User profiles for Gustavo Chavez
Gustavo ChávezPostdoctoral researcher, Lawrence Berkeley National Laboratory Verified email at kaust.edu.sa Cited by 111 |
[HTML][HTML] Levels of neural progenitors in the hippocampus predict memory impairment and relapse to drug seeking as a function of excessive methamphetamine self …
P Recinto, ARH Samant, G Chavez, A Kim… - …, 2012 - nature.com
Methamphetamine affects the hippocampus, a brain region crucial for learning and memory,
as well as relapse to drug seeking. Rats self-administered methamphetamine for 1 h twice …
as well as relapse to drug seeking. Rats self-administered methamphetamine for 1 h twice …
Irregularity detection on low tension electric installations by neural network ensembles
…, K Figueiredo, M Vellasco, G Chavez… - … Joint Conference on …, 2009 - ieeexplore.ieee.org
The volume of energy loss that Brazilian electric utilities have to deal with has been ever
increasing. The electricity concessionaries are suffering significant and increasing loss in the …
increasing. The electricity concessionaries are suffering significant and increasing loss in the …
Distal femur replacement versus surgical fixation for the treatment of geriatric distal femur fractures: a systematic review
…, MR DeBaun, MF Githens, GA Chavez… - Journal of …, 2021 - journals.lww.com
Objectives: The management of geriatric distal femur fractures is controversial, and both
primary distal femur replacement (DFR) and surgical fixation (SF) are viable treatment options. …
primary distal femur replacement (DFR) and surgical fixation (SF) are viable treatment options. …
A study of clustering techniques and hierarchical matrix formats for kernel ridge regression
We present memory-efficient and scalable algorithms for kernel methods used in machine
learning. Using hierarchical matrix approximations for the kernel matrix the memory …
learning. Using hierarchical matrix approximations for the kernel matrix the memory …
Artificial neural networks predict 30-day mortality after hip fracture: insights from machine learning
MR DeBaun, G Chavez, A Fithian… - JAAOS-Journal of the …, 2021 - journals.lww.com
Objectives: Accurately stratifying patients in the preoperative period according to mortality risk
informs treatment considerations and guides adjustments to bundled reimbursements. We …
informs treatment considerations and guides adjustments to bundled reimbursements. We …
Scalable and memory-efficient kernel ridge regression
We present a scalable and memory-efficient framework for kernel ridge regression. We
exploit the inherent rank deficiency of the kernel ridge regression matrix by constructing an …
exploit the inherent rank deficiency of the kernel ridge regression matrix by constructing an …
Exhpd: Exploiting human, physical, and driving behaviors to detect vehicle cyber attacks
…, J Castillo, KC Roy, G Chavez… - IEEE Internet of …, 2021 - ieeexplore.ieee.org
As increasingly more vehicles are connected to the Internet, cyber attacks against vehicles
are becoming a real threat with devastating consequences. This highlights the importance of …
are becoming a real threat with devastating consequences. This highlights the importance of …
Robust and accurate stopping criteria for adaptive randomized sampling in matrix-free hierarchically semiseparable construction
We present new algorithms for randomized construction of hierarchically semiseparable (HSS)
matrices, addressing several practical issues. The HSS construction algorithms use a …
matrices, addressing several practical issues. The HSS construction algorithms use a …
[HTML][HTML] Reversals and limitations on high-intensity, life-sustaining treatments
G Chavez, IB Richman, R Kaimal, J Bentley… - PloS one, 2018 - journals.plos.org
Importance Critically ill patients often receive high-intensity life sustaining treatments (LST)
in the intensive care unit (ICU), although they can be ineffective and eventually undesired. …
in the intensive care unit (ICU), although they can be ineffective and eventually undesired. …
[HTML][HTML] Accelerated cyclic reduction: a distributed-memory fast solver for structured linear systems
We present Accelerated Cyclic Reduction (ACR), a distributed-memory fast solver for rank-compressible
block tridiagonal linear systems arising from the discretization of elliptic …
block tridiagonal linear systems arising from the discretization of elliptic …