CLOVER: Contrastive Learning for Onboard Vision-Enabled Robotics
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Grace M. Vincent↗
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Automatic Detection and Classification of Aurora in THEMIS All‐Sky Images
We report a novel machine-learning algorithm for automatically detecting and classifying aurora in all–sky images (ASI) that is largely trained without requiring ground–truth labels. By including a small number of labeled images, we are able to automatically label all of the approximately 700 million images in the Time History of Events and Macroscale Interactions during Substorms (THEMIS) ASI data set from 2008 to 2022. We use a two–stage approach. In the first stage, we adapt the Simple framework for Contrastive Learning of Representations (SimCLR) algorithm to learn latent representations of THEMIS all–sky images. We then finetune a classifier network on the latent representations our model learns of the manually labeled Oslo aurora THEMIS (OATH) data set. We demonstrate that this two–stage approach achieves excellent classification results on data for which there is no current ML classification benchmark. The outcome of this work will facilitate efficient information retrieval for researchers interested in specific categories of aurora and will enable large scale statistical studies and machine learning analyses of THEMIS all–sky images that have not previously been possible. To demonstrate possible ways to utilize this database, we performed a statistical analysis of the occurrence rates of auroral labels with respect to solar wind parameters, interplanetary magnetic field vector, and geomagnetic indices. We further investigate the occurrence rates of auroral phenomena in the annotated data set and their geoeffectiveness by utilizing the co–located THEMIS ground magnetometer data set.
Jeremiah W Johnson↗
A New Monte Carlo Filtering Method for the Diagnosis of Mission-Critical Failures
Testing large-scale systems is expensive in terms of both time and money. Running simulations early in the process is a proven method of finding the design faults likely to lead to critical system failures, but determining the exact cause of those errors is still time-consuming and requires access to a limited number of domain experts. It is desirable to find an automated method that explores the large number of combinations and is able to isolate likely fault points. Treatment learning is a subset of minimal contrast-set learning that, rather than classifying data into distinct categories, focuses on finding the unique factors that lead to a particular classification. That is, they find the smallest change to the data that causes the largest change in the class distribution. These treatments, when imposed, are able to identify the settings most likely to cause a mission-critical failure. This research benchmarks two treatment learning methods against standard optimization techniques across three complex systems, including two projects from the Robust Software Engineering (RSE) group within the National Aeronautics and Space Administration (NASA) Ames Research Center. It is shown that these treatment learners are both faster than traditional methods and show demonstrably better results.
Gay, Gregory↗
Automatically Finding the Control Variables for Complex System Behavior
Testing large-scale systems is expensive in terms of both time and money. Running simulations early in the process is a proven method of finding the design faults likely to lead to critical system failures, but determining the exact cause of those errors is still time-consuming and requires access to a limited number of domain experts. It is desirable to find an automated method that explores the large number of combinations and is able to isolate likely fault points. Treatment learning is a subset of minimal contrast-set learning that, rather than classifying data into distinct categories, focuses on finding the unique factors that lead to a particular classification. That is, they find the smallest change to the data that causes the largest change in the class distribution. These treatments, when imposed, are able to identify the factors most likely to cause a mission-critical failure. The goal of this research is to comparatively assess treatment learning against state-of-the-art numerical optimization techniques. To achieve this, this paper benchmarks the TAR3 and TAR4.1 treatment learners against optimization techniques across three complex systems, including two projects from the Robust Software Engineering (RSE) group within the National Aeronautics and Space Administration (NASA) Ames Research Center. The results clearly show that treatment learning is both faster and more accurate than traditional optimization methods.
Gay, Gregory↗
Identification of linear multivariable systems from a single set of data by identification of observers with assigned real eigenvalues
This paper presents a formulation for identification of linear multivariable systems from a single set of input-output data. The identification method is formulated with the mathematical framework of learning identification, by extension of the repetition domain concept to include shifting time intervals. This contrasts existing learning approaches that require data from multiple experiments. In this method, the system input-output relationship is expressed in terms of an observer, which is made asymptotically stable by an embedded real eigenvalue assignment procedure. Through this relationship, the Markov parameters of the observer are identified. The Markov parameters of the actual system are recovered from those of the observer, and then used to obtain a state space model of the system by standard realization techniques. The basic mathematical formulation is derived, and numerical examples presented to illustrate the proposed method.
Phan, Minh↗
Establishing a Distance Learning Plan for International Space Station (ISS) Interactive Video Education Events (IVEE)
Educational outreach is an integral part of the International Space Station (ISS) mandate. In a few scant years, the International Space Station has already established a tradition of successful, general outreach activities. However, as the number of outreach events increased and began to reach school classrooms, those events came under greater scrutiny by the education community. Some of the ISS electronic field trips, while informative and helpful, did not meet the generally accepted criteria for education events, especially within the context of the classroom. To make classroom outreach events more acceptable to educators, the ISS outreach program must differentiate between communication events (meant to disseminate information to the general public) and education events (designed to facilitate student learning). In contrast to communication events, education events: are directed toward a relatively homogeneous audience who are gathered together for the purpose of learning, have specific performance objectives which the students are expected to master, include a method of assessing student performance, and include a series of structured activities that will help the students to master the desired skill(s). The core of the ISS education events is an interactive videoconference between students and ISS representatives. This interactive videoconference is to be preceded by and followed by classroom activities which help the students aftain the specified learning objectives. Using the interactive videoconference as the centerpiece of the education event lends a special excitement and allows students to ask questions about what they are learning and about the International Space Station and NASA. Whenever possible, the ISS outreach education events should be congruent with national guidelines for student achievement. ISS outreach staff should recognize that there are a number of different groups that will review the events, and that each group has different criteria for acceptance. For example, school administrators are more likely to be concerned about an event meeting national standards and the cost of the event. In contrast, a teacher's acceptance of an education event may be directly related to the amount of extra work the event imposes upon that teacher. ISS education events must be marketed differently to the different groups of educators, and must never increase the workload of the average teacher.
Wallington, Clint↗
Uncertain responses by humans and rhesus monkeys (Macaca mulatta) in a psychophysical same-different task
The authors asked whether animals, like humans, use an uncertain response adaptively to escape indeterminate stimulus relations. Humans and monkeys were placed in a same-different task, known to be challenging for animals. Its difficulty was increased further by reducing the size of the stimulus differences, thereby making many same and different trials difficult to tell apart. Monkeys do escape selectively from these threshold trials, even while coping with 7 absolute stimulus levels concurrently. Monkeys even adjust their response strategies on short time scales according to the local task conditions. Signal-detection and optimality analyses confirm the similarity of humans' and animals' performances. Whereas associative interpretations account poorly for these results, an intuitive uncertainty construct does so easily. The authors discuss the cognitive processes that allow uncertainty's adaptive use and recommend further comparative studies of metacognition.
NASA Discipline Space Human Factors↗
An LES Survey of Cold-Air Outbreaks Spanning ACTIVATE and COMBLE
Two recent field campaigns offer unique new opportunities to survey coupled aerosol and cloud processes operating within cold-air outbreaks (CAOs). NASA's 2019-2023 Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project provided airborne in situ and remote sensing observations in multiple CAOs off the central US Eastern Seaboard. Contemporaneously, DOE's 2019-2020 Cold-air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) provided continuous ground-based in situ and remote sensing measurements during hundreds of hours of CAO conditions at two sites flanking the Norwegian Sea. Here we highlight lessons learned from contrasting sets of aerosol-aware Lagrangian large-eddy simulation case studies derived from each campaign, as well as outstanding uncertainties. Cases simulated include all of the most extensively sampled CAO flights during ACTIVATE, and a wide range of CAO strengths and mesoscale structures during COMBLE. Each case study is also suitable for simulation with large-scale models in single-column model mode or with a limited-domain approach using periodic boundary conditions. We seek to address the central question: what microphysical process pathways control the Lagrangian evolution of surface and top-of-atmosphere radiative fluxes under ACTIVATE and COMBLE conditions? Many science team members, co-investigators, and collaborators will be gratefully acknowledged in association with their contributions to making this work possible.
cold-air outbreaks↗
Identification of linear multivariable systems from a single set of data by identification of observers with assigned real eigenvalues
A formulation is presented for identification of linear multivariable from a single set of input-output data. The identification method is formulated with the mathematical framework of learning identifications, by extension of the repetition domain concept to include shifting time intervals. This method contrasts with existing learning approaches that require data from multiple experiments. In this method, the system input-output relationship is expressed in terms of an observer, which is made asymptotically stable by an embedded real eigenvalue assignment procedure. Through this relationship, the Markov parameters of the observer are identified. The Markov parameters of the actual system are recovered from those of the observer, and then used to obtain a state space model of the system by standard realization techniques. The basic mathematical formulation is derived, and numerical examples presented to illustrate.
Phan, Minh↗
Detection in fixed and random noise in foveal and parafoveal vision explained by template learning
Foveal and parafoveal contrast detection thresholds for Gabor and checkerboard targets were measured in white noise by means of a two-interval forced-choice paradigm. Two white-noise conditions were used: fixed and twin. In the fixed noise condition a single noise sample was presented in both intervals of all the trials. In the twin noise condition the same noise sample was used in the two intervals of a trial, but a new sample was generated for each trial. Fixed noise conditions usually resulted in lower thresholds than twin noise. Template learning models are presented that attribute this advantage of fixed over twin noise either to fixed memory templates' reducing uncertainty by incorporation of the noise or to the introduction, by the learning process itself, of more variability in the twin noise condition. Quantitative predictions of the template learning process show that it contributes to the accelerating nonlinear increase in performance with signal amplitude at low signal-to-noise ratios.
NASA Center ARC↗
Reinforcement Learning in a Nonstationary Environment: The El Farol Problem
This paper examines the performance of simple learning rules in a complex adaptive system based on a coordination problem modeled on the El Farol problem. The key features of the El Farol problem are that it typically involves a medium number of agents and that agents' pay-off functions have a discontinuous response to increased congestion. First we consider a single adaptive agent facing a stationary environment. We demonstrate that the simple learning rules proposed by Roth and Er'ev can be extremely sensitive to small changes in the initial conditions and that events early in a simulation can affect the performance of the rule over a relatively long time horizon. In contrast, a reinforcement learning rule based on standard practice in the computer science literature converges rapidly and robustly. The situation is reversed when multiple adaptive agents interact: the RE algorithms often converge rapidly to a stable average aggregate attendance despite the slow and erratic behavior of individual learners, while the CS based learners frequently over-attend in the early and intermediate terms. The symmetric mixed strategy equilibria is unstable: all three learning rules ultimately tend towards pure strategies or stabilize in the medium term at non-equilibrium probabilities of attendance. The brittleness of the algorithms in different contexts emphasize the importance of thorough and thoughtful examination of simulation-based results.
Bell, Ann Maria↗
The Costs of Knowledge
Acquiring knowledge-genuinely learning something new-requires the consent and commitment of the person you're trying to learn from. In contrast to information, which can usually be effectively transmitted in a document or diagram, knowledge comes from explaining, clarifying, questioning, and sometimes actually working together. Getting this kind of attention and commitment often involves some form of negotiation, since even the most generous person's time and energy are limited. Few experts sit around waiting to share their knowledge with strangers or casual acquaintances. In reasonably collaborative enterprises- I think NASA is one-this sort of negotiation isn't too onerous. People want to help each other and share what they know, so the "cost" of acquiring knowledge is relatively low. In many organizations (and many communities and countries), however, there are considerable costs associated with this activity, and many situations in which negotiations fail. The greatest knowledge cost is in and adopting knowledge to one's own use. Sometimes this means formally organizing what one learns in writing. Sometimes it means just taking time to reflect on someone else's thoughts and experiences-thinking about knowledge that is not exactly what you need but can lead you to develop ideas that will be useful. A long, discursive conversation, with all the back-and-forth that defines conversation, can be a mechanism of knowledge exchange. I have seen many participants at NASA APPEL Masters Forums talking, reflecting, and thinking-adapting what they are hearing to their own needs. Knowledge transfer is not a simple proposition. An enormous amount of information flows through the world every day, but knowledge is local, contextual, and "stickyn-that is, it takes real effort to move it from one place to another. There is no way around this. To really learn a subject, you have to work at it, you have to pay your "knowledge dues." So while, thanks to advances in technology, almost infinite amounts of information are instantly available, it still takes the same amount of time and work to learn French as it did in the year 1800-or to master physics or philosophy.
Prusak, Laurence↗
Integration of Online Parameter Identification and Neural Network for In-Flight Adaptive Control
An indirect adaptive system has been constructed for robust control of an aircraft with uncertain aerodynamic characteristics. This system consists of a multilayer perceptron pre-trained neural network, online stability and control derivative identification, a dynamic cell structure online learning neural network, and a model following control system based on the stochastic optimal feedforward and feedback technique. The pre-trained neural network and model following control system have been flight-tested, but the online parameter identification and online learning neural network are new additions used for in-flight adaptation of the control system model. A description of the modification and integration of these two stand-alone software packages into the complete system in preparation for initial flight tests is presented. Open-loop results using both simulation and flight data, as well as closed-loop performance of the complete system in a nonlinear, six-degree-of-freedom, flight validated simulation, are analyzed. Results show that this online learning system, in contrast to the nonlearning system, has the ability to adapt to changes in aerodynamic characteristics in a real-time, closed-loop, piloted simulation, resulting in improved flying qualities.
Hageman, Jacob↗
Integration of Online Parameter Identification and Neural Network for In-Flight Adaptive Control
An indirect adaptive system has been constructed for robust control of an aircraft with uncertain aerodynamic characteristics. This system consists of a multilayer perceptron pre-trained neural network, online stability and control derivative identification, a dynamic cell structure online learning neural network, and a model following control system based on the stochastic optimal feedforward and feedback technique. The pre-trained neural network and model following control system have been flight-tested, but the online parameter identification and online learning neural network are new additions used for in-flight adaptation of the control system model. A description of the modification and integration of these two stand-alone software packages into the complete system in preparation for initial flight tests is presented. Open-loop results using both simulation and flight data, as well as closed-loop performance of the complete system in a nonlinear, six-degree-of-freedom, flight validated simulation, are analyzed. Results show that this online learning system, in contrast to the nonlearning system, has the ability to adapt to changes in aerodynamic characteristics in a real-time, closed-loop, piloted simulation, resulting in improved flying qualities.
Hageman, Jacob J.↗
The CHARIS High-Contrast Integral-Field Spectrograph
One of the leading direct Imaging techniques, particularly in ground-based imaging, uses a coronagraphic system and integral field spectrograph (IFS). The Coronagraphic High Angular Resolution Imaging Spectrograph (CHARIS) is an IFS that has been built for the Subaru telescope. CHARIS has been delivered to the observatory and now sits behind the Subaru Coronagraphic Extreme Adaptive Optics (SCExAO) system. CHARIS has 'high' and 'low' resolution operating modes. The "high-resolution" mode is used to characterize targets in J, H, and K bands at ~R70. The "low-resolution" prism is meant for discovery and spans J+H+K bands (1.15-2.37 microns) with a spectral resolution of ~R18. This discovery mode has already proven better than 15-sigma detections of HR8799c,d,e when combining ADI+SDI. Using SDI alone, planets c and d have been detected in a single 24 second image. The CHARIS team is optimizing instrument performance and refining ADI+SDI recombination to maximize our contrast detection limit. In addition to the new observing modes, CHARIS has demonstrated a design with high robustness to spectral crosstalk. CHARIS is in the final stages of commissioning, with the instrument open for science observations beginning February 2017. Here we review the science case, design, on-sky performance, engineering observations of exoplanet and disk targets, and specific lessons learned for extremely high contrast imagers. Key design aspects that will be demonstrated are crosstalk optimization, wavefront correction using the IFS image, lenslet tolerancing, the required spectral resolution to fit exoplanet atmospheres, and the utility of the spectrum in achieving higher contrast detection limits.
Groff, Tyler D.↗
Low melting temperature alloy deployment mechanism and recent experiments
This paper describes the concept of a low melting temperature alloy deployment mechanism, U.S. Patent 4,842,106. It begins with a brief history of conventional dimethyl-silicone fluid damped mechanisms. Design fundamentals of the new melting alloy mechanism are then introduced. Benefits of the new over the old are compared and contrasted. Recent experiments and lessons learned complete this paper.
Madden, M. J.↗
Crew Roles and Interactions in Scientific Space Exploration
Future piloted space exploration missions will focus more on science than engineering, a change which will challenge existing concepts for flight crew tasking and demand that participants with contrasting skills, values, and backgrounds learn to cooperate as equals. In terrestrial space flight analogs such as Desert Research And Technology Studies, engineers, pilots, and scientists can practice working together, taking advantage of the full breadth of all team members training to produce harmonious, effective missions that maximize the time and attention the crew can devote to science. This paper presents, in a format usable as a reference by participants in the field, a successfully tested crew interaction model for such missions. The model builds upon the basic framework of a scientific field expedition by adding proven concepts from aviation and human spaceflight, including expeditionary behavior and cockpit resource management, cooperative crew tasking and adaptive leadership and followership, formal techniques for radio communication, and increased attention to operational considerations. The crews of future spaceflight analogs can use this model to demonstrate effective techniques, learn from each other, develop positive working relationships, and make their expeditions more successful, even if they have limited time to train together beforehand. This model can also inform the preparation and execution of actual future spaceflights.
Love, Stanley G.↗
The Twin Decadal Survey Testbeds in the High Contrast Imaging Testbed facility at NASA’s Jet Propulsion Laboratory
Direct imaging of an Earth-like exoplanet will require future missions, such as the proposed HabEx or LUVOIR concepts, to achieve starlight suppression with planet/star contrast ratios on the order of 10−10 at angular separations <100 milliarcseconds in visible wavelengths. To facilitate the technology and algorithm development required to enable this ambitious scientific goal, NASA’s Exoplanet Exploration Program (ExEP) High Contrast Imaging Testbed (HCIT) facility at the Jet Propulsion Laboratory (JPL) recently commissioned the Decadal Survey Testbed (DST), a state-of-the-art, flexible, and extremely stable coronagraph testbed. This testbed integrates years of experience and lessons learned at HCIT and has now demonstrated contrast ratios as low as few times 10−10 in 10% bandwidth. Following the success of DST, the HCIT team has developed a companion testbed, the Decadal Survey Testbed 2 (DST2) that further implements lessons learned from DST, and will provide the HCIT with additional coronagraph testbed capacity and efficiency. Commissioning for DST2 is currently targeted for Fall 2021. Here we provide an update on the status of DST, and report on the design, performance modeling simulations, and status of integration and testing for DST2, highlighting updates to the design from the original DST.
Siegler, Nick↗