Visual Dynamics Models for Robotic Planning and Control

For a robot to interact with its environment, it must perceive the world and understand how the world evolves as a consequence of its actions. This thesis studies a few methods that a robot can use to respond to its observations, with a focus on instances that can leverage visual dynamic models. In general, these are models of how the visual observations of a robot evolves as a consequence of its actions. This could be in the form of predictive models that directly predict the future in the space of image pixels, in the space of visual features extracted from these images, or in the space of compact learned latent representations. The three instances that this thesis studies are in the context of visual servoing, visual planning, and representation learning for reinforcement learning. In the first case, we combine learned visual features with learning single-step predictive dynamics models and reinforcement learning to learn visual servoing mechanisms. In the second case, we use a deterministic multi-step video prediction model to achieve various manipulation tasks through visual planning. In addition, we show that conventional video prediction models are unequipped to model uncertainty and multiple futures, which could limit the planning capabilities of the robot. To address this, we propose a stochastic video prediction model that is trained with a combination of variational losses, adversarial losses, and perceptual losses, and show that this model can predict futures that are more realistic, diverse, and accurate. Unlike the first two cases, in which the dynamics model is used to make predictions for decision-making, the third case learns the model solely for representation learning. We learn a stochastic sequential latent variable model to learn a latent representation, and then use it as an intermediate representation for reinforcement learning. We show that this approach improves final performance and sample efficiency

Modeling network traffic on a global network-centric system with artificial neural networks

This dissertation proposes a new methodology for modeling and predicting network traffic. It features an adaptive architecture based on artificial neural networks and is especially suited for large-scale, global, network-centric systems. Accurate characterization and prediction of network traffic is essential for network resource sizing and real-time network traffic management. As networks continue to increase in size and complexity, the task has become increasingly difficult and current methodology is not sufficiently adaptable or scaleable. Current methods model network traffic with express mathematical equations which are not easily maintained or adjusted. The accuracy of these models is based on detailed characterization of the traffic stream which is measured at points along the network where the data is often subject to constant variation and rapid evolution. The main contribution of this dissertation is development of a methodology that allows utilization of artificial neural networks with increased capability for adaptation and scalability. Application on an operating global, broadband network, the Connexion by Boeingʼ network, was evaluated to establish feasibility. A simulation model was constructed and testing was conducted with operational scenarios to demonstrate applicability on the case study network and to evaluate improvements in accuracy over existing methods --Abstract, page iii

2022 Review of Data-Driven Plasma Science

Data-driven science and technology offer transformative tools and methods to science. This review article highlights the latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS), i.e., plasma science whose progress is driven strongly by data and data analyses. Plasma is considered to be the most ubiquitous form of observable matter in the universe. Data associated with plasmas can, therefore, cover extremely large spatial and temporal scales, and often provide essential information for other scientific disciplines. Thanks to the latest technological developments, plasma experiments, observations, and computation now produce a large amount of data that can no longer be analyzed or interpreted manually. This trend now necessitates a highly sophisticated use of high-performance computers for data analyses, making artificial intelligence and machine learning vital components of DDPS. This article contains seven primary sections, in addition to the introduction and summary. Following an overview of fundamental data-driven science, five other sections cover widely studied topics of plasma science and technologies, i.e., basic plasma physics and laboratory experiments, magnetic confinement fusion, inertial confinement fusion and high-energy-density physics, space and astronomical plasmas, and plasma technologies for industrial and other applications. The final section before the summary discusses plasma-related databases that could significantly contribute to DDPS. Each primary section starts with a brief introduction to the topic, discusses the state-of-the-art developments in the use of data and/or data-scientific approaches, and presents the summary and outlook. Despite the recent impressive signs of progress, the DDPS is still in its infancy. This article attempts to offer a broad perspective on the development of this field and identify where further innovations are required

Proceedings, MSVSCC 2015

The Virginia Modeling, Analysis and Simulation Center (VMASC) of Old Dominion University hosted the 2015 Modeling, Simulation, & Visualization Student capstone Conference on April 16th. The Capstone Conference features students in Modeling and Simulation, undergraduates and graduate degree programs, and fields from many colleges and/or universities. Students present their research to an audience of fellow students, faculty, judges, and other distinguished guests. For the students, these presentations afford them the opportunity to impart their innovative research to members of the M&S community from academic, industry, and government backgrounds. Also participating in the conference are faculty and judges who have volunteered their time to impart direct support to their students’ research, facilitate the various conference tracks, serve as judges for each of the tracks, and provide overall assistance to this conference. 2015 marks the ninth year of the VMASC Capstone Conference for Modeling, Simulation and Visualization. This year our conference attracted a number of fine student written papers and presentations, resulting in a total of 51 research works that were presented. This year’s conference had record attendance thanks to the support from the various different departments at Old Dominion University, other local Universities, and the United States Military Academy, at West Point. We greatly appreciated all of the work and energy that has gone into this year’s conference, it truly was a highly collaborative effort that has resulted in a very successful symposium for the M&S community and all of those involved. Below you will find a brief summary of the best papers and best presentations with some simple statistics of the overall conference contribution. Followed by that is a table of contents that breaks down by conference track category with a copy of each included body of work. Thank you again for your time and your contribution as this conference is designed to continuously evolve and adapt to better suit the authors and M&S supporters. Dr.Yuzhong Shen Graduate Program Director, MSVE Capstone Conference Chair John ShullGraduate Student, MSVE Capstone Conference Student Chai

