Index Catalog // ScholarsArchive@OSU

Adaptive Movement Intent Decoding for Intuitive Control of Neuroprostheses

Creator:: George, Damon A.
Abstract:: Movement intent decoders, which interpret volitional movement intent from human bioelectric signals, can be incorporated into modern neuroprostheses to offer people living with limb loss or paralysis the potential to regain their lost motor control. Machine learning methods have become the research standard for continuous decoders with high degrees of...
Resource Type:: Masters Thesis

Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression

Creator:: Wang, Xinyao
Abstract:: Heatmap regression has became one of the mainstream approaches to localize facial landmarks. As Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) are becoming popular in solving computer vision tasks, extensive research has been done on these architectures. However, the loss function for heatmap regression is rarely studied. In...
Resource Type:: Masters Thesis

Advances in Simultaneous Translation

Creator:: Zheng, Renjie
Abstract:: Simultaneous translation, which translates concurrently with the source language speech, is widely used in many scenarios including multilateral organizations. However, it is well known to be one of the most challenging tasks for humans due to the simultaneous perception and production in two languages. On the other hand, simultaneous translation...
Resource Type:: Dissertation
Full Text:: . Lizhong Chen, Prof. Brett Tyler and Prof. Alan Fern, for their insightful comments and encouragement, but

An Investigation of the Effects of Merge Conflicts on Collaborative Software Development

Creator:: Brindescu, Caius
Abstract:: Merge conflicts have long plagued software development. With larger and more dispersed teams comes greater risk of developers working on the same code at the same time. While merge conflicts are known to be painful, their exact impact on software is still largely unknown. Are merge conflicts an isolated problem,...
Resource Type:: Dissertation

Attentional flow networks : a real-time adaptive display technique

Creator:: Bible, John
Abstract:: Graphics resource allocation has often existed on the periphery of graphics research, taking second place to faster or more realistic algorithms for global illumination and modeling. Most research has focused on generating sets of images that best match the effects one might see in photographs. While this approach works well...
Resource Type:: Masters Thesis

Characterizing and Addressing Dataset Shift with Deep Learning

Creator:: Olson, Matthew L.
Abstract:: This dissertation delves into understanding, characterizing, and addressing dataset shift in deep learning, a pervasive issue for deployed machine learning systems. Integral aspects of the problem are examined: We start with the use of counterfactual explanations in order to characterize the behavior of deep reinforcement learning agents in visual input...
Resource Type:: Dissertation

Characterizing Server Compromise: Resilience Models, Instantiations, and Impossibilities

Creator:: McQuoid, Ian M.
Abstract:: One of the pervasive problems arising in our modern, digital world surrounds data breaches where an adversary, through zero-day exploitations, phishing, or old-fashioned social engineering attacks, gains access to a service’s data stores. Our society increasingly relies on these cloud-based services for everything from our taxes to personal communication. As...
Resource Type:: Dissertation

Computational Elucidation of the Regulatory SNPs in the Non-Coding Regions of the Human Genome

Creator:: Yao, Yao
Abstract:: We describe a series of novel computational models, CERENKOV (Computational Elucidation of the REgulatory NonKOding Variome) and its successors CERENKOV2, CERENKOV3, and Convolutional CERENKOV3, for discriminating regulatory single nucleotide polymorphisms (rSNPs) from non-regulatory SNPs within non-coding genetic loci. The CERENKOV models are designed for recognizing rSNPs in the context of...
Resource Type:: Dissertation

Convergence Analysis Framework for Fixed-Point Algorithms in Machine Learning and Signal Processing

Creator:: Vu, Trung
Abstract:: Iterative algorithms are simple yet efficient in solving large-scale optimization problems in practice. With a surge in the amount of data in past decades, these methods have become increasingly important in many application areas including matrix/tensor recovery, deep learning, data mining, and reinforcement learning. To optimize or improve iterative algorithms,...
Resource Type:: Dissertation

Design and Analysis of Distributed Resource Allocation Techniques for Large-Scale Dynamic Spectrum Access Networks

Creator:: NoroozOliaee, MohammadJavad
Abstract:: The widespread use of wireless devices that we have recently been witnessing, such as smartphones, tablets, laptops, and wirelessly accessible devices in general, is causing an unprecedented growth in the required amount of the wireless radio spectrum. On the other hand, the spectrum resource has, for the last several decades,...
Resource Type:: Dissertation

ScholarsArchive@OSU

Adaptive Movement Intent Decoding for Intuitive Control of Neuroprostheses

Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression

Advances in Simultaneous Translation

An Investigation of the Effects of Merge Conflicts on Collaborative Software Development

Attentional flow networks : a real-time adaptive display technique

Characterizing and Addressing Dataset Shift with Deep Learning

Characterizing Server Compromise: Resilience Models, Instantiations, and Impossibilities

Computational Elucidation of the Regulatory SNPs in the Non-Coding Regions of the Human Genome

Convergence Analysis Framework for Fixed-Point Algorithms in Machine Learning and Signal Processing

Design and Analysis of Distributed Resource Allocation Techniques for Large-Scale Dynamic Spectrum Access Networks

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