Zobrazeno 1 - 10
of 129
pro vyhledávání: '"Kasim Muhammad"'
Publikováno v:
E3S Web of Conferences, Vol 400, p 01011 (2023)
The research was conducted in Bualemo Area, Kwandang District, North Gorontalo Regency. The aim of this study was to analyze the petrogenesis of volcanic rocks in the Bualemo Area, North Gorontalo Regency. A mapping technique was utilized in this wor
Externí odkaz:
https://doaj.org/article/4bf709a51ae344e186f80320f7c662d3
Sequential models, such as Recurrent Neural Networks and Neural Ordinary Differential Equations, have long suffered from slow training due to their inherent sequential nature. For many years this bottleneck has persisted, as many thought sequential m
Externí odkaz:
http://arxiv.org/abs/2309.12252
Autor:
Gawne, Thomas, Campbell, Thomas, Forte, Alessandro, Hollebon, Patrick, Perez-Callejo, Gabriel, Humphries, Oliver, Karnbach, Oliver, Kasim, Muhammad F., Preston, Thomas R., Lee, Hae Ja, Miscampbell, Alan, Berg, Quincy Y. van den, Nagler, Bob, Ren, Shenyuan, Royle, Ryan B., Wark, Justin S., Vinko, Sam M.
We present the first experimental observation of K$_{\beta}$ emission from highly charged Mg ions at solid density, driven by intense x-rays from a free electron laser. The presence of K$_{\beta}$ emission indicates the $n=3$ atomic shell is relocali
Externí odkaz:
http://arxiv.org/abs/2302.04079
The beauty of physics is that there is usually a conserved quantity in an always-changing system, known as the constant of motion. Finding the constant of motion is important in understanding the dynamics of the system, but typically requires mathema
Externí odkaz:
http://arxiv.org/abs/2208.10387
Conservation of energy is at the core of many physical phenomena and dynamical systems. There have been a significant number of works in the past few years aimed at predicting the trajectory of motion of dynamical systems using neural networks while
Externí odkaz:
http://arxiv.org/abs/2208.02632
Autor:
Aguas, Ricardo, Amswych, Ma'ayan, Andersen-Waine, Billie, Bajaj, Sumali, Bimpong, Kweku, Bodley, Adam, Cantrell, Liberty, Chen, Siyu, Creswell, Richard, Dahal, Prabin, Dickinson, Sophie, Dittrich, Sabine, Evans, Tracy, Ferguson-Lewis, Angus, Franco, Caroline, Gao, Bo, Hounsell, Rachel, Kasim, Muhammad, Keene, Claire, Lambert, Ben, Mahmood, Umar, Mills, Melinda, Moldokmatova, Ainura, Molyneux, Sassy, Naidoo, Reshania, Ngwafor Anye, Randolph, Norman, Jared, Pan-Ngum, Wirichada, Pokharel, Sunil, Polner, Anastasiia, Rowe, Emily, Saralamba, Sompob, Shretta, Rima, Silal, Sheetal, Stepniewska, Kasia, L-H Tsui, Joseph, Voysey, Merryn, Wanat, Marta, White, Lisa J, Tsui, Joseph L-H *, Kolade, Olumide, Nicholson, George, Lehmann, Brieuc, Hay, James A, Kraemer, Moritz U G, Donnelly, Christl A, Fowler, Tom, Hopkins, Susan
Publikováno v:
In The Lancet Digital Health November 2024 6(11):e778-e790
Deep-learning-based models are increasingly used to emulate scientific simulations to accelerate scientific research. However, accurate, supervised deep learning models require huge amount of labelled data, and that often becomes the bottleneck in em
Externí odkaz:
http://arxiv.org/abs/2111.08498
Publikováno v:
J. Chem. Phys. 156, 084801 (2022)
Automatic differentiation represents a paradigm shift in scientific programming, where evaluating both functions and their derivatives is required for most applications. By removing the need to explicitly derive expressions for gradients, development
Externí odkaz:
http://arxiv.org/abs/2110.11678
The lack of equitable access to radiotherapy linear accelerators (LINACs) is a substantial barrier to cancer care in Low and Middle-Income Countries (LMICs). Aside from the issue of cost, there are also issues of robustness of state-of-the-art LINACs
Externí odkaz:
http://arxiv.org/abs/2105.08906
Autor:
Kasim, Muhammad F., Vinko, Sam M.
Publikováno v:
Phys. Rev. Lett. 127, 126403 (2021)
Improving the predictive capability of molecular properties in ab initio simulations is essential for advanced material discovery. Despite recent progress making use of machine learning, utilizing deep neural networks to improve quantum chemistry mod
Externí odkaz:
http://arxiv.org/abs/2102.04229