Results 91 to 100 of about 313,735 (187)

Grounds for Argument: Local Understandings, Science, and Global Processes in Special Forest Products Harvesting [PDF]

open access: yes, 1997
In posing the question Where are the pickers? , Love and Jones suggest that the shifting paradigm in forestry is real and that academia is not leading the shift.
Jones, Eric, Love, Thomas
core   +1 more source

The Value of Hyperparameter Optimization in Phase-Picking Neural Networks

open access: yesThe Seismic Record
Abstract The effectiveness of neural networks for picking seismic phase arrival times has been demonstrated through several case studies, and seismic monitoring programs are starting to adopt the technology into their workflows. However, published models were designed and trained using rather arbitrary choices of hyperparameters ...
Yongsoo Park, David R. Shelly
openaire   +1 more source

Reducing the Parameter Dependency of Phase-Picking Neural Networks with Dice Loss

open access: yesThe Seismic Record
Training a neural network for picking seismic phase arrivals has been commonly posed as a segmentation problem. It is a highly imbalanced segmentation problem in the sense that the background vastly dominates the foreground because we are trying to pick ...
Yongsoo Park, Gregory C. Beroza
doaj   +1 more source

Toward single particle reconstruction without particle picking: Breaking the detection limit

open access: yes, 2018
Single-particle cryo-electron microscopy (cryo-EM) has recently joined X-ray crystallography and NMR spectroscopy as a high-resolution structural method for biological macromolecules.
Bendory, Tamir   +4 more
core  

A systemic methodology for risk management in healthcare sector [PDF]

open access: yes, 2011
Anna Corinna Cagliano   +62 more
core   +1 more source

SeismicSense: Phase Picking of Seismic Events with Embedded Machine Learning

open access: yesProceedings of the 40th ACM/SIGAPP Symposium on Applied Computing
Analyzing seismic data is essential for understanding natural geological processes and anthropogenic activities, particularly in localizing seismic events. While recent advances in seismic analysis rely heavily on resource-intensive machine learning approaches, these methods are impractical in resource-constrained environments such as underwater ...
Tayyaba Zainab   +3 more
openaire   +2 more sources

Evaluating Multi-station Phase Picking Algorithm Phase Neural Operator (PhaseNO) on Local Seismic Networks

open access: yes
Reliable automatic phase picking is important for many seismic applications. With the development of machine learning approaches, many algorithms are proposed, evaluated and applied to different areas. Many of these algorithms are single station based, while recent proposed methods start to combine surrounding stations into consideration in the problem
Kong, Qingkai   +8 more
openaire   +2 more sources

Deep‐Learning‐Based Phase Picking for Volcano‐Tectonic and Long‐Period Earthquakes

open access: yesGeophysical Research Letters
The application of deep‐learning‐based seismic phase pickers has surged in recent years. However, the efficacy of these models when applied to monitoring volcano seismicity has yet to be fully evaluated.
Yiyuan Zhong, Yen Joe Tan
doaj   +1 more source

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