Conditional Visualisation for Statistical Models

It is difficult to understand data and statistical models in high-dimensional space. One way to approach the problem is conditional visualisation, but methods in this area have lagged behind the considerable advances in statistical modelling in recent decades. This thesis presents a new approach to conditional visualisation which uses interactive computer graphics, and supports the exploration of a broad range of statistical models. The new approach to conditional visualisation consists of visualising a single lowdimensional section at a time, showing fitted models on the section, and enhancing the section by displaying observed data which are near the section according to a similarity measure. Two ways of choosing sections are given |choosing sections interactively using data summary graphics, and choosing sections programmatically according to some criteria. The visualisations in this thesis necessitate interactive graphics, which are implemented in the condvis package in R

Multi-plane Neural Networks for Event Reconstruction in LArTPCs

With the advent of high precision Liquid Argon Time Projection Chambers (LArTPCs), the need for fast and efficient data analysis has grown exponentially. The Shorte Baseline Neutrino (SBN) program [17], consisting of MicroBooNE, ICARUS, and SBND and located along the Booster Neutrino Beam (BNB) at Fermilab aims to collect millions of neutrino events across its experimental lifetime. The high statistics and and rate of data acquisition serves to elucidate certain mysteries about the neutrino, such as the existence of a sterile flavour, but comes with several challenges. Primary concerns such as the high flux of cosmic rays and the difficulty in classifying certain particle types in the detector add to the need for fast and automated data analysis software to discriminate between neutrino event varieties. Machine Learning (ML) and specifically Convolutional Neural Networks (CNN’s) offer a unique and attractive option towards event reconstruction in LArTPCs. Neural networks, modelled after the connections between neurons in brains, learn deep and often complex features based on data supplied during training. With the firm backing of simulation software such as GENIE [36], training data driven by simulation and experiment is abundantly available. While perfect translation of information from experiment to simulated data is difficult, such difficulties in the data generation domain is left for future work. By utilising these generated datasets, one can train neural networks to predict energies, particle types, interaction modes, and even segment separate instances of particles in the pursuit of reconstructing a neutrino event. By reconstructing the contents in a LArTPC through this computational tool to determine event topologies, evaluation of different physical models can be tested quickly and automatically at SBN. This thesis aims to apply Sparse Submanifold Convolutions to create network architectures suited for LArTPC data with minimal memory footprints in GPUs. Furthermore, contrary to some contemporary studies, this thesis focuses on the application of feature fusion in order to co-learn features across multiple 2D planes. This approach, contrary to using 3D (full detector volume) data, takes inspiration from the structural output of wire-based LArTPC detectors, and serves to demonstrate the efficacy of deep learning techniques over single-plane 2D methods on individual wire planes. These feature fusion models, deemed multi-plane models in this work, demonstrate increased classification accuracy on particle types on the order of 90%. Furthermore, cosmic neutrinos have been demonstrated to be removed with 99% efficiency, allowing event selection for further analysis. Simulated events were segmented by semantic classes based on geometry with an overall efficiency of 98%, leading over other 2D models. Segmenting by particle ini stance was determined at an overall clustering efficiency of 97%, or an overall Adjusted Rand Index (ARI) of 0.9. The clustering works well on the majority of particles, but key error modes prevent the current model from applicability in an end-to-end reconstruction chain. Lastly, the tools developed for use in LArTPC physics was applied to the problem of thyroid cancer diagnostics as a separate project. Employing a dense multi-scale (Inceptionstyle) network a 98 % diagnostic accuracy was reported per-slide. Furthermore, a novel voting strategy is proposed which reports a per-patient diagnostic performance of 98%. Neural networks are demonstrated to be valuable tools both in physcs and outside for various classification and clustering tasks, with the goal of creating a larger analysis pipeline for end-to-end reconstruction

AVATAR - Machine Learning Pipeline Evaluation Using Surrogate Model

© 2020, The Author(s). The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation. The previous methods such as Bayesian-based and genetic-based optimisation, which are implemented in Auto-Weka, Auto-sklearn and TPOT, evaluate pipelines by executing them. Therefore, the pipeline composition and optimisation of these methods requires a tremendous amount of time that prevents them from exploring complex pipelines to find better predictive models. To further explore this research challenge, we have conducted experiments showing that many of the generated pipelines are invalid, and it is unnecessary to execute them to find out whether they are good pipelines. To address this issue, we propose a novel method to evaluate the validity of ML pipelines using a surrogate model (AVATAR). The AVATAR enables to accelerate automatic ML pipeline composition and optimisation by quickly ignoring invalid pipelines. Our experiments show that the AVATAR is more efficient in evaluating complex pipelines in comparison with the traditional evaluation approaches requiring their execution