University of New South Wales

About: University of New South Wales is a education organization based out in Sydney, New South Wales, Australia. It is known for research contribution in the topics: Population & Poison control. The organization has 51197 authors who have published 153634 publications receiving 4880608 citations. The organization is also known as: UNSW & UNSW Australia.

Challenges and opportunities for mixed-matrix membranes for gas separation

Abstract: Mixed-Matrix Membranes (MMMs) combining the benefits of both polymeric and inorganic materials have become a focus for the next-generation gas separation membranes. In this review, major challenges surrounding MMMs and the strategies to tackle these challenges are given in detail. The selection criteria of polymeric and inorganic materials are discussed in terms of their physical and chemical compatibility as well as large scale fabrication issues. Major models for separation performance prediction of MMMs are reviewed in terms of their interrelations and limitations. A discussion is provided regarding the future direction of MMMs.

Deep Learning based Recommender System: A Survey and New Perspectives.

Abstract: With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.

Galaxy ecology: groups and low-density environments in the SDSS and 2dFGRS

Abstract: We analyse the observed correlation between galaxy environment and Halpha emission-line strength, using volume-limited samples and group catalogues of 24 968 galaxies at 0.05 < z < 0.095, drawn from the 2dF Galaxy Redshift Survey (M-bJ < -19.5) and the Sloan Digital Sky Survey (M-r < -20.6). We characterize the environment by: (1) Sigma(5), the surface number density of galaxies determined by the projected distance to the fifth nearest neighbour; and (2) rho(1.1) and rho(5.5), three-dimensional density estimates obtained by convolving the galaxy distribution with Gaussian kernels of dispersion 1.1 and 5.5 Mpc, respectively. We find that star-forming and quiescent galaxies form two distinct populations, as characterized by their H equivalent width, W-0(Halpha). The relative numbers of star-forming and quiescent galaxies vary strongly and continuously with local density. However, the distribution of W-0(Halpha) amongst the star-forming population is independent of environment. The fraction of star-forming galaxies shows strong sensitivity to the density on large scales, rho(5.5), which is likely independent of the trend with local density, rho(1.1). We use two differently selected group catalogues to demonstrate that the correlation with galaxy density is approximately independent of group velocity dispersion, for sigma = 200-1000 km s(-1). Even in the lowest-density environments, no more than similar to70 per cent of galaxies show significant Halpha emission. Based on these results, we conclude that the present-day correlation between star formation rate and environment is a result of short-time-scale mechanisms that take place preferentially at high redshift, such as starbursts induced by galaxy-galaxy interactions.

Task complexity as a moderator of goal effects: A meta-analysis.

Abstract: Much evidence exists that supports the use of goal setting as a motivational technique for enhancing task performance; howevei; little attention has been given to the role of task characteristics as potential moderating conditions of goal effects. Meta-analysis procedures were used to assess the moderator effects of task complexity for goal-setting studies conducted from 1966 to 1985 (n = 125). The reliability ofthe task complexity ratings was .92. Three sets of analyses were conducted: for goaldifficulty results (hard vs. easy), for goal specificity-difficulty (specific difficult goals vs. do-best or no goal), and for all studies collapsed across goal difficulty and goal specificity-difficulty. It was generally found that goal-setting effects were strongest for easy tasks (reaction time, brainstorming), d = .76, and weakest for more complex tasks (business game simulations, scientific and engineering work, faculty research productivity), d = .42. Implications for future research on goal setting and the validity of generalizing results are discussed.

So Many Variables: Joint Modeling in Community Ecology.

Abstract: Technological advances have enabled a new class of multivariate models for ecology, with the potential now to specify a statistical model for abundances jointly across many taxa, to simultaneously explore interactions across taxa and the response of abundance to environmental variables. Joint models can be used for several purposes of interest to ecologists, including estimating patterns of residual correlation across taxa, ordination, multivariate inference about environmental effects and environment-by-trait interactions, accounting for missing predictors, and improving predictions in situations where one can leverage knowledge of some species to predict others. We demonstrate this by example and discuss recent computation tools and future directions.