Zobrazeno 1 - 10
of 2 665
pro vyhledávání: '"Johnson Erik"'
Autor:
Guittari, Nicole K., Wimbish, Miguel E., Rivlin, Patricia K., Hinton, Mark A., Matelsky, Jordan K., Rose, Victoria A., Rivera Jr., Jorge L., Stock, Nicole E., Wester, Brock A., Johnson, Erik C., Gray-Roncal, William R.
The promise of large-scale, high-resolution datasets from Electron Microscopy (EM) and X-ray Microtomography (XRM) lies in their ability to reveal neural structures and synaptic connectivity, which is critical for understanding the brain. Effectively
Externí odkaz:
http://arxiv.org/abs/2410.22320
Autor:
Norman-Tenazas, Raphael, Kleinberg, David, Johnson, Erik C., Lathrop, Daniel P., Roos, Matthew J.
It has been shown that unclocked, recurrent networks of Boolean gates in FPGAs can be used for low-SWaP reservoir computing. In such systems, topology and node functionality of the network are randomly initialized. To create a network that solves a t
Externí odkaz:
http://arxiv.org/abs/2403.13105
The Geiger photodiodes, or single photon avalanche photodiodes, for a radiation tolerant solid-state photomultiplier (SSPM) are being designed using Aluminum Gallium Arsenide (AlGaAs) and are less than 1 micrometer thick. Studies on the changes in da
Externí odkaz:
http://arxiv.org/abs/2402.08838
Autor:
Wimbish, Miguel E., Guittari, Nicole K., Rose, Victoria A., Rivera Jr, Jorge L., Rivlin, Patricia K., Hinton, Mark A., Matelsky, Jordan K., Stock, Nicole E., Wester, Brock A., Johnson, Erik C., Gray-Roncal, William R.
High resolution volumetric neuroimaging datasets from electron microscopy (EM) and x-ray micro and holographic-nano tomography (XRM/XHN) are being generated at an increasing rate and by a growing number of research teams. These datasets are derived f
Externí odkaz:
http://arxiv.org/abs/2401.15251
Autor:
HERNÁNDEZ, RAFAEL RUEDA
Publikováno v:
The Wilson Journal of Ornithology, 2018 Jun 01. 130(2), 576-577.
Externí odkaz:
https://www.jstor.org/stable/26501282
Autor:
Johnson, Erik C., Nguyen, Thinh T., Dichter, Benjamin K., Zappulla, Frank, Kosma, Montgomery, Gunalan, Kabilar, Halchenko, Yaroslav O., Neufeld, Shay Q., Ratan, Kristen, Edwards, Nicholas J., Ressl, Susanne, Heilbronner, Sarah R., Schirner, Michael, Ritter, Petra, Wester, Brock, Ghosh, Satrajit, Martone, Maryann E., Pestilli, Franco, Yatsenko, Dimitri
Scientists are increasingly leveraging advances in instruments, automation, and collaborative tools to scale up their experiments and research goals, leading to new bursts of discovery. Various scientific disciplines, including neuroscience, have ado
Externí odkaz:
http://arxiv.org/abs/2401.00077
We propose a novel modular inference approach combining two different generative models -- generative adversarial networks (GAN) and normalizing flows -- to approximate the posterior distribution of physics-based Bayesian inverse problems framed in h
Externí odkaz:
http://arxiv.org/abs/2310.04690
Autor:
Johnson, Erik C., Robinson, Brian S., Vallabha, Gautam K., Joyce, Justin, Matelsky, Jordan K., Norman-Tenazas, Raphael, Western, Isaac, Villafañe-Delgado, Marisel, Cervantes, Martha, Robinette, Michael S., Reddy, Arun V., Kitchell, Lindsey, Rivlin, Patricia K., Reilly, Elizabeth P., Drenkow, Nathan, Roos, Matthew J., Wang, I-Jeng, Wester, Brock A., Gray-Roncal, William R., Hoffmann, Joan A.
Despite the progress in deep learning networks, efficient learning at the edge (enabling adaptable, low-complexity machine learning solutions) remains a critical need for defense and commercial applications. We envision a pipeline to utilize large ne
Externí odkaz:
http://arxiv.org/abs/2305.17300
Autor:
Jabini, Amin, Johnson, Erik A.
Optimal sensor placement enhances the efficiency of a variety of applications for monitoring dynamical systems. It has been established that deterministic solutions to the sensor placement problem are insufficient due to the many uncertainties in sys
Externí odkaz:
http://arxiv.org/abs/2303.09750