Monash University, Clayton campus

About: Monash University, Clayton campus is a based out in . It is known for research contribution in the topics: Population & Poison control. The organization has 21634 authors who have published 41131 publications receiving 1401421 citations.

GW170817: observation of gravitational waves from a binary neutron star inspiral

Abstract: On August 17, 2017 at 12∶41:04 UTC the Advanced LIGO and Advanced Virgo gravitational-wave detectors made their first observation of a binary neutron star inspiral. The signal, GW170817, was detected with a combined signal-to-noise ratio of 32.4 and a false-alarm-rate estimate of less than one per 8.0×10^{4} years. We infer the component masses of the binary to be between 0.86 and 2.26 M_{⊙}, in agreement with masses of known neutron stars. Restricting the component spins to the range inferred in binary neutron stars, we find the component masses to be in the range 1.17-1.60 M_{⊙}, with the total mass of the system 2.74_{-0.01}^{+0.04}M_{⊙}. The source was localized within a sky region of 28 deg^{2} (90% probability) and had a luminosity distance of 40_{-14}^{+8} Mpc, the closest and most precisely localized gravitational-wave signal yet. The association with the γ-ray burst GRB 170817A, detected by Fermi-GBM 1.7 s after the coalescence, corroborates the hypothesis of a neutron star merger and provides the first direct evidence of a link between these mergers and short γ-ray bursts. Subsequent identification of transient counterparts across the electromagnetic spectrum in the same location further supports the interpretation of this event as a neutron star merger. This unprecedented joint gravitational and electromagnetic observation provides insight into astrophysics, dense matter, gravitation, and cosmology.

A Comprehensive Survey on Graph Neural Networks

Abstract: Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications, where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects. The complexity of graph data has imposed significant challenges on the existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. In this article, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. We propose a new taxonomy to divide the state-of-the-art GNNs into four categories, namely, recurrent GNNs, convolutional GNNs, graph autoencoders, and spatial–temporal GNNs. We further discuss the applications of GNNs across various domains and summarize the open-source codes, benchmark data sets, and model evaluation of GNNs. Finally, we propose potential research directions in this rapidly growing field.

Encoding specificity and retrieval processes in episodic memory.

Abstract: Recent changes in prctheorclical orientation toward problems of human memory have brought with them a concern with retrieval processes, and a number of early versions of theories of retrieval have been constructed. This paper describes and evaluates explanations offered by these theories to account for the effect of extralist cuing, facilitation of recall of list items by nonlist items. Experiments designed to test the currently most popular theory of retrieval, the generation-recognition theory, yielded results incompatible not only with generation-recognition models, but most other theories as well: under certain conditions subjects consistently failed to recognize many recallable list words. Several tentative explanations of this phenomenon of recognition failure were subsumed under the encoding specificity principle according to which the memory trace of an event and hence the properties of effective retrieval cue are determined by the specific encoding operations performed by the system on the input stimuli.

Ionic-liquid materials for the electrochemical challenges of the future.

Abstract: Ionic liquids are room-temperature molten salts, composed mostly of organic ions that may undergo almost unlimited structural variations. This review covers the newest aspects of ionic liquids in applications where their ion conductivity is exploited; as electrochemical solvents for metal/semiconductor electrodeposition, and as batteries and fuel cells where conventional media, organic solvents (in batteries) or water (in polymer-electrolyte-membrane fuel cells), fail. Biology and biomimetic processes in ionic liquids are also discussed. In these decidedly different materials, some enzymes show activity that is not exhibited in more traditional systems, creating huge potential for bioinspired catalysis and biofuel cells. Our goal in this review is to survey the recent key developments and issues within ionic-liquid research in these areas. As well as informing materials scientists, we hope to generate interest in the wider community and encourage others to make use of ionic liquids in tackling scientific challenges.