Zobrazeno 1 - 10
of 2 335
pro vyhledávání: '"Aden P"'
Autor:
Joshua V. Chen, Yi Li, Felicia Tang, Gunvant Chaudhari, Christopher Lew, Amanda Lee, Andreas M. Rauschecker, Aden P. Haskell-Mendoza, Yvonne W. Wu, Evan Calabrese
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-10 (2024)
Abstract Brain extraction, or skull-stripping, is an essential data preprocessing step for machine learning approaches to brain MRI analysis. Currently, there are limited extraction algorithms for the neonatal brain. We aim to adapt an established de
Externí odkaz:
https://doaj.org/article/589caae17a3a4f28953eb0778a03a7b9
Autor:
Jihad Abdelgadir, Aden P Haskell-Mendoza, Amanda R Magno, Alexander D Suarez, Prince Antwi, Alankrita Raghavan, Patricia Nelson, Lexie Zidanyue Yang, Sin-Ho Jung, Ali R Zomorodi
Publikováno v:
PLoS ONE, Vol 18, Iss 5, p e0285982 (2023)
ObjectiveDespite advances in the nonsurgical management of cerebrovascular atherosclerotic steno-occlusive disease, approximately 15-20% of patients remain at high risk for recurrent ischemia. The benefit of revascularization with flow augmentation b
Externí odkaz:
https://doaj.org/article/835102d1977c45d9ad1cb3cb57293c67
Autor:
Emily C. Lerner, BA, Ethan S. Srinivasan, BS, Gloria Broadwater, MS, Aden P. Haskell-Mendoza, MS, Ryan M. Edwards, BA, David Huie, MD, Eugene J. Vaios, MD, MBA, Scott R. Floyd, MD, PhD, Justus D. Adamson, PhD, Peter E. Fecci, MD, PhD
Publikováno v:
Advances in Radiation Oncology, Vol 7, Iss 6, Pp 101054- (2022)
Purpose: Stereotactic radiosurgery (SRS) is a highly effective therapy for newly diagnosed brain metastases. Prophylactic antiepileptic drugs are no longer routinely used in current SRS practice, owing to a perceived low overall frequency of new-onse
Externí odkaz:
https://doaj.org/article/e3e881a6e942428493cfa1f34286d66c
Autor:
Forrow, Aden
Progress on modern scientific questions regularly depends on using large-scale datasets to understand complex dynamical systems. An especially challenging case that has grown to prominence with advances in single-cell sequencing technologies is learn
Externí odkaz:
http://arxiv.org/abs/2408.14408
Autor:
Aditya A. Mohan, William H. Tomaszewski, Aden P. Haskell-Mendoza, Kelly M. Hotchkiss, Kirit Singh, Jessica L. Reedy, Peter E. Fecci, John H. Sampson, Mustafa Khasraw
Publikováno v:
Frontiers in Oncology, Vol 11 (2021)
We have only recently begun to understand how cancer metabolism affects antitumor responses and immunotherapy outcomes. Certain immunometabolic targets have been actively pursued in other tumor types, however, glioblastoma research has been slow to e
Externí odkaz:
https://doaj.org/article/fde8852ef67c421689dcd357438a1646
Software testing is an important and essential part of the software development life cycle and accounts for almost one-third of system development costs. In the software industry, testing costs can account for about 35% to 40% of the total cost of a
Externí odkaz:
http://arxiv.org/abs/2404.06568
Developing an optimal PAC learning algorithm in the realizable setting, where empirical risk minimization (ERM) is suboptimal, was a major open problem in learning theory for decades. The problem was finally resolved by Hanneke a few years ago. Unfor
Externí odkaz:
http://arxiv.org/abs/2403.08831
Publikováno v:
Journal of Differential Equations, Volume 416, Part 1, 2025, Pages 449-490
We investigate dispersive estimates for the massless three dimensional Dirac equation with a potential. In particular, we show that the Dirac evolution satisfies a $\langle t\rangle^{-1}$ decay rate as an operator from $L^1$ to $L^\infty$ regardless
Externí odkaz:
http://arxiv.org/abs/2402.07675
Hybrid dynamical systems are systems which posses both continuous and discrete transitions. Assuming that the discrete transitions (resets) occur a finite number of times, the optimal control problem can be solved by gluing together the optimal arcs
Externí odkaz:
http://arxiv.org/abs/2401.14476
Autor:
Singireddy, Suraj, Nwaorgu, Precious, Beckus, Andre, McKinney, Aden, Enyioha, Chinwendu, Jha, Sumit Kumar, Atia, George K., Velasquez, Alvaro
Reinforcement learning (RL) is a powerful tool for finding optimal policies in sequential decision processes. However, deep RL methods have two weaknesses: collecting the amount of agent experience required for practical RL problems is prohibitively
Externí odkaz:
http://arxiv.org/abs/2310.19137