Learning Over All Contracting and Lipschitz Closed-Loops for Partially-Observed Nonlinear Systems

This paper presents a policy parameterization for learning-based control on nonlinear, partially-observed dynamical systems. The parameterization is based on a nonlinear version of the Youla parameterization and the recently proposed Recurrent Equilibrium Network (REN) class of models. We prove that the resulting Youla-REN parameterization automatically satisfies stability (contraction) and user-tunable robustness (Lipschitz) conditions on the closed-loop system. This means it can be used for safe learning-based control with no additional constraints or projections required to enforce stability or robustness. We test the new policy class in simulation on two reinforcement learning tasks: 1) magnetic suspension, and 2) inverting a rotary-arm pendulum. We find that the Youla-REN performs similarly to existing learning-based and optimal control methods while also ensuring stability and exhibiting improved robustness to adversarial disturbances

Learning over All Stabilizing Nonlinear Controllers for a Partially-Observed Linear System

This paper proposes a nonlinear policy architecture for control of partially-observed linear dynamical systems providing built-in closed-loop stability guarantees. The policy is based on a nonlinear version of the Youla parameterization, and augments a known stabilizing linear controller with a nonlinear operator from a recently developed class of dynamic neural network models called the recurrent equilibrium network (REN). We prove that RENs are universal approximators of contracting and Lipschitz nonlinear systems, and subsequently show that the the proposed Youla-REN architecture is a universal approximator of stabilizing nonlinear controllers. The REN architecture simplifies learning since unconstrained optimization can be applied, and we consider both a model-based case where exact gradients are available and reinforcement learning using random search with zeroth-order oracles. In simulation examples our method converges faster to better controllers and is more scalable than existing methods, while guaranteeing stability during learning transients

RobustNeuralNetworks.jl: a Package for Machine Learning and Data-Driven Control with Certified Robustness

Neural networks are typically sensitive to small input perturbations, leading to unexpected or brittle behaviour. We present RobustNeuralNetworks.jl: a Julia package for neural network models that are constructed to naturally satisfy a set of user-defined robustness constraints. The package is based on the recently proposed Recurrent Equilibrium Network (REN) and Lipschitz-Bounded Deep Network (LBDN) model classes, and is designed to interface directly with Julia's most widely-used machine learning package, Flux.jl. We discuss the theory behind our model parameterization, give an overview of the package, and provide a tutorial demonstrating its use in image classification, reinforcement learning, and nonlinear state-observer design

Finding binaries from phase modulation of pulsating stars with \textit{Kepler}: VI. Orbits for 10 new binaries with mischaracterised primaries

Measuring phase modulation in pulsating stars has proved to be a highly successful way of finding binary systems. The class of pulsating main-sequence A and F variables known as delta Scuti stars are particularly good targets for this, and the \textit{Kepler} sample of these has been almost fully exploited. However, some \textit{Kepler} $\delta$ Scuti stars have incorrect temperatures in stellar properties catalogues, and were missed in previous analyses. We used an automated pulsation classification algorithm to find 93 new $\delta$ Scuti pulsators among tens of thousands of F-type stars, which we then searched for phase modulation attributable to binarity. We discovered 10 new binary systems and calculated their orbital parameters, which we compared with those of binaries previously discovered in the same way. The results suggest that some of the new companions may be white dwarfs.Comment: 8 pages, 6 figures that make liberal use of colou

Nonextendible Latin Cuboids

We show that for all integers m >= 4 there exists a 2m x 2m x m latin cuboid that cannot be completed to a 2mx2mx2m latin cube. We also show that for all even m > 2 there exists a (2m-1) x (2m-1) x (m-1) latin cuboid that cannot be extended to any (2m-1) x (2m-1) x m latin cuboid

New GEO paradigm: Re-purposing satellite components from the GEO graveyard

The rising production rate of space debris poses an increasingly severe threat of collision to satellites in the crowded Geostationary Orbit (GEO). It also presents a unique opportunity to make use of a growing supply of in-space resources for the benefit of the satellite community. “The Recycler” is a mission proposed to source replacements for failed components in GEO satellites by extracting functioning components from non-operational spacecraft in the GEO graveyard. This paper demonstrates a method of analyzing in-space re-purposing missions such as the Recycler, using real satellite data to provide a strong platform for accurate performance estimates. An inventory of 1107 satellites in the extended GEO region is presented, and a review into past GEO satellite anomalies is conducted to show that solar arrays would be in the greatest demand for re-purposing. This inventory is used as an input to a greedy selection algorithm and trajectory simulation to show that the Recycler spacecraft could harvest components for 67 client satellites with its allotted fuel budget. This capacity directly meets the levels of customer demand estimated from the GEO satellite anomaly data, placing the Recycler as a strong contender in a future second-hand satellite-component industry. Propellant mass is found to be a greater restriction on the Recycler mission than its 15-year lifetime — a problem which could be solved by on-orbit refueling

Virtual Simulation Training Using the Storz C-HUB to Support Distance Airway Training for the Spanish Medical Corps and NATO Partners

In medicine, the advancement of new technologies creates challenges to providers both in learning and in maintaining competency in required skills. For those medical providers located in remote environments, access to learning can be even more formidable. This work describes a collaboration created to facilitate the use of new communication technologies in providing distance training and support to health care personnel deployed in remote areas

Staphylococcus aureus Protein A Disrupts Immunity Mediated by Long-Lived Plasma Cells

Infection with Staphylococcus aureus does not induce long-lived protective immunity for reasons that are not completely understood. Human and murine vaccine studies support a role for antibodies in protecting against recurring infections, but S. aureus modulates the B cell response through expression of Staphylococcal Protein A (SpA), a surface protein that drives polyclonal B cell expansion and induces cell death in the absence of co-stimulation. In this murine study, we show that SpA altered the fate of plasmablasts and plasma cells (PCs) by enhancing the short-lived extrafollicular response and reducing the pool of bone marrow (BM)-resident long-lived PCs (LLPCs). The absence of LLPCs was associated with a rapid decline in antigen-specific, class-switched antibody. In contrast, when previously inoculated mice were challenged with isogenic Δspa S. aureus, cells proliferated in the BM survival niches and sustained long-term antibody titers. The effects of SpA on PC fate were limited to the secondary response, as antibody levels and the formation of B cell memory occurred normally during the primary response in mice inoculated with either WT or Δspa S. aureus. Thus, failure to establish long-term protective antibody titers against S. aureus was not a consequence of diminished formation of B cell memory; instead, SpA reduced the proliferative capacity of PCs that entered the BM, diminishing the number of cells in the long-lived pool

Reef state and performance as indicators of cumulative impacts on coral reefs

Coral bleaching, cyclones, outbreaks of crown-of-thorns seastar, and reduced water quality (WQ) threaten the health and resilience of coral reefs. The cumulative impacts from multiple acute and chronic stressors on “reef State” (i.e., total coral cover) and “reef Performance” (i.e., the deviation from expected rate of total coral cover increase) have rarely been assessed simultaneously, despite their management relevance. We evaluated the dynamics of coral cover (total and per morphological groups) in the Central and Southern Great Barrier Reef over 25 years, and identified and compared the main environmental drivers of State and Performance at the reef level (i.e. based on total coral cover) and per coral group. Using a combination of 25 environmental metrics that consider both the frequency and magnitude of impacts and their lagged effects, we find that the stressors that correlate with State differed from those correlating with Performance. Importantly, we demonstrate that WQ metrics better predict Performance than State. Further, inter-annual dynamics in WQ (here available for a subset of the data) improved the explanatory power of WQ metrics on Performance over long-term WQ averages. The lagged effects of cumulative acute stressors, and to a lesser extent poor water quality, correlated negatively with the Performance of some but not all coral groups. Tabular Acropora and branching non-Acropora were the most affected by water quality demonstrating that group-specific approaches aid in the interpretation of monitoring data and can be crucial for the detection of the impact of chronic pressures. We highlight the complexity of coral reef dynamics and the need of evaluating Performance metrics in order to prioritise local management interventions