cFAT-GAN: Conditional Simulation of Electron–Proton Scattering Events with Variate Beam Energies by a Feature Augmented and Transformed Generative Adversarial Network

2021 ◽  
pp. 245-261
Author(s):  
Luisa Velasco ◽  
Evan McClellan ◽  
N. Sato ◽  
Pawel Ambrozewicz ◽  
Tianbo Liu ◽  
...  
Keyword(s):  
Conditional Simulation ◽  
Proton Scattering ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Scattering Events

scholarly journals Simulation of Electron-Proton Scattering Events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)

2021 ◽  
Author(s):  
Yasir Alanazi ◽  
Nobuo Sato ◽  
Tianbo Liu ◽  
Wally Melnitchouk ◽  
Pawel Ambrozewicz ◽  
...  
Keyword(s):  
Electron Scattering ◽  
Particle Production ◽  
Event Generator ◽  
Proton Scattering ◽  
Final State ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Theoretical Assumptions ◽  
New Feature ◽  
Inclusive Electron

We apply generative adversarial network (GAN) technology to build an event generator that simulates particle production in electron-proton scattering that is free of theoretical assumptions about underlying particle dynamics. The difficulty of efficiently training a GAN event simulator lies in learning the complicated patterns of the distributions of the particles physical properties. We develop a GAN that selects a set of transformed features from particle momenta that can be generated easily by the generator, and uses these to produce a set of augmented features that improve the sensitivity of the discriminator. The new Feature-Augmented and Transformed GAN (FAT-GAN) is able to faithfully reproduce the distribution of final state electron momenta in inclusive electron scattering, without the need for input derived from domain-based theoretical assumptions. The developed technology can play a significant role in boosting the science of existing and future accelerator facilities, such as the Electron-Ion Collider.

ORGANIC (1).pdf

2017 ◽  
Author(s):  
Benjamin Sanchez-Lengeling ◽  
Carlos Outeiral ◽  
Gabriel L. Guimaraes ◽  
Alan Aspuru-Guzik
Keyword(s):  
Machine Learning ◽  
Learning Community ◽  
Chemical Species ◽  
Material Design ◽  
Organic Photovoltaic ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Photovoltaic Material

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.

Role of General Adversarial Networks in Mammogram Analysis: A Review

2020 ◽  
Vol 16 (7) ◽  
pp. 863-877
Author(s):  
Annapoorani Gopal ◽  
Lathaselvi Gandhimaruthian ◽  
Javid Ali
Keyword(s):  
Breast Tumor ◽  
Deep Neural Networks ◽  
Training Data ◽  
Learning Technology ◽  
Breast Cancers ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Tumor Extraction

The Deep Neural Networks have gained prominence in the biomedical domain, becoming the most commonly used networks after machine learning technology. Mammograms can be used to detect breast cancers with high precision with the help of Convolutional Neural Network (CNN) which is deep learning technology. An exhaustive labeled data is required to train the CNN from scratch. This can be overcome by deploying Generative Adversarial Network (GAN) which comparatively needs lesser training data during a mammogram screening. In the proposed study, the application of GANs in estimating breast density, high-resolution mammogram synthesis for clustered microcalcification analysis, effective segmentation of breast tumor, analysis of the shape of breast tumor, extraction of features and augmentation of the image during mammogram classification have been extensively reviewed.

scholarly journals Realistic Sonar Image Simulation Using Generative Adversarial Network

2019 ◽  
Vol 52 (21) ◽  
pp. 291-296 ◽  
Author(s):  
Minsung Sung ◽  
Jason Kim ◽  
Juhwan Kim ◽  
Son-Cheol Yu
Keyword(s):  
Image Simulation ◽  
Generative Adversarial Network ◽  
Sonar Image ◽  
Adversarial Network
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