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Articles 1 - 9 of 9
Full-Text Articles in Physical Sciences and Mathematics
Vaim For Solving Inverse Problems, Manal Almaeen, Yasir Alanazi, Michelle Kuchera, Nobuo Sato, Wally Melnitchouk, Yaohang Li
Vaim For Solving Inverse Problems, Manal Almaeen, Yasir Alanazi, Michelle Kuchera, Nobuo Sato, Wally Melnitchouk, Yaohang Li
College of Sciences Posters
In this work, we propose the Variational Autoencoder Inverse Mapper (VAIM) to solve inverse problems, where there is a demand to accurately restore hidden parameters from indirect observations. VAIM is an autoencoder-based neural network architecture. The encoder and decoder networks approximate the forward and backward mapping, respectively, and a variational latent layer is incorporated into VAIM to learn the posterior parameter distributions with respect to the given observables. VAIM shows promising results on several artificial inverse problems. VAIM further demonstrates preliminary effectiveness in constructing the inverse function mapping quantum correlation functions to observables in a quantum chromodynamics analysis of nucleon …
Data-Limited Domain Adaptation And Transfer Learning For Learning Latent Expression Labels Of Child Facial Expression Images, Megan Witherow, Winston Shields, Manar Samad, Khan Iftekharuddin
Data-Limited Domain Adaptation And Transfer Learning For Learning Latent Expression Labels Of Child Facial Expression Images, Megan Witherow, Winston Shields, Manar Samad, Khan Iftekharuddin
College of Engineering & Technology (Batten) Posters
While state-of-the-art deep learning models have demonstrated success in adult facial expression classification by leveraging large, labeled datasets, labeled data for child facial expression classification is limited. Due to differences in facial morphology and development in child and adult faces, deep learning models trained on adult data do not generalize well to child data. Recent deep domain adaptation approaches have improved the generalizability of models trained on a source domain to a target domain with few labeled samples. We propose that incorporating steps of deep transfer learning, e.g. weights initialization from the pre-trained source model and freezing model layers, may …
Analysis Of Reading Patterns Of Scientific Literature Using Eye-Tracking Measures, Gavindya Jayawardena, Sampath Jayarathna, Jian Wu
Analysis Of Reading Patterns Of Scientific Literature Using Eye-Tracking Measures, Gavindya Jayawardena, Sampath Jayarathna, Jian Wu
College of Sciences Posters
Scientific literature is crucial for researchers to inspire novel research ideas and find solutions to various problems. This study presents a reading task for novice researchers using eye-tracking measures. The study focused on the scan paths, fixation, and pupil dilation frequency of the participants. In this study, 3 participants were asked to read a pre-selected research paper while wearing an eye-tracking device (PupilLabs Core 200Hz). We specified sections of the research paper as areas of interest (title, abstract, motivation, methodology, conclusion)to analyze the eye-movements. Then we extracted eye-movements data from the recordings and processed them using an eye-movement processing pipeline. …
Multi-Dimensional Numerical Integration On Parallel Architectures, Ioannis Sakiotis, Marc Paterno, Balsa Terzic, Mohammad Zubair, Desh Ranjan
Multi-Dimensional Numerical Integration On Parallel Architectures, Ioannis Sakiotis, Marc Paterno, Balsa Terzic, Mohammad Zubair, Desh Ranjan
College of Sciences Posters
Multi-dimensional numerical integration is a challenging computational problem that is encountered in many scientific computing applications. Despite extensive research and the development of efficient techniques such as adaptive and Monte Carlo methods, many complex high-dimensional integrands can be too computationally intense even for state-of-the-art numerical libraries such as CUBA, QUADPACK, NAG, and MSL. However, adaptive integration has few dependencies and is very well suited for parallel architectures where processors can operate on different partitions of the integration-space. While existing parallel methods exist, most are simple extensions of their sequential versions. This results in moderate speedup and in many cases failure …
Nanopore Guided Regional Assembly, Eleni Adam, Desh Ranjan, Harold Riethman
Nanopore Guided Regional Assembly, Eleni Adam, Desh Ranjan, Harold Riethman
College of Sciences Posters
The telomeres are the “caps” of the chromosomes and their vital role is to protect them. Possible telomere dysfunction caused by telomere rearrangements can be fatal for the cell and result in age-related diseases, including cancer. The telomeres and subtelomeres are regions that are hard to investigate. The current technology cannot provide their complete sequence, instead the DNA is given in multiple pieces. Current methods of assembling the pieces of these regions are not accurate enough due to the region’s high variability and complex repeated patterns. We propose a hybrid assembly method, the NPGREAT, which utilizes two of the latest …
Combine Cryo-Em Density Map And Residue Contact For Protein Structure Prediction: A Case Study, Maytha Alshammari, Jing He
Combine Cryo-Em Density Map And Residue Contact For Protein Structure Prediction: A Case Study, Maytha Alshammari, Jing He
College of Sciences Posters
Although atomic structures have been determined directly from cryo-EM density maps with high resolutions, current structure determination methods for medium resolution (5 to 10 Å) cryo-EM maps are limited by the availability of structure templates. Secondary structure traces are lines detected from a cryo-EM density map for α-helices and β-strands of a protein. A topology of secondary structures defines the mapping between a set of sequence segments in 1D and a set of traces of secondary structures in 3D. In order to enhance the accuracy in ranking secondary structure topologies, we propose a method that combines three sources of information …
End-To-End Physics Event Generator, Yasir Alanazi, N. Sato, Tianbo Liu, W. Melnitchouk, Michelle P. Kuchera, Evan Pritchard, Michael Robertson, Ryan Strauss, Luisa Velasco, Yaohang Li
End-To-End Physics Event Generator, Yasir Alanazi, N. Sato, Tianbo Liu, W. Melnitchouk, Michelle P. Kuchera, Evan Pritchard, Michael Robertson, Ryan Strauss, Luisa Velasco, Yaohang Li
College of Sciences Posters
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 pat- terns 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 …
Using Torchattacks To Improve The Robustness Of Models With Adversarial Training, William S. Matos Díaz
Using Torchattacks To Improve The Robustness Of Models With Adversarial Training, William S. Matos Díaz
Cybersecurity: Deep Learning Driven Cybersecurity Research in a Multidisciplinary Environment
Adversarial training has proven to be one of the most successful ways to defend models against adversarial examples. This process consists of training a model with an adversarial example to improve the robustness of the model. In this experiment, Torchattacks, a Pytorch library made for importing adversarial examples more easily, was used to determine which attack was the strongest. Later on, the strongest attack was used to train the model and make it more robust against adversarial examples. The datasets used to perform the experiments were MNIST and CIFAR-10. Both datasets were put to the test using PGD, FGSM, and …
Measurement Study Of Energy Impact On Blockchain Technologies: Cryptocurrency Mining, Qaylin Holliman
Measurement Study Of Energy Impact On Blockchain Technologies: Cryptocurrency Mining, Qaylin Holliman
Cybersecurity: Deep Learning Driven Cybersecurity Research in a Multidisciplinary Environment
Blockchain technology facilitates the flow of information and the speed of information through a faster and more decentralized network. It has its advantages as compared to more centralized networks and legacy networks. With the evolution of mainstream technology, blockchains is predicted to be more effective and sufficient to consumers and commercial companies. In this paper, blockchains will be scaled to cryptocurrency mining, where cryptocurrencies utilize blockchain technology to record transactions and orders. Mining will also be examined through energy consumption, the algorithms behind some cryptocurrencies, their sustainability issue, and resolutions to combat high energy consumption. While the pace of energy …