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We present in this position paper a methodology to validate legal governance regulatory models from an empirical approach, as illustrated by means of three diagrams: (i) a scheme drawing the rule and meta-rule of law; (ii) a metamodel for legal gover
Externí odkaz:
http://arxiv.org/abs/2407.20691
Autor:
Gao, Shanghua, Koker, Teddy, Queen, Owen, Hartvigsen, Thomas, Tsiligkaridis, Theodoros, Zitnik, Marinka
Although pre-trained transformers and reprogrammed text-based LLMs have shown strong performance on time series tasks, the best-performing architectures vary widely across tasks, with most models narrowly focused on specific areas, such as time serie
Externí odkaz:
http://arxiv.org/abs/2403.00131
The calculation of electron density distribution using density functional theory (DFT) in materials and molecules is central to the study of their quantum and macro-scale properties, yet accurate and efficient calculation remains a long-standing chal
Externí odkaz:
http://arxiv.org/abs/2312.05388
Autor:
Queen, Owen, Hartvigsen, Thomas, Koker, Teddy, He, Huan, Tsiligkaridis, Theodoros, Zitnik, Marinka
Interpreting time series models is uniquely challenging because it requires identifying both the location of time series signals that drive model predictions and their matching to an interpretable temporal pattern. While explainers from other modalit
Externí odkaz:
http://arxiv.org/abs/2306.02109
Publikováno v:
npj Computational Materials, Vol 10, Iss 1, Pp 1-10 (2024)
Abstract The calculation of electron density distribution using density functional theory (DFT) in materials and molecules is central to the study of their quantum and macro-scale properties, yet accurate and efficient calculation remains a long-stan
Externí odkaz:
https://doaj.org/article/792824976d4446059520c41531eac566
Autor:
He, Huan, Queen, Owen, Koker, Teddy, Cuevas, Consuelo, Tsiligkaridis, Theodoros, Zitnik, Marinka
Unsupervised domain adaptation (UDA) enables the transfer of models trained on source domains to unlabeled target domains. However, transferring complex time series models presents challenges due to the dynamic temporal structure variations across do
Externí odkaz:
http://arxiv.org/abs/2302.03133
Autor:
De Koker, Louis
Publikováno v:
Journal of Money Laundering Control, 2024, Vol. 27, Issue 4, pp. 621-624.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/JMLC-07-2024-206
Recent work has shown the potential of graph neural networks to efficiently predict material properties, enabling high-throughput screening of materials. Training these models, however, often requires large quantities of labelled data, obtained via c
Externí odkaz:
http://arxiv.org/abs/2211.13408
Early adverse physiological event detection using commercial wearables: challenges and opportunities
Autor:
Jesse Phipps, Bryant Passage, Kaan Sel, Jonathan Martinez, Milad Saadat, Teddy Koker, Natalie Damaso, Shakti Davis, Jeffrey Palmer, Kajal Claypool, Christopher Kiley, Roderic I. Pettigrew, Roozbeh Jafari
Publikováno v:
npj Digital Medicine, Vol 7, Iss 1, Pp 1-13 (2024)
Abstract Data from commercial off-the-shelf (COTS) wearables leveraged with machine learning algorithms provide an unprecedented potential for the early detection of adverse physiological events. However, several challenges inhibit this potential, in
Externí odkaz:
https://doaj.org/article/85d5c684c49d4f8bbbd581cf64b1a736
Autor:
Lucian T. De Koker, Tanya Du Plessis
Publikováno v:
South African Journal of Information Management, Vol 26, Iss 1, Pp e1-e13 (2024)
Background: The scholarship of teaching and learning (SoTL) makes provision for methodology contributions across disciplines. The information and knowledge management (IKM) discipline prepares students to function optimally in the Fourth Industrial R
Externí odkaz:
https://doaj.org/article/e17d00c839c2444bace1aed46d025273