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Exploring The Impact Of Pretrained Bidirectional Language Models On Protein Secondary Structure Prediction, Dillon G. Daudert
Exploring The Impact Of Pretrained Bidirectional Language Models On Protein Secondary Structure Prediction, Dillon G. Daudert
Masters Theses
Protein secondary structure prediction (PSSP) involves determining the local conformations of the peptide backbone in a folded protein, and is often the first step in resolving a protein's global folded structure. Accurate structure prediction has important implications for understanding protein function and de novo protein design, with progress in recent years being driven by the application of deep learning methods such as convolutional and recurrent neural networks. Language models pretrained on large text corpora have been shown to learn useful representations for feature extraction and transfer learning across problem domains in natural language processing, most notably in instances where the …
Improving The Quality Of Neural Machine Translation Using Terminology Injection, Duane K. Dougal
Improving The Quality Of Neural Machine Translation Using Terminology Injection, Duane K. Dougal
Theses and Dissertations
Most organizations use an increasing number of domain- or organization-specific words and phrases. A translation process, whether human or automated, must also be able to accurately and efficiently use these specific multilingual terminology collections. However, comparatively little has been done to explore the use of vetted terminology as an input to machine translation (MT) for improved results. In fact, no single established process currently exists to integrate terminology into MT as a general practice, and especially no established process for neural machine translation (NMT) exists to ensure that the translation of individual terms is consistent with an approved terminology collection. …
Cmos Compatible Memristor Networks For Brain-Inspired Computing, Can Li
Cmos Compatible Memristor Networks For Brain-Inspired Computing, Can Li
Doctoral Dissertations
In the past decades, the computing capability has shown an exponential growth trend, which is observed as Moore’s law. However, this growth speed is slowing down in recent years mostly because the down-scaled size of transistors is approaching their physical limit. On the other hand, recent advances in software, especially in big data analysis and artificial intelligence, call for a break-through in computing hardware. The memristor, or the resistive switching device, is believed to be a potential building block of the future generation of integrated circuits. The underlying mechanism of this device is different from that of complementary metal-oxide-semiconductor (CMOS) …
Leveraging Eye Structure And Motion To Build A Low-Power Wearable Gaze Tracking System, Addison Mayberry
Leveraging Eye Structure And Motion To Build A Low-Power Wearable Gaze Tracking System, Addison Mayberry
Doctoral Dissertations
Clinical studies have shown that features of a person's eyes can function as an effective proxy for cognitive state and neurological function. Technological advances in recent decades have allowed us to deepen this understanding and discover that the actions of the eyes are in fact very tightly coupled to the operation of the brain. Researchers have used camera-based eye monitoring technology to exploit this connection and analyze mental state across across many different metrics of interest. These range from simple things like attention and scene processing, to impairments such as a fatigue or substance use, and even significant mental disorders …
Identification And Optimal Linear Tracking Control Of Odu Autonomous Surface Vehicle, Nadeem Khan
Identification And Optimal Linear Tracking Control Of Odu Autonomous Surface Vehicle, Nadeem Khan
Mechanical & Aerospace Engineering Theses & Dissertations
Autonomous surface vehicles (ASVs) are being used for diverse applications of civilian and military importance such as: military reconnaissance, sea patrol, bathymetry, environmental monitoring, and oceanographic research. Currently, these unmanned tasks can accurately be accomplished by ASVs due to recent advancements in computing, sensing, and actuating systems. For this reason, researchers around the world have been taking interest in ASVs for the last decade. Due to the ever-changing surface of water and stochastic disturbances such as wind and tidal currents that greatly affect the path-following ability of ASVs, identification of an accurate model of inherently nonlinear and stochastic ASV system …
Parameterizing And Aggregating Activation Functions In Deep Neural Networks, Luke Benjamin Godfrey
Parameterizing And Aggregating Activation Functions In Deep Neural Networks, Luke Benjamin Godfrey
Graduate Theses and Dissertations
The nonlinear activation functions applied by each neuron in a neural network are essential for making neural networks powerful representational models. If these are omitted, even deep neural networks reduce to simple linear regression due to the fact that a linear combination of linear combinations is still a linear combination. In much of the existing literature on neural networks, just one or two activation functions are selected for the entire network, even though the use of heterogenous activation functions has been shown to produce superior results in some cases. Even less often employed are activation functions that can adapt their …
Virtualized Cloud Platform Management Using A Combined Neural Network And Wavelet Transform Strategy, Chunyu Liu
Virtualized Cloud Platform Management Using A Combined Neural Network And Wavelet Transform Strategy, Chunyu Liu
Electronic Theses, Projects, and Dissertations
This study focuses on implementing a log analysis strategy that combines a neural network algorithm and wavelet transform. Wavelet transform allows us to extract the important hidden information and features of the original time series log data and offers a precise framework for the analysis of input information. While neural network algorithm constitutes a powerfulnonlinear function approximation which can provide detection and prediction functions. The combination of the two techniques is based on the idea of using wavelet transform to denoise the log data by decomposing it into a set of coefficients, then feed the denoised data into a neural …
Rnn-Based Generation Of Polyphonic Music And Jazz Improvisation, Andrew Hannum
Rnn-Based Generation Of Polyphonic Music And Jazz Improvisation, Andrew Hannum
Electronic Theses and Dissertations
This paper presents techniques developed for algorithmic composition of both polyphonic music, and of simulated jazz improvisation, using multiple novel data sources and the character-based recurrent neural network architecture char-rnn. In addition, techniques and tooling are presented aimed at using the results of the algorithmic composition to create exercises for musical pedagogy.