Australian National University

About: Australian National University is a education organization based out in Canberra, Australian Capital Territory, Australia. It is known for research contribution in the topics: Population & Galaxy. The organization has 34419 authors who have published 109261 publications receiving 4315448 citations. The organization is also known as: The Australian National University & ANU.

A systematic review of the impact of adherence on the effectiveness of e-therapies.

Abstract: Background: As the popularity of e-therapies grows, so too has the body of literature supporting their effectiveness. However, these interventions are often plagued by high attrition rates and varying levels of user adherence. Understanding the role of adherence may be crucial to understanding how program usage influences the effectiveness of e-therapy interventions. Objective: The aim of this study was to systematically review the e-therapy literature to (1) describe the methods used to assess adherence and (2) evaluate the association of adherence with outcome of these interventions. Methods: A systematic review of e-therapy interventions was conducted across disease states and behavioral targets. Data were collected on adherence measures, outcomes, and analyses exploring the relationship between adherence measures and outcomes. Results: Of 69 studies that reported an adherence measure, only 33 (48%) examined the relationship between adherence and outcomes. The number of logins was the most commonly reported measure of adherence, followed by the number of modules completed. The heterogeneity of adherence and outcome measures limited analysis. However, logins appeared to be the measure of adherence most consistently related to outcomes in physical health interventions, while module completion was found to be most related to outcomes in psychological health interventions. Conclusions: There is large variation in the reporting of adherence and the association of adherence with outcomes. A lack of agreement about how best to measure adherence is likely to contribute to the variation in findings. Physical and psychological outcomes seem influenced by different types of adherence. A composite measure encompassing time online, activity completion, and active engagements with the intervention may be the best measure of adherence. Further research is required to establish a consensus for measuring adherence and to understand the role of adherence in influencing outcomes.

The GALAH survey: scientific motivation

Abstract: The Galactic Archaeology with HERMES (GALAH) survey is a large high-resolution spectroscopic survey using the newly commissioned High Efficiency and Resolution Multi-Element Spectrograph (HERMES) on the Anglo-Australian Telescope. The HERMES spectrograph provides high-resolution (R ~ 28 000) spectra in four passbands for 392 stars simultaneously over a 2 deg field of view. The goal of the survey is to unravel the formation and evolutionary history of the Milky Way, using fossil remnants of ancient star formation events which have been disrupted and are now dispersed throughout the Galaxy. Chemical tagging seeks to identify such dispersed remnants solely from their common and unique chemical signatures; these groups are unidentifiable from their spatial, photometric or kinematic properties. To carry out chemical tagging, the GALAH survey will acquire spectra for a million stars down to V ~ 14. The HERMES spectra of FGK stars contain absorption lines from 29 elements including light proton-capture elements, α-elements, odd-Z elements, iron-peak elements and n-capture elements from the light and heavy s-process and the r-process. This paper describes the motivation and planned execution of the GALAH survey, and presents some results on the first-light performance of HERMES.

SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives

Abstract: In this work we introduce a new optimisation method called SAGA in the spirit of SAG, SDCA, MISO and SVRG, a set of recently proposed incremental gradient algorithms with fast linear convergence rates. SAGA improves on the theory behind SAG and SVRG, with better theoretical convergence rates, and has support for composite objectives where a proximal operator is used on the regulariser. Unlike SDCA, SAGA supports non-strongly convex problems directly, and is adaptive to any inherent strong convexity of the problem. We give experimental results showing the effectiveness of our method.