Grounds for Argument: Local Understandings, Science, and Global Processes in Special Forest Products Harvesting [PDF]
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
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The Value of Hyperparameter Optimization in Phase-Picking Neural Networks
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
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Reducing the Parameter Dependency of Phase-Picking Neural Networks with Dice Loss
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
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Research Profile: Protein conjugates pick polymer phase [PDF]
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Toward single particle reconstruction without particle picking: Breaking the detection limit
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]
Anna Corinna Cagliano +62 more
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SeismicSense: Phase Picking of Seismic Events with Embedded Machine Learning
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
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
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Deep‐Learning‐Based Phase Picking for Volcano‐Tectonic and Long‐Period Earthquakes
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
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Case Report: Behavioral analysis guided intervention targeting triggers and urges in skin-picking disorder with comorbid onychophagia. [PDF]
Kawahito M +4 more
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