Delayed Slater determinant update algorithms for high efficiency quantum Monte Carlo

Autor: McDaniel, T., D'Azevedo, E. F., Li, Y. W., Wong, K., Kent, P. R. C.

Within ab initio Quantum Monte Carlo simulations, the leading numerical cost for large systems is the computation of the values of the Slater determinants in the trial wavefunction. Each Monte Carlo step requires finding the determinant of a dense ma

Externí odkaz: http://arxiv.org/abs/1708.00744

LB1045 Inhibition of pyruvate oxidation activates human hair follicle stem cells ex vivo

Autor: Flores, A.A., McDaniel, T., Sun, D., Christofk, H., Jung, M., Weng, Q., Gil, D., Lowry, W.

Publikováno v: In Journal of Investigative Dermatology August 2024 144(8) Supplement:S186-S186

Emotion Recognition with Simulated Phosphene Vision

Autor: Bollen, C.J.M., Wezel, R.J.A. van, Gerven, M.A.J. van, Güçlütürk, Y., Liu, X., Min, R., McDaniel, T.

Publikováno v: Liu, X.; Min, R.; McDaniel, T. (ed.), MAHCI '19: Proceedings of the 2nd Workshop on Multimedia for Accessible Human Computer Interfaces, pp. 1-8
Liu, X.; Min, R.; McDaniel, T. (ed.), MAHCI '19: Proceedings of the 2nd Workshop on Multimedia for Accessible Human Computer Interfaces, 1-8. New York, NY : Association for Computing Machinery (ACM)
STARTPAGE=1;ENDPAGE=8;TITLE=Liu, X.; Min, R.; McDaniel, T. (ed.), MAHCI '19: Proceedings of the 2nd Workshop on Multimedia for Accessible Human Computer Interfaces

Contains fulltext : 215179.pdf (Publisher’s version ) (Open Access) Electrical stimulation of retina, optic nerve or cortex is found to elicit visual sensations, known as phosphenes. This allows visual prosthetics to partially restore vision by rep

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8bca70c7be866a0d8dbbc43c5b1a5ee8
https://doi.org/10.1145/3347319.3356836

Model Selection with Nonlinear Embedding for Unsupervised Domain Adaptation

Autor: Hemanth Venkateswara, Chakraborty, S., Mcdaniel, T., Panchanathan, S.

Publikováno v: Scopus-Elsevier

Domain adaptation deals with adapting classifiers trained on data from a source distribution, to work effectively on data from a target distribution. In this paper, we introduce the Nonlinear Embedding Transform (NET) for unsupervised domain adaptati

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b31c21223a6d04c1767f499046a4eda9
http://arxiv.org/abs/1706.07527

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