Index Catalog // ScholarsArchive@OSU

Dimensionality Reduction Techniques for Compositional Data : An Empirical Study

Creator:: George, Rogen
Abstract:: Compositional data is a type of data where the features are non-negative and always sum to a constant. This type of data is frequently encountered in many ﬁelds such as microbiology, geology, economics and natural language processing. Compositional data has unique statistical properties that makes it difficult to apply standard...
Resource Type:: Masters Thesis

Exploratory Study to Uncover Student Mental Models of Singly Linked Lists in the C Programming Language

Creator:: Almadhoun, Eman
Abstract:: In computer science, learning abstract fundamental programming concepts requiring students to understand memory management can be very difficult and lead to misunderstandings that carry on into advanced topics. This is especially true in data structures with abstract data types. Understanding how novice students think and reason about data structures is...
Resource Type:: Dissertation

Deep Segmentation in Plant Phenomics

Creator:: Kaur, Damanpreet
Abstract:: The abilities of plant biologists to characterize the genetic basis of physiological traits are limited by their abilities to obtain quantitative data representing precise details of trait variation and mainly to collect this data on a high-throughput scale at low cost. Deep learning-based methods have demonstrated unprecedented potential to automate...
Resource Type:: Masters Thesis

Facilitating Code Comprehension by Annotations in Canvas-Based IDE

Creator:: Adeli, Marjan
Abstract:: Developers spend a considerable amount of time comprehending code and building accurate mental models of the code. Understanding the relationships between software features within IDEs is difficult, with information split across different visual hierarchies making navigation cumbersome. Canvas-based IDEs mitigate some of the navigation costs by allowing relevant information to...
Resource Type:: Masters Thesis

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

Exploration of regression models for cancer noncoding mutation recurrence

Creator:: Xu, Tanjin
Abstract:: An important impact of the genome technology revolution will be the elucidation of mechanisms of cancer pathogenesis, leading to improvements in the diagnosis of cancer and the selection of cancer treatment. Integrated with current well-studied massive knowledge and findings about the role of protein-coding mutations in cancer, demystifying the functional...
Resource Type:: Masters Thesis

Investigating Latent State and Uncertainty Representations in Reinforcement Learning

Creator:: Koul, Anurag
Abstract:: Learning latent space representations of high-dimensional world states has been at the core of recent rapid growth in reinforcement learning(RL). At the same time, RL algo- rithms have suffered from ignored uncertainties in the predicted estimates of model-free or model-based methods. In our work, we investigate both of these aspects...
Resource Type:: Dissertation

Redesigning the Structure and Access Paths of Databases for Effective and Efficient Query Processing

Creator:: Ghadakchi, Vahid
Abstract:: Many database users are not familiar with formal query languages, the concept of schema, or the exact content of their database. Thus, it is challenging for these users to formulate their information needs over semi-structured and structured databases. To address this problem, researchers have proposed usable query interfaces over which...
Resource Type:: Dissertation

Anomaly Detection and Probabilistic Diagnosis for Automated Data Quality Control

Creator:: Zemicheal, Tadesse H.
Abstract:: Advances in sensor technology are greatly expanding the range of quantities that can be measured while simultaneously reducing the cost. However, deployed sensors drift out of calibration and fail, so every sensor network requires quality control procedures to promptly detect these failures. To address these problems, we propose a two-level...
Resource Type:: Dissertation

Millimeter-Wave Wideband Low-Noise Amplifiers in 22FFL Technology

Creator:: Gupta, Soumya
Abstract:: The deployment of advanced technology standards for 5G and beyond in cellular networks has resulted in interest in integrated circuits (ICs) operating at frequencies above 10GHz. This has sparked research on wideband circuits in commercial low-cost silicon technologies, operating at high RF and mm-wave frequencies. Given the wide range of...
Resource Type:: Masters Thesis

ScholarsArchive@OSU

Dimensionality Reduction Techniques for Compositional Data : An Empirical Study

Exploratory Study to Uncover Student Mental Models of Singly Linked Lists in the C Programming Language

Deep Segmentation in Plant Phenomics

Facilitating Code Comprehension by Annotations in Canvas-Based IDE

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

Exploration of regression models for cancer noncoding mutation recurrence

Investigating Latent State and Uncertainty Representations in Reinforcement Learning

Redesigning the Structure and Access Paths of Databases for Effective and Efficient Query Processing

Anomaly Detection and Probabilistic Diagnosis for Automated Data Quality Control

Millimeter-Wave Wideband Low-Noise Amplifiers in 22FFL Technology

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