Faster Evolutionary Multi-Objective Optimization via GALE, the Geometric Active Learner

Goal optimization has long been a topic of great interest in computer science. The literature contains many thousands of papers that discuss methods for the search of optimal solutions to complex problems. In the case of multi-objective optimization, such a search yields iteratively improved approximations to the Pareto frontier, i.e. the set of best solutions contained along a trade-off curve of competing objectives.;To approximate the Pareto frontier, one method that is ubiquitous throughout the field of optimization is stochastic search. Stochastic search engines explore solution spaces by randomly mutating candidate guesses to generate new solutions. This mutation policy is employed by the most commonly used tools (e.g. NSGA-II, SPEA2, etc.), with the goal of a) avoiding local optima, and b) expand upon diversity in the set of generated approximations. Such blind mutation policies explore many sub-optimal solutions that are discarded when better solutions are found. Hence, this approach has two problems. Firstly, stochastic search can be unnecessarily computationally expensive due to evaluating an overwhelming number of candidates. Secondly, the generated approximations to the Pareto frontier are usually very large, and can be difficult to understand.;To solve these two problems, a more-directed, less-stochastic approach than standard search tools is necessary. This thesis presents GALE (Geometric Active Learning). GALE is an active learner that finds approximations to the Pareto frontier by spectrally clustering candidates using a near-linear time recursive descent algorithm that iteratively divides candidates into halves (called leaves at the bottom level). Active learning in GALE selects a minimally most-informative subset of candidates by only evaluating the two-most different candidates during each descending split; hence, GALE only requires at most, 2Log2(N) evaluations per generation. The candidates of each leaf are thereafter non-stochastically mutated in the most promising directions along each piece. Those leafs are piece-wise approximations to the Pareto frontier.;The experiments of this thesis lead to the following conclusion: a near-linear time recursive binary division of the decision space of candidates in a multi-objective optimization algorithm can find useful directions to mutate instances and find quality solutions much faster than traditional randomization approaches. Specifically, in comparative studies with standard methods (NSGA-II and SPEA2) applied to a variety of models, GALE required orders of magnitude fewer evaluations to find solutions. As a result, GALE can perform dramatically faster than the other methods, especially for realistic models

Learning the Task Management Space of an Aircraft Approach Model

Validating models of airspace operations is a particular challenge. These models are often aimed at finding and exploring safety violations, and aim to be accurate representations of real-world behavior. However, the rules governing the behavior are quite complex: nonlinear physics, operational modes, human behavior, and stochastic environmental concerns all determine the responses of the system. In this paper, we present a study on aircraft runway approaches as modeled in Georgia Tech's Work Models that Compute (WMC) simulation. We use a new learner, Genetic-Active Learning for Search-Based Software Engineering (GALE) to discover the Pareto frontiers defined by cognitive structures. These cognitive structures organize the prioritization and assignment of tasks of each pilot during approaches. We discuss the benefits of our approach, and also discuss future work necessary to enable uncertainty quantification

Stable Electromyographic Sequence Prediction During Movement Transitions using Temporal Convolutional Networks

Transient muscle movements influence the temporal structure of myoelectric signal patterns, often leading to unstable prediction behavior from movement-pattern classification methods. We show that temporal convolutional network sequential models leverage the myoelectric signal's history to discover contextual temporal features that aid in correctly predicting movement intentions, especially during interclass transitions. We demonstrate myoelectric classification using temporal convolutional networks to effect 3 simultaneous hand and wrist degrees-of-freedom in an experiment involving nine human-subjects. Temporal convolutional networks yield significant $(p<0.001)$ performance improvements over other state-of-the-art methods in terms of both classification accuracy and stability.Comment: 4 pages, 5 figures, accepted for Neural Engineering (NER) 2019 Conferenc

Exploiting Laboratory and Heliophysics Plasma Synergies

Recent advances in space-based heliospheric observations, laboratory experimentation, and plasma simulation codes are creating an exciting new cross-disciplinary opportunity for understanding fast energy release and transport mechanisms in heliophysics and laboratory plasma dynamics, which had not been previously accessible. This article provides an overview of some new observational, experimental, and computational assets, and discusses current and near-term activities towards exploitation of synergies involving those assets. This overview does not claim to be comprehensive, but instead covers mainly activities closely associated with the authors’ interests and reearch. Heliospheric observations reviewed include the Sun Earth Connection Coronal and Heliospheric Investigation (SECCHI) on the National Aeronautics and Space Administration (NASA) Solar Terrestrial Relations Observatory (STEREO) mission, the first instrument to provide remote sensing imagery observations with spatial continuity extending from the Sun to the Earth, and the Extreme-ultraviolet Imaging Spectrometer (EIS) on the Japanese Hinode spacecraft that is measuring spectroscopically physical parameters of the solar atmosphere towards obtaining plasma temperatures, densities, and mass motions. The Solar Dynamics Observatory (SDO) and the upcoming Solar Orbiter with the Heliospheric Imager (SoloHI) on-board will also be discussed. Laboratory plasma experiments surveyed include the line-tied magnetic reconnection experiments at University of Wisconsin (relevant to coronal heating magnetic flux tube observations and simulations), and a dynamo facility under construction there; the Space Plasma Simulation Chamber at the Naval Research Laboratory that currently produces plasmas scalable to ionospheric and magnetospheric conditions and in the future also will be suited to study the physics of the solar corona; the Versatile Toroidal Facility at the Massachusetts Institute of Technology that provides direct experimental observation of reconnection dynamics; and the Swarthmore Spheromak Experiment, which provides well-diagnosed data on three-dimensional (3D) null-point magnetic reconnection that is also applicable to solar active regions embedded in pre-existing coronal fields. New computer capabilities highlighted include: HYPERION, a fully compressible 3D magnetohydrodynamics (MHD) code with radiation transport and thermal conduction; ORBIT-RF, a 4D Monte-Carlo code for the study of wave interactions with fast ions embedded in background MHD plasmas; the 3D implicit multi-fluid MHD spectral element code, HiFi; and, the 3D Hall MHD code VooDoo. Research synergies for these new tools are primarily in the areas of magnetic reconnection, plasma charged particle acceleration, plasma wave propagation and turbulence in a diverging magnetic field, plasma atomic processes, and magnetic dynamo behavior.United States. Office of Naval ResearchNaval Research Laboratory (U.S.

Electron‐ion Coulomb scattering and the electron Landau damping of Alfvén waves in the solar wind

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95528/1/jgra21228.pd

An Anomalous Type IV Secretion System in Rickettsia Is Evolutionarily Conserved

Bacterial type IV secretion systems (T4SSs) comprise a diverse transporter family functioning in conjugation, competence, and effector molecule (DNA and/or protein) translocation. Thirteen genome sequences from Rickettsia, obligate intracellular symbionts/pathogens of a wide range of eukaryotes, have revealed a reduced T4SS relative to the Agrobacterium tumefaciens archetype (vir). However, the Rickettsia T4SS has not been functionally characterized for its role in symbiosis/virulence, and none of its substrates are known.Superimposition of T4SS structural/functional information over previously identified Rickettsia components implicate a functional Rickettsia T4SS. virB4, virB8 and virB9 are duplicated, yet only one copy of each has the conserved features of similar genes in other T4SSs. An extraordinarily duplicated VirB6 gene encodes five hydrophobic proteins conserved only in a short region known to be involved in DNA transfer in A. tumefaciens. virB1, virB2 and virB7 are newly identified, revealing a Rickettsia T4SS lacking only virB5 relative to the vir archetype. Phylogeny estimation suggests vertical inheritance of all components, despite gene rearrangements into an archipelago of five islets. Similarities of Rickettsia VirB7/VirB9 to ComB7/ComB9 proteins of epsilon-proteobacteria, as well as phylogenetic affinities to the Legionella lvh T4SS, imply the Rickettsiales ancestor acquired a vir-like locus from distantly related bacteria, perhaps while residing in a protozoan host. Modern modifications of these systems likely reflect diversification with various eukaryotic host cells.We present the rvh (Rickettsiales vir homolog) T4SS, an evolutionary conserved transporter with an unknown role in rickettsial biology. This work lays the foundation for future laboratory characterization of this system, and also identifies the Legionella lvh T4SS as a suitable genetic model

Faster Evolutionary Multi-Objective Optimization via GALE, the Geometric Active Learner

Goal optimization has long been a topic of great interest in computer science. The literature contains many thousands of papers that discuss methods for the search of optimal solutions to complex problems. In the case of multi-objective optimization, such a search yields iteratively improved approximations to the Pareto frontier, i.e. the set of best solutions contained along a trade-off curve of competing objectives. To approximate the Pareto frontier, one method that is ubiquitous throughout the field of optimization is stochastic search. Stochastic search engines explore solution spaces by randomly mutating candidate guesses to generate new solutions. This mutation policy is employed by the most commonly used tools (e.g. NSGA-II, SPEA2, etc.), with the goal of a) avoiding local optima, and b) expand upon diversity in the set of generated approximations. Such "blind" mutation policies explore many sub-optimal solutions that are discarded when better solutions are found. Hence, this approach has two problems. Firstly, stochastic search can be unnecessarily computationally expensive due to evaluating an overwhelming number of candidates. Secondly, the generated approximations to the Pareto frontier are usually very large, and can be difficult to understand. To solve these two problems, a more-directed, less-stochastic approach than standard search tools is necessary. This thesis presents GALE (Geometric Active Learning). GALE is an active learner that finds approximations to the Pareto frontier by spectrally clustering candidates using a near-linear time recursive descent algorithm that iteratively divides candidates into halves (called leaves at the bottom level). Active learning in GALE selects a minimally most-informative subset of candidates by only evaluating the two-most different candidates during each descending split; hence, GALE only requires at most, 2Log2(N) evaluations per generation. The candidates of each leaf are thereafter non-stochastically mutated in the most promising directions along each piece. Those leafs are piece-wise approximations to the Pareto frontier. The experiments of this thesis lead to the following conclusion: a near-linear time recursive binary division of the decision space of candidates in a multi-objective optimization algorithm can find useful directions to mutate instances and find quality solutions much faster than traditional randomization approaches. Specifically, in comparative studies with standard methods (NSGA-II and SPEA2) applied to a variety of models, GALE required orders of magnitude fewer evaluations to find solutions. As a result, GALE can perform dramatically faster than the other methods, especially for realistic models