Institution
Dublin City University
Education•Dublin, Ireland•
About: Dublin City University is a education organization based out in Dublin, Ireland. It is known for research contribution in the topics: Machine translation & Laser. The organization has 5904 authors who have published 17178 publications receiving 389376 citations. The organization is also known as: National Institute for Higher Education, Dublin & DCU.
Topics: Machine translation, Laser, Irish, Population, Context (language use)
Papers published on a yearly basis
Papers
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TL;DR: Results provide further evidence of the non-protective nature of specific immune responses in cattle following F. hepatica infection, and demonstrate that vaccination can induce a qualitatively different, and protective, response.
148 citations
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TL;DR: In this paper, the authors examined the impact of job involvement on self-report measures of in-role job performance and organizational citizenship behavior and found that job involvement was positively correlated with both inrole jobperformance and OCB (r = 0.30, p<0.01).
Abstract: This study examines the impact of job involvement on the self-report measures of inrole
job performance and organizational citizenship behaviour. The results of this study
revealed that job involvement was positively correlated with both in-role job
performance (r = 0.30, p<0.01) and OCB (r = 0.43, p<0.01). In addition to this it was
found that organizational commitment partially mediated the job involvement performance relationship. Furthermore the findings of this research uncovered that job involvement exerted a stronger impact on OCB than on in-role performance. Finally the practical implications of this research for organizations are discussed.
148 citations
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TL;DR: In this paper, the authors identify a new Raman peak which occurs between the commonly found G and D bands of carbon nitride films and identify this new peak as being due to nitrogen-nitrogen bonding.
147 citations
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01 Jan 2017TL;DR: This work introduces multi-modal, attention-based neural machine translation (NMT) models which incorporate visual features into different parts of both the encoder and the decoder, and reports new state-of-the-art results.
Abstract: We introduce multi-modal, attention-based neural machine translation (NMT) models which incorporate visual features into different parts of both the encoder and the decoder. Global image features are extracted using a pre-trained convolutional neural network and are incorporated (i) as words in the source sentence, (ii) to initialise the encoder hidden state, and (iii) as additional data to initialise the decoder hidden state. In our experiments, we evaluate translations into English and German, how different strategies to incorporate global image features compare and which ones perform best. We also study the impact that adding synthetic multi-modal, multilingual data brings and find that the additional data have a positive impact on multi-modal NMT models. We report new state-of-the-art results and our best models also significantly improve on a comparable phrase-based Statistical MT (PBSMT) model trained on the Multi30k data set according to all metrics evaluated. To the best of our knowledge, it is the first time a purely neural model significantly improves over a PBSMT model on all metrics evaluated on this data set.
147 citations
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TL;DR: In this paper, the authors demonstrate the generation of a highly coherent multicarrier signal that consists of eight clearly resolved 10.7 GHz coherent sidebands generated within 3 dB of the spectral envelope peak and with an extinction ratio in excess of 45 dB by gain switching a discrete mode (DM) laser.
Abstract: The authors demonstrate the generation of a highly coherent multicarrier signal that consists of eight clearly resolved 10.7-GHz coherent sidebands generated within 3 dB of the spectral envelope peak and with an extinction ratio in excess of 45 dB by gain switching a discrete mode (DM) laser. The generated spectral comb displays a corresponding picosecond pulse train at a repetition rate of 10.7 GHz with a pulse duration of 24 ps and a temporal jitter of ~450 fs. The optical spectra and associated pulses of the gain-switched DM laser are subsequently compared with a gain-switched distributed feedback (DFB) laser that generates a spectrum with no discernible sidebands and corresponding pulses with ~3 ps of temporal jitter. By means of external injection, the temporal jitter of the gain-switched DFB laser is then reduced to <; 1 ps, resulting in visible tones on the output spectrum. Finally, a nonlinear scheme is employed and initially tailored to compress the optical pulses, after which, the setup is slightly altered to expand the original frequency comb from the gain-switched DM laser.
147 citations
Authors
Showing all 6059 results
Name | H-index | Papers | Citations |
---|---|---|---|
Joseph Wang | 158 | 1282 | 98799 |
David Cameron | 154 | 1586 | 126067 |
David Taylor | 131 | 2469 | 93220 |
Gordon G. Wallace | 114 | 1267 | 69095 |
David A. Morrow | 113 | 598 | 56776 |
G. Hughes | 103 | 957 | 46632 |
David Wilson | 102 | 757 | 49388 |
Muhammad Imran | 94 | 3053 | 51728 |
Haibo Zeng | 94 | 604 | 39226 |
David Lloyd | 90 | 1017 | 37691 |
Vikas Kumar | 89 | 859 | 39185 |
Luke P. Lee | 84 | 413 | 22803 |
James Chapman | 82 | 483 | 36468 |
Muhammad Iqbal | 77 | 961 | 23821 |
Michael C. Berndt | 76 | 228 | 16897 |