3 research outputs found
Ten simple rules to make your computing more environmentally sustainable.
Funder: Victorian Government’s Operational Infrastructure Support (OIS) programFunder: Health Data Research UKFunder: La Trobe University Postgraduate Research ScholarshipFunder: Munz Chair of Cardiovascular Prediction and Preventio
Generation of Highlights from Research Papers Using Pointer-Generator Networks and SciBERT Embeddings
Nowadays many research articles are prefaced with research highlights to
summarize the main findings of the paper. Highlights not only help researchers
precisely and quickly identify the contributions of a paper, they also enhance
the discoverability of the article via search engines. We aim to automatically
construct research highlights given certain segments of the research paper. We
use a pointer-generator network with coverage mechanism and a contextual
embedding layer at the input that encodes the input tokens into SciBERT
embeddings. We test our model on a benchmark dataset, CSPubSum and also present
MixSub, a new multi-disciplinary corpus of papers for automatic research
highlight generation. For both CSPubSum and MixSub, we have observed that the
proposed model achieves the best performance compared to related variants and
other models proposed in the literature. On the CSPubSum data set, our model
achieves the best performance when the input is only the abstract of a paper as
opposed to other segments of the paper. It produces ROUGE-1, ROUGE-2 and
ROUGE-L F1-scores of 38.26, 14.26 and 35.51, respectively, METEOR F1-score of
32.62, and BERTScore F1 of 86.65 which outperform all other baselines. On the
new MixSub data set, where only the abstract is the input, our proposed model
(when trained on the whole training corpus without distinguishing between the
subject categories) achieves ROUGE-1, ROUGE-2 and ROUGE-L F1-scores of 31.78,
9.76 and 29.3, respectively, METEOR F1-score of 24.00, and BERTScore F1 of
85.25, outperforming other models.Comment: 18 pages, 7 figures, 7 table
High-Density Solid-State Memory Devices and Technologies
This Special Issue aims to examine high-density solid-state memory devices and technologies from various standpoints in an attempt to foster their continuous success in the future. Considering that broadening of the range of applications will likely offer different types of solid-state memories their chance in the spotlight, the Special Issue is not focused on a specific storage solution but rather embraces all the most relevant solid-state memory devices and technologies currently on stage. Even the subjects dealt with in this Special Issue are widespread, ranging from process and design issues/innovations to the experimental and theoretical analysis of the operation and from the performance and reliability of memory devices and arrays to the exploitation of solid-state memories to pursue new computing paradigms