Digital Commons Network^™

Solving Multiple Inference In Graphical Models, Cong Chen

Dissertations, Theses, and Capstone Projects

For inference problems in graphical models, much effort has been directed at algorithms for obtaining one single optimal prediction. In practice, the data is often noisy or incomplete, which makes one single optimal solution unreliable. To address this problem, multiple Inference is proposed to find several best solutions, M-Best, where multiple hypotheses are preferred for advanced reasoning. People use oracle accuracy as an evaluation criterion expecting one of the solutions has high accuracy with the ground truth. It has been shown that it is beneficial for the top solutions to be diverse. Approaches for solving diverse multiple inference are proposed …

Molecular Dynamics Simulations Of Self-Assemblies In Nature And Nanotechnology, Phu Khanh Tang

Dissertations, Theses, and Capstone Projects

Nature usually divides complex systems into smaller building blocks specializing in a few tasks since one entity cannot achieve everything. Therefore, self-assembly is a robust tool exploited by Nature to build hierarchical systems that accomplish unique functions. The cell membrane distinguishes itself as an example of Nature’s self-assembly, defining and protecting the cell. By mimicking Nature’s designs using synthetically designed self-assemblies, researchers with advanced nanotechnological comprehension can manipulate these synthetic self-assemblies to improve many aspects of modern medicine and materials science. Understanding the competing underlying molecular interactions in self-assembly is always of interest to the academic scientific community and industry. …

Piecewise Linear Manifold Clustering, Artyom Diky

Dissertations, Theses, and Capstone Projects

This work studies the application of topological analysis to non-linear manifold clustering. A novel method, that exploits the data clustering structure, allows to generate a topological representation of the point dataset. An analysis of topological construction under different simulated conditions is performed to explore the capabilities and limitations of the method, and demonstrated statistically significant improvements in performance. Furthermore, we introduce a new information-theoretical validation measure for clustering, that exploits geometrical properties of clusters to estimate clustering compressibility, for evaluation of the clustering goodness-of-fit without any prior information about true class assignments. We show how the new validation measure, when …

Novel Hybrid Resampling Algorithms For Parallel/Distributed Particle Filters, Xudong Zhang

Dissertations, Theses, and Capstone Projects

Particle filters, also known as sequential Monte Carlo (SMC) methods, use the Bayesian inference and the stochastic sampling technique to estimate the states of dynamic systems from given observations. Parallel/Distributed particle filters were introduced to improve the performance of sequential particle filters by using multiple processing units (PUs). The classical resampling algorithm used in parallel/distributed particle filters is a centralized scheme, called centralized resampling, which needs a central unit (CU) to serve as a hub for data transfers. As a result, the centralized resampling procedures produce extra communication costs, which lowers the speedup factors in parallel computing. Even though some …

Logics Of Resource And Justification, Hirohiko Kushida

Dissertations, Theses, and Capstone Projects

It is a well-known result by G ̈odel in 1933 that the Intuitionistic Logic can be embedded into a system which is essentially equivalent to the modal logic S4. This can be considered to be an attempt to provide a provability semantics to the Intuitionistic Logic. This work had caused some problems: the exact arithmetical meaning of the Intuitionistic Logic and S4, the exact axiomatization of formal provability in formal arithmetic and the standard model of arithmetic. These days the arithmetical interpretation has been extended and generalized to epistemological interpretation for various modal logics, which resulted in various systems of …

Adversarial Training For Skill Learning In A Mobile Robot, Todd W. Flyr

Dissertations, Theses, and Capstone Projects

Machine Learning in mobile robotics is sometimes hampered by the difficulties associated with the creation of a large corpus of labeled data that most neural network based learning algorithms demand. In recent years, advances in the field of machine learning have been facilitated via the creation of large collaboratively-created labeled training datasets that researchers can use as the basis for experiments to validate and improve their candidate neural network architectures. For the field of robotics, however, tasks are so disparate and the physical devices so varied that in most cases the creation of collaborative benchmark datasets are impractical. Obtaining data …

Mechanism Design And Modeling To Analyze Complex Social Systems For Public Policy, Haripriya Chakraborty

Dissertations, Theses, and Capstone Projects

The study of complex systems is an important area of research. Many scenarios require the ability to simulate large multi-agent systems with minimal artificial assumptions. We are currently living in a world where the adoption of artificial intelligence (AI) in various areas is increasing rapidly. This, in turn, has serious consequences from a computational and policy perspective. The focus needs to be on designing systems that are not only computationally elegant and efficient but also ethical. The goal of this thesis is to examine some of the ways AI can be used to simulate complex social systems. In addition, we …

Towards Automated Software Evolution Of Data-Intensive Applications, Yiming Tang

Dissertations, Theses, and Capstone Projects

Recent years have witnessed an explosion of work on Big Data. Data-intensive applications analyze and produce large volumes of data typically terabyte and petabyte in size. Many techniques for facilitating data processing are integrated into data-intensive applications. API is a software interface that allows two applications to communicate with each other. Streaming APIs are widely used in today's Object-Oriented programming development that can support parallel processing. In this dissertation, an approach that automatically suggests stream code run in parallel or sequentially is proposed. However, using streams efficiently and properly needs many subtle considerations. The use and misuse patterns for stream …

Learn Biologically Meaningful Representation With Transfer Learning, Di He

Dissertations, Theses, and Capstone Projects

Machine learning has made significant contributions to bioinformatics and computational biol­ogy. In particular, supervised learning approaches have been widely used in solving problems such as bio­marker identification, drug response prediction, and so on. However, because of the limited availability of comprehensively labeled and clean data, constructing predictive models in super­ vised settings is not always desirable or possible, especially when using data­hunger, red­hot learning paradigms such as deep learning methods. Hence, there are urgent needs to develop new approaches that could leverage more readily available unlabeled data in driving successful machine learning ap­ plications in this area.

In my dissertation, …

Metareasoning, Opportunistic Exploration, And Explanations For Autonomous Indoor Navigation, Raj Korpan

Dissertations, Theses, and Capstone Projects

Autonomous indoor navigation is an important task for mobile robots deployed without a map in real-world environments, such as museums or offices. While it travels, an autonomous robot navigator must contend with lack of prior knowledge, sensor noise, actuator error, and inquisitive people. This dissertation addresses these challenges with a cognitively-based hierarchical reasoning architecture that incorporates learning, exploration, reactivity, planning, heuristics, and explanations. Evaluation by simulation in large, complex, indoor environments shows that a robot controller can successfully navigate without a detailed map of every obstruction's location when it performs limited initial global exploration and plans in its learned spatial …

A New Feature Selection Method Based On Class Association Rule, Sami A. Al-Dhaheri

Dissertations, Theses, and Capstone Projects

Feature selection is a key process for supervised learning algorithms. It involves discarding irrelevant attributes from the training dataset from which the models are derived. One of the vital feature selection approaches is Filtering, which often uses mathematical models to compute the relevance for each feature in the training dataset and then sorts the features into descending order based on their computed scores. However, most Filtering methods face several challenges including, but not limited to, merely considering feature-class correlation when defining a feature’s relevance; additionally, not recommending which subset of features to retain. Leaving this decision to the end-user may …

Modeling And Analysis Of Affiliation Networks With Subsumption, Alexey Nikolaev

Dissertations, Theses, and Capstone Projects

An affiliation (or two-mode) network is an abstraction commonly used for representing systems with group interactions. It consists of a set of nodes and a set of their groupings called affiliations. We introduce the notion of affiliation network with subsumption, in which no affiliation can be a subset of another. A network with this property can be modeled by an abstract simplicial complex whose facets are the affiliations of the network.

We introduce a new model for generating affiliation networks with and without subsumption (represented as simplicial complexes and hypergraphs, respectively). In this model, at each iteration, a constant number …

3d Object Detection, Instance Segmentation And Classification From 3d Range And 2d Color Images, Xiaoke Shen

Dissertations, Theses, and Capstone Projects

We address the problem of 3D object detection and instance segmentation by proposing a novel object segmentation and detection system. First, we detect 2D objects based on RGB, Depth only, or RGB-D images. A 3D convolutional-based system, named Frustum VoxNet, is proposed. This system 1) generates frustums from 2D detection results, 2) proposes 3D candidate voxelized images for each frustum, and uses a 3D convolutional neural network (CNN) based on these candidates voxelized images to perform the 3D instance segmentation and object detection. Although the volumetric data representation is widely used for 3D object classification, there are fewer works on …

Speech Enhancement Using Speech Synthesis Techniques, Soumi Maiti

Dissertations, Theses, and Capstone Projects

Traditional speech enhancement systems reduce noise by modifying the noisy signal to make it more like a clean signal, which suffers from two problems: under-suppression of noise and over-suppression of speech. These problems create distortions in enhanced speech and hurt the quality of the enhanced signal. We propose to utilize speech synthesis techniques for a higher quality speech enhancement system. Synthesizing clean speech based on the noisy signal could produce outputs that are both noise-free and high quality. We first show that we can replace the noisy speech with its clean resynthesis from a previously recorded clean speech dictionary from …