In Situ Diazotroph Population Dynamics Under Different Resource Ratios in the North Pacific Subtropical Gyre.

Major advances in understanding the diversity, distribution, and activity of marine N2-fixing microorganisms (diazotrophs) have been made in the past decades, however, large gaps in knowledge remain about the environmental controls on growth and mortality rates. In order to measure diazotroph net growth rates and microzooplankton grazing rates on diazotrophs, nutrient perturbation experiments and dilution grazing experiments were conducted using free-floating in situ incubation arrays in the vicinity of Station ALOHA in March 2016. Net growth rates for targeted diazotroph taxa as well as Prochlorococcus, Synechococcus and photosynthetic picoeukaryotes were determined under high (H) and low (L) nitrate:phosphate (NP) ratio conditions at four depths in the photic zone (25, 45, 75, and 100 m) using quantitative PCR and flow cytometry. Changes in the prokaryote community composition in response to HNP and LNP treatments were characterized using 16S rRNA variable region tag sequencing. Microzooplankton grazing rates on diazotrophs were measured using a modified dilution technique at two depths in the photic zone (15 and 125 m). Net growth rates for most of the targeted diazotrophs after 48 h were not stimulated as expected by LNP conditions, rather enhanced growth rates were often measured in HNP treatments. Interestingly, net growth rates of the uncultivated prymnesiophyte symbiont UCYN-A1 were stimulated in HNP treatments at 75 and 100 m, suggesting that N used for growth was acquired through continuing to fix N2 in the presence of nitrate. Net growth rates for UCYN-A1, UCYN-C, Crocosphaera sp. (UCYN-B) and the diatom symbiont Richelia (associated with Rhizosolenia) were uniformly high at 45 m (up to 1.6 ± 0.5 d-1), implying that all were growing optimally at the onset of the experiment at that depth. Differences in microzooplankton grazing rates on UCYN-A1 and UCYN-C in 15 m waters indicate that the grazer assemblage preyed preferentially on UCYN-A1. Deeper in the water column (125 m), both diazotrophs were grazed at substantial rates, suggesting grazing pressure may increase with depth in the photic zone. Constraining in situ diazotroph growth and mortality rates are important steps for improving parameterization for diazotrophs in global ecosystem models

Radio-frequency dressing of multiple Feshbach resonances

We demonstrate and theoretically analyze the dressing of several proximate Feshbach resonances in Rb-87 using radio-frequency (rf) radiation. We present accurate measurements and characterizations of the resonances, and the dramatic changes in scattering properties that can arise through the rf dressing. Our scattering theory analysis yields quantitative agreement with the experimental data. We also present a simple interpretation of our results in terms of rf-coupled bound states interacting with the collision threshold.Comment: 4+ pages, 3 figures, 1 table; revised introduction & references to reflect published versio

Simulations of a Scintillator Compton Gamma Imager for Safety and Security

We are designing an all-scintillator Compton gamma imager for use in security investigations and remediation actions involving radioactive threat material. To satisfy requirements for a rugged and portable instrument, we have chosen solid scintillator for the active volumes of both the scatter and absorber detectors. Using the BEAMnrc/EGSnrc Monte Carlo simulation package, we have constructed models using four different materials for the scatter detector: LaBr_3, NaI, CaF_2 and PVT. We have compared the detector performances using angular resolution, efficiency, and image resolution. We find that while PVT provides worse performance than that of the detectors based entirely on inorganic scintillators, all of the materials investigated for the scatter detector have the potential to provide performance adequate for our purposes.Comment: Revised text and figures, Presented at SORMA West 2008, Published in IEEE Transactions on Nuclear Scienc

On-Policy Policy Gradient Reinforcement Learning Without On-Policy Sampling

On-policy reinforcement learning (RL) algorithms perform policy updates using i.i.d. trajectories collected by the current policy. However, after observing only a finite number of trajectories, on-policy sampling may produce data that fails to match the expected on-policy data distribution. This sampling error leads to noisy updates and data inefficient on-policy learning. Recent work in the policy evaluation setting has shown that non-i.i.d., off-policy sampling can produce data with lower sampling error than on-policy sampling can produce. Motivated by this observation, we introduce an adaptive, off-policy sampling method to improve the data efficiency of on-policy policy gradient algorithms. Our method, Proximal Robust On-Policy Sampling (PROPS), reduces sampling error by collecting data with a behavior policy that increases the probability of sampling actions that are under-sampled with respect to the current policy. Rather than discarding data from old policies -- as is commonly done in on-policy algorithms -- PROPS uses data collection to adjust the distribution of previously collected data to be approximately on-policy. We empirically evaluate PROPS on both continuous-action MuJoCo benchmark tasks as well as discrete-action tasks and demonstrate that (1) PROPS decreases sampling error throughout training and (2) improves the data efficiency of on-policy policy gradient algorithms. Our work improves the RL community's understanding of a nuance in the on-policy vs off-policy dichotomy: on-policy learning requires on-policy data, not on-policy sampling

Understanding when Dynamics-Invariant Data Augmentations Benefit Model-Free Reinforcement Learning Updates

Recently, data augmentation (DA) has emerged as a method for leveraging domain knowledge to inexpensively generate additional data in reinforcement learning (RL) tasks, often yielding substantial improvements in data efficiency. While prior work has demonstrated the utility of incorporating augmented data directly into model-free RL updates, it is not well-understood when a particular DA strategy will improve data efficiency. In this paper, we seek to identify general aspects of DA responsible for observed learning improvements. Our study focuses on sparse-reward tasks with dynamics-invariant data augmentation functions, serving as an initial step towards a more general understanding of DA and its integration into RL training. Experimentally, we isolate three relevant aspects of DA: state-action coverage, reward density, and the number of augmented transitions generated per update (the augmented replay ratio). From our experiments, we draw two conclusions: (1) increasing state-action coverage often has a much greater impact on data efficiency than increasing reward density, and (2) decreasing the augmented replay ratio substantially improves data efficiency. In fact, certain tasks in our empirical study are solvable only when the replay ratio is sufficiently low

Correlation of ERTS multispectral imagery with suspended matter and chlorophyll in lower Chesapeake Bay

The feasibility of using multispectral satellite imagery to monitor the characteristics of estuarine waters is being investigated. Preliminary comparisons of MSS imagery with suspended matter concentrations, particle counts, chlorophyll, transmittance and bathymetry have been made. Some visual correlation of radiance with particulates and chlorophyll has been established. Effects of bathymetry are present, and their relation to transmittance and radiance is being investigated. Greatest detail in suspended matter is revealed by MSS band 5. Near-surface suspended sediment load and chlorophyll can be observed in bands 6 and 7. Images received to date have partially defined extent and location of high suspensate concentrations. Net quantity of suspended matter in the lower Bay has been decreasing since the inception of the study, and represents the diminution of turbid flood waters carried into the Bay in late September, 1972. The results so far point to the utility of MSS imagery in monitoring estuarine water character for the assessment of siltation, productivity, and water types

Vortices in Spatially Inhomogeneous Superfluids

We study vortices in a radially inhomogeneous superfluid, as realized by a trapped degenerate Bose gas in a uniaxially symmetric potential. We show that, in contrast to a homogeneous superfluid, an off-axis vortex corresponds to an anisotropic superflow whose profile strongly depends on the distance to the trap axis. One consequence of this superflow anisotropy is vortex precession about the trap axis in the absence of an imposed rotation. In the complementary regime of a finite prescribed rotation, we compute the minimum-energy vortex density, showing that in the rapid-rotation limit it is extremely uniform, despite a strongly inhomogeneous (nearly) Thomas-Fermi condensate density $\rho_s(r)$. The weak radially-dependent contribution ($\propto \nabla^2\ln\rho_s(r)$) to the vortex distribution, that vanishes with the number of vortices $N_v$ as $\frac{1}{N_v}$, arises from the interplay between vortex quantum discretness (namely their inability to faithfully support the imposed rigid-body rotation) and the inhomogeneous superfluid density. This leads to an enhancement of the vortex density at the center of a typical concave trap, a prediction that is in quantitative agreement with recent experiments (cond-mat/0405240). One striking consequence of the inhomogeneous vortex distribution is an azimuthally-directed, radially-shearing superflow.Comment: 22 RevTeX pages, 20 figures, Submitted to PR