421 research outputs found

    Scaling Ant Colony Optimization with Hierarchical Reinforcement Learning Partitioning

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    This paper merges hierarchical reinforcement learning (HRL) with ant colony optimization (ACO) to produce a HRL ACO algorithm capable of generating solutions for large domains. This paper describes two specific implementations of the new algorithm: the first a modification to Dietterichā€™s MAXQ-Q HRL algorithm, the second a hierarchical ant colony system algorithm. These implementations generate faster results, with little to no significant change in the quality of solutions for the tested problem domains. The application of ACO to the MAXQ-Q algorithm replaces the reinforcement learning, Q-learning, with the modified ant colony optimization method, Ant-Q. This algorithm, MAXQ-AntQ, converges to solutions not significantly different from MAXQ-Q in 88% of the time. This paper then transfers HRL techniques to the ACO domain and traveling salesman problem (TSP). To apply HRL to ACO, a hierarchy must be created for the TSP. A data clustering algorithm creates these subtasks, with an ACO algorithm to solve the individual and complete problems. This paper tests two clustering algorithms, k-means and G-means. The results demonstrate the algorithm with data clustering produces solutions 20 times faster with 5-10% decrease in solution quality due to the effects of clustering

    Nonlinear Mixed-Effect Pharmacokinetic Modeling and Distribution of Doxycycline in Healthy Female Donkeys after Multiple Intragastric Dosingā€“Preliminary Investigation

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    Doxycycline (DXC) is a broad-spectrum antibacterial antimicrobial administered to horses for the treatment of bacterial infections which may also affect donkeys. Donkeys have a different metabolism than horses, leading to differences in the pharmacokinetics of drugs compared to horses. This study aimed to describe the population pharmacokinetics of DXC in donkeys. Five doses of DXC hyclate (10 mg/kg) were administered via a nasogastric tube, q12 h, to eight non-fasted, healthy, adult jennies. Serum, urine, synovial fluid and endometrium were collected for 72 h following the first administration. Doxycycline concentration was measured by competitive enzyme immunoassay. Serum concentrations versus time data were fitted simultaneously using the stochastic approximation expectation-maximization algorithm for nonlinear mixed effects. A one-compartment model with linear elimination and first-order absorption after intragastric administration, best described the available pharmacokinetic data. Final parameter estimates indicate that DXC has a high volume of distribution (108 L/kg) as well as high absorption (10.3 h-1) in donkeys. However, results suggest that oral DXC at 10 mg/kg q12 h in donkeys would not result in a therapeutic concentration in serum, urine, synovial fluid or endometrium by comparison to the minimum inhibitory concentration of common equine pathogens. Further studies are recommended to identify appropriate dosage and dosing intervals of oral DXC in donkeys

    Hawai`i Supernova Flows: A Peculiar Velocity Survey Using Over a Thousand Supernovae in the Near-Infrared

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    We introduce the Hawai`i Supernova Flows project and present summary statistics of the first 1218 astronomical transients observed, 669 of which are spectroscopically classified Type Ia Supernovae (SNe Ia). Our project is designed to obtain systematics-limited distances to SNe Ia while consuming minimal dedicated observational resources. This growing sample will provide increasing resolution into peculiar velocities as a function of position on the sky and redshift, allowing us to more accurately map the structure of dark matter. This can be used to derive cosmological parameters such as Ļƒ8\sigma_8 and can be compared with large scale flow maps from other methods such as luminosity-line width or luminosity-velocity dispersion correlations in galaxies. Additionally, our photometry will provide a valuable test bed for analyses of SNe Ia incorporating near-infrared data. In this survey paper, we describe the methodology used to select targets, collect and reduce data, and calculate distances.Comment: 33 pages, 23 figure

    Draft Genome Sequencing of Three Glutaraldehyde-Tolerant Bacteria from Produced Water from Hydraulic Fracturing

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    Here, we report the draft genome sequence of three glutaraldehyde-resistant isolates from produced water from hydraulic fracturing operations. The three strains were identified as sp. strain G11, sp. strain G15, and sp. strain G16. The genome sequences of these isolates will provide insights into biocide resistance in hydraulic fracturing operations

    The Lick AGN Monitoring Project 2011: Dynamical Modeling of the Broad Line Region in Mrk 50

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    We present dynamical modeling of the broad line region (BLR) in the Seyfert 1 galaxy Mrk 50 using reverberation mapping data taken as part of the Lick AGN Monitoring Project (LAMP) 2011. We model the reverberation mapping data directly, constraining the geometry and kinematics of the BLR, as well as deriving a black hole mass estimate that does not depend on a normalizing factor or virial coefficient. We find that the geometry of the BLR in Mrk 50 is a nearly face-on thick disk, with a mean radius of 9.6(+1.2,-0.9) light days, a width of the BLR of 6.9(+1.2,-1.1) light days, and a disk opening angle of 25\pm10 degrees above the plane. We also constrain the inclination angle to be 9(+7,-5) degrees, close to face-on. Finally, the black hole mass of Mrk 50 is inferred to be log10(M(BH)/Msun) = 7.57(+0.44,-0.27). By comparison to the virial black hole mass estimate from traditional reverberation mapping analysis, we find the normalizing constant (virial coefficient) to be log10(f) = 0.78(+0.44,-0.27), consistent with the commonly adopted mean value of 0.74 based on aligning the M(BH)-{\sigma}* relation for AGN and quiescent galaxies. While our dynamical model includes the possibility of a net inflow or outflow in the BLR, we cannot distinguish between these two scenarios.Comment: Accepted for publication in ApJ. 8 pages, 6 figure

    Simulation Intelligence: Towards a New Generation of Scientific Methods

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    The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of computation and data movement. We present the "Nine Motifs of Simulation Intelligence", a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence. We call this merger simulation intelligence (SI), for short. We argue the motifs of simulation intelligence are interconnected and interdependent, much like the components within the layers of an operating system. Using this metaphor, we explore the nature of each layer of the simulation intelligence operating system stack (SI-stack) and the motifs therein: (1) Multi-physics and multi-scale modeling; (2) Surrogate modeling and emulation; (3) Simulation-based inference; (4) Causal modeling and inference; (5) Agent-based modeling; (6) Probabilistic programming; (7) Differentiable programming; (8) Open-ended optimization; (9) Machine programming. We believe coordinated efforts between motifs offers immense opportunity to accelerate scientific discovery, from solving inverse problems in synthetic biology and climate science, to directing nuclear energy experiments and predicting emergent behavior in socioeconomic settings. We elaborate on each layer of the SI-stack, detailing the state-of-art methods, presenting examples to highlight challenges and opportunities, and advocating for specific ways to advance the motifs and the synergies from their combinations. Advancing and integrating these technologies can enable a robust and efficient hypothesis-simulation-analysis type of scientific method, which we introduce with several use-cases for human-machine teaming and automated science

    The DEHVILS Survey Overview and Initial Data Release: High-Quality Near-Infrared Type Ia Supernova Light Curves at Low Redshift

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    While the sample of optical Type Ia Supernova (SN Ia) light curves (LCs) usable for cosmological parameter measurements surpasses 2000, the sample of published, cosmologically viable near-infrared (NIR) SN Ia LCs, which have been shown to be good "standard candles," is still ā‰²\lesssim 200. Here, we present high-quality NIR LCs for 83 SNe Ia ranging from 0.002<z<0.090.002 < z < 0.09 as a part of the Dark Energy, H0_0, and peculiar Velocities using Infrared Light from Supernovae (DEHVILS) survey. Observations are taken using UKIRT's WFCAM, where the median depth of the images is 20.7, 20.1, and 19.3 mag (Vega) for YY, JJ, and HH-bands, respectively. The median number of epochs per SN Ia is 18 for all three bands (YJHYJH) combined and 6 for each band individually. We fit 47 SN Ia LCs that pass strict quality cuts using three LC models, SALT3, SNooPy, and BayeSN and find scatter on the Hubble diagram to be comparable to or better than scatter from optical-only fits in the literature. Fitting NIR-only LCs, we obtain standard deviations ranging from 0.128-0.135 mag. Additionally, we present a refined calibration method for transforming 2MASS magnitudes to WFCAM magnitudes using HST CALSPEC stars that results in a 0.03 mag shift in the WFCAM YY-band magnitudes.Comment: 24 pages, 9 figures. Accepted by MNRA

    Managing for RADical ecosystem change: applying the Resist-Accept- Direct (RAD) framework

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    Ecosystem transformation involves the emergence of persistent ecological or socialā€“ecological systems that diverge, dramatically and irreversibly, from prior ecosystem structure and function. Such transformations are occurring at increasing rates across the planet in response to changes in climate, land use, and other factors. Consequently, a dynamic view of ecosystem processes that accommodates rapid, irreversible change will be critical for effectively conserving fish, wildlife, and other natural resources, and maintaining ecosystem services. However, managing ecosystems toward states with novel structure and function is an inherently unpredictable and difficult task. Managers navigating ecosystem transformation can benefit from considering broader objectives, beyond a traditional focus on resisting ecosystem change, by also considering whether accepting inevitable change or directing it along some desirable pathway is more feasible (that is, practical and appropriate) under some circumstances (the RAD framework). By explicitly acknowledging transformation and implementing an iterative RAD approach, natural resource managers can be deliberate and strategic in addressing profound ecosystem change
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