Index Catalog // ScholarsArchive@OSU

A tool for visualizing sequence alignments and associating annotations

Creator:: Zhang, Wenpeng
Abstract:: Alignment of genomic sequences from different species is becoming an increasingly powerful method in biology, and is being used for many purposes. The result of sequence alignments is a list of pairs of matched locations between the pattern string and the text string. However, without any proper visualization tools to...
Resource Type:: Capstone Project

Decentralized Multi-Robot Exploration with Sparse Communication through Deep Imitation Learning

Creator:: Senewiratne, Yomal K.
Abstract:: We present a method for decentralized, multi-robot exploration in adverse environments where communication is minimal. A key conceptual feature of our method is enabling implicit coordination between robots by training a Convolutional Neural Network (CNN) as a heuristic for planning using Monte Carlo Tree Search (MCTS). Our method consists of...
Resource Type:: Masters Thesis

Detecting Anomalies in Object Appearance and Motion Dynamics

Creator:: Alotaibi, Mazen
Abstract:: Machine common sense remains a broad, potentially unbounded problem in AI. Our focus is to move toward AI systems that can develop common-sense reasoning similar to humans to detect anomalies. In particular, we study the problem of detecting the violation of expectations when object appearance or motion dynamics change from...
Resource Type:: Capstone Project

Agile Bipedal Maneuvers Optimized then Learned from Single Rigid-Body Model Dynamics

Creator:: Batke, Ryan
Abstract:: As bipedal robots move ever closer to being integrated into all manner of real world envi-ronments there is a necessity to push their dynamic capabilities to meet or exceed those of humans and animals. Advancements must be made to address ordinary challenges that arise everyday in the same environments that...
Resource Type:: Masters Thesis

Collaborative Online Learning For Join Processing

Creator:: Xie, Mian
Abstract:: Relational binary operators, such as join, are arguably the most costly and frequently used operations in relational data systems. In many join algorithms, the majority of the process time is spent on scanning and attempting to join the parts of the relations that do not satisfy the join condition and...
Resource Type:: Capstone Project

Learning to Estimate Multi-Relation Aggregation Functions

Creator:: Jadhav, Rashmi Suresh
Abstract:: Multi-relation aggregation queries process the join operator before computing the aggregation function. This join is arguably the most costly operation since traditional join algorithms spend majority of their time trying to join the parts of the relations that do not generate any output tuples. This causes slow response times with...
Resource Type:: Masters Thesis

Customizable Visual Traces for Program Explanations

Creator:: Bajaj, Divya
Abstract:: Correctness and efficiency are important properties of programs. However, to support maintenance and debugging, the programs should also be understandable. Program explanations also play a vital role in educational settings, enhancing the understanding of programs among students. Proof trees provide a sound basis for generating dynamic explanations of programs. But...
Resource Type:: Masters Thesis

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

Action Selection for Heterologous Control of a Continuous-Output Myoelectric Prosthesis

Creator:: Sylwester, Sean T.
Abstract:: Signiﬁcance: Movement intent decoding algorithms can interpret human bioelectrical signals to control prosthetic limbs with many degrees of freedom (DOFs). This work involves decoding volitional movement intent from surface electromyogram (sEMG) signals to control prosthetic arms. To train these algorithms, patients ﬂex their muscles to “follow” a movement prompt, and...
Resource Type:: Masters Thesis

Toward Disentangling the Activations of the Deep Networks via Low-dimensional Embedding and Non-negative Factorization

Creator:: Khorram, Saeed
Abstract:: In this thesis, we introduce a novel Explanation Neural Network (XNN) to explain the predictions made by a deep network. The XNN works by embedding a high-dimensional activation vector of a deep network layer non-linearly into a low-dimensional explanation space while retaining faithfulness i.e., the original deep learning predictions can...
Resource Type:: Masters Thesis

ScholarsArchive@OSU

A tool for visualizing sequence alignments and associating annotations

Decentralized Multi-Robot Exploration with Sparse Communication through Deep Imitation Learning

Detecting Anomalies in Object Appearance and Motion Dynamics

Agile Bipedal Maneuvers Optimized then Learned from Single Rigid-Body Model Dynamics

Collaborative Online Learning For Join Processing

Learning to Estimate Multi-Relation Aggregation Functions

Customizable Visual Traces for Program Explanations

Adaptive Movement Intent Decoding for Intuitive Control of Neuroprostheses

Action Selection for Heterologous Control of a Continuous-Output Myoelectric Prosthesis

Toward Disentangling the Activations of the Deep Networks via Low-dimensional Embedding and Non-negative Factorization

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