Bayesian averaging for ground state masses of atomic nuclei in a Machine Learning approach

We present global predictions of the ground state mass of atomic nuclei based on a novel Machine Learning (ML) algorithm. We combine precision nuclear experimental measurements together with theoretical predictions of unmeasured nuclei. This hybrid data set is used to train a probabilistic neural network. In addition to training on this data, a physics-based loss function is employed to help refine the solutions. The resultant Bayesian averaged predictions have excellent performance compared to the testing set and come with well-quantified uncertainties which are critical for contemporary scientific applications. We assess extrapolations of the model's predictions and estimate the growth of uncertainties in the region far from measurements.Comment: 15 pages, 10 figures, comments welcom

Constraining inputs to realistic kilonova simulations through comparison to observed rr-process abundances

Kilonovae, one source of electromagnetic emission associated with neutron star mergers, are powered by the decay of radioactive isotopes in the neutron-rich merger ejecta. Models for kilonova emission consistent with the electromagnetic counterpart to GW170817 predict characteristic abundance patterns, determined by the relative balance of different types of material in the outflow. Assuming the observed source is prototypical, this inferred abundance pattern in turn must match $r$-process abundances deduced by other means, such as what is observed in the solar system. We report on analysis comparing the input mass-weighted elemental compositions adopted in our radiative transfer simulations to the mass fractions of elements in the Sun, as a practical prototype for the potentially universal abundance signature from neutron-star mergers. We characterize the extent to which our parameter inference results depend on our assumed composition for the dynamical and wind ejecta and examine how the new results compare to previous work. We find that a dynamical ejecta composition calculated using the FRDM2012 nuclear mass and FRLDM fission models with extremely neutron-rich ejecta ($Y_{\rm{e}} = 0.035$) along with moderately neutron-rich ($Y_{\rm e} = 0.27$) wind ejecta composition yields a wind-to-dynamical mass ratio of $M_{\rm{w}}/M_{\rm{d}}$ = 0.47 which best matches the observed AT2017gfo kilonova light curves while also producing the best-matching abundance of neutron-capture elements in the solar system.Comment: 16 pages, 9 figures, submitted to PR

Defining NELF-E RNA binding in HIV-1 and promoter-proximal pause regions

The four-subunit Negative Elongation Factor (NELF) is a major regulator of RNA Polymerase II (Pol II) pausing. The subunit NELF-E contains a conserved RNA Recognition Motif (RRM) and is proposed to facilitate Poll II pausing through its association with nascent transcribed RNA. However, conflicting ideas have emerged for the function of its RNA binding activity. Here, we use in vitro selection strategies and quantitative biochemistry to identify and characterize the consensus NELF-E binding element (NBE) that is required for sequence specific RNA recognition (NBE: CUGAGGA(U) for Drosophila). An NBE-like element is present within the loop region of the transactivation-response element (TAR) of HIV-1 RNA, a known regulatory target of human NELF-E. The NBE is required for high affinity binding, as opposed to the lower stem of TAR, as previously claimed. We also identify a non-conserved region within the RRM that contributes to the RNA recognition of Drosophila NELF-E. To understand the broader functional relevance of NBEs, we analyzed promoter-proximal regions genome-wide in Drosophila and show that the NBE is enriched +20 to +30 nucleotides downstream of the transcription start site. Consistent with the role of NELF in pausing, we observe a significant increase in NBEs among paused genes compared to non-paused genes. In addition to these observations, SELEX with nuclear run-on RNA enrich for NBE-like sequences. Together, these results describe the RNA binding behavior of NELF-E and supports a biological role for NELF-E in promoter-proximal pausing of both HIV-1 and cellular genes

A validation of Amazon Mechanical Turk for the collection of acceptability judgments in linguistic theory

Amazon’s Mechanical Turk (AMT) is a Web application that provides instant access to thousands of potential participants for survey-based psychology experiments, such as the acceptability judgment task used extensively in syntactic theory. Because AMT is a Web-based system, syntacticians may worry that the move out of the experimenter-controlled environment of the laboratory and onto the user-controlled environment of AMT could adversely affect the quality of the judgment data collected. This article reports a quantitative comparison of two identical acceptability judgment experiments, each with 176 participants (352 total): one conducted in the laboratory, and one conducted on AMT. Crucial indicators of data quality—such as participant rejection rates, statistical power, and the shape of the distributions of the judgments for each sentence type—are compared between the two samples. The results suggest that aside from slightly higher participant rejection rates, AMT data are almost indistinguishable from laboratory data