scholarly journals Recent Science from Australian Large-Scale Millimetre Mapping Projects: Proceedings from a Swinburne University Workshop

2009 ◽  
Vol 26 (2) ◽  
pp. 110-120 ◽  
Author(s):  
I. Bains ◽  
S. L. Breen ◽  
M. G. Burton ◽  
M. R. Cunningham ◽  
P. A. Jones ◽  
...  
Keyword(s):  
Large Scale ◽  
The State ◽  
International Competition ◽  
Future Research ◽  
Research Directions ◽  
Current State ◽  
Receiver System ◽  
Future Research Directions

AbstractSince the recent upgrades to the Australia Telescope National Facility (ATNF) Mopra telescope back-end and receiver system, it has risen from an under-subscribed facility to a sought-after instrument with heavy international competition to gain time. Furthermore, the introduction of the on-the-fly mapping capability in 2004 has made this technique one of Mopra's most popular observing modes. In addition, the recent upgrade of the NANTEN millimetre-wavelength telescope to the sub-millimetre NANTEN2 instrument, has provided a complementary, higher-frequency facility to Mopra. A two-day workshop was held at Swinburne University in June 2008 to disseminate the current state of ongoing large-scale mapping projects and associated spin-offs that the telescopes' upgrades have facilitated, and to decide upon future research directions. Here, we provide a summary of the result-oriented talks as a record of the state of Australian-access single-dish millimetre science in 2008.

Life cycle assessment of carbon capture and storage/utilization: From current state to future research directions and opportunities

2021 ◽  
Vol 108 ◽  
pp. 103309
Author(s):  
Tatiane Tobias da Cruz ◽  
José A. Perrella Balestieri ◽  
João M. de Toledo Silva ◽  
Mateus R.N. Vilanova ◽  
Otávio J. Oliveira ◽  
...  
Keyword(s):  
Life Cycle Assessment ◽  
Life Cycle ◽  
Carbon Capture ◽  
Carbon Capture And Storage ◽  
Future Research ◽  
Research Directions ◽  
Current State ◽  
Future Research Directions ◽  
And Storage

scholarly journals Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development

2020 ◽  
Vol 13 (3) ◽  
pp. 795-848
Author(s):  
Alina Köchling ◽  
Marius Claus Wehner
Keyword(s):  
Systematic Review ◽  
Decision Making ◽  
Current Knowledge ◽  
Future Research ◽  
Journal Articles ◽  
Research Directions ◽  
Current State ◽  
Management Context ◽  
Future Research Directions ◽  
State Of Research

AbstractAlgorithmic decision-making is becoming increasingly common as a new source of advice in HR recruitment and HR development. While firms implement algorithmic decision-making to save costs as well as increase efficiency and objectivity, algorithmic decision-making might also lead to the unfair treatment of certain groups of people, implicit discrimination, and perceived unfairness. Current knowledge about the threats of unfairness and (implicit) discrimination by algorithmic decision-making is mostly unexplored in the human resource management context. Our goal is to clarify the current state of research related to HR recruitment and HR development, identify research gaps, and provide crucial future research directions. Based on a systematic review of 36 journal articles from 2014 to 2020, we present some applications of algorithmic decision-making and evaluate the possible pitfalls in these two essential HR functions. In doing this, we inform researchers and practitioners, offer important theoretical and practical implications, and suggest fruitful avenues for future research.

Introduction to L2 attrition

2019 ◽  
pp. 329-348 ◽  
Author(s):  
Teodora H. Mehotcheva ◽  
Barbara Köpke
Keyword(s):  
Second Language ◽  
Large Scale ◽  
First Language ◽  
Foreign Languages ◽  
Future Research ◽  
Theoretical Frameworks ◽  
Research Directions ◽  
Short Overview ◽  
Future Research Directions ◽  
The Subject

As the introduction to the section on second language (L2) attrition, this chapter provides a broad presentation to research on attrition of L2 and foreign languages (FL). We will first discuss the terminology used in the field, focusing on some important differences in the terminology used in first language (L1) attrition studies. It provides a short overview of the development of the field, outlining major challenges and obstacles that research on the topic has to deal with. Next, it briefly describes the major findings and knowledge amassed on the subject before reviewing in more detail the findings of some of the most significant and large-scale projects carried out on L2/FL attrition. A final presentation of several theoretical frameworks of interest for L2/FL attrition research will allow us to show how L2/FL attrition is commonly explained but also to provide some ideas for future research directions.

Performance and trends in recent opinion retrieval techniques

2013 ◽  
Vol 30 (1) ◽  
pp. 76-105 ◽  
Author(s):  
Sylvester O. Orimaye ◽  
Saadat M. Alhashmi ◽  
Eu-Gene Siew
Keyword(s):  
State Of The Art ◽  
Performance Comparison ◽  
The State ◽  
Future Research ◽  
Research Directions ◽  
Opinion Retrieval ◽  
Future Research Directions ◽  
And Performance ◽  
Comparison Table ◽  
Art Techniques

AbstractThis paper presents trends and performance of opinion retrieval techniques proposed within the last 8 years. We identify major techniques in opinion retrieval and group them into four popular categories. We describe the state-of-the-art techniques for each category and emphasize on their performance and limitations. We then summarize with a performance comparison table for the techniques on different datasets. Finally, we highlight possible future research directions that can help solve existing challenges in opinion retrieval.

scholarly journals Compressing Large-Scale Transformer-Based Models: A Case Study on BERT

2021 ◽  
Vol 9 ◽  
pp. 1061-1080
Author(s):  
Prakhar Ganesh ◽  
Yao Chen ◽  
Xin Lou ◽  
Mohammad Ali Khan ◽  
Yin Yang ◽  
...  
Keyword(s):  
Language Processing ◽  
Large Scale ◽  
State Of The Art ◽  
Future Research ◽  
Research Directions ◽  
Research Attention ◽  
Model Compression ◽  
Future Research Directions ◽  
Or Applications

Abstract Pre-trained Transformer-based models have achieved state-of-the-art performance for various Natural Language Processing (NLP) tasks. However, these models often have billions of parameters, and thus are too resource- hungry and computation-intensive to suit low- capability devices or applications with strict latency requirements. One potential remedy for this is model compression, which has attracted considerable research attention. Here, we summarize the research in compressing Transformers, focusing on the especially popular BERT model. In particular, we survey the state of the art in compression for BERT, we clarify the current best practices for compressing large-scale Transformer models, and we provide insights into the workings of various methods. Our categorization and analysis also shed light on promising future research directions for achieving lightweight, accurate, and generic NLP models.

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