalities in the evaluation of pulmonary thromboembolism.

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