TauREx 2D: Modelling 2D effects in retrievals

2021 ◽  
Author(s):  
Tiziano Zingales ◽  
Aurélien Falco ◽  
William Pluriel ◽  
Jéremy Leconte
Keyword(s):  
Three Dimensional ◽  
Synthetic Spectrum ◽  
Computational Power ◽  
Atmospheric Parameters ◽  
One Dimensional ◽  
Hot Jupiters ◽  
Future Space ◽  
Computationally Intensive ◽  
Exoplanetary Atmospheres ◽  
New Generation

<div data-canvas-width="636.8035259153738">New-generation spectrographs dedicated to the study of exoplanetary atmospheres, require a higher precision in the atmospheric</div> <div data-canvas-width="636.8035259153735">models to better interpret the new spectra. Thanks to future space missions like JWST, ARIEL and Twinkle, indeed, the observed</div> <div data-canvas-width="636.8035259153738">spectra will be precise enough to reveal features which cannot be modeled with a one-dimensional plane parallel atmosphere,</div> <div data-canvas-width="636.8035259153739">especially in the case of Ultra Hot Jupiters. Bayesian frameworks are computationally intensive and prevent us from using complete</div> <div data-canvas-width="636.803525915374">three-dimensional self-consistent models to retrieve an exoplanetary atmosphere, and, they constrain us to use simplified models to</div> <div data-canvas-width="636.8035259153739">converge to a set of atmospheric parameters. We propose the TauREx2D retrieval code, which uses two-dimensional atmospheric</div> <div data-canvas-width="636.8035259153738">models as a good compromise between computational power and model precision to better infer exoplanetary atmospheres. Finally,</div> <div data-canvas-width="636.8035259153736">we apply such a model on synthetic spectrum computed from a GCM simulation of WASP121b and show the parameters retrieved by</div> <div data-canvas-width="167.95345291125463">the new TauREx 2D retrieval code.</div>

scholarly journals From Auto-encoders to Capsule Networks: A Survey

2021 ◽  
Vol 229 ◽  
pp. 01048
Author(s):  
Omaima El Alaoui-Elfels ◽  
Taoufiq Gadi
Keyword(s):  
Neural Networks ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Three Dimensional ◽  
Future Research ◽  
Two Dimensional ◽  
One Dimensional ◽  
Deep Learning Algorithm ◽  
New Generation ◽  
Open Issues

Convolutional Neural Networks are a very powerful Deep Learning algorithm used in image processing, object classification and segmentation. They are very robust in extracting features from data and largely used in several domains. Nonetheless, they require a large number of training datasets and relations between features get lost in the Max-pooling step, which can lead to a wrong classification. Capsule Networks (CapsNets) were introduced to overcome these limitations by extracting features and their pose using capsules instead of neurons. This technique shows an impressive performance in one-dimensional, two-dimensional and three-dimensional datasets as well as in sparse datasets. In this paper, we present an initial understanding of CapsNets, their concept, structure and learning algorithm. We introduce the progress made by CapsNets from their introduction in 2011 until 2020. We compare different CapsNets series to demonstrate strengths and challenges. Finally, we quote different implementations of Capsule Networks and show their robustness in a variety of domains. This survey provides the state-of-the-art of Capsule Networks and allows other researchers to get a clear view of this new field. Besides, we discuss the open issues and the promising directions of future research, which may lead to a new generation of CapsNets.

scholarly journals From Auto-encoders to Capsule Networks: A Survey

2021 ◽  
Vol 229 ◽  
pp. 01003
Author(s):  
Omaima El Alaoui-Elfels ◽  
Taoufiq Gadi
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Three Dimensional ◽  
Future Research ◽  
Two Dimensional ◽  
One Dimensional ◽  
New Generation ◽  
Open Issues

Convolutional Neural Networks are a very powerful Deep Learning structure used in image processing, object classification and segmentation. They are very robust in extracting features from data and largely used in several domains. Nonetheless, they require a large number of training datasets and relations between features get lost in the Max-pooling step, which can lead to a wrong classification. Capsule Networks(CapsNets) were introduced to overcome these limitations by extracting features and their pose using capsules instead of neurons. This technique shows an impressive performance in one-dimensional, two-dimensional and three-dimensional datasets as well as in sparse datasets. In this paper, we present an initial understanding of CapsNets, their concept, structure and learning algorithm. We introduce the progress made by CapsNets from their introduction in 2011 until 2020. We compare different CapsNets series architectures to demonstrate strengths and challenges. Finally, we quote different implementations of Capsule Networks and show their robustness in a variety of domains. This survey provides the state-of-theartof Capsule Networks and allows other researchers to get a clear view of this new field. Besides, we discuss the open issues and the promising directions of future research, which may lead to a new generation of CapsNets.

Structure and function of neural networks in the retina

1988 ◽  
Vol 46 ◽  
pp. 26-27
Author(s):  
Peter Sterling
Keyword(s):  
Ganglion Cells ◽  
Three Dimensional ◽  
Structure And Function ◽  
Synaptic Connections ◽  
Visual Axis ◽  
One Dimensional ◽  
Cat Retina ◽  
Ultrathin Sections ◽  
And Function ◽  
Insight Into

The synaptic connections in cat retina that link photoreceptors to ganglion cells have been analyzed quantitatively. Our approach has been to prepare serial, ultrathin sections and photograph en montage at low magnification (˜2000X) in the electron microscope. Six series, 100-300 sections long, have been prepared over the last decade. They derive from different cats but always from the same region of retina, about one degree from the center of the visual axis. The material has been analyzed by reconstructing adjacent neurons in each array and then identifying systematically the synaptic connections between arrays. Most reconstructions were done manually by tracing the outlines of processes in successive sections onto acetate sheets aligned on a cartoonist's jig. The tracings were then digitized, stacked by computer, and printed with the hidden lines removed. The results have provided rather than the usual one-dimensional account of pathways, a three-dimensional account of circuits. From this has emerged insight into the functional architecture.

Exploring the Multidimensional Facets of Authoritarianism: Authoritarian Aggression and Social Dominance Orientation

2008 ◽  
Vol 67 (1) ◽  
pp. 51-60 ◽  
Author(s):  
Stefano Passini
Keyword(s):  
Social Dominance ◽  
Factor Model ◽  
Three Dimensional ◽  
Social Dominance Orientation ◽  
Model Fit ◽  
Regression Analyses ◽  
One Dimensional ◽  
Confirmatory Factor ◽  
The One ◽  
Better Than

The relation between authoritarianism and social dominance orientation was analyzed, with authoritarianism measured using a three-dimensional scale. The implicit multidimensional structure (authoritarian submission, conventionalism, authoritarian aggression) of Altemeyer’s (1981, 1988) conceptualization of authoritarianism is inconsistent with its one-dimensional methodological operationalization. The dimensionality of authoritarianism was investigated using confirmatory factor analysis in a sample of 713 university students. As hypothesized, the three-factor model fit the data significantly better than the one-factor model. Regression analyses revealed that only authoritarian aggression was related to social dominance orientation. That is, only intolerance of deviance was related to high social dominance, whereas submissiveness was not.

Pavement Damage Due to Conventional and New Generation of Wide-Base Super Single Tires

2005 ◽  
Vol 33 (4) ◽  
pp. 210-226 ◽  
Author(s):  
I. L. Al-Qadi ◽  
M. A. Elseifi ◽  
P. J. Yoo ◽  
I. Janajreh
Keyword(s):  
Carrying Capacity ◽  
Material Properties ◽  
Three Dimensional ◽  
Fe Analysis ◽  
Load Carrying Capacity ◽  
Inflation Pressure ◽  
3D Finite Element ◽  
Load Carrying ◽  
Pavement Damage ◽  
New Generation

Abstract The objective of this study was to quantify pavement damage due to a conventional (385/65R22.5) and a new generation of wide-base (445/50R22.5) tires using three-dimensional (3D) finite element (FE) analysis. The investigated new generation of wide-base tires has wider treads and greater load-carrying capacity than the conventional wide-base tire. In addition, the contact patch is less sensitive to loading and is especially designed to operate at 690kPa inflation pressure at 121km/hr speed for full load of 151kN tandem axle. The developed FE models simulated the tread sizes and applicable contact pressure for each tread and utilized laboratory-measured pavement material properties. In addition, the models were calibrated and properly validated using field-measured stresses and strains. Comparison was established between the two wide-base tire types and the dual-tire assembly. Results indicated that the 445/50R22.5 wide-base tire would cause more fatigue damage, approximately the same rutting damage and less surface-initiated top-down cracking than the conventional dual-tire assembly. On the other hand, the conventional 385/65R22.5 wide-base tire, which was introduced more than two decades ago, caused the most damage.

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