Index Catalog // ScholarsArchive@OSU

Graph Imitation Learning for Optimal Joint Beamforming and Antenna Selection

Creator:: Shrestha, Sagar
Abstract:: Transmit beamforming is an important technique employed to improve efficiency and signal quality in wireless communication systems by steering signals towards their in- tended users. It often arises jointly with the antenna selection problem due to various reasons, such as limited number of radio frequency (RF) chains and energy/resource effi-...
Resource Type:: Masters Thesis

Regularization-Based Data Integration and Its Application to Genome-Wide Association Studies

Creator:: Li, Kai
Abstract:: In genome-wide association studies, it is often challenging to have consistent findings from independent studies. This "lack of reproducibility" issue is partially caused by the relatively small sample size compared to the number of genetic markers in every single study. Therefore, researchers have developed statistical methods to integrate multiple studies...
Resource Type:: Dissertation
Full Text:: across studies. On the other hand, for different but often related phenotypes, Li et al. (2014), Shi et

Ideal Thumbnail-Preserving Encryption for Balancing Image Privacy and Usability

Creator:: Tajik, Kimia
Abstract:: In this dissertation, we propose Ideal Thumbnail-Preserving Encryption (Ideal TPE), as a special case of format-preserving encryption, to balance image privacy and usability concerns in a cloud environment. We first introduce a concrete construction for Ideal TPE, that provably leaks nothing about the plaintext (unencrypted) image beyond its thumbnail. We...
Resource Type:: Dissertation

Machine Learning Methods for the Discovery and Analysis of MicroRNAs

Creator:: Bell, Jimmy D.
Abstract:: MicroRNAs are a highly conserved class of small endogenous RNA, about ~22nt in length, involved in post-transcriptional gene silencing and have prominent roles in disease and development. Though the process of microRNA discovery was once an arduous task, the advent of high throughput sequencing technology has resulted in novel microRNAs...
Resource Type:: Masters Thesis

Action Segmentation in Videos: Deep Models, Prediction Diffusion, and Deep-Temporal Augmentations

Creator:: Aziere, Nicolas
Abstract:: This paper addresses the high model complexity and overconfident frame labeling of state-of-the-art (SOTA) action segmenters. Their complexity is typically justified by the need to sequentially refine action segmentation through multiple stages of a deep architecture. However, this multistage refinement does not take into account uncertainty of frame labeling predicted...
Resource Type:: Dissertation

Pixel- and Frame-level Video Labeling using Spatial and Temporal Convolutional Networks

Creator:: Lei, Peng
Abstract:: This dissertation addresses the problem of video labeling at both the frame and pixel levels using deep learning. For pixel-level video labeling, we have studied two problems: i) Spatiotemporal video segmentation and ii) Boundary detection and boundary flow estimation. For the problem of spatiotemporal video segmentation, we have developed recurrent...
Resource Type:: Dissertation
Full Text:: , Dimitrios Trigkakis, Xinze Guan, Xu Hu, Qingkai Lu, Zhongyuan Feng, Xu Xu, Zeyu You, Yuanli Pei, Khoi

Object Tracking-by-Segmentation in Videos

Creator:: Chen, Sheng
Abstract:: This thesis focuses on the problem of object tracking. Given a video, the general objective of tracking is to track the location over time of one or more targets in the image sequence. This is a very challenging task as algorithms need to deal with problems such as appearance variations,...
Resource Type:: Dissertation

Part-based and Uncertainty-Aware Few-shot Object Segmentation in Images

Creator:: Nguyen, Khoi D.
Abstract:: This dissertation addresses few-shot object segmentation in images. The goal of segmentation is to label every image pixel with a class of the object occupying that pixel, where the class may represent a semantic object category or instance. In few-shot segmentation, training and test datasets have different classes. Every new...
Resource Type:: Dissertation

Explanations and Processes to Enable Humans to Assess AI with Respect to Manipulable Properties

Creator:: Dodge, Jonathan E.
Abstract:: Assessing AI systems is difficult. Humans rely on AI systems in increasing ways, both visible and invisible, meaning a variety of stakeholders need a variety of assessment tools (e.g., a professional auditor, a developer, and an end user all have different needs). We posit that it is possible to provide...
Resource Type:: Dissertation

Semantic embodied navigation : developing agents that navigate from language and vision

Creator:: Krantz, Jacob
Abstract:: Autonomous robotic agents are on their way to becoming in-home personal assistants, construction assistants, and warehouse workers. The degree of autonomy of such systems is reflected by the manner in which we specify goals to them; the abstraction of low-level commands to high-level goals goes hand-in-hand with increased autonomy. In...
Resource Type:: Dissertation

ScholarsArchive@OSU

Graph Imitation Learning for Optimal Joint Beamforming and Antenna Selection

Regularization-Based Data Integration and Its Application to Genome-Wide Association Studies

Ideal Thumbnail-Preserving Encryption for Balancing Image Privacy and Usability

Machine Learning Methods for the Discovery and Analysis of MicroRNAs

Action Segmentation in Videos: Deep Models, Prediction Diffusion, and Deep-Temporal Augmentations

Pixel- and Frame-level Video Labeling using Spatial and Temporal Convolutional Networks

Object Tracking-by-Segmentation in Videos

Part-based and Uncertainty-Aware Few-shot Object Segmentation in Images

Explanations and Processes to Enable Humans to Assess AI with Respect to Manipulable Properties

Semantic embodied navigation : developing agents that navigate from language and vision

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