Finding the Right Tree: Topology Inference Despite Spatial Dependences

© 1963-2012 IEEE. Network tomographic techniques have almost exclusively been built on a strong assumption of mutual independence of link processes. We introduce model classes for link loss processes with non-Trivial spatial dependencies, for which the tree topology is nonetheless identifiable from leaf measurements using multicast probing. We show that these classes are large in a well-defined sense, and we provide an algorithm, SLTD, capable of returning the correct topology with certainty in the limit of infinite data

Systematic challenges for future gravitational wave measurements of precessing binary black holes

The properties of precessing, coalescing binary black holes are presently inferred through comparison with two approximate models of compact binary coalescence. In this work we show these two models often disagree substantially when binaries have modestly large spins ($a\gtrsim 0.4$) and modest mass ratios ($q\gtrsim 2$). We demonstrate these disagreements using standard figures of merit and the parameters inferred for recent detections of binary black holes. By comparing to numerical relativity, we confirm these disagreements reflect systematic errors. We provide concrete examples to demonstrate that these systematic errors can significantly impact inferences about astrophysically significant binary parameters. For the immediate future, parameter inference for binary black holes should be performed with multiple models (including numerical relativity), and carefully validated by performing inference under controlled circumstances with similar synthetic events.Comment: 12 pages, 9 figure

Should I stay or should I go? Exploring the job preferences of allied health professionals working with people with disability in rural Australia

Introduction: The uneven distribution of allied health professionals (AHPs) in rural and remote Australia and other countries is well documented. In Australia, like elsewhere, service delivery to rural and remote communities is complicated because relatively small numbers of clients are dispersed over large geographic areas. This uneven distribution of AHPs impacts significantly on the provision of services particularly in areas of special need such as mental health, aged care and disability services. Objective: This study aimed to determine the relative importance that AHPs (physiotherapists, occupational therapists, speech pathologists and psychologists – “therapists”) living in a rural area of Australia and working with people with disability, place on different job characteristics and how these may affect their retention. Methods: A cross-sectional survey was conducted using an online questionnaire distributed to AHPs working with people with disability in a rural area of Australia over a 3-month period. Information was sought about various aspects of the AHPs’ current job, and their workforce preferences were explored using a best–worst scaling discrete choice experiment (BWSDCE). Conditional logistic and latent class regression models were used to determine AHPs’ relative preferences for six different job attributes. Results: One hundred ninety-nine AHPs completed the survey; response rate was 51 %. Of those, 165 completed the BWSDCE task. For this group of AHPs, “high autonomy of practice” is the most valued attribute level, followed by “travel BWSDCE arrangements: one or less nights away per month”, “travel arrangements: two or three nights away per month” and “adequate access to professional development”. On the other hand, the least valued attribute levels were “travel arrangements: four or more nights per month”, “limited autonomy of practice” and “minimal access to professional development”. Except for “some job flexibility”, all other attributes had a statistical influence on AHPs’ job preference. Preferences differed according to age, marital status and having dependent children. Conclusions: This study allowed the identification of factors that contribute to AHPs’ employment decisions about staying and working in a rural area. This information can improve job designs in rural areas to increase retention

Parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library

The Advanced LIGO and Advanced Virgo gravitational-wave (GW) detectors will begin operation in the coming years, with compact binary coalescence events a likely source for the first detections. The gravitational waveforms emitted directly encode information about the sources, including the masses and spins of the compact objects. Recovering the physical parameters of the sources from the GW observations is a key analysis task. This work describes the LALInference software library for Bayesian parameter estimation of compact binary signals, which builds on several previous methods to provide a well-tested toolkit which has already been used for several studies. We show that our implementation is able to correctly recover the parameters of compact binary signals from simulated data from the advanced GW detectors. We demonstrate this with a detailed comparison on three compact binary systems: a binary neutron star, a neutron star–black hole binary and a binary black hole, where we show a cross comparison of results obtained using three independent sampling algorithms. These systems were analyzed with nonspinning, aligned spin and generic spin configurations respectively, showing that consistent results can be obtained even with the full 15-dimensional parameter space of the generic spin configurations. We also demonstrate statistically that the Bayesian credible intervals we recover correspond to frequentist confidence intervals under correct prior assumptions by analyzing a set of 100 signals drawn from the prior. We discuss the computational cost of these algorithms, and describe the general and problem-specific sampling techniques we have used to improve the efficiency of sampling the compact binary coalescence parameter space