Statistically-informed deep learning for gravitational wave parameter estimation

We introduce deep learning models to estimate the masses of the binary components of black hole mergers, (m1, m2), and three astrophysical properties of the post-merger compact remnant, namely, the final spin, af, and the frequency and damping time of the ringdown oscillations of the fundamental (l=m=2) bar mode, (ωR, ωI). Our neural networks combine a modified WaveNet architecture with contrastive learning and normalizing flow. We validate these models against a Gaussian conjugate prior family whose posterior distribution is described by a closed analytical expression. Upon confirming that our models produce statistically consistent results, we used them to estimate the astrophysical parameters (m1, m2, af, ωR, ωI) of five binary black holes: GW150914, GW170104, GW170814, GW190521 and GW190630. We use PyCBC Inference to directly compare traditional Bayesian methodologies for parameter estimation with our deep learning based posterior distributions. Our results show that our neural network models predict posterior distributions that encode physical correlations, and that our data-driven median results and 90\% confidence intervals are similar to those produced with gravitational wave Bayesian analyses. This methodology requires a single V100 NVIDIA GPU to produce median values and posterior distributions within two milliseconds for each event. This neural network, and a tutorial for its use, are available at the Data and Learning Hub for Science.

Baseline Energy Use Modeling and Characterization in Tertiary Buildings Using an Interpretable Bayesian Linear Regression Methodology

Interpretable and scalable data-driven methodologies providing high granularity baseline predictions of energy use in buildings are essential for the accurate measurement and verification of energy renovation projects and have the potential of unlocking considerable investments in energy efficiency worldwide. Bayesian methodologies have been demonstrated to hold great potential for energy baseline modelling, by providing richer and more valuable information using intuitive mathematics. This paper proposes a Bayesian linear regression methodology for hourly baseline energy consumption predictions in commercial buildings. The methodology also enables a detailed characterization of the analyzed buildings through the detection of typical electricity usage profiles and the estimation of the weather dependence. The effects of different Bayesian model specifications were tested, including the use of different prior distributions, predictor variables, posterior estimation techniques, and the implementation of multilevel regression. The approach was tested on an open dataset containing two years of electricity meter readings at an hourly frequency for 1578 non-residential buildings. The best performing model specifications were identified, among the ones tested. The results show that the methodology developed is able to provide accurate high granularity baseline predictions, while also being intuitive and explainable. The building consumption characterization provides actionable information that can be used by energy managers to improve the performance of the analyzed facilities.

Prevalence of Obesity and Trends of Body Mass Index in Azad Jammu and Kashmir

In the present study an attempt has been made to figure out the remarkable factors that were responsible for higher body mass index (BMI) and obesity as well as to observe the existing trends of BMI in Azad Jammu and Kashmir. The factors were age, sex, diabetes, blood pressure, total cholesterol, triglycerides, blood glucose and random glucose. The statistical analysis with both classical and Bayesian methodologies was carried out for the investigation of significant risk factors. The results showed that with the increase in age the BMI also increases i.e., respondents of age group > 60 have the highest percentage of 82.76% for BMI. Moreover, it was found that except sex and blood pressure all other factors had significant association with BMI. Additionally, four factors namely, age, diabetes, total cholesterol and triglycerides were selected for the development of the parsimonious model of BMI based on generalized linear model, step wise regression and Bayesian model averaging.

A Methodology for the Analysis of Consistent Hashing

The synthesis of IPv4 is a practical obstacle. After years of unproven research into the World Wide Web, we demonstrate the synthesis of access points. In our research we use Bayesian methodologies to argue that the much-touted empathic algorithm for the analysis of superblocks by Takahashi et al. [19] is impossible.

Study of repeatability and phenotypical stabilization in kale using frequentist, Bayesian and bootstrap resampling approaches

The aim of this study was to obtain information for the genetic improvement of kale through repeatability and phenotypic stabilization studies and to compare methodologies that represent the reliability of the estimated parameters. Thirty-three half-sib progenies were evaluated in a randomized block design with three replicates and six plants per plot. Eight harvests were evaluated in terms of the yield of fresh leaves, number of shoots, number of leaves and average mass of leaves. Then, a phenotypic repeatability and stabilization study was performed, estimating the genetic parameters σ2a, σ²g, σ²e, and the coefficient of environmental variation and repeatability using the frequentist and Bayesian methodologies. To evaluate the reliability of these estimates, intervals were obtained using the frequentist, Bayesian and bootstrap methods. It was verified that the reliable selection of progenies of half-sib of kale can be achieved in four harvests that were realized between 95 and 170 days after planting. It was observed that the frequentist and Bayesian methodologies are better suited to obtain reliable estimates of the genetic parameters evaluated, as the last one provided smaller amplitudes for the obtained intervals. The bootstrap methodologies are not recommended for phenotypic repeatability and stabilization studies in kale.

Specimen-level phylogenetics in paleontology using the Fossilized Birth-Death model with sampled ancestors

Bayesian phylogenetic methods integrating simultaneously morphological and stratigraphic information have been applied increasingly among paleontologists. Most of these studies have used Bayesian methods as an alternative to the widely-used parsimony analysis, to infer macroevolutionary patterns and relationships among species-level or higher taxa. Among recently introduced Bayesian methodologies, the Fossilized Birth-Death (FBD) model allows incorporation of hypotheses on ancestor-descendant relationships in phylogenetic analyses including fossil taxa. Here, the FBD model is used to infer the relationships among an ingroup formed exclusively by fossil individuals, i.e., dipnoan tooth plates from four localities in the Ain el Guettar Formation of Tunisia. Previous analyses of this sample compared the results of phylogenetic analysis using parsimony with stratigraphic methods, inferred a high diversity (five or more genera) in the Ain el Guettar Formation, and interpreted it as an artifact inflated by depositional factors. In the analysis performed here, the uncertainty on the chronostratigraphic relationships among the specimens was included among the prior settings. The results of the analysis confirm the referral of most of the specimens to the taxaAsiatoceratodus,Equinoxiodus, LavocatodusandNeoceratodus, but reject those toCeratodusandFerganoceratodus. The resulting phylogeny constrained the evolution of the Tunisian sample exclusively in the Early Cretaceous, contrasting with the previous scenario inferred by the stratigraphically-calibrated topology resulting from parsimony analysis. The phylogenetic framework also suggests that (1) the sampled localities are laterally equivalent, (2) but three localities are restricted to the youngest part of the section; both results are in agreement with previous stratigraphic analyses of these localities. The FBD model of specimen-level units provides a novel tool for phylogenetic inference among fossils but also for independent tests of stratigraphic scenarios.

Contrasting Meteorological Wide-Area Networks and Evolutionary Programming

Recent advances in secure information and Bayesian methodologies have paved the way for randomized algorithms. Here, we verify the evaluation of multi-processors. This is an important point to understand. In order to fix this issue, we concentrate our efforts on arguing that courseware and consistent hashing can collaborate to overcome this riddle.

Phylogenetic revision of the Late Ordovician orthid brachiopod generaPlaesiomysandHebertellafrom Laurentia

The orthidine brachiopod generaPlaesiomysandHebertellaare significant constituents of Late Ordovician benthic marine communities throughout Laurentia. Species-level phylogenetic analyses were conducted on both genera to inform systematic revisions and document evolutionary relationships. Phylogenetic analyses combined discrete and continuous characters, from which character states were determined using a statistical approach, and utilized both cladistic and Bayesian methodologies.Plaesiomys cutterensis,P. idahoensis, andP. occidentalisare herein recognized as distinct species rather than subspecies ofP. subquadratus. Similarly,Hebertella montoyensisandH. prestonensisare recognized as distinct species separate fromH. occidentalis, andH. richmondensisis recognized as a distinct species rather than a geographical variant ofH. alveata.Hebertella subjugatais removed from its tentative synonymy withH. occidentalisand revalidated.The development of species-level evolutionary hypotheses forPlaesiomysandHebertellaprovides a detailed framework for assessing evolutionary and paleobiogeographic patterns of Late Ordovician brachiopods from Laurentia. The geographic range ofHebertellaexpanded throughout Laurentia during the Richmondian into both intracratonic and marginal basins.Plaesiomys subquadratusparticipated in the Late Ordovician Richmondian Invasion. The recovered phylogenetic topology forPlaesiomyssuggests thatP. subquadratusmay have migrated into the Cincinnati region from a basin situated to the paleo-northeast